277 results on '"Risk prediction model"'
Search Results
2. Risk Prediction Models as an Emerging Trend for Managing Cancer‐Related Fatigue: A Systematic Review.
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Zhang, Yun, Li, Linna, Li, Xia, Zhang, Shu, Zhou, Lin, Chen, Xiaoli, and Hu, Xiaolin
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ABSTRACT Aim Design Data Sources Review Methods Results Conclusion Implications for the Profession Impact Reporting Method Public Contribution To systematically identify, describe and evaluate the existing risk prediction models for cancer‐related fatigue.Systematic review.Seven databases (EMBASE, Cochrane Database, MEDLINE, CINAHL, CNKI, SinoMed and Wanfang) were conducted from inception to August 14, 2023 and updated in September 15, 2024.A systematic search was conducted to identify studies that reported the development of risk prediction models for cancer‐related fatigue. Two researchers independently performed a comprehensive assessment of the included studies. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability.Eighteen studies were included in this review. These models predicted cancer‐related fatigue in various cancers, including breast cancer, prostate cancer, gynaecological tumours and lung cancer. The most commonly included predictors were anxiety and depression, age, chemotherapy status, sleep quality and pain. Thirteen studies assessed the model performance by using the receiver operating characteristic curve. Although most models exhibited good predictive performance, a higher risk of bias was observed because of inappropriate handling of missing data methods and an imbalance in events per variable.Prediction models show promise for cancer‐related fatigue management and precision care, but few are ready for clinical application due to methodological limitations.Future research should focus on improving the clinical utility of cancer‐related fatigue models while balancing predictive accuracy with cost‐effectiveness to promote equitable care.This study critically systematically evaluated the prediction models of cancer‐related fatigue. The existing prediction models have a weak methodological foundation, with only a few having the potential to be implemented in clinical practice.The review is reported using the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis in Systematic Reviews and Meta‐Analyses checklist (TRIPOD‐SRMA).No patient or public contribution. [ABSTRACT FROM AUTHOR] more...
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- 2024
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3. Construction of a risk prediction model of diquat poisoning based on clinical indicators.
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Qian, Weiwei, Zhou, Jian, Ren, Yan, Bai, Yangjuan, Peng, Aihua, Lv, Lin, Ma, Zengwen, He, Chengtong, Zhou, Yue, Tong, Jiale, Zhang, Yanzi, Cao, Yu, and Xu, Shuyun
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AbstractThis study aims to explore the clinical characteristics and prognostic factors in patients with diquat (DQ) poisoning and to develop a clinical risk assessment model to improve diagnosis and treatment strategies. Data from 60 patients with DQ poisoning, including basic characteristics, poisoning severity, and inflammatory response indicators, were collected. The plasma concentration of DQ was measured using liquid chromatography–mass spectrometry. The included patients were categorized into survival and death groups based on their 30-day outcomes. Fisher’s exact test was used to identify statistically significant clinical indicators (
p < .05), and logistic regression within a generalized linear model (GLM) framework was employed to analyze these indicators alongside the severity index of diquat poisoning (SIDP), followed by the construction of a prognostic model. The performance of the model was evaluated through receiver operating characteristic (ROC) analysis, and the accuracy of the model was assessed. Additionally, two independent sample Wilcoxon tests compared the clinical indicators between high-risk and low-risk groups. Fisher’s exact test identified significant differences in variables such as oral drug dosage (ODD), time from poisoning to admission (TFPTA), state of consciousness (SOC), Glasgow Coma Scale (GCS), white blood cells (WBC), myoglobin (Myo), high-flow nasal cannula (HFNC), invasive mechanical ventilation (IMV), acute kidney injury (AKI), and acute lung injury (ALI) (p < .05) between the survival and death groups. The GLM-based risk assessment model demonstrated high predictive accuracy, with an area under the ROC curve (AUC) of 0.97 (SE 0.017, 95% CI 0.939–1.001), indicating potent prognostic capability. The Wilcoxon test revealed that ODD, Myo, SIDP, aspartate transferase (AST), creatine kinase (CK), hemoglobin (Hb), cardiac troponin (cTnT), and serum creatinine (Cr) levels were significantly higher in the high-risk group. The clinical risk assessment model effectively predicts the prognosis of patients with DQ poisoning, aiding clinicians in personalizing treatment strategies to improve patient outcomes. [ABSTRACT FROM AUTHOR] more...- Published
- 2024
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4. Risk factors for SARS-CoV-2 pneumonia among renal transplant recipients in Omicron pandemic—a prospective cohort study.
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Zhang, Sai, Ding, Xiang, Geng, Chunmi, and Zhang, Hong
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VIRAL pneumonia , *COVID-19 , *RECEIVER operating characteristic curves , *ELECTRONIC health records , *SARS-CoV-2 Omicron variant - Abstract
Background: The coronavirus disease (COVID-19) pandemic is a global health emergency, and SARS-CoV-2 pneumonia poses significant challenges to health systems worldwide. Renal transplant recipients (RTRs) are a special group and are more vulnerable to viral pneumonia. However, few studies have elucidated the risk factors of SARS-CoV-2 pneumonia in RTRs infected with COVID-19. This study aimed to build a risk prediction model for SARS-CoV-2 pneumonia among RTRs based on demographic and clinical information. Methods: We conducted a prospective cohort study among 383 RTRs (age ≥ 18 years) diagnosed with COVID-19 from December 21, 2022, to March 26, 2023. Patients' demographic and clinical information was collected through a questionnaire survey combined with electronic medical records. A stepwise logistic regression model was established to test the predictors of SARS-CoV-2 pneumonia. We assessed the diagnostic performance of the model by calculating the area under the curve (AUC) of the receiver operating characteristic (ROC) and calibration using the Hosmer–Lemeshow (HL) goodness-of-fit test. Results: Our study showed that the incidence of SARS-CoV-2 pneumonia among RTRs was 31.1%. Older age (OR = 2.08–3.37,95%CI:1.05–7.23), shorter post-transplantation duration (OR = 0.92,95% CI: 0.87,0.99), higher post-transplant Charlson Comorbidity Index (CCI) (OR = 1.84, 95%CI: 1.14,2.98), pulmonary infection history (OR = 3.44, 95%CI: 1.459, 8.099, P = 0.005), fatigue (OR = 2.11, 95%CI: 1.14, 3.90), cough (OR = 2.03, 95%CI: 1.08, 3.81), and lower estimated glomerular filtration rate (eGFR) at COVID-19 diagnosis (OR = 0.98, 95%CI:0.97,0.99) predicted a higher risk for SARS-CoV-2 pneumonia. The model showed good diagnostic performance with Chi-Square = 10.832 (P > 0.05) and AUC = 0.839 (P < 0.001). Conclusions: Our study showed a high incidence of SARS-CoV-2 pneumonia among RTRs, and we built a risk prediction model for SARS-CoV-2 pneumonia based on patients' demographic and clinical characteristics. The model can help identify RTRs infected with COVID-19 at high risk of SARS-CoV-2 pneumonia to inform timely, targeted, and effective prevention and intervention efforts. [ABSTRACT FROM AUTHOR] more...
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- 2024
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5. A risk prediction model of gestational diabetes mellitus based on traditional and genetic factors.
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Li, Ying, Zhong, Xinli, Yang, Mengjiao, Yuan, Lu, Wang, Dandan, Li, Ting, and Guo, Yanying
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Background: Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication during pregnancy. We aimed to evaluate a risk prediction model of GDM based on traditional and genetic factors. Methods: A total of 2744 eligible pregnant women were included. Face-to-face questionnaire surveys were conducted to gather general data. Serum test results were collected from the laboratory information system. Independent risk factors for GDM were identified using univariate and multivariate logistic regression analyses. A GDM risk prediction model was constructed and evaluated with the Hosmer–Lemeshow goodness-of-fit test, goodness-of-fit calibration plot, receiver operating characteristic curve and area under the curve. Results: Among traditional factors, age ≥30 years, family history, GDM history, impaired glucose tolerance history, systolic blood pressure ≥116.22 mmHg, diastolic blood pressure ≥74.52 mmHg, fasting plasma glucose ≥5.0 mmol/L, 1-hour postprandial blood glucose ≥8.8 mmol/L, 2-h postprandial blood glucose ≥7.9 mmol/L, total cholesterol ≥4.50 mmol/L, low-density lipoprotein ≥2.09 mmol/L and insulin ≥11.5 mIU/L were independent risk factors for GDM. Among genetic factors, 11 single nucleotide polymorphisms (SNPs) (rs2779116, rs5215, rs11605924, rs7072268, rs7172432, rs10811661, rs2191349, rs10830963, rs174550, rs13266634 and rs11071657) were identified as potential predictors of the risk of postpartum DM among women with GDM history, collectively accounting for 3.6% of the genetic risk. Conclusions: Both genetic and traditional factors contribute to the risk of GDM in women, operating through diverse mechanisms. Strengthening the risk prediction of SNPs for postpartum DM among women with GDM history is crucial for maternal and child health protection. PLAIN LANGUAGE SUMMARY: We aimed to evaluate a risk prediction model of gestational diabetes mellitus (GDM) based on traditional and genetic factors. A total of 2744 eligible pregnant women were included. Face-to-face questionnaire surveys were conducted to collect general data. Among traditional factors, age ≥30 years old, family history, GDM history, impaired glucose tolerance history, systolic blood pressure ≥116.22 mmHg, diastolic blood pressure ≥74.52 mmHg, fasting plasma glucose ≥5.0 mmol/L, 1-hour postprandial blood glucose ≥8.8 mmol/L, 2-h postprandial blood glucose ≥7.9 mmol/L, total cholesterol ≥4.50 mmol/L, low-density lipoprotein ≥2.09 mmol/L and insulin ≥11.5 mIU/L were independent risk factors for GDM. Among genetic factors, 11 single nucleotide polymorphisms were identified as potential predictors of the risk of postpartum DM among women with GDM history, collectively accounting for 3.6% of the genetic risk. Both genetic and traditional factors increase the risk of GDM in women. [ABSTRACT FROM AUTHOR] more...
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- 2024
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6. Incorporating low haemoglobin into a risk prediction model for conversion in minimally invasive gynaecologic oncology surgeries.
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Nguyen, Kevin H., Joo, Hyundeok, Manuel, Solmaz, Chen, Lee-may, and Chen, Lee-lynn
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Background: A well-known complication of laparoscopic management of gynaecologic masses and cancers is the need to perform an intraoperative conversion to laparotomy. The purpose of this study was to identify novel patient risk factors for conversion from minimally invasive to open surgeries for gynaecologic oncology operations. Methods: This was a retrospective cohort study of 1356 patients ≥18 years of age who underwent surgeries for gynaecologic masses or malignancies between February 2015 and May 2020 at a single academic medical centre. Multivariable logistic regression was used to study the effects of older age, higher body mass index (BMI), higher American Society of Anaesthesiologist (ASA) physical status, and lower preoperative haemoglobin (Hb) on odds of converting from minimally invasive to open surgery. Receiver operating characteristic (ROC) curve analysis assessed the discriminatory ability of a risk prediction model for conversion. Results: A total of 704 planned minimally invasive surgeries were included with an overall conversion rate of 6.1% (43/704). Preoperative Hb was lowest for conversion cases, compared to minimally invasive and open cases (11.6 ± 1.9 vs 12.8 ± 1.5 vs 11.8 ± 1.9 g/dL, p<.001). Patients with preoperative Hb <10 g/dL had an adjusted odds ratio (OR) of 3.94 (CI: 1.65–9.41, p=.002) for conversion while patients with BMI ≥30 kg/m2 had an adjusted OR of 2.86 (CI: 1.50–5.46, p=.001) for conversion. ROC curve analysis using predictive variables of age >50 years, BMI ≥30 kg/m2, ASA physical status >2, and preoperative haemoglobin <10 g/dL resulted in an area under the ROC curve of 0.71. Patients with 2 or more risk factors were at highest risk of requiring an intraoperative conversion (12.0%). Conclusions: Lower preoperative haemoglobin is a novel risk factor for conversion from minimally invasive to open gynaecologic oncology surgeries and stratifying patients based on conversion risk may be helpful for preoperative planning. PLAIN LANGUAGE SUMMARY: Minimally invasive surgery for management of gynaecologic masses (masses that affect the female reproductive organs) is often preferred over more invasive surgery, because it involves smaller surgical incisions and can have overall better recovery time. However, one unwanted complication of minimally invasive surgery is the need to unexpectedly convert the surgery to an open surgery, which entails a larger incision and is a higher risk procedure. In our study, we aimed to find patient characteristics that are associated with higher risk of converting a minimally invasive surgery to an open surgery. Our study identified that lower levels of preoperative haemoglobin, the protein that carries oxygen within red blood cells, is correlated with higher risk for conversion. This new risk factor was used with other known risk factors, including having higher age, higher body mass index, and higher baseline medical complexity to create a model to help surgical teams identify high risk patients for conversion. This model may be useful for surgical planning before and during the operation to improve patient outcomes. [ABSTRACT FROM AUTHOR] more...
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- 2024
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7. A nomogram prediction model for mild cognitive impairment in non-dialysis outpatient patients with chronic kidney disease.
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Yang, Qin, Xiang, Yuhe, Ma, Guoting, Cao, Min, Fang, Yixi, Xu, Wenbin, Li, Lin, Li, Qin, Feng, Yu, and Yang, Qian
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MILD cognitive impairment , *CHRONIC kidney failure , *PREDICTION models , *CHRONICALLY ill , *NOMOGRAPHY (Mathematics) - Abstract
The high prevalence of mild cognitive impairment (MCI) in non-dialysis individuals with chronic kidney disease (CKD) impacts their prognosis and quality of life. This study aims to investigate the variables associated with MCI in non-dialysis outpatient patients with CKD and to construct and verify a nomogram prediction model. 416 participants selected from two hospitals in Chengdu, between January 2023 and June 2023. They were categorized into two groups: the MCI group (n = 210) and the non-MCI (n = 206). Univariate and multivariate binary logistic regression analyses were employed to identify independent influences (candidate predictor variables). Subsequently, regression models was constructed, and a nomogram was drawn. The restricted cubic spline diagram was drawn to further analyze the relationship between the continuous numerical variables and MCI. Internally validated using a bootstrap resampling procedure. Among 416 patients, 210 (50.9%) had MCI. Logistic regression analysis revealed that age, educational level, occupational status, use of smartphones, sleep disorder, and hemoglobin were independent influencing factors of MCI (all p<.05). The model's area under the curve was 0.926,95% CI (0.902, 0.951), which was a good discriminatory measure; the Calibration curve, the Hosmer–Lemeshow test, and the Clinical Decision Curve suggested that the model had good calibration and clinical benefit. Internal validation results showed the consistency index was 0.926, 95%CI (0.925, 0.927). The nomogram prediction model demonstrates good performance and can be used for early screening and prediction of MCI in non-dialysis patients with CKD. It provides valuable reference for medical staff to formulate corresponding intervention strategies. [ABSTRACT FROM AUTHOR] more...
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- 2024
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8. Prediction of the risk of 3-year chronic kidney disease among elderly people: a community-based cohort study.
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Wang, Tao, Zhou, Zhitong, Ren, Longbing, Shen, Zhiping, Li, Jue, and Zhang, Lijuan
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OLDER people , *CHRONIC kidney failure , *COHORT analysis , *SYSTOLIC blood pressure , *DECISION making - Abstract
We conducted a community-based cohort study to predict the 3-year occurrence of chronic kidney disease (CKD) among population aged ≥60 years. Participants were selected from two communities through randomized cluster sampling in Jiading District of Shanghai, China. The two communities were randomly divided into a development cohort (n = 12012) and a validation cohort (n = 6248) with a 3-year follow-up. Logistic regression analysis was used to determine the independent predictors. A nomogram was established to predict the occurrence of CKD within 3 years. The area under the curve (AUC), the calibration curve and decision curve analysis (DCA) curve were used to evaluate the model. At baseline, participants in development cohort and validation cohort were with the mean age of 68.24 ± 5.87 and 67.68 ± 5.26 years old, respectively. During 3 years, 1516 (12.6%) and 544 (8.9%) new cases developed CKD in the development and validation cohorts, respectively. Nine variables (age, systolic blood pressure, body mass index, exercise, previous hypertension, triglycerides, fasting plasma glucose, glycated hemoglobin and serum creatinine) were included in the prediction model. The AUC value was 0.742 [95% confidence interval (CI), 0.728–0.756] in the development cohort and 0.881(95%CI, 0.867–0.895) in the validation cohort, respectively. The calibration curves and DCA curves demonstrate an effective predictive model. Our nomogram model is a simple, reasonable and reliable tool for predicting the risk of 3-year CKD in community-dwelling elderly people, which is helpful for timely intervention and reducing the incidence of CKD. [ABSTRACT FROM AUTHOR] more...
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- 2024
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9. Prediction models for the risk of ventilator-associated pneumonia in patients on mechanical ventilation: A systematic review and meta-analysis.
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Li, Jiaying, Li, Guifang, Liu, Ziqing, Yang, Xingyu, and Yang, Qiuyan
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Identifying patients at risk of ventilator-associated pneumonia through prediction models can facilitate medical decision-making. Our objective was to evaluate the current models for ventilator-associated pneumonia in patients with mechanical ventilation. Nine databases systematically retrieved from establishment to March 6, 2024. Two independent reviewers performed study selection, data extraction, and quality assessment, respectively. The Prediction Model Risk of Bias Assessment Tool was used to evaluate the risk of model bias and applicability. Stata 17.0 was used to conduct a meta-analysis of discrimination of model validation. The total of 34 studies were included, with reported 52 prediction models. The most frequent predictors in the models were mechanical ventilation duration, length of intensive care unit stay, and age. Each study was essentially considered having a high risk of bias. A meta-analysis of 17 studies containing 33 models with validation was performed with a pooled area under the receiver-operating curve of 0.80 (95% confidence interval: 0.78-0.83). Despite the relatively excellent performance of the models, there is a high risk of bias of the model development process. Enhancing the methodological quality, especially the external validation, practical application, and optimization of the models need urgent attention. • Applying credible models becomes beneficial to identifying the risk of ventilator-associated pneumonia. • In total, 52 prediction models showed relatively excellent performance, but were at high risk of bias. • More attention is needed to focus on external validation, practical application of the models. [ABSTRACT FROM AUTHOR] more...
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- 2024
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10. Development and Validation of a Predictive Model for Early Identification of Cognitive Impairment Risk in Community-Based Hypertensive Patients.
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Li, Yan, Xin, Jimei, Fang, Sen, Wang, Fang, Jin, Yufei, and Wang, Lei
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Objective: To investigate the risk factors for the development of mild cognitive dysfunction in hypertensive patients in the community and to develop a risk prediction model. Method: The data used in this study were obtained from two sources: the China Health and Retirement Longitudinal Study (CHARLS) and the Chinese Longitudinal Healthy Longevity Survey (CLHLS). A total of 1121 participants from CHARLS were randomly allocated into a training set and a validation set, following a 70:30 ratio. Meanwhile, an additional 4016 participants from CLHLS were employed for external validation of the model. The patients in this study were divided into two groups: those with mild cognitive impairment and those without. General information, employment status, pension, health insurance, and presence of depressive symptoms were compared between the two groups. LASSO regression analysis was employed to identify the most predictive variables for the model, utilizing 14-fold cross-validation. The risk prediction model for cognitive impairment in hypertensive populations was developed using generalized linear models. The model's discriminatory power was evaluated through the area under the receiver operating characteristic (ROC) curve and calibration curves. Results: In the modeling group, eight variables such as gender, age, residence, education, alcohol use, depression, employment status, and health insurance were ultimately selected from an initial pool of 21 potential predictors to construct the risk prediction model. The area under the curve (AUC) values for the training, internal, and external validation sets were 0.777, 0.785, and 0.782, respectively. All exceeded the threshold of 0.7, suggesting that the model effectively predicts the incidence of mild cognitive dysfunction in community-based hypertensive patients. A risk prediction model was developed using a generalized linear model in conjunction with Lasso regression. The model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve. Hosmer–Lemeshow test values yielded p =.346 and p =.626, both of which exceeded the 0.05 threshold. Calibration curves demonstrated a significant agreement between the nomogram model and observed outcomes, serving as an effective tool for evaluating the model's predictive performance. Discussion: The predictive model developed in this study serves as a promising and efficient tool for evaluating cognitive impairment in hypertensive patients, aiding community healthcare workers in identifying at-risk populations. [ABSTRACT FROM AUTHOR] more...
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- 2024
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11. Tumor‐Resident Microbiota‐Based Risk Model Predicts Neoadjuvant Therapy Response of Locally Advanced Esophageal Squamous Cell Carcinoma Patients.
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Wu, Hong, Liu, Qianshi, Li, Jingpei, Leng, Xuefeng, He, Yazhou, Liu, Yiqiang, Zhang, Xia, Ouyang, Yujie, Liu, Yang, Liang, Wenhua, and Xu, Chuan
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SQUAMOUS cell carcinoma , *NEOADJUVANT chemotherapy , *OVERALL survival , *PROGNOSIS , *REGRESSION analysis - Abstract
Few predictive biomarkers exist for identifying patients who may benefit from neoadjuvant therapy (NAT). The intratumoral microbial composition is comprehensively profiled to predict the efficacy and prognosis of patients with esophageal squamous cell carcinoma (ESCC) who underwent NAT and curative esophagectomy. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis is conducted to screen for the most closely related microbiota and develop a microbiota‐based risk prediction (MRP) model on the genera of TM7x, Sphingobacterium, and Prevotella. The predictive accuracy and prognostic value of the MRP model across multiple centers are validated. The MRP model demonstrates good predictive accuracy for therapeutic responses in the training, validation, and independent validation sets. The MRP model also predicts disease‐free survival (p = 0.00074 in the internal validation set and p = 0.0017 in the independent validation set) and overall survival (p = 0.00023 in the internal validation set and p = 0.11 in the independent validation set) of patients. The MRP‐plus model basing on MRP, tumor stage, and tumor size can also predict the patients who can benefit from NAT. In conclusion, the developed MRP and MRP‐plus models may function as promising biomarkers and prognostic indicators accessible at the time of diagnosis. [ABSTRACT FROM AUTHOR] more...
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- 2024
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12. Construction of a Risk Prediction Model for Ureteral Stricture after Ureteroscopic Holmium Laser Lithotripsy.
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Li, Ping, Wang, Kangning, Luo, Lin, Xie, Qingzhi, Wu, Yunchou, and Liao, Qiuling
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LASER lithotripsy , *RECEIVER operating characteristic curves , *PREOPERATIVE risk factors , *URETERIC obstruction , *LOGISTIC regression analysis , *URINARY calculi - Abstract
Introduction: To analyze the influencing factors of ureteral stenosis after ureteroscopic holmium laser lithotripsy. Methods: The clinical data of 427 patients treated with ureteroscopic holmium laser lithotripsy were selected, and the patients were divided into two groups based on the presence or absence of ureteral stenosis after the operation. Univariate and multivariate logistic regression were used to analyze the independent risk factors for postoperative ureteral stenosis, and R software and regression coefficients were used to construct a predictive model. Results: After a 1-year follow-up of 427 patients, 28 patients (6.56%) developed ureteral stenosis; univariate analysis showed that the occurrence of ureteral stenosis after subureteral holmium laser lithotripsy was related to stone diameter, stone incarceration, degree of hydronephrosis, holmium laser injury of mucosa, and operation time (p < 0.05); further logistic regression analysis showed that a large stone diameter, stone incarceration, and moderate to severe hydronephrosis were independent risk factors for ureteral stenosis after ureteroscopic holmium laser lithotripsy (p < 0.05); According to H-L deviation degree and area under receiver operating characteristic curve test, the results show that the model has high accuracy (χ2 = 2.475, p = 0.613) and differentiation (0.875 [95% confidence interval or CI: 0.817–0.919]), and the external verification of the nomogram prediction model was carried out by the verification group. The results showed that the prediction probability of the calibration curve was close to the actual probability and had a good consistency (area under the curve: 0.873 [95 CI: 0.822–0.914]). Conclusion: The established nomogram model exhibits high accuracy and discriminative ability. It can effectively identify high-risk groups, enabling timely prevention of ureteral stenosis and minimizing the risk of postoperative ureteral stenosis. [ABSTRACT FROM AUTHOR] more...
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- 2024
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13. Predictors of post-healing recurrence in patients with diabetic foot ulcers: A systematic review and meta-analysis.
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Sun, Yujian, Zhou, Yue, Dai, Yu, Pan, Yufan, Xiao, Yi, and Yu, Yufeng
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Diabetic foot ulcer is one of the most prevalent, serious, and costly consequences of diabetes, often associated with peripheral neuropathy and peripheral arterial disease. These ulcers contribute to high disability and mortality rates in patients and pose a major challenge to clinical management. To systematically review the risk prediction models for post-healing recurrence in diabetic foot ulcer (DFU) patients, so as to provide a reference for clinical staff to choose appropriate prediction models. The authors searched five databases (Cochrane Library, PubMed, Web of Science, EMBASE, and Chinese Biomedical Database) from their inception to September 23, 2023, for relevant literature. After data extraction, the quality of the literature was evaluated using the Predictive Model Research Bias Risk and Suitability Assessment tool (PROBAST). Meta-analysis was performed using STATA 17.0 software. A total of 9 studies involving 5956 patients were included. The recurrence rate after DFU healing ranged from 6.2 % to 41.4 %. Nine studies established 15 risk prediction models, and the area under the curve (AUC) ranged from 0.660 to 0.940, of which 12 models had an AUC≥0.7, indicating good prediction performance. The combined AUC value of the 9 validation models was 0.83 (95 % confidence interval: 0.79–0.88). Hosmer-Lemeshow test was performed for 10 models, external validation for 5 models, and internal validation for 6 models. Meta-analysis showed that 14 predictors, such as age and living alone, could predict post-healing recurrence in DFU patients (p < 0.05). To enhance the quality of these risk prediction models, there is potential for future improvements in terms of follow-up duration, model calibration, and validation processes. • First review of the Risk prediction model for post-healing recurrence in DFU patients. • Existing models showed good prediction accuracy, but all had high bias risk. Joint AUC for nine models was 0.830. • Meta-analysis showed that 14 predictors, such as age, could predict post-healing recurrence in DFU patients (P < 0.05). [ABSTRACT FROM AUTHOR] more...
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- 2024
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14. Construction and Validation of a Predictive Model for Long-Term Major Adverse Cardiovascular Events in Patients with Acute Myocardial Infarction.
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Yang, Peng, Duan, Jieying, Li, Mingxuan, Tan, Rui, Li, Yuan, Zhang, Zeqing, and Wang, Ying
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MAJOR adverse cardiovascular events ,MYOCARDIAL infarction ,PERCUTANEOUS coronary intervention ,DISEASE risk factors ,DECISION making - Abstract
Purpose: Current scoring systems used to predict major adverse cardiovascular events (MACE) in patients with acute myocardial infarction (AMI) lack some key components and their predictive ability needs improvement. This study aimed to develop a more effective scoring system for predicting 3-year MACE in patients with AMI. Patients and Methods: Our statistical analyses included data for 461 patients with AMI. Eighty percent of patients (n=369) were randomly assigned to the training set and the remaining patients (n=92) to the validation set. Independent risk factors for MACE were identified in univariate and multifactorial logistic regression analyses. A nomogram was used to create the scoring system, the predictive ability of which was assessed using calibration curve, decision curve analysis, receiver-operating characteristic curve, and survival analysis. Results: The nomogram model included the following seven variables: age, diabetes, prior myocardial infarction, Killip class, chronic kidney disease, lipoprotein(a), and percutaneous coronary intervention during hospitalization. The predicted and observed values for the nomogram model were in good agreement based on the calibration curves. Decision curve analysis showed that the clinical nomogram model had good predictive ability. The area under the curve (AUC) for the scoring system was 0.775 (95% confidence interval [CI] 0.728– 0.823) in the training set and 0.789 (95% CI 0.693– 0.886) in the validation set. Risk stratification based on the scoring system found that the risk of MACE was 4.51-fold higher (95% CI 3.24– 6.28) in the high-risk group than in the low-risk group. Notably, this scoring system demonstrated better predictive ability than the GRACE risk score (AUC 0.776 vs 0.731; P=0.007). Conclusion: The scoring system developed from the nomogram in this study showed favorable performance in prediction of MACE and risk stratification of patients with AMI. [ABSTRACT FROM AUTHOR] more...
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- 2024
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15. Investigation of Nutritional Factors and Malnutrition Risk Prediction Model in Hospitalized Patients with Systemic Lupus Erythematosus in China.
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Xia, Lijuan, Yang, Fanxing, Hayashi, Naoko, Ma, Yuan, Yan, Bin, Du, Yingxin, Chen, Sujuan, Xia, Yuke, Feng, Fang, and Ma, Zhifang
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SLEEP quality ,SYSTEMIC lupus erythematosus ,COMPLEMENT (Immunology) ,CONVENIENCE sampling (Statistics) ,LOGISTIC regression analysis - Abstract
Introduction: Nutritional status is a critical indicator of overall health and immune function, significantly influencing treatment outcomes. Despite its importance, the nutritional status of patients with systemic lupus erythematosus (SLE) often receives insufficient attention. This study aims to evaluate the nutritional status of patients with SLE, identify factors associated with malnutrition, and develop a risk prediction model for malnutrition in this population. Methods: We collected clinical data from a convenience sample of SLE patients at a general hospital in Ningxia Province, China, between January and December 2022. Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors for malnutrition. A risk prediction model was constructed and evaluated using the receiver operating characteristic (ROC) curve. Results: This study included 420 patients with SLE (mean age: 41.43 years, 91.7% women), of whom 46.2% were malnourished based on their serum albumin levels. Multivariate logistic regression analysis identified monthly income (OR=0.192, P< 0.05), sleep quality (OR=2.559, P< 0.05), kidney involvement (OR=4.269, P< 0.05), disease activity (OR=2.743, P< 0.05), leukocyte count (OR=1.576, P< 0.05), lymphocyte count (OR=0.393, P< 0.05), hemoglobin (OR=0.972, P< 0.05), complement C3 (OR=0.802, P< 0.05), and complement C4 (OR=0.493, P< 0.05) as independent risk factors for malnutrition. The prediction model showed good predictive value with an area under the ROC curve of 0.895 (95% CI: 0.823– 0.840), sensitivity of 0.907, and specificity of 0.827. The Hosmer-Lemeshow test indicated a good model fit (χ²=10.779, P=0.215). Discussion: Malnutrition is a significant concern among SLE patients, influenced by a range of socioeconomic and clinical factors. Our risk prediction model, with its high sensitivity and specificity, provides a robust tool for early identification of malnutrition in this population. Implementing this model in clinical practice can guide healthcare providers in prioritizing at-risk patients, enabling proactive nutritional interventions that could potentially improve clinical outcomes, enhance quality of life, and reduce healthcare costs associated with SLE. [ABSTRACT FROM AUTHOR] more...
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- 2024
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16. A Model to Predict the Risk of Adverse Ocular Outcomes in Pregnant Women.
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Liu, Xintian, Wen, Yiyi, Zou, Haiqing, and Wang, Shuangyong
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Aims/Background Pregnancy can affect various bodily functions, including metabolism, cardiovascular function, and eyesight. Pathological ocular changes observed during pregnancy are linked to the development of pregnancy-specific conditions, such as preeclampsia/eclampsia and gestational diabetes. This study aims to analyze clinical data disease history and maternal characteristics collected during pregnancy, to determine ocular parameters and develop a risk prediction model for adverse ocular outcomes. Methods We retrospectively analyzed the medical records of 760 pregnant women (1520 eyes) from September 2020 to September 2022 at The Third Affiliated Hospital of Guangzhou Medical University. We identified maternal variables that could influence adverse ocular outcomes, including maternal age, pregnancy-induced hypertension (PIH), gestational diabetes mellitus (GDM), eclampsia, pre-eclampsia, uterine disease, fetal abnormalities, in vitro fertilization with embryo transfer, hypoproteinemia, and major comorbidities during pregnancy. Univariate and multivariate logistic regression analyses were conducted to evaluate the effects of these independent predictors on adverse ocular outcomes. Additionally, receiver operating characteristic (ROC) curve analysis was performed to determine the cut-off probability with for optimal sensitivity and specificity. Results Eclampsia, pre-eclampsia, GDM, a history of chronic hypertension, and hypoproteinemia were identified as independent predictors of adverse ocular outcomes during pregnancy (p < 0.05). Maternal age, PIH, intrauterine growth retardation (IUGR), obesity, and pregnancy with immunoglobulin A nephropathy were predictors of moderate and severe retinal arteriole sclerosis during pregnancy (p < 0.05). Additionally, hemolysis, elevated liver enzymes, and low platelets (HELLP) syndrome were predictors of retinal hemorrhage and exudate during pregnancy (p < 0.05). The area under the ROC curve for adverse ocular outcomes were 0.75 and 0.74, respectively. Conclusion Our predictive model effectively forecasts adverse ocular outcomes during pregnancy, incorporating risk factors such as maternal age, eclampsia and pre-eclampsia, GDM, obesity, a history of chronic hypertension, hypoproteinemia, IUGR, pregnancy with immunoglobulin A nephropathy, and HELLP syndrome. [ABSTRACT FROM AUTHOR] more...
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- 2024
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17. Feasibility of risk assessment for breast cancer molecular subtypes.
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McCarthy, Anne Marie, Ehsan, Sarah, Hughes, Kevin S., Lehman, Constance D., Conant, Emily F., Kontos, Despina, Armstrong, Katrina, and Chen, Jinbo
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Purpose: Few breast cancer risk assessment models account for the risk profiles of different tumor subtypes. This study evaluated whether a subtype-specific approach improves discrimination. Methods: Among 3389 women who had a screening mammogram and were later diagnosed with invasive breast cancer we performed multinomial logistic regression with tumor subtype as the outcome and known breast cancer risk factors as predictors. Tumor subtypes were defined by expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) based on immunohistochemistry. Discrimination was assessed with the area under the receiver operating curve (AUC). Absolute risk of each subtype was estimated by proportioning Gail absolute risk estimates by the predicted probabilities for each subtype. We then compared risk factor distributions for women in the highest deciles of risk for each subtype. Results: There were 3,073 ER/PR+ HER2 − , 340 ER/PR +HER2 + , 126 ER/PR−ER2+, and 300 triple-negative breast cancers (TNBC). Discrimination differed by subtype; ER/PR−HER2+ (AUC: 0.64, 95% CI 0.59, 0.69) and TNBC (AUC: 0.64, 95% CI 0.61, 0.68) had better discrimination than ER/PR+HER2+ (AUC: 0.61, 95% CI 0.58, 0.64). Compared to other subtypes, patients at high absolute risk of TNBC were younger, mostly Black, had no family history of breast cancer, and higher BMI. Those at high absolute risk of HER2+ cancers were younger and had lower BMI. Conclusion: Our study provides proof of concept that stratifying risk prediction for breast cancer subtypes may enable identification of patients with unique profiles conferring increased risk for tumor subtypes. [ABSTRACT FROM AUTHOR] more...
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- 2024
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18. Predictive value of methylene blue combined with indocyanine green in sentinel lymph node metastasis in breast cancer: a prospective pilot cohort study.
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Zecheng He, Fan Guo, Yuhan Liu, Yan Lin, Changjun Wang, Yidong Zhou, and Qiang Sun
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METASTATIC breast cancer ,SENTINEL lymph nodes ,METHYLENE blue ,LYMPHATIC metastasis ,INDOCYANINE green ,SENTINEL lymph node biopsy - Abstract
Background: The status of sentinel lymph nodes is crucial for prognosis and treatment decisions in breast cancer patients. This study aimed to evaluate the predictive value of combined methylene blue and indocyanine green for sentinel lymph node metastasis in breast cancer. Methods: This prospective cohort study enrolled 90 clinically node-negative breast cancer patients. Methylene blue and indocyanine green were injected locally before surgery. Sentinel lymph nodes were grouped based on fluorescence intensity and methylene blue staining. A binary logistic regression model was established using 285 lymph node groups to predict metastatic risk. Results: A total of 475 lymph nodes were identified, with 33 being metastatic. The metastatic risk reached 70% for partially blue-stained and weakly fluorescent lymph nodes between 1-2 cm. The model revealed associations between lymph node size, dye staining patterns, and metastatic risks (P<0.05). The AUC of the ROC curve was 0.855. Conclusions: The staining pattern of combined methylene blue and indocyanine green could predict risks of sentinel lymph node metastasis and facilitate rapid intraoperative identification of high-risk lymph nodes. [ABSTRACT FROM AUTHOR] more...
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- 2024
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19. Nomogram to Predict Nodal Recurrence‐Free Survival in Early Oral Squamous Cell Carcinoma.
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Liu, Ying, Liu, Limin, He, Yining, Jiang, Wen, Fang, Tianyi, Huang, Yingying, Zhou, Xinyu, Zhu, Dongwang, Li, Jiang, and Zhong, Laiping
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LYMPHATIC metastasis , *SQUAMOUS cell carcinoma , *NOMOGRAPHY (Mathematics) , *ORAL cancer , *TUMOR grading , *HEAD & neck cancer - Abstract
ABSTRACT Objective Materials and Methods Results Conclusions This study aimed to develop and internally validate a prognostic nomogram for predicting nodal recurrence‐free survival (NRFS) in patients with early‐stage oral squamous cell carcinoma (OSCC) with clinically negative neck lymph nodes.The management of early‐stage oral cancer patients with clinically negative neck lymph nodes (cN0) remains controversial, especially concerning the need for elective neck dissection. Data from a single institution spanning 2010 to 2020 were utilized to develop and evaluate the nomogram. The nomogram was constructed using multivariable Cox regression and LASSO regression analyses to identify independent risk factors for lymph node metastasis. Internal validation was performed using bootstrap resampling to assess the nomogram's predictive accuracy.A total of 930 cN0 patients with T1 and T2 stage OSCC were randomly divided into training and validation cohorts (8:2 ratio). Independent risk factors for lymph node metastasis included tumor pathological grade (well: reference, moderate/poor: OR 1.69), cT (cT1: reference, cT2: OR 2.01), history of drinking (never: reference, current/former: OR 1.72), and depth of invasion (0 mm < DOI ≤ 5 mm: reference, 5 mm < DOI ≤ 10 mm: OR 1.31). The nomogram, incorporating these variables, demonstrated good predictive accuracy with a C‐index of 0.67 (95% CI: 0.58–0.76) in the validation set. In both training and validation groups, the nomogram effectively stratified patients into low‐risk and high‐risk groups for occult cervical nodal metastases (p < 0.05).The nomogram enables risk stratification and improved identification of occult cervical nodal metastases in clinically node‐negative OSCC patients by incorporating tumor‐specific and patient‐specific risk factors. [ABSTRACT FROM AUTHOR] more...
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- 2024
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20. Risk prediction models for autogenous arteriovenous fistula failure in maintenance hemodialysis patients: A systematic review and meta‐analysis.
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Han, Minghua, Zhao, Qian, Zhao, Jian, Xue, Xiaoxiao, and Wu, Hongxia
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ARTERIOVENOUS fistula , *MEDICAL personnel , *DATABASES , *HEMODIALYSIS patients , *PREDICTION models - Abstract
Background: The aim of this study was to systematically retrieve and evaluate published risk prediction models for autogenous arteriovenous fistula (AVF) failure post‐formation in maintenance hemodialysis (MHD) patients, with the goal of assisting healthcare providers in selecting or developing appropriate risk assessment tools and providing a reference for future research. Methods: A systematic search of relevant studies was conducted in PubMed, Web of Science, Cochrane Library, CINAHL, Embase, CNKI, Wanfang Database, VIP Database, and CBM Database up to February 1, 2024. Two researchers independently performed literature screening, data extraction, and methodological quality assessment using the Prediction Model Risk of bias (ROB) Assessment Tool. Results: A total of 4869 studies were identified, from which 25 studies with 28 prediction models were ultimately included. The incidence of autogenous AVF failure in MHD patients ranged from 3.9% to 39%. The most commonly used predictors were age, vein diameter, history of diabetes, AVF blood flow, and sex. The reported area under the curve (AUC) ranged from 0.61 to 0.911. All studies were found to have a high ROB, primarily due to inappropriate data sources and a lack of rigorous reporting in the analysis domain. The pooled AUC of five validation models was 0.80 (95% confidence interval: 0.79–0.81), indicating good predictive accuracy. Conclusion: The included studies indicated that the predictive models for AVF failure post‐formation in MHD patients are biased to some extent. Future research should focus on developing new models with larger sample sizes, strict adherence to reporting procedures, and external validation across multiple centers. [ABSTRACT FROM AUTHOR] more...
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- 2024
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21. Construction and Validation of a Risk Prediction Model for Colon Adenocarcinoma Prognosis Based on 11 Depression‐Related Genes.
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Yuan, Yunchuan, Xia, Lili, Wu, Xintong, Yang, Hongjing, Dou, Lu, and Li, Zhengrui
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REGULATORY T cells , *RECEIVER operating characteristic curves , *DISEASE risk factors , *IMMUNOSUPPRESSION , *IMMUNOLOGIC memory - Abstract
Background: The influences of depression on cancer have noticeably attracted scholars' attention. This study is aimed at exploring the relationships between depression and colon adenocarcinoma (COAD). Methods: Differentially expressed genes in COAD were overlapped with depression‐related genes (DRGs) to obtain COAD‐DRGs. A risk model was constructed to predict overall survival (OS) using univariate and multivariate Cox regression analyses. GSE39582 dataset was utilized to validate the model. A nomogram was developed utilizing the clinical data. Results: A risk model containing 11 genes was constructed. The results of receiver operating characteristic curve analysis revealed that the model could well predict the OS. In the high‐risk group, the infiltration levels of plasma cells, resting/activated memory CD4 T cells, and monocytes were reduced, and only the infiltration levels of CD8 T cells and regulatory T cells were elevated. Cox regression analysis indicated that the risk score emerged as an independent prognostic factor. Finally, a nomogram of comprehensive risk score, age, and pM stage was established, and the predictions of this model aligned well with the actual OS data. Conclusion: A COAD risk prediction model was successfully constructed utilizing 11 DRGs. This model assists in implementing more effective treatment and care strategies, enhancing the clinical outcomes for COAD. [ABSTRACT FROM AUTHOR] more...
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- 2024
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22. Risk prediction models for disability in older adults: a systematic review and critical appraisal.
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Zhou, Jinyan, Xu, Yihong, Yang, Dan, Zhou, Qianya, Ding, Shanni, and Pan, Hongying
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CINAHL database ,LOGISTIC regression analysis ,BODY mass index ,OLDER people ,PREDICTION models - Abstract
Background: The amount of prediction models for disability in older adults is increasing but the prediction performance of different models varies greatly, and the quality of prediction models is still unclear. Objectives: To systematically review and critically appraise the studies on risk prediction models for disability in older adults. Methods: A systematic literature search was conducted on PubMed, Embase, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), and Wanfang Database, published up until June 30, 2023. Data were extracted according to the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and applicability of the included studies. In addition, all included studies were evaluated for clinical value. Results: A total of 5722 articles were initially retrieved from databases, 16 studies and 17 prediction models were finally included after screening. The sample sizes of studies ranged from 420 to 90,889. Model development methods mainly included logistic regression analysis, Cox proportional hazards regression, and machine learning methods. The C statistic or area under the curve (AUC) of models ranged from 0.650 to 0.853, and nine models had C statistic/AUC higher than 0.75. Age, chronic disease, gender, self-rated health, body mass index (BMI), drinking, smoking and education level were the most common predictors. According to the PROBAST, all included studies were at high risk of bias, and 10 studies were at high concerns for applicability. Only two studies reported following the Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement. After evaluation, only two models reached the standard of clinical value. Conclusion: Although most of the included prediction models had acceptable discrimination, the overall quality and clinical value of the current studies were poor. In the future, researchers should follow the TRIPOD statement and PROBAST checklist to develop prediction models with larger sample sizes, more reasonable study designs, and more scientific analysis methods, to improve the predictive performance and application value. Trial registration: The review protocol was registered in PROSPERO (registration ID: CRD42023446657). [ABSTRACT FROM AUTHOR] more...
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- 2024
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23. Risk Factors Analysis and Prediction Model Establishment for Carbapenem-Resistant Enterobacteriaceae Colonization: A Retrospective Cohort Study.
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Guo, Xiaolan, Wu, Dansen, Chen, Xiaoping, Lin, Jing, Chen, Jialong, Wang, Liming, Shi, Songjing, Yang, Huobao, Liu, Ziyi, and Hong, Donghuang
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APACHE (Disease classification system) ,RANDOM forest algorithms ,CARBAPENEM-resistant bacteria ,INTENSIVE care units ,COLONIZATION (Ecology) ,MANN Whitney U Test ,LOGISTIC regression analysis - Abstract
Objective of this study was to identify the risk factors associated with Carbapenem-resistant Enterobacteriaceae (CRE) colonization in intensive care unit (ICU) patients and to develop a predictive risk model for CRE colonization. Patients and Methods: In this study, 121 ICU patients from Fujian Provincial Hospital were enrolled between January 2021 and July 2022. Based on bacterial culture results from rectal and throat swabs, patients were categorized into two groups: CRE-colonized (n = 18) and non-CRE-colonized (n = 103). To address class imbalance, Synthetic Minority Over-sampling Technique (SMOTE) was applied. Statistical analyses including T-tests, Chi-square tests, and Mann–Whitney U-tests were employed to compare differences between the groups. Feature selection was performed using Lasso regression and Random Forest algorithms. A Logistic regression model was then developed to predict CRE colonization risk, and the results were presented in a nomogram. Results: After applying SMOTE, the dataset included 198 CRE-colonized patients and 180 non-CRE-colonized patients, ensuring balanced groups. The two groups were comparable in most clinical characteristics except for diabetes, previous emergency department admission, and abdominal infection. Eight independent risk factors for CRE colonization were identified through Random Forest, Lasso regression, and Logistic regression, including Acute Physiology and Chronic Health Evaluation (APACHE) II score > 16, length of hospital stay > 31 days, female gender, previous carbapenem antibiotic exposure, skin infection, multi-site infection, immunosuppressant exposure, and tracheal intubation. The risk prediction model for CRE colonization demonstrated high accuracy (87.83%), recall rate (89.9%), precision (85.6%), and an AUC value of 0.877. Patients were categorized into low-risk (0– 90 points), medium-risk (91– 160 points), and high-risk (161– 381 points) groups, with corresponding CRE colonization rates of 1.82%, 7.14%, and 58.33%, respectively. Conclusion: This study identified independent risk factors for CRE colonization and developed a predictive model for assessing the risk of CRE colonization. [ABSTRACT FROM AUTHOR] more...
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- 2024
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24. Construction and verification of a model for predicting fall risk in patients with maintenance hemodialysis†
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Liu Yue, Zeng Yan-Li, Zhanga Shan, Meng Li, He Xiao-Hua, and Tang Qing
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construction ,fall ,maintenance hemodialysis ,risk prediction model ,verification ,Nursing ,RT1-120 - Abstract
To construct a risk prediction model for fall in patients with maintenance hemodialysis (MHD) and to verify the prediction effect of the model.
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- 2024
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25. Risk factors for SARS-CoV-2 pneumonia among renal transplant recipients in Omicron pandemic—a prospective cohort study
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Sai Zhang, Xiang Ding, Chunmi Geng, and Hong Zhang
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SARS-CoV-2 ,Pneumonia ,Omicron ,Renal transplant recipients ,Risk factors ,Risk prediction model ,Infectious and parasitic diseases ,RC109-216 - Abstract
Abstract Background The coronavirus disease (COVID-19) pandemic is a global health emergency, and SARS-CoV-2 pneumonia poses significant challenges to health systems worldwide. Renal transplant recipients (RTRs) are a special group and are more vulnerable to viral pneumonia. However, few studies have elucidated the risk factors of SARS-CoV-2 pneumonia in RTRs infected with COVID-19. This study aimed to build a risk prediction model for SARS-CoV-2 pneumonia among RTRs based on demographic and clinical information. Methods We conducted a prospective cohort study among 383 RTRs (age ≥ 18 years) diagnosed with COVID-19 from December 21, 2022, to March 26, 2023. Patients’ demographic and clinical information was collected through a questionnaire survey combined with electronic medical records. A stepwise logistic regression model was established to test the predictors of SARS-CoV-2 pneumonia. We assessed the diagnostic performance of the model by calculating the area under the curve (AUC) of the receiver operating characteristic (ROC) and calibration using the Hosmer–Lemeshow (HL) goodness-of-fit test. Results Our study showed that the incidence of SARS-CoV-2 pneumonia among RTRs was 31.1%. Older age (OR = 2.08–3.37,95%CI:1.05–7.23), shorter post-transplantation duration (OR = 0.92,95% CI: 0.87,0.99), higher post-transplant Charlson Comorbidity Index (CCI) (OR = 1.84, 95%CI: 1.14,2.98), pulmonary infection history (OR = 3.44, 95%CI: 1.459, 8.099, P = 0.005), fatigue (OR = 2.11, 95%CI: 1.14, 3.90), cough (OR = 2.03, 95%CI: 1.08, 3.81), and lower estimated glomerular filtration rate (eGFR) at COVID-19 diagnosis (OR = 0.98, 95%CI:0.97,0.99) predicted a higher risk for SARS-CoV-2 pneumonia. The model showed good diagnostic performance with Chi-Square = 10.832 (P > 0.05) and AUC = 0.839 (P more...
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- 2024
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26. Risk prediction models for disability in older adults: a systematic review and critical appraisal
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Jinyan Zhou, Yihong Xu, Dan Yang, Qianya Zhou, Shanni Ding, and Hongying Pan
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Older adults ,Disability ,Risk prediction model ,Systematic review ,Geriatrics ,RC952-954.6 - Abstract
Abstract Background The amount of prediction models for disability in older adults is increasing but the prediction performance of different models varies greatly, and the quality of prediction models is still unclear. Objectives To systematically review and critically appraise the studies on risk prediction models for disability in older adults. Methods A systematic literature search was conducted on PubMed, Embase, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), and Wanfang Database, published up until June 30, 2023. Data were extracted according to the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and applicability of the included studies. In addition, all included studies were evaluated for clinical value. Results A total of 5722 articles were initially retrieved from databases, 16 studies and 17 prediction models were finally included after screening. The sample sizes of studies ranged from 420 to 90,889. Model development methods mainly included logistic regression analysis, Cox proportional hazards regression, and machine learning methods. The C statistic or area under the curve (AUC) of models ranged from 0.650 to 0.853, and nine models had C statistic/AUC higher than 0.75. Age, chronic disease, gender, self-rated health, body mass index (BMI), drinking, smoking and education level were the most common predictors. According to the PROBAST, all included studies were at high risk of bias, and 10 studies were at high concerns for applicability. Only two studies reported following the Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement. After evaluation, only two models reached the standard of clinical value. Conclusion Although most of the included prediction models had acceptable discrimination, the overall quality and clinical value of the current studies were poor. In the future, researchers should follow the TRIPOD statement and PROBAST checklist to develop prediction models with larger sample sizes, more reasonable study designs, and more scientific analysis methods, to improve the predictive performance and application value. Trial registration The review protocol was registered in PROSPERO (registration ID: CRD42023446657). more...
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- 2024
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27. Nomogram for predicting the risk of nosocomial infections among obstetric inpatients: a large-scale retrospective study in China
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Lei Huang, Houzhi Chen, Jielong Wu, Huiping Huang, and Jing Ran
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Large-scale retrospective study ,Obstetric inpatients ,Postpartum infections ,Risk prediction model ,Nomogram ,Infectious and parasitic diseases ,RC109-216 - Abstract
Abstract Objective This study aimed to develop and validate a nomogram for assessing the risk of nosocomial infections among obstetric inpatients, providing a valuable reference for predicting and mitigating the risk of postpartum infections. Methods A retrospective observational study was performed on a cohort of 28,608 obstetric patients admitted for childbirth between 2017 and 2022. Data from the year 2022, comprising 4,153 inpatients, were utilized for model validation. Univariable and multivariable stepwise logistic regression analyses were employed to identify the factors influencing nosocomial infections among obstetric inpatients. A nomogram was subsequently developed based on the final predictive model. The receiver operating characteristic (ROC) curve was utilized to calculate the area under the curve (AUC) to evaluate the predictive accuracy of the nomogram in both the training and validation datasets. Results The gestational weeks > = 37, prenatal anemia, prenatal hypoproteinemia, premature rupture of membranes (PROM), cesarean sction, operative delivery, adverse birth outcomes, length of hospitalization (days) > 5, CVC use and catheterization of ureter were included in the ultimate prediction model. The AUC of the nomogram was 0.828 (0.823, 0.833) in the training dataset and 0.855 (0.844, 0.865) in the validation dataset. Conclusion Through a large-scale retrospective study conducted in China, we developed and independently validated a nomogram to enable personalized postpartum infections risk estimates for obstetric inpatients. Its clinical application can facilitate early identification of high-risk groups, enabling timely infection prevention and control measures. more...
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- 2024
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28. Analysis of influencing factors and construction of risk prediction model for postoperative thrombocytopenia in critically ill patients with heart disease
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Changjun Song, Yicai Wu, Yuanyuan Liu, Jun Zhang, Jingliang Peng, Yuming Zhou, and Lulu Yi
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Heart ,Critical illness ,Thrombocytopenia ,Influencing factors ,Risk prediction model ,Surgery ,RD1-811 ,Anesthesiology ,RD78.3-87.3 - Abstract
Abstract Objective To analyze the influencing factors of postoperative thrombocytopenia in critically ill patients with heart disease and construct a nomogram prediction model. Methods From October 2022 to October 2023, 319 critically ill patients with heart disease who visited our hospital were collected and separated into postoperative thrombocytopenia group (n = 142) and no postoperative thrombocytopenia group (n = 177) based on their postoperative thrombocytopenia, Logistic regression analysis was applied to screen risk factors for postoperative thrombocytopenia in critically ill patients with heart disease; R software was applied to construct a nomogram for predicting postoperative thrombocytopenia in critically ill patients with heart disease, and ROC curves, calibration curves, and Hosmer-Lemeshow goodness of fit tests were applied to evaluate nomogram. Results A total of 142 out of 319 critically ill patients had postoperative thrombocytopenia, accounting for 44.51%. Logistic regression analysis showed that gender (95% CI 1.607–4.402, P = 0.000), age ≥ 60 years (95% CI 1.380–3.697, P = 0.001), preoperative antiplatelet therapy (95% CI 1.254–3.420, P = 0.004), and extracorporeal circulation time > 120 min (95% CI 1.681–4.652, P = 0.000) were independent risk factors for postoperative thrombocytopenia in critically ill patients with heart disease. The area under the ROC curve was 0.719 (95% CI: 0.663–0.774). The slope of the calibration curve was close to 1, and the Hosmer-Lemeshow goodness of fit test was χ 2 = 6.422, P = 0.491. Conclusion Postoperative thrombocytopenia in critically ill patients with heart disease is influenced by gender, age ≥ 60 years, preoperative antiplatelet therapy, and extracorporeal circulation time > 120 min. A nomogram established based on above multiple independent risk factors provides a method for clinical prediction of the risk of postoperative thrombocytopenia in critically ill patients with heart disease. more...
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- 2024
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29. A risk prediction model for unexplained early neurological deterioration following intravenous thrombolysis
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Bifeng Zhu, Dan Wang, Jing Zuo, Yi Huang, Chang Gao, Haiwei Jiang, and Dan Yan
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Early neurological deterioration ,Intravenous thrombolysis ,Acute ischemic stroke ,Risk prediction model ,Nomogram ,Prospective study ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Abstract Background and objectives Early neurological deterioration (END) post-intravenous thrombolysis significantly impacts the long-term prognosis of stroke patients. This study aimed to establish a rapid risk prediction model for unexplained END following intravenous thrombolysis. Methods This prospective study consecutively enrolled patients with acute ischemic stroke treated with recombinant tissue plasminogen activator intravenous thrombolysis at the Department of Neurology, Third People’s Hospital of Hubei Province, and Yangluo Hospital District between June 2019 and December 2022. Unexplained END was defined as an increase of ≥ 4 points in the National Institutes of Health Stroke Scale (NIHSS) score between admission and 24 h. A nomogram was developed and assessed by calculating the area under the receiver operating characteristic curve (AUC-ROC). The calibration was assessed using the Hosmer–Lemeshow test. Results A total of 211 patients (130 males and 110 patients aged more...
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- 2024
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30. Web-Based Dynamic Nomogram for Predicting Risk of Mortality in Heart Failure with Mildly Reduced Ejection Fraction
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Guo W, Tian J, Wang Y, Zhang Y, Yan J, Du Y, and Han Q
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heart failure with mildly reduced ejection fraction ,all-cause mortality ,risk prediction model ,risk strategy ,dynamic nomogram ,Public aspects of medicine ,RA1-1270 - Abstract
Wei Guo,1 Jing Tian,1 Yajing Wang,1 Yajing Zhang,1 Jingjing Yan,2 Yutao Du,2 Yanbo Zhang,2,3 Qinghua Han1,4 1Department of Cardiology, the 1st Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, People’s Republic of China; 2Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi Province, People’s Republic of China; 3Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, Shanxi Province, People’s Republic of China; 4Key Laboratory of Cellular Physiology at Shanxi Medical University, Ministry of Education, Taiyuan, Shanxi Province, People’s Republic of ChinaCorrespondence: Qinghua Han, Department of Cardiology, the 1st Hospital of Shanxi Medical University, No. 85 South JieFang Road, Yingze District, Taiyuan, Shanxi, People’s Republic of China, Email syhqh@sohu.com Yanbo Zhang, Department of Health Statistics, School of Public Health, Shanxi Medical University, No. 56 South XinJian Road, Yingze District, Taiyuan, Shanxi, People’s Republic of China, Email sxmuzyb@126.comPurpose: This study aimed to develop an integrative dynamic nomogram, including N-terminal pro-B type natural peptide (NT-proBNP) and estimated glomerular filtration rate (eGFR), for predicting the risk of all-cause mortality in HFmrEF patients.Patients and Methods: 790 HFmrEF patients were prospectively enrolled in the development cohort for the model. The least absolute shrinkage and selection operator (LASSO) regression and Random Survival Forest (RSF) were employed to select predictors for all-cause mortality. Develop a nomogram based on the Cox proportional hazard model for predicting long-term mortality (1-, 3-, and 5-year) in HFmrEF. Internal validation was conducted using Bootstrap, and the final model was validated in an external cohort of 338 consecutive adult patients. Discrimination and predictive performance were evaluated by calculating the time-dependent concordance index (C-index), area under the ROC curve (AUC), and calibration curve, with clinical value assessed via decision curve analysis (DCA). Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to assess the contributions of NT-proBNP and eGFR to the nomogram. Finally, develop a dynamic nomogram using the “Dynnom” package.Results: The optimal independent predictors for all-cause mortality (APSELNH: A: angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor-neprilysin inhibitor (ACEI/ARB/ARNI), P: percutaneous coronary intervention/coronary artery bypass graft (PCI/CABG), S: stroke, E: eGFR, L: lg of NT-proBNP, N: NYHA, H: healthcare) were incorporated into the dynamic nomogram. The C-index in the development cohort and validation cohort were 0.858 and 0.826, respectively, with AUCs exceeding 0.8, indicating good discrimination and predictive ability. DCA curves and calibration curves demonstrated clinical applicability and good consistency of the nomogram. NT-proBNP and eGFR provided significant net benefits to the nomogram.Conclusion: In this study, the dynamic APSELNH nomogram developed serves as an accessible, functional, and effective clinical decision support calculator, offering accurate prognostic assessment for patients with HFmrEF.Keywords: heart failure with mildly reduced ejection fraction, all-cause mortality, risk prediction model, risk strategy, dynamic nomogram more...
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- 2024
31. Risk Prediction Model for Non-Suicidal Self-Injury in Chinese Adolescents with Major Depressive Disorder Based on Machine Learning
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Sun T, Liu J, Wang H, Yang BX, Liu Z, Wan Z, Li Y, Xie X, Li X, Gong X, and Cai Z
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non-suicidal self-injury ,adolescents ,major depressive disorder ,risk prediction model ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 ,Neurology. Diseases of the nervous system ,RC346-429 - Abstract
Ting Sun,1,2,* Jingfang Liu,3,* Hui Wang,3,* Bing Xiang Yang,3– 5 Zhongchun Liu,3 Jie Liu,6 Zhiying Wan,3 Yinglin Li,1 Xiangying Xie,1 Xiaofen Li,3 Xuan Gong,3 Zhongxiang Cai1 1Department of Nursing, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China; 2Health Science Center, Yangtze University, Jingzhou, People’s Republic of China; 3Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China; 4School of Nursing, Wuhan University, Wuhan, People’s Republic of China; 5Population and Health Research Center, Wuhan University, Wuhan, People’s Republic of China; 6Anesthesiology, Virginia Commonwealth University Health System, Richmond, VA, USA*These authors contributed equally to this workCorrespondence: Zhongxiang Cai, Department of Nursing, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuhan, Hubei Province, 430060, People’s Republic of China, Email tg20201228@163.com Xuan Gong, Department of Psychiatry, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuhan, Hubei Province, 430060, People’s Republic of China, Email 12048387@qq.comBackground: Non-suicidal self-injury (NSSI) is a significant social issue, especially among adolescents with major depressive disorder (MDD). This study aimed to construct a risk prediction model using machine learning (ML) algorithms, such as XGBoost and random forest, to identify interventions for healthcare professionals working with adolescents with MDD.Methods: This study investigated 488 adolescents with MDD. Adolescents was randomly divided into 75% training set and 25% test set to testify the predictive value of risk prediction model. The prediction model was constructed using XGBoost and random forest algorithms. We evaluated the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, recall, F Score of the two models for comparing the performance of the two models.Results: There were 161 (33.00%) participants having NSSI. Compared without NSSI, there were statistically significant differences in gender (P=0.035), age (P=0.036), depressive symptoms (P=0.042), sleep quality (P=0.030), dysfunctional attitudes (P=0.048), childhood trauma (P=0.046), interpersonal problems (P=0.047), psychoticism (P) (P=0.049), neuroticism (N) (P=0.044), punishing and Severe (F2) (P=0.045) and Overly-intervening and Protecting (M2) (P=0.047) with NSSI. The AUC values for random forest and XGBoost were 0.780 and 0.807, respectively. The top five most important risk predictors identified by both machine learning methods were dysfunctional attitude, childhood trauma, depressive symptoms, F2 and M2.Conclusion: The study demonstrates the suitability of prediction models for predicting NSSI behavior in Chinese adolescents with MDD based on ML. This model improves the assessment of NSSI in adolescents with MDD by health care professionals working. This provides a foundation for focused prevention and interventions by health care professionals working with these adolescents.Keywords: non-suicidal self-injury, adolescents, major depressive disorder, risk prediction model more...
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- 2024
32. Development and validation of a nomogram model of depression and sleep disorders and the risk of disease progression in patients with breast cancer
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Jun Shen, Dan Zhou, Meng Wang, Fan Li, Huan-Huan Yan, Jun Zhou, and Wen-Wen Sun
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Depression ,Progressive disease ,Risk prediction model ,Sleep disorder ,Gynecology and obstetrics ,RG1-991 ,Public aspects of medicine ,RA1-1270 - Abstract
Abstract Background In this study, we investigated the relationship between the risk of postoperative progressive disease (PD) in breast cancer and depression and sleep disorders in order to develop and validate a suitable risk prevention model. Methods A total of 750 postoperative patients with breast cancer were selected from the First People’s Hospital of LianYunGang, and the indices of two groups (an event group and a non-event group) were compared to develop and validate a risk prediction model. The relationship between depression, sleep disorders, and PD events was investigated using the follow-up data of the 750 patients. Results SAS, SDS, and AIS scores differed in the group of patients who experienced postoperative disease progression versus those who did not; the differences were statistically significant and the ability to differentiate prognosis was high. The area under the receiver operating characteristic (ROC) curves (AUC) were: 0.8049 (0.7685–0.8613), 0.768 (0.727–0.809), and 0.7661 (0.724-–0.808), with cut-off values of 43.5, 48.5, and 4.5, respectively. Significant variables were screened by single-factor analysis and multi-factor analysis to create model 1, by lasso regression and cross-lasso regression analysis to create model 2, by random forest calculation method to create model 3, by stepwise regression method (backward method) to create model 4, and by including all variables for Cox regression to include significant variables to create model 5. The AUC of model 2 was 0.883 (0.848–0.918) and 0.937 (0.893–0.981) in the training set and validation set, respectively. The clinical efficacy of the model was evaluated using decision curve analysis and clinical impact curve, and then the model 2 variables were transformed into scores, which were validated in two datasets, the training and validation sets, with AUCs of 0.884 (0.848–0.919) and 0.885 (0.818–0.951), respectively. Conclusion We established and verified a model including SAS, SDS and AIS to predict the prognosis of breast cancer patients, and simplified it by scoring, making it convenient for clinical use, providing a theoretical basis for precise intervention in these patients. However, further research is needed to verify the generalization ability of our model. more...
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- 2024
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33. Construction and Validation of a Risk Prediction Model for Mild Cognitive Impairment in Non-Dialysis Chronic Kidney Disease Patient
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Wenbin Xu, Qin Yang, Lin Li, Yuhe Xiang, and Qian Yang
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chronic kidney disease ,non-dialysis patients ,mild cognitive impairment ,nomogram ,risk prediction model ,Dermatology ,RL1-803 ,Diseases of the circulatory (Cardiovascular) system ,RC666-701 ,Diseases of the genitourinary system. Urology ,RC870-923 - Abstract
Introduction: The aims of this study are to explore the factors affecting mild cognitive impairment in patients with chronic kidney disease (CKD) who are not undergoing dialysis and to construct and validate a nomogram risk prediction model. Methods: Using a convenience sampling method, 383 non-dialysis CKD patients from two tertiary hospitals in Chengdu were selected between February 2023 and August 2023 to form the modeling group. The patients were divided into a mild cognitive impairment group (n = 192) and a non-mild cognitive impairment group (n = 191), and factors such as demographics, disease data, and sleep disorders were compared between the two groups. Univariate and multivariate binary logistic regression analyses were used to identify independent influencing factors, followed by collinearity testing, and construction of the regression model. The final risk prediction model was presented through a nomogram and an online calculator, with internal validation using Bootstrap sampling. For external validation, 137 non-dialysis CKD patients from another tertiary hospital in Chengdu were selected between October 2023 and December 2023. Results: In the modeling group, 192 (50.1%) of the non-dialysis CKD patients developed mild cognitive impairment, and in the validation group, 56 (40.9%) patients developed mild cognitive impairment, totaling 248 (47.7%) of all sampled non-dialysis CKD patients. Age, educational level, Occupation status, Use of smartphone, sleep disorders, hemoglobin, and platelet count were independent factors influencing the occurrence of mild cognitive impairment in non-dialysis CKD patients (all p < 0.05). The model evaluation showed an area under the ROC curve of 0.928, 95% CI (0.902, 0.953) in the modeling group, and 0.897, 95% CI (0.844, 0.950) in the validation group. The model's Youden index was 0.707, with an optimal cutoff value of 0.494, sensitivity of 0.853, and specificity of 0.854, indicating good predictive performance; calibration curves, Hosmer-Lemeshow test, and clinical decision curves indicated good calibration and clinical benefit. Internal validation results showed a consistency index (C-index) of 0.928, 95% CI (0.902, 0.953). Conclusion: The risk prediction model developed in this study shows excellent performance, demonstrating significant predictive potential for early screening of mild cognitive impairment in non-dialysis CKD patients. The application of this model will provide a reference for healthcare professionals, helping them formulate more targeted intervention strategies to optimize patient treatment and management outcomes. more...
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- 2024
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34. 急性冠脉综合征不良心血管事件机器学习预测 模型的研究进展.
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周仟慧 and 古满平
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The adverse cardiovascular events in patients with acute coronary syndrome can seriously affect the prognosis. In recent years, the development of artificial intelligence and big data in the medical fields has provided new ideas for the risk prediction of such people. At present, a variety of machine learning prediction models have been used to predict various adverse cardiovascular events in patients with acute coronary syndrome. This article reviewed the research and application status of these machine learning prediction models, and reviewed the model construction methods, data sources and characteristics, model validation and risk factors, so as to provide references for patient risk assessment, early prevention and prognosis assessment. [ABSTRACT FROM AUTHOR] more...
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- 2024
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35. Nomogram for predicting the risk of nosocomial infections among obstetric inpatients: a large-scale retrospective study in China.
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Huang, Lei, Chen, Houzhi, Wu, Jielong, Huang, Huiping, and Ran, Jing
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DELIVERY (Obstetrics) , *RECEIVER operating characteristic curves , *NOSOCOMIAL infections , *LOGISTIC regression analysis , *INFECTION prevention - Abstract
Objective: This study aimed to develop and validate a nomogram for assessing the risk of nosocomial infections among obstetric inpatients, providing a valuable reference for predicting and mitigating the risk of postpartum infections. Methods: A retrospective observational study was performed on a cohort of 28,608 obstetric patients admitted for childbirth between 2017 and 2022. Data from the year 2022, comprising 4,153 inpatients, were utilized for model validation. Univariable and multivariable stepwise logistic regression analyses were employed to identify the factors influencing nosocomial infections among obstetric inpatients. A nomogram was subsequently developed based on the final predictive model. The receiver operating characteristic (ROC) curve was utilized to calculate the area under the curve (AUC) to evaluate the predictive accuracy of the nomogram in both the training and validation datasets. Results: The gestational weeks > = 37, prenatal anemia, prenatal hypoproteinemia, premature rupture of membranes (PROM), cesarean sction, operative delivery, adverse birth outcomes, length of hospitalization (days) > 5, CVC use and catheterization of ureter were included in the ultimate prediction model. The AUC of the nomogram was 0.828 (0.823, 0.833) in the training dataset and 0.855 (0.844, 0.865) in the validation dataset. Conclusion: Through a large-scale retrospective study conducted in China, we developed and independently validated a nomogram to enable personalized postpartum infections risk estimates for obstetric inpatients. Its clinical application can facilitate early identification of high-risk groups, enabling timely infection prevention and control measures. [ABSTRACT FROM AUTHOR] more...
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- 2024
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36. Analysis of influencing factors and construction of risk prediction model for postoperative thrombocytopenia in critically ill patients with heart disease.
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Song, Changjun, Wu, Yicai, Liu, Yuanyuan, Zhang, Jun, Peng, Jingliang, Zhou, Yuming, and Yi, Lulu
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CARDIAC patients , *PREOPERATIVE risk factors , *LOGISTIC regression analysis , *ARTIFICIAL blood circulation , *CRITICALLY ill - Abstract
Objective: To analyze the influencing factors of postoperative thrombocytopenia in critically ill patients with heart disease and construct a nomogram prediction model. Methods: From October 2022 to October 2023, 319 critically ill patients with heart disease who visited our hospital were collected and separated into postoperative thrombocytopenia group (n = 142) and no postoperative thrombocytopenia group (n = 177) based on their postoperative thrombocytopenia, Logistic regression analysis was applied to screen risk factors for postoperative thrombocytopenia in critically ill patients with heart disease; R software was applied to construct a nomogram for predicting postoperative thrombocytopenia in critically ill patients with heart disease, and ROC curves, calibration curves, and Hosmer-Lemeshow goodness of fit tests were applied to evaluate nomogram. Results: A total of 142 out of 319 critically ill patients had postoperative thrombocytopenia, accounting for 44.51%. Logistic regression analysis showed that gender (95% CI 1.607–4.402, P = 0.000), age ≥ 60 years (95% CI 1.380–3.697, P = 0.001), preoperative antiplatelet therapy (95% CI 1.254–3.420, P = 0.004), and extracorporeal circulation time > 120 min (95% CI 1.681–4.652, P = 0.000) were independent risk factors for postoperative thrombocytopenia in critically ill patients with heart disease. The area under the ROC curve was 0.719 (95% CI: 0.663–0.774). The slope of the calibration curve was close to 1, and the Hosmer-Lemeshow goodness of fit test was χ2 = 6.422, P = 0.491. Conclusion: Postoperative thrombocytopenia in critically ill patients with heart disease is influenced by gender, age ≥ 60 years, preoperative antiplatelet therapy, and extracorporeal circulation time > 120 min. A nomogram established based on above multiple independent risk factors provides a method for clinical prediction of the risk of postoperative thrombocytopenia in critically ill patients with heart disease. [ABSTRACT FROM AUTHOR] more...
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- 2024
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37. Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.
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Ahmad, Faraz S., Hu, Ted Ling, Adler, Eric D., Petito, Lucia C., Wehbe, Ramsey M., Wilcox, Jane E., Mutharasan, R. Kannan, Nardone, Beatrice, Tadel, Matevz, Greenberg, Barry, Yagil, Avi, and Campagnari, Claudio more...
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Background: Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems. Objective: To assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta‐Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score. Design: Retrospective, cohort study. Participants: Data from 6764 adults with HF were abstracted from EHRs at a large integrated health system from 1/1/10 to 12/31/19. Main measures: One-year survival from time of first cardiology or primary care visit was estimated using MARKER-HF, SHFM, and MAGGIC. Discrimination was measured by the area under the receiver operating curve (AUC). Calibration was assessed graphically. Key results: Compared to MARKER-HF, both SHFM and MAGGIC required a considerably larger amount of data engineering and imputation to generate risk score estimates. MARKER-HF, SHFM, and MAGGIC exhibited similar discriminations with AUCs of 0.70 (0.69–0.73), 0.71 (0.69–0.72), and 0.71 (95% CI 0.70–0.73), respectively. All three scores showed good calibration across the full risk spectrum. Conclusions: These findings suggest that MARKER-HF, which uses readily available clinical and lab measurements in the EHR and required less imputation and data engineering than SHFM and MAGGIC, is an easier tool to identify high-risk patients in ambulatory clinics who could benefit from referral to a HF specialist. [ABSTRACT FROM AUTHOR] more...
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- 2024
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38. Identifying Key Genes as Progression Indicators of Prostate Cancer with Castration Resistance Based on Dynamic Network Biomarker Algorithm and Weighted Gene Correlation Network Analysis.
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Liu, Siyuan, Hu, Yi, Liu, Fei, Jiang, Yizheng, Wang, Hongrui, Wu, Xusheng, and Hu, Dehua
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CASTRATION-resistant prostate cancer ,NUCLEOTIDE sequence ,RNA sequencing ,ANDROGEN deprivation therapy ,GENE expression profiling - Abstract
Background: Androgen deprivation therapy (ADT) is the mainstay of treatment for prostate cancer, yet dynamic molecular changes from hormone-sensitive to castration-resistant states in patients treated with ADT remain unclear. Methods: In this study, we combined the dynamic network biomarker (DNB) method and the weighted gene co-expression network analysis (WGCNA) to identify key genes associated with the progression to a castration-resistant state in prostate cancer via the integration of single-cell and bulk RNA sequencing data. Based on the gene expression profiles of CRPC in the GEO dataset, the DNB method was used to clarify the condition of epithelial cells and find out the most significant transition signal DNB modules and genes included. Then, we calculated gene modules associated with the clinical phenotype stage based on the WGCNA. IHC was conducted to validate the expression of the key genes in CRPC and primary PCa patients Results:Nomograms, calibration plots, and ROC curves were applied to evaluate the good prognostic accuracy of the risk prediction model. Results: By combining single-cell RNA sequence data and bulk RNA sequence data, we identified a set of DNBs, whose roles involved in androgen-associated activities indicated the signals of a prostate cancer cell transition from an androgen-dependent state to a castration-resistant state. In addition, a risk prediction model including the risk score of four key genes (SCD, NARS2, ALDH1A1, and NFXL1) and other clinical–pathological characteristics was constructed and verified to be able to reasonably predict the prognosis of patients receiving ADT. Conclusions: In summary, four key genes from DNBs were identified as potential diagnostic markers for patients treated with ADT and a risk score-based nomogram will facilitate precise prognosis prediction and individualized therapeutic interventions of CRPC. [ABSTRACT FROM AUTHOR] more...
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- 2024
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39. 难治性肺炎支原体肺炎患儿并发闭塞性 细支气管炎风险预测模型的构建.
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刘铁虎, 刘小雪, 汤洋, 齐飞, and 刘登品
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BRONCHIOLITIS obliterans ,MYCOPLASMA pneumoniae infections ,MYCOPLASMA pneumoniae ,RECEIVER operating characteristic curves ,LENGTH of stay in hospitals - Abstract
Copyright of Chinese Journal of Contemporary Pediatrics is the property of Xiangya Medical Periodical Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) more...
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- 2024
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40. A risk prediction model for unexplained early neurological deterioration following intravenous thrombolysis.
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Zhu, Bifeng, Wang, Dan, Zuo, Jing, Huang, Yi, Gao, Chang, Jiang, Haiwei, and Yan, Dan
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STROKE patients , *TISSUE plasminogen activator , *RECEIVER operating characteristic curves , *ISCHEMIC stroke , *CLINICAL deterioration - Abstract
Background and objectives: Early neurological deterioration (END) post-intravenous thrombolysis significantly impacts the long-term prognosis of stroke patients. This study aimed to establish a rapid risk prediction model for unexplained END following intravenous thrombolysis. Methods: This prospective study consecutively enrolled patients with acute ischemic stroke treated with recombinant tissue plasminogen activator intravenous thrombolysis at the Department of Neurology, Third People's Hospital of Hubei Province, and Yangluo Hospital District between June 2019 and December 2022. Unexplained END was defined as an increase of ≥ 4 points in the National Institutes of Health Stroke Scale (NIHSS) score between admission and 24 h. A nomogram was developed and assessed by calculating the area under the receiver operating characteristic curve (AUC-ROC). The calibration was assessed using the Hosmer–Lemeshow test. Results: A total of 211 patients (130 males and 110 patients aged < 65 years) were included, with 66 experiencing unexplained END. Multivariate logistic regression analysis identified large arterial disease, transient ischemic attack, high blood glucose, high neutrophil/lymphocyte ratio, important perforator disease, and low the Alberta Stroke Program Early CT scores (APSECTS) as independent risk factors for END and established the nomogram used above indicators. The nomogram showed an AUC-ROC of 0.809 (95% CI 0.7429–0.8751), with a specificity of 0.862 and sensitivity of 0.712. The positive predictive value was 0.702, and the negative predictive value was 0.868. The Hosmer–Lemeshow goodness-of-fit test (χ2 = 1.069, P = 0.169) indicated acceptable model calibration. Conclusion: This study successfully established a risk prediction model for END following intravenous thrombolysis and the model demonstrates good stability and predictive capacity. Further validation through a prospective, multicenter study is necessary. [ABSTRACT FROM AUTHOR] more...
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- 2024
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41. 基于随机森林算法的云南女性产后抑郁风险 预测模型构建.
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夏修, 潢睿, 邓春燕, 邓睿, and 黄源
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EDINBURGH Postnatal Depression Scale , *FAMILY support , *RANDOM forest algorithms , *POSTPARTUM depression , *RECEIVER operating characteristic curves , *MOTHER-infant relationship - Abstract
Objective To construct a postpartum depression risk prediction model for multi - ethnic population in Yunnan Province of China, and identify predictive factors. Methods Women who were 42 days and within 1 year after childbirth were screened, and the Edinburgh Postnatal Depression Scale (EPDS M 9) was used for postpartum depression. 52 influencing factors from economics, social psychology, obstetrics, neonatology, spouse and family dynamics and other characteristics were included in the survey. A random forest algorithm was employed to construct a predictive model for postnatal depression risk in the multi 一 ethnic population of Yunnan Province. The model was evaluated on test sets with accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristic curve (Area Under Curve, AUC) to assess its performance. Results A total of 459 women were analyzed, with a postpartum depression detection rate of 11. 55%. Among them, the detection rates for Han, Zhuang and other ethnic minorities were 7. 56%, 13. 94% and 13.92%, respectively. The top 14 variables in terms of importance scores were: anxiety, history of previous negative emotions, marital relationship, family support level, physical and mental exhaustion in caring for newborns, pregnancy risk classification, mother - infant rooming - in, feeding mode, education level, spouse's education level, frequency of nighttime newborn care, ethnicity, parity and age. The accuracy was 92.74%, specificity was 95. 50%, sensitivity was 69.23%, positive predictive value was 64. 29%, negative predictive value was 96. 36%, and the AUC value was 0. 925, using Han, Zhuang, and other ethnic minorities as validation sets respectively. The model also demonstrated good stability. Conclusion The random forest algorithm 一 based postpartum depression risk prediction model for the multi 一 ethnic population in Yunnan performed well, which can be utilized to predict risk factors for postpartum depression among women in minority ethnic areas, thereby facilitating targeted intervention measures. [ABSTRACT FROM AUTHOR] more...
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- 2024
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42. Development and validation of a risk prediction model for community-acquired pressure injury in a cancer population: A case-control study.
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Zhang, Zhi-li, Luo, Man, Sun, Ru-yin, and Liu, Yan
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Patients with cancer are susceptible to pressure injuries, which accelerate deterioration and death. In patients with post-acute cancer, the risk of pressure injury is ignored in home or community settings. To develop and validate a community-acquired pressure injury risk prediction model for cancer patients. All research data were extracted from the hospital's electronic medical record system. The identification of optimal predictors is based on least absolute shrinkage and selection operator regression analysis combined with clinical judgment. The performance of the model was evaluated by drawing a receiver operating characteristic curve and calculating the area under the curve (AUC), calibration analysis and decision curve analysis. The model was used for internal and external validation, and was presented as a nomogram. In total, 6257 participants were recruited for this study. Age, malnutrition, chronic respiratory failure, body mass index, and activities of daily living scores were identified as the final predictors. The AUC of the model in the training and validation set was 0.87 (95 % confidence interval [CI], 0.85–0.89), 0.88 (95 % CI, 0.85–0.91), respectively. The model demonstrated acceptable calibration and clinical benefits. Comorbidities in patients with cancer are closely related to the etiology of pressure injury, and can be used to predict the risk of pressure injury. This study provides a tool to predict the risk of pressure injury for cancer patients. This suggests that improving the respiratory function and nutritional status of cancer patients may reduce the risk of community-acquired pressure injury. • We believe that the prevention of pressure injury in cancer patients should begin with the family. • Comorbidities in patients with cancer are closely related to the etiology of pressure injury. • Age, malnutrition, chronic respiratory failure, BMI, and ADL were predictors of pressure injury in cancer patients. [ABSTRACT FROM AUTHOR] more...
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- 2024
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43. Evaluation of the risk prediction model of pressure injuries in hospitalized patient: A systematic review and meta‐analysis.
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Ma, Yuxia, He, Xiang, Yang, Tingting, Yang, Yifang, Yang, Ziyan, Gao, Tian, Yan, Fanghong, Yan, Boling, Wang, Juan, and Han, Lin
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PRESSURE ulcers , *PREDICTION models , *HOSPITAL patients , *RISK assessment , *LENGTH of stay in hospitals , *CRITICALLY ill patient care - Abstract
Aims and Objectives Background Design Methods Results Conclusion Relevance to Clinical Practice Registration Number (PROSPERO) The main aim of this study is to synthesize the prevalent predictive models for pressure injuries in hospitalized patients, with the goal of identifying common predictive factors linked to pressure injuries in hospitalized patients. This endeavour holds the potential to provide clinical nurses with a valuable reference for providing targeted care to high‐risk patients.Pressure injuries (PIs) are a frequently occurring health problem throughout the world. There are mounting studies about risk prediction model of PIs reported and published. However, the prediction performance of the models is still unclear.Systematic review and meta‐analysis: The Cochrane Library, PubMed, Embase, CINAHL, Web of Science and Chinese databases including CNKI (China National Knowledge Infrastructure), Wanfang Database, Weipu Database and CBM (China Biology Medicine).This systematic review was conducted following PRISMA recommendations. The databases of Cochrane Library, PubMed, Embase, CINAHL, Web of Science, and CNKI, Weipu Database, Wanfang Database and CBM were searched for all studies published before September 2023. We included studies with cohort, case–control designs, reporting the development of risk model and have been validated externally and internally among the hospitalized patients. Two researchers selected the retrieved studies according to the inclusion and exclusion criteria, and critically evaluated the quality of studies based on the CHARMS checklist. The PRISMA guideline was used to report the systematic review and meta‐analysis.Sixty‐two studies were included, which contained 99 pressure injuries risk prediction models. The AUC (area under ROC curve) of modelling in 32 prediction models were reported ranged from .70 to .99, while the AUC of verification in 38 models were reported ranged from .70 to .98. Gender (OR = 1.41, CI: .99 ~ 1.31), age (WMD = 8.81, CI: 8.11 ~ 9.57), diabetes mellitus (OR = 1.64, CI: 1.36 ~ 1.99), mechanical ventilation (OR = 2.71, CI: 2.05 ~ 3.57), length of hospital stay (WMD = 7.65, CI: 7.24 ~ 8.05) were the most common predictors of pressure injuries.Studies of PIs risk prediction model in hospitalized patients had high research quality, and the risk prediction models also had good predictive performance. However, some of the included studies lacked of internal or external validation in modelling, which affected the stability and extendibility. The aged, male patient in ICU, albumin, haematocrit, low haemoglobin level, diabetes, mechanical ventilation and length of stay in hospital were high‐risk factors for pressure injuries in hospitalized patients. In the future, it is recommended that clinical nurses, in practice, select predictive models with better performance to identify high‐risk patients based on the actual situation and provide care targeting the high‐risk factors to prevent the occurrence of diseases.The risk prediction model is an effective tool for identifying patients at the risk of developing PIs. With the help of risk prediction tool, nurses can identify the high‐risk patients and common predictive factors, predict the probability of developing PIs, then provide specific preventive measures to improve the outcomes of these patients.CRD42023445258. [ABSTRACT FROM AUTHOR] more...
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- 2024
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44. 三角肌结节指数联合术前因素构建老年肱骨近端骨折锁定钢板内固定失效的 风险预测模型.
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徐大星, 纪木强, 涂泽松, 许伟鹏, 徐伟龙, and 牛 维
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BACKGROUND: Proximal humeral fracture in older adults is one of the three major osteoporotic fractures. Anatomic locking plate fixation is the first choice for most scholars to treat difficult-to-reduce and complex fracture types. However, the probability of reduction failure after the operation is high, which seriously affects patients’ quality of life. OBJECTIVE: To investigate the correlation between deltoid tuberosity index and postoperative reduction failure of proximal humeral fractures in the elderly, analyze and filter preoperative independent risk factors for reduction failure of proximal humeral fractures in the elderly, and construct and verify the effectiveness of a clinical prediction model. METHODS: The clinical data of 153 elderly patients with proximal humeral fractures who met the diagnosis and inclusion criteria and received open reduction and locking plate surgery in Foshan Hospital of TCM from June 2012 to June 2021 were collected. The patients were divided into the reduction failure subgroup and the reduction maintenance subgroup. The independent risk factors were selected by multivariate Logistic regression analysis, and the nomogram was constructed by R language. After 1000 times of resampling by Bootstrap method, the Hosmer-Lemeshow goodness of fit correlation test, receiver operating characteristic curve, calibration curve, clinical decision, and influence curve were plotted to evaluate its goodness of fit, discrimination, calibration ability, and clinical application value. Fifty-five elderly patients with proximal humeral fractures from June 2013 to August 2021 were selected as the model’s external validation group to evaluate the prediction model’s stability and accuracy. RESULTS AND CONCLUSION: (1) Of the 153 patients in the training group, 44 patients met reduction failure after internal plate fixation. The prevalence of postoperative reduction failure was 28.8%. Multivariate Logistic regression analysis identified that deltoid tuberosity index [OR=9.782, 95%CI (3.798, 25.194)], varus displacement [OR=4.209, 95%CI (1.472, 12.031)], and medial metaphyseal comminution [OR=4.278, 95%CI (1.670, 10.959)] were independent risk factors for postoperative reduction failure of proximal humeral fractures in older adults (P < 0.05). (2) A nomogram based on independent risk factors was then constructed. The Hosmer-Lemeshow test results for the model of the training group showed that χ² =0.812 (P=0.976) and area under curve=0.830[95%CI (0.762, 0.898)]. The calibration plot results showed that the model’s predicted risk was in good agreement with the actual risk. The decision and clinical influence curves showed good clinical applicability. (3) In the validation group, the accuracy rate in practical applications was 86%, area under curve=0.902[95%CI (0.819, 0.985)]. (4) It is concluded that deltoid tuberosity index < 1.44, medial metaphyseal comminution, and varus displacement were independent risk factors for reduction failure. (5) The internal and external validation of the risk prediction model demonstrated high discrimination, accuracy, and clinical applicability could be used to individually predict and screen the high-risk population of postoperative reduction failure of proximal humeral fractures in the elderly. The predicted number of patients at high risk is highly matched to the actual number of patients who occur when the model’s threshold risk probability is above 65%, and clinicians should use targeted treatment. [ABSTRACT FROM AUTHOR] more...
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- 2024
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45. Development and validation of a nomogram model of depression and sleep disorders and the risk of disease progression in patients with breast cancer.
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Shen, Jun, Zhou, Dan, Wang, Meng, Li, Fan, Yan, Huan-Huan, Zhou, Jun, and Sun, Wen-Wen
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SLEEP disorders , *BREAST cancer , *CANCER patients , *DISEASE progression , *RECEIVER operating characteristic curves , *ADOLESCENT idiopathic scoliosis - Abstract
Background: In this study, we investigated the relationship between the risk of postoperative progressive disease (PD) in breast cancer and depression and sleep disorders in order to develop and validate a suitable risk prevention model. Methods: A total of 750 postoperative patients with breast cancer were selected from the First People's Hospital of LianYunGang, and the indices of two groups (an event group and a non-event group) were compared to develop and validate a risk prediction model. The relationship between depression, sleep disorders, and PD events was investigated using the follow-up data of the 750 patients. Results: SAS, SDS, and AIS scores differed in the group of patients who experienced postoperative disease progression versus those who did not; the differences were statistically significant and the ability to differentiate prognosis was high. The area under the receiver operating characteristic (ROC) curves (AUC) were: 0.8049 (0.7685–0.8613), 0.768 (0.727–0.809), and 0.7661 (0.724-–0.808), with cut-off values of 43.5, 48.5, and 4.5, respectively. Significant variables were screened by single-factor analysis and multi-factor analysis to create model 1, by lasso regression and cross-lasso regression analysis to create model 2, by random forest calculation method to create model 3, by stepwise regression method (backward method) to create model 4, and by including all variables for Cox regression to include significant variables to create model 5. The AUC of model 2 was 0.883 (0.848–0.918) and 0.937 (0.893–0.981) in the training set and validation set, respectively. The clinical efficacy of the model was evaluated using decision curve analysis and clinical impact curve, and then the model 2 variables were transformed into scores, which were validated in two datasets, the training and validation sets, with AUCs of 0.884 (0.848–0.919) and 0.885 (0.818–0.951), respectively. Conclusion: We established and verified a model including SAS, SDS and AIS to predict the prognosis of breast cancer patients, and simplified it by scoring, making it convenient for clinical use, providing a theoretical basis for precise intervention in these patients. However, further research is needed to verify the generalization ability of our model. [ABSTRACT FROM AUTHOR] more...
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- 2024
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46. Risk prediction model for gastric cancer within 5 years in healthy Korean adults.
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Oh, Hyungseok, Cho, Sunwoo, Lee, Jung Ah, Ryu, Seungho, and Chang, Yoosoo
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STOMACH cancer , *KOREANS , *PREDICTION models , *PROPORTIONAL hazards models , *FAMILY history (Medicine) - Abstract
Background: Although endoscopy is commonly used for gastric cancer screening in South Korea, predictive models that integrate endoscopy results are scarce. We aimed to develop a 5-year gastric cancer risk prediction model using endoscopy results as a predictor. Methods: We developed a predictive model using the cohort data of the Kangbuk Samsung Health Study from 2011 to 2019. Among the 260,407 participants aged ≥20 years who did not have any previous history of cancer, 435 cases of gastric cancer were observed. A Cox proportional hazard regression model was used to evaluate the predictors and calculate the 5-year risk of gastric cancer. Harrell's C-statistics and Nam-D'Agostino χ2 test were used to measure the quality of discrimination and calibration ability, respectively. Results: We included age, sex, smoking status, alcohol consumption, family history of cancer, and previous results for endoscopy in the risk prediction model. This model showed sufficient discrimination ability [development cohort: C-Statistics: 0.800, 95% confidence interval (CI) 0.770–0.829; validation cohort: C-Statistics: 0.799, 95% CI 0.743–0.856]. It also performed well with effective calibration (development cohort: χ2 = 13.65, P = 0.135; validation cohort: χ2 = 15.57, P = 0.056). Conclusion: Our prediction model, including young adults, showed good discrimination and calibration. Furthermore, this model considered a fixed time interval of 5 years to predict the risk of developing gastric cancer, considering endoscopic results. Thus, it could be clinically useful, especially for adults with endoscopic results. [ABSTRACT FROM AUTHOR] more...
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- 2024
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47. Preoperative Variables of 30-Day Mortality in Adults Undergoing Percutaneous Coronary Intervention: A Systematic Review.
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Chowdhury, Mohammad Rocky Khan, Stub, Dion, Dinh, Diem, Karim, Md Nazmul, Siddiquea, Bodrun Naher, and Billah, Baki
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PERCUTANEOUS coronary intervention , *CLINICAL prediction rules , *ACUTE coronary syndrome , *CARDIOGENIC shock , *MISSING data (Statistics) , *MORTALITY - Abstract
Risk adjustment following percutaneous coronary intervention (PCI) is vital for clinical quality registries, performance monitoring, and clinical decision-making. There remains significant variation in the accuracy and nature of risk adjustment models utilised in international PCI registries/databases. Therefore, the current systematic review aims to summarise preoperative variables associated with 30-day mortality among patients undergoing PCI, and the other methodologies used in risk adjustments. The MEDLINE, EMBASE, CINAHL, and Web of Science databases until October 2022 without any language restriction were systematically searched to identify preoperative independent variables related to 30-day mortality following PCI. Information was systematically summarised in a descriptive manner following the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. The quality and risk of bias of all included articles were assessed using the Prediction Model Risk Of Bias Assessment Tool. Two independent investigators took part in screening and quality assessment. The search yielded 2,941 studies, of which 42 articles were included in the final assessment. Logistic regression, Cox-proportional hazard model, and machine learning were utilised by 27 (64.3%), 14 (33.3%), and one (2.4%) article, respectively. A total of 74 independent preoperative variables were identified that were significantly associated with 30-day mortality following PCI. Variables that repeatedly used in various models were, but not limited to, age (n=36, 85.7%), renal disease (n=29, 69.0%), diabetes mellitus (n=17, 40.5%), cardiogenic shock (n=14, 33.3%), gender (n=14, 33.3%), ejection fraction (n=13, 30.9%), acute coronary syndrome (n=12, 28.6%), and heart failure (n=10, 23.8%). Nine (9; 21.4%) studies used missing values imputation, and 15 (35.7%) articles reported the model's performance (discrimination) with values ranging from 0.501 (95% confidence interval [CI] 0.472–0.530) to 0.928 (95% CI 0.900–0.956), and four studies (9.5%) validated the model on external/out-of-sample data. Risk adjustment models need further improvement in their quality through the inclusion of a parsimonious set of clinically relevant variables, appropriately handling missing values and model validation, and utilising machine learning methods. [ABSTRACT FROM AUTHOR] more...
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- 2024
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48. 脑卒中恢复期患者下肢深静脉血栓风险预测 模型的构建及应用.
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朱乐英, 许钦玲, 彭银英, 阳静, and 黎慕佳
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Objective To investigate the incidence and influencing factors of lower limb deep vein thrombosis (DVT) in patients during the stroke recovery phase and to develop a predictive risk model for DVT in this patient popula- tion. Methods A retrospective study was conducted involving 431 stroke recovery patients. Influencing factors for DVT were identified using univariate and multivariate logistic regression analyses. A risk prediction model was constructed based on these factors and presented using a nomogram. The model's predictive performance was evaluated using the area under the receiver operating characteristics (ROC) curve (AUC), sensitivity, and specificity. Internal validation was performed using the Bootstrap method. Results Among the 431 patients, 36 developed DVT, yielding an incidence rate of 8.35%. In the modeling group (301 patients), 26 cases of DVT were observed (8.64%), while in the validation group (130 patients), 10 cases of DVT were recorded (7.69%). Logistic regression analysis identified modified Barthel index (MBI) score, Caprini DVT risk score, mean platelet volume (MPV), creatinine (Cr), thrombin time (TT), and D-dimer as independent risk factors for DVT in stroke recovery patients (OR=0.978, 1. 186, 0.662, 0.979, 1.043, and 1. 766, respectively, all P<0.1). A nomogram based on these six factors was developed. The model showed good predictive performance with an AUC of 0.842 in the modeling group, a Youden index of 0. 525, a diagnostic value of 0. 115, sensitivity of 0. 833, and specificity of 0.692. Hosmer Lemeshow goodness of fit test yielded x² = 7.458, P = 0.589 In the validation group, the AUC was 0.720, with a Youden index of 0. 425, a diagnostic value of 0.072, sensitivity of 0. 675, and specificity of 0.700 (Hosmer - Lemeshow chi x² = 11.414 P = 0.248 ) Conclusion Stroke recovery patients exhibit a high incidence of DVT. Key factors such as MBI score, Caprini DVT risk score, MPV, Cr, TT, and D-dimer significantly influence the risk of DVT. The constructed nomogram provides personalized risk predictions for DVT, facilitating targeted intervention measures by healthcare professionals. [ABSTRACT FROM AUTHOR] more...
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- 2024
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49. Risk prediction models for diabetic nephropathy among type 2 diabetes patients in China: a systematic review and meta-analysis.
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Wenbin Xu, Yanfei Zhou, Qian Jiang, Yiqian Fang, and Qian Yang
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Objective: This study systematically reviews and meta-analyzes existing risk prediction models for diabetic kidney disease (DKD) among patients with type 2 diabetes, aiming to provide references for scholars in China to develop higherquality risk prediction models. Methods: We searched databases including China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP Chinese Science and Technology Journal Database, Chinese Biomedical Literature Database (CBM), PubMed, Web of Science, Embase, and the Cochrane Library for studies on the construction of DKD risk prediction models among type 2 diabetes patients, up until 28 December 2023. Two researchers independently screened the literature and extracted and evaluated information according to a data extraction form and bias risk assessment tool for prediction model studies. The area under the curve (AUC) values of the models were meta-analyzed using STATA 14.0 software. Results: A total of 32 studies were included, with 31 performing internal validation and 22 reporting calibration. The incidence rate of DKD among patients with type 2 diabetes ranged from 6.0% to 62.3%. The AUC ranged from 0.713 to 0.949, indicating the prediction models have fair to excellent prediction accuracy. The overall applicability of the included studies was good; however, there was a high overall risk of bias, mainly due to the retrospective nature of most studies, unreasonable sample sizes, and studies conducted in a single center. Meta-analysis of the models yielded a combined AUC of 0.810 (95% CI: 0.780–0.840), indicating good predictive performance. Conclusion: Research on DKD risk prediction models for patients with type 2 diabetes in China is still in its initial stages, with a high overall risk of bias and a lack of clinical application. Future efforts could focus on constructing highperformance, easy-to-use prediction models based on interpretable machine learning methods and applying them in clinical settings. [ABSTRACT FROM AUTHOR] more...
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- 2024
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50. Construction of a clinically significant prostate cancer risk prediction model based on traditional diagnostic methods
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Wen-Tong Ji, Yong-Kun Wang, Zhan-Yang Han, Si-Qi Wang, and Yao Wang
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prostate biopsy ,clinically significant prostate cancer ,risk prediction model ,diagnosis ,nomogram ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Objectivesto construct a prediction model for clinically significant prostate cancer (csPCa) based on prostate-specific antigen (PSA) levels, digital rectal examination (DRE), and transrectal ultrasonography (TRUS).MethodsWe retrospectively analysed 1196 Asian patients who underwent transrectal ultrasound-guided biopsy (TRUSB) between June 2000 and February 2023. Patients were randomly divided into a training set of 837 cases (70%) and a validation set of 359 patients (30%). A csPCa risk prediction model was established using the logistic regression. The performance of the model was examined based on calibration curves, receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and clinical impact curves (CIC).ResultsSerum PSA levels, age, DRE results, prostatic shape, prostatic border and hypoechoic area were associated with pathological outcomes. The area under the ROC curve of the training set was 0.890 (95%CI: 0.865-0.816). The optimal cut-off value was 0.279. The calibration curves indicated good calibration, and the DCA and CIC results demonstrated good clinical utility. Significantly, the prediction model has higher negative predictive value (89.8%) and positive predictive value (68.0%) compared with MRI. Subsequently, we developed an online calculator (https://jiwentong0.shinyapps.io/dynnomapp/) with six variables for biopsy optimization.ConclusionThis study incorporated the results of three traditional diagnostic methods to establish a cost-effective and highly accurate model for predicting csPCa before biopsy. With this model, we aim to provide a non-invasive and cost-effective tool for csPCa detection in Asia and other underdeveloped areas. more...
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- 2024
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