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The T315I mutation poses a significant threat to patients with chronic phase chronic myeloid leukemia (CP-CML). This study aimed to establish a nomogram to predict the risk of T315I mutation in CP-CML patients. The training cohort included 1,466 patients from 24 hematology centers, and the validation cohort included 820 patients from an additional 20 centers. Peripheral blood blast (PBB), additional chromosomal abnormality (ACA), dasatinib use, non-EMR at 3 months, and BCR::ABLIS > 1% at 6 months were identified as independent risk factors through multivariate Cox regression analysis. The performance of the nomogram was assessed via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The area under the ROC curve (AUC) values at 5, 10, and 15 years were 0.874, 0.925, and 0.930 for the training cohort, and 0.864, 0.814, and 0.803 for the validation cohort, respectively. The calibration curves for both cohorts were close to the ideal diagonal, and the decision curves indicated clinical net benefit. In conclusion, we developed a nomogram to predict the 5-year, 10-year, and 15-year T315I-free survival probabilities of CP-CML patients. This tool can aid clinicians in the early prediction and timely management of high-risk CP-CML patients with the T315I mutation. [ABSTRACT FROM AUTHOR]

Background: We aimed to establish a predictive nomogram to evaluate the incidence of residual tissue in patients with endogenic cesarean scar ectopic pregnancy after negative pressure aspiration. Methods: This retrospective study included patients treated in the gynecology department of our institution from May 2017 to August 2023 who underwent negative pressure suction treatment, ultrasound examinations before and after treatment, and received telephone follow-up for at least 6 months. A total of 899 patients met the inclusion criteria and were divided into a training cohort (629 patients, 70%) and a validation cohort (270 patients, 30%). Independent predictive factors were established using multivariate logistic regression. The resulting nomogram was validated using 1,000 bootstrap resampling, and calibration curves were plotted. Receiver operating characteristic (ROC) analysis was performed to calculate the area under the curve, sensitivity, specificity, and other metrics to assess its discriminative performance. Clinical decision curves were constructed to evaluate clinical applicability and quantify the net benefit within a range of threshold probabilities. The model was externally validated in the validation cohort. Results: Predictive factors included in the nomogram included age (hazard ratio [HR]: 1.220, 95% confidence interval [CI]: 1.135—1.316), BMI (HR: 0.890, 95% CI: 0.796—0.986), intraoperative major hemorrhage (HR: 4.457, 95% CI: 1.610—12.292), maximum diameter of the gestational sac (HR: 1.572, 95% CI: 1.295, 1.914), and thickness of the remaining muscle layer of the lower uterine segment (HR: 1.572, 95% CI: 0.014, 0.430). The ROC curve of the resulting nomogram showed similar area under the curve values for the training (0.809, 95% CI: 0.751—0.867) and validation cohorts (0.814, 95% CI: 0.739, 0.888). The Hosmer–Lemeshow test indicated good model fit (P = 0.861), and the calibration curve was close to the ideal diagonal line. Decision curve analysis demonstrated good net benefit, and external validation confirmed its reliability. Conclusions: The model may aid in individual clinical decision-making, allowing clinicians to perform immediate postoperative assessments for patients with endogenous ectopic pregnancy in cesarean section scars treated with negative pressure suction, identify high-risk subpopulations, and select appropriate supplementary treatment in advance, making it particularly suitable for low-income areas and resource-limited primary hospitals. [ABSTRACT FROM AUTHOR]

Background: Clinical studies on atrial fibrillation (AF) recurrence after catheter ablation in patients diagnosed with patent foramen ovale (PFO) and paroxysmal AF (PAF) are scarce. Here, we aimed to develop a nomogram model utilizing multimodal data for the risk stratification of AF recurrence following catheter ablation in individuals diagnosed with PFO and new-onset PAF. Methods: Patients with PFO and PAF who underwent catheter ablation at the Renmin Hospital of Wuhan University from January 2018 to June 2020 were consecutively enrolled. The identification of potential risk factors was conducted using the regression method known as least absolute shrinkage and selection operator. Subsequently, multivariate COX regression analysis was conducted to determine the independent risk factors, after which a nomogram scoring system was developed. The nomogram's performance was assessed via various statistical measures, including receiver operating characteristic curve analysis, calibration curve, and decision curve analysis (DCA). Results: The dataset was partitioned into the development cohort (n = 102) and the validation cohort (n = 43) using a 7:3 ratio. The constructed nomogram included four clinical variables: age, diabetes mellitus, lipoprotein (a), and right ventricular diameter. The area under the curve values of the development and validation cohorts at 1, 2, and 3 years post-catheter ablation were 0.911, 0.812, and 0.786 and 0.842, 0.761, and 0.785, respectively. Additionally, the nomogram demonstrated a significant correlation between the predicted and actual outcomes in the development and validation cohorts, indicating its excellent calibration. Lastly, the DCA findings suggested that the model had notable clinical applicability in predicting the likelihood of AF recurrence within 1, 2, and 3 years after catheter ablation. Conclusion: The incorporation of multimodal data in a nomogram visualization tool facilitates the concise representation of multimodal data, thereby enhancing the comprehension of the clinical status of patients with PFO and PAF following catheter ablation and providing accurate risk stratification at 1, 2, and 3 years post-treatment. Trial registration: This trial was registered in the Chinese Clinical Trial Registry. (ChiCTR2300072320). [ABSTRACT FROM AUTHOR]

Objective: To build a prediction nomogram for early prediction of live birth probabilities according to number of oocytes retrieved in women ≤ 35 years of age. Methods: A prediction model was built including 9265 infertile women ≤ 35 years of age accepting their first ovum pick-up cycle from January 2018 to December 2022. Least absolute shrinkage and selection operator (LASSO) regression was performed to identify independent predictors and establish a nomogram to predict reproductive outcomes. Both discrimination and calibration were assessed by bootstrapping with 1000 resamples. Results: The critical threshold for the number of retrieved oocytes associated with cumulative live birth was determined as 10.5 (AUC: 0.824). Consequently, a nomogram was constructed to predict the likelihood of obtaining fewer than 10 oocytes at one oocyte retrieval cycle. There were five indicators significantly related to the risk of obtaining less than 10 oocytes at one oocyte retrieval cycle, including age, antral follicle count (AFC), anti-Mullerian hormone (AMH), follicle-stimulating hormone (FSH), and FSH to luteinizing hormone ratio. These factors were subsequently used to develop a nomogram prediction model. The model's performance was evaluated using the area under the curve (AUC), concordance index (C-index), and calibration curves, which indicated fair predictive ability and good calibration. Conclusion: We developed and validated a nomogram based on five ovarian reserve indicators to predict the risk of retrieving fewer than 10 oocytes at one oocyte retrieval cycle in women ≤ 35 years of age. The model demonstrated good discrimination and calibration, indicating its reliability for clinical application. This nomogram offers a practical and accurate tool for early identification of young women with potentially decreased ovarian reserve, enabling timely intervention and personalized management strategies. [ABSTRACT FROM AUTHOR]

Purpose: Preoperative albumin to alkaline phosphatase ratio (AAPR) and inflammatory burden index (IBI) are prognostic indicators for a multitude of cancers, and our study focuses on evaluating the prognostic significance of the AAPR and the IBI on rectal cancer (RC) patients to provide a more accurate guideline for patient prognosis. Patients and Methods: This study enrolled patients who underwent laparoscopic rectal cancer surgery from January 2016 to January 2021. We utilized three machine learning approaches to select variables most relevant to prognosis in the training cohort. Finally, based on the screened variables, a nomogram was established to predict RC patients' overall survival (OS). The improvement in predictive ability and clinical benefit was assessed through the concordance index (C-index), receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA). Results: A total of 356 patients were enrolled and they were randomly divided into a training cohort (60%, n=214) and a validation cohort (40%, n=143). Overall survival (OS) was worse for patients in either the low AAPR or the high AAPR group, whereas patients in the low AAPR with both high IBI group had the lowest OS (P< 0.001). Finally, five variables were obtained after screening the best variables by three machine learning, and the nomogram was constructed. In both the development and validation cohorts, the C-index values exceeded 0.85, indicating that the predictive model has a strong predictive performance in terms of overall survival. The calibration curves and the decision curve analysis (DCA) showed that the nomogram demonstrated a superior benefit. Conclusion: Preoperative AAPR and IBI can serve as effective indicators for predicting the OS of RC patients. We have developed a nomogram for predicting the OS of patients who underwent laparoscopic rectal cancer surgery. [ABSTRACT FROM AUTHOR]

Angina, a prevalent manifestation of coronary artery disease, is primarily associated with inflammation, an established contributor to the pathogenesis of atherosclerosis and acute coronary syndromes (ACS). Various inflammatory markers are employed in clinical practice to predict patient prognosis and optimize clinical decision-making in the management of ACS. This study investigated the prognostic significance of integrating commonly used, easily repeatable inflammatory biomarkers within a multimodal preoperative prediction model in patients presenting with unstable Angina Pectoris (UAP) and intermediate coronary lesions. Methods: This retrospective analysis included patients diagnosed with UAP and intermediate coronary lesions (50%– 70% stenosis) who underwent coronary angiography at our hospital between January 2019 and June 2021. The assessed outcome was the occurrence of major adverse cardiac and cerebrovascular events (MACCEs). The Boruta algorithm was applied to identify potential risk factors and develop a prognostic multimodal model. Results: A total of 773 patients were enrolled and divided into a training cohort (n=463) and validation cohort (n=310). A nomogram was constructed to predict the probability of MACCE-free survival based on five clinical features: diabetes mellitus, current smoking, history of myocardial infarction, neutrophil-to-lymphocyte ratio, and fasting blood glucose. In the training cohort, the area under the curve values for the nomogram at 24, 32, and 40 months were 0.669, 0.707, and 0.718, respectively, while those in the validation cohort were 0.613, 0.612 and 0.630, respectively. The model demonstrated good calibration in both cohorts with predicted outcomes aligning well with actual results at all time points up to 40 months. Furthermore, decision curve analysis showed significant clinical utility of the model across the specified time intervals. Conclusion: The developed preoperative prognostic model visually illustrates the association among inflammation, blood glucose level, established risk factors, and long-term MACCEs in UAP patients with intermediate coronary lesions. [ABSTRACT FROM AUTHOR]

Abstract Background Clinical studies on atrial fibrillation (AF) recurrence after catheter ablation in patients diagnosed with patent foramen ovale (PFO) and paroxysmal AF (PAF) are scarce. Here, we aimed to develop a nomogram model utilizing multimodal data for the risk stratification of AF recurrence following catheter ablation in individuals diagnosed with PFO and new-onset PAF. Methods Patients with PFO and PAF who underwent catheter ablation at the Renmin Hospital of Wuhan University from January 2018 to June 2020 were consecutively enrolled. The identification of potential risk factors was conducted using the regression method known as least absolute shrinkage and selection operator. Subsequently, multivariate COX regression analysis was conducted to determine the independent risk factors, after which a nomogram scoring system was developed. The nomogram's performance was assessed via various statistical measures, including receiver operating characteristic curve analysis, calibration curve, and decision curve analysis (DCA). Results The dataset was partitioned into the development cohort (n = 102) and the validation cohort (n = 43) using a 7:3 ratio. The constructed nomogram included four clinical variables: age, diabetes mellitus, lipoprotein (a), and right ventricular diameter. The area under the curve values of the development and validation cohorts at 1, 2, and 3 years post-catheter ablation were 0.911, 0.812, and 0.786 and 0.842, 0.761, and 0.785, respectively. Additionally, the nomogram demonstrated a significant correlation between the predicted and actual outcomes in the development and validation cohorts, indicating its excellent calibration. Lastly, the DCA findings suggested that the model had notable clinical applicability in predicting the likelihood of AF recurrence within 1, 2, and 3 years after catheter ablation. Conclusion The incorporation of multimodal data in a nomogram visualization tool facilitates the concise representation of multimodal data, thereby enhancing the comprehension of the clinical status of patients with PFO and PAF following catheter ablation and providing accurate risk stratification at 1, 2, and 3 years post-treatment. Trial registration: This trial was registered in the Chinese Clinical Trial Registry. (ChiCTR2300072320). Graphical Abstract

Abstract Background We aimed to analyze the correlation between serum lipid levels [total cholesterol (TC), triglyceride (TG), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C)] and recurrence after uterine fibroids (UF) resection, and explore the predictive value of serum lipid levels in determining recurrence after myomectomy. Methods In this retrospective cohort study, 323 patients undergoing first myomectomy who came from Li Huili Hospital, Ningbo Medical Center between December 2019 and January 2023 were included. The primary endpoint was the recurrence of UF within 12 months following surgery. Univariate and multivariate logistic regression analyses were adopted to evaluate the association between four serum lipid parameters and the risk of UF recurrence. All included patients were randomly assigned to the training group for nomogram development and the testing group for nomogram validation, with a ratio of 7:3. Receiver operator characteristic, calibration curves, and decision curve analysis were used to assess the predicting performance of constructed nomograms. Results Totally, 98 developed the recurrence of UF within 12 months following surgery. Multivariate logistic regression analyses indicated that high levels of TC [odds ratio (OR) = 9.98, 95% confidence interval (CI): 4.28–23.30], LDL-C (OR = 11.31, 95% CI: 4.66–27.47) and HDL-C (OR = 2.37, 95% CI: 1.21–4.64) were associated with recurrence of UF risk. The association between TG level and UF recurrence risk did not statistical significance (P > 0.05). Four online prediction nomograms by integrating serum lipid levels and clinical features for predicting the risk of recurrence of UF were developed (TC-model, TG-model, LDL-C-model and HDL-C-model). Through verification, these models may have good prediction performance for predicting the recurrence of UF risk. Conclusion This study developed and validated prediction nomograms for predicting the risk of UF recurrence. These nomograms can provide individual risk assessment for UF recurrence.

Dexian Zhang,1,2,&ast; Xinxin Xu,2,3,&ast; Zhibin Ye,1,2 Zhenxing Zhang,1,2 Jing Xiao1,2 1Department of Nephrology, Huadong Hospital, Fudan University, Shanghai, People’s Republic of China; 2Shanghai Key Laboratory of Clinical Geriatric Medicine, Huadong Hospital, Fudan University, Shanghai, People’s Republic of China; 3Clinical Research Center for Geriatric Medicine, Huadong Hospital, Fudan University, Shanghai, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Zhenxing Zhang; Jing Xiao, Department of Nephrology, Huadong Hospital, Fudan University, Shanghai, People’s Republic of China, Email contraindication@163.com; jingxiao13@fudan.edu.cnObjective: To develop and externally validate a nomogram to predict elevated serum uric acid (SUA) levels in older adults.Study Design: This is a longitudinal Chinese cohort study.Methods: A cohort of 2788 older adults was established at Huadong Hospital, followed-up for at least one year, and screened for risk factors for elevated SUA levels. A logistic regression model was built to predict elevated SUA, and its performance was validated.Results: The risk prediction model showed good discrimination ability in both the development cohort (area under the curve (AUC) = 0.82; 95% confidence interval (CI) =0.79~0.86) and the external validation cohort (AUC=0.76; 95% CI=0.70~0.82). The model was adequately calibrated, and the predictions correlated with the observed outcome (χ2 = 6.36, P = 0.607). Men were more prone to elevated SUA levels than women were, and a baseline SUA level ≥ 360 μmol/L was a common risk factor for both males and females. Proteinuria status was an additional risk factor for males, whereas a baseline estimated glomerular filtration rate (eGFR)< 60 mL/min· 1.73 m2 and diabetes status were additional risk factors for females.Conclusion: The externally validated nomogram, which is predictive of elevated SUA in older adults, might aid in the detection of individual diseases, the development of preventive interventions and clinical decision-making.Keywords: elevated serum uric acid, prediction nomogram, risk factor

Objective To analyze the risk factors for the occurrence of coronary slow flow (CSF) and to construct a predictive nomogram for the occurrence of CSF. Methods Totally 103 patients with CSF diagnosed by CAG examination were recorded as the CSF group. A total of 121 patients with normal blood flow in the same period were selected as the control group. The following data of the two groups were collected: age, gender, smoking history, drinking history, body mass index (BMI), blood pressure, past medical history and other basic information, blood potassium, blood sodium, blood phosphorus, blood magnesium, urea nitrogen, creatinine, uric acid, blood glucose, albumin, globulin, creatine kinase, creatine kinase isoenzyme, triglycerides, total cholesterol, free fatty acids, low-density lipoprotein (LDL), high-density lipoprotein (HDL), lipoproteins, leukocytes, monocytes, lymphocytes, neutrophils, hemoglobin, red blood cells, red blood cell volume, red blood cell width, hematocrit, platelets, platelet volume, platelet distribution width, Ddimer and other laboratory test information, end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), fractional shortening (FS) and other cardiac ultrasound examination results, the percentage of adjacent RR intervals ≥ 50 ms (PNN50), mean standard deviation of normal RR intervals per 5 min (SDNNI), standard deviation of average RR interval (SDNN), root mean square difference of consecutive normal RR intervals (RMSSD), standard deviation of the mean of all NN intervals within 5 min (SDANN), standard deviation of adjacent NN intervals (SDSD), triangular index (TI) and other indexes related to heart rate variability. The indexes with statistical differences in univariate analysis were included in least absolute shrinkage and selection operator (LASSO) regression and multivariate Logistic regression analysis using the "glmnet" package of R statistical software to screen characteristic variables and analyze the risk factors for CSF. The "rms" package of R statistical software was used to construct a prediction nomogram for CSF. The ROC curve of the nomogram was drawn to evaluate the discrimination of the nomogram. The calibration curve was used to evaluate the consistency of the nomogram. The clinical decision curve was used to evaluate the clinical value of the nomogram. Results There were statistically significant differences in the smoking history, hypertension, systolic blood pressure, diastolic blood pressure, BMI, uric acid, blood sugar, triglycerides, total cholesterol, LDL, HDL, lymphocytes, EDV, ESV, EF, FS, PNN50, RMSSD, SDSD, SDNN, SDANN, age, blood sodium, blood magnesium, neutrophils, red blood cell width, D-dimer, and TI between the CSF group and the control group (all P<0. 05). The results of LASSO regression and multivariate Logistic regression analysis showed that smoking, systolic blood pressure, triglycerides, lymphocytes, SDNN, and EF were independent risk factors for the occurrence of CSF (all P<0. 05) . Based on this, a prediction nomogram for the occurrence of CSF was constructed, and the nomogram had good discrimination, consistency, and clinical value. Conclusions Smoking, systolic blood pressure, triglycerides, lymphocytes, SDNN, and EF are independent risk factors for the occurrence of CSF. The prediction nomogram constructed based on the above risk factors has a high predictive value for the occurrence of CSF [ABSTRACT FROM AUTHOR]

ObjectiveTo establish a prediction nomogram for early prediction of neonatal acute respiratory distress syndrome (NARDS).MethodsThis is a retrospective cross-sectional study conducted between January 2021 and December 2023. Clinical characteristics and laboratory results of cases with neonatal pneumonia were compared in terms of presence of NARDS diagnosis based on the Montreux Definition. The NARDS group and non-NARDS group were then compared to establish a prediction nomogram for early prediction of NARDS. The predictive accuracy and compliance of the model were evaluated using subject operating characteristic curves, area under the ROC curve, and calibration curves, and the model performance was estimated by self-lifting weight sampling. The Hosmer–Lemeshow test was used to assess the goodness of fit of the model.FindingsNARDS group consisted of 104, non-NARDS group consisted of 238 newborns in our study. Gestational age, triple concave sign, blood glucose measurement after birth (Glu), Apgar score at the 5th minute (Apgar5), neutrophil count (ANC) and platelet count (PLT) are independent predictors of NARDS in late preterm and term newborns who present with progressive respiratory distress and require varying degrees of respiratory support within the first 24 h of life to minimize work of breathing and restore organismal oxygenation. The area under the ROC curve was 0.829 (95% CI = 0.785–0.873), indicating the model's strong predictive power. In addition, decision curve analysis showed that the model had significantly better net benefits.ConclusionIn this study, a predictive column-line plot was constructed based on six clinically accessible conventional variables. Early application of this model has a better predictive effect on the early diagnosis of NARDS, thus facilitating more timely and effective interventions.

ObjectiveTo build a prediction nomogram for early prediction of live birth probabilities according to number of oocytes retrieved in women ≤ 35 years of age.MethodsA prediction model was built including 9265 infertile women ≤ 35 years of age accepting their first ovum pick-up cycle from January 2018 to December 2022. Least absolute shrinkage and selection operator (LASSO) regression was performed to identify independent predictors and establish a nomogram to predict reproductive outcomes. Both discrimination and calibration were assessed by bootstrapping with 1000 resamples.ResultsThe critical threshold for the number of retrieved oocytes associated with cumulative live birth was determined as 10.5 (AUC: 0.824). Consequently, a nomogram was constructed to predict the likelihood of obtaining fewer than 10 oocytes at one oocyte retrieval cycle. There were five indicators significantly related to the risk of obtaining less than 10 oocytes at one oocyte retrieval cycle, including age, antral follicle count (AFC), anti-Mullerian hormone (AMH), follicle-stimulating hormone (FSH), and FSH to luteinizing hormone ratio. These factors were subsequently used to develop a nomogram prediction model. The model’s performance was evaluated using the area under the curve (AUC), concordance index (C-index), and calibration curves, which indicated fair predictive ability and good calibration.ConclusionWe developed and validated a nomogram based on five ovarian reserve indicators to predict the risk of retrieving fewer than 10 oocytes at one oocyte retrieval cycle in women ≤ 35 years of age. The model demonstrated good discrimination and calibration, indicating its reliability for clinical application. This nomogram offers a practical and accurate tool for early identification of young women with potentially decreased ovarian reserve, enabling timely intervention and personalized management strategies.

Background Limited data from the literature are available to assess the efficacy of coronary artery bypass grafting in patients with ischemic cardiomyopathy and heart failure with preserved ejection fraction. Therefore, our objective was to use machine learning techniques integrating clinical features, biomarker data, and echocardiography data to enhance comprehension and risk stratification in patients diagnosed with ischemic cardiomyopathy and heart failure with preserved ejection fraction who have undergone coronary artery bypass grafting surgery. Methods and Results For this study, 294 patients with ischemic cardiomyopathy and heart failure with preserved ejection fraction who underwent coronary artery bypass grafting surgery were assigned to the development cohort (n=176) and the independent validation cohort (n=118). A total of 52 clinical variables were extracted for each patient. The principal clinical end point was the incidence of major adverse cardiovascular events, encompassing cardiac mortality, acute myocardial infarction, acute heart failure, and graft failure. From least absolute shrinkage and selection operator regression, 4 predictors were selected for the final prediction nomogram: diabetes, hypertension, the systemic immune‐inflammation index, and NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide). The prediction nomogram achieved satisfactory prediction performance in both the development cohort (C index, 0.768 [95% CI, 0.701–0.835]) and independent validation cohort (C index, 0.633 [95% CI, 0.521–0.745]). Adequate calibration was noted for the likelihood of major adverse cardiovascular events in both the development and independent validation cohorts. Decision curve analysis confirmed the clinical usefulness of the established prediction nomogram. Conclusions A clinically feasible prognostic model, based on preoperative multimodal data, was developed for risk stratification of patients with ischemic heart and heart failure with preserved ejection fraction who receive coronary artery bypass grafting surgery. Registration https://www.chictr.org.cn; Unique identifier: ChiCTR2300074439.

Abstract Background This dynamic nomogram model was developed to predict the probability of fetal loss in pregnant patients with systemic lupus erythematosus (SLE) with mild disease severity before conception. Methods An analysis was conducted on 314 pregnancy records of patients with SLE who were hospitalized between January 2015 and January 2022 at Shenzhen People's Hospital, and the Longhua Branch of Shenzhen People's Hospital. Data from the Longhua Branch of the Shenzhen People's Hospital were utilized as an independent external validation cohort. The nomogram, a widely used statistical visualization tool to predict disease onset, progression, prognosis, and survival, was created after feature selection using multivariate logistic regression analysis. To evaluate the model prediction performance, we employed the receiver operating characteristic curve, calibration curve, and decision curve analysis. Results Lupus nephritis, complement 3, immunoglobulin G, serum albumin, C-reactive protein, and hydroxychloroquine were all included in the nomogram model. The model demonstrated good calibration and discriminatory power, with an area under the curve of 0.867 (95% confidence interval: 0.787–0.947). According to decision curve analysis, the nomogram model exhibited clinical importance when the probability of fetal loss in patients with SLE ranged between 10 and 70%. The predictive ability of the model was demonstrated through external validation. Conclusion The predictive nomogram approach may facilitate precise management of pregnant patients with SLE with mild disease severity before conception.

Abstract Purpose Sleep disorders are becoming more prevalent in hospitalized patients with acute myocardial infarction (AMI). We aimed to investigate the risk factors for sleep disorders in hospitalized patients with AMI, then develop and validate a prediction nomogram for the risk of sleep disorders. Methods Clinical data were collected from patients with AMI hospitalized in our hospital from January 2020 to June 2023. All patients were divided into the training group and the validation group with a ratio of 7:3 in sequential order. The LASSO regression analysis and multivariate logistic regression analysis were used to screen potential risk factors for sleep disorders. The concordance index (C-index), calibration curves, and decision curve analysis (DCA) were plotted. Results A total of 256 hospitalized patients with AMI were enrolled. Patients were divided into the training group (180) and the validation group (76) according to a scale of 7:3. Of the 256 patients, 90 patients (35.16%) suffered from sleep disorders, and 33 patients (12.89%) needed hypnotics. The variables screened by LASSO regression included age, smoking, NYHA class, anxiety status at admission, depression status at admission, and strangeness of environment. A nomogram model was established by incorporating the risk factors selected. The C-index, calibration curve, and DCA showed good predictive performance. Conclusions We identified six clinical characteristics as predictors of sleep disorders in hospitalized patients with AMI. It helps nurses make appropriate decisions in clinical practice.

Abstract The timely detection and management of hemorrhagic shock hold paramount importance in clinical practice. This study was designed to establish a nomogram that may facilitate early identification of hemorrhagic shock in pediatric patients with multiple-trauma. A retrospective study was conducted utilizing a cohort comprising 325 pediatric patients diagnosed with multiple-trauma, who received treatment at the Children's Hospital, Zhejiang University School of Medicine, Zhejiang, China. For external validation, an additional cohort of 144 patients from a children's hospital in Taizhou was included. The model's predictor selection was optimized through the application of the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Subsequently, a prediction nomogram was constructed using multivariable logistic regression analysis. The performance and clinical utility of the developed model were comprehensively assessed utilizing various statistical metrics, including Harrell's Concordance Index (C-index), receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA). Multivariate logistic regression analysis identified systolic blood pressure (ΔSBP), platelet count, activated partial thromboplastin time (APTT), and injury severity score (ISS) as independent predictors for hemorrhagic shock. The nomogram constructed using these predictors demonstrated robust predictive capabilities, as evidenced by an impressive area under the curve (AUC) value of 0.963. The model's goodness-of-fit was assessed using the Hosmer–Lemeshow test (χ2 = 10.023, P = 0.209). Furthermore, decision curve analysis revealed significantly improved net benefits with the model. External validation further confirmed the reliability of the proposed predictive nomogram. This study successfully developed a nomogram for predicting the occurrence of hemorrhagic shock in pediatric patients with multiple trauma. This nomogram may serve as an accurate and effective tool for timely and efficient management of children with multiple trauma.

Yaping Wang,1 Yiyi Zhao,1 Chaolu Shi,2 Juanqing Li,1,3,4 Xiufeng Huang1,3,4 1Zhejiang University, Womens Hospital, Sch Med, Department Obstet & Gynecol, Hangzhou, Zhejiang, People’s Republic of China; 2Cixi maternity&health Care Hospital, Department Obstet & Gynecol Ningbo, Ningbo, Zhejiang, People’s Republic of China; 3Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China; 4Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of ChinaCorrespondence: Juanqing Li; Xiufeng Huang, Email ljq0313@zju.edu.cn; huangxiufeng@zju.edu.cnPurpose: The aim of this study was to investigate the risk factors of postmenopausal special uterine leiomyoma pathological types or leiomyosarcoma and to develop a nomogram for clinical risk assessment, ultimately to reduce unnecessary surgical interventions and corresponding economic expenses.Methods: A total of 707 patients with complete information were enrolled from 1 August 2012 to 1 August 2022. Univariate and multivariate logistic regression models were used to analyse the association between variables and special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients. A nomogram for special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients was developed and validated by bootstrap resampling. The calibration curve was used to assess the accuracy of the model and receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were compared with the clinical experience model.Results: The increasing trend after menopause, the diameter of the largest uterine fibroid, serum carcinoembryonic antigen 125 concentration, Serum neutrophil to lymphocyte ratio, and Serum phosphorus ion concentration were independent risk factors for special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients. We developed a user-friendly nomogram which showed good diagnostic performance (AUC=0.724). The model was consistent and the calibration curve of our cohort was close to the ideal diagonal line. DCA indicated that the model has potential value for clinical application. Furthermore, our model was superior to the previous clinical experience model in terms of ROC and DCA.Conclusion: We have developed a prediction nomogram for special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients. This nomogram could serve as an important warning signal and evaluation method for special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients.Keywords: special uterine leiomyoma pathological types, leiomyosarcoma, postmenopausal women, prediction nomogram, bootstrap

Background: Carbapenem-resistant Gram-negative organism (CRO) infection is a critical clinical disease with high mortality rates. The 30-day mortality rate following antibiotic treatment serves as a benchmark for assessing the quality of care. Colistin sulfate is currently considered the last resort therapy against infections caused by CRO. Nevertheless, there is a scarcity of reliable tools for personalized prognosis of CRO infections. This study aimed to develop and validate a nomogram to predict the 30-day all-cause mortality in patients with CRO infection who underwent colistin sulfate treatment. Methods: A prediction model was developed and preliminarily validated using CRO-infected patients treated with colistin sulfate at Tongji Hospital in Wuhan, China, who were hospitalized between May 2018 and May 2023, forming the study cohort. Patients admitted to Xianning Central Hospital in Xianning, China, between May 2018 and May 2023 were considered for external validation. Multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of 30-day allcause mortality. The receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and the calibration curve were used to evaluate model performance. The decision curve analysis (DCA) was used to assess the model clinical utility. Results: A total of 170 patients in the study cohort and 65 patients in the external validation cohort were included. Factors such as age, duration of combination therapy, nasogastric tube placement, history of previous surgery, presence of polymicrobial infections, and occurrence of septic shock were independently associated with 30-day all-cause mortality and were used to construct the nomogram. The AUC of the nomogram constructed from the above six factors was 0.888 in the training set. The Hosmer-Lemeshow test showed that the model was a good fit (p = 0.944). The calibration curve of the nomogram was close to the ideal diagonal line. Furthermore, the decision curve analysis demonstrated significantly better net benefit in the model. The external validation proved the reliability of the prediction nomogram. Conclusion: A nomogram was developed and validated to predict the occurrence of 30-day all-cause mortality in patients with CRO infection treated with colistin sulfate. This nomogram offers healthcare providers a precise and efficient means for early prediction, treatment management, and patient notification in cases of CRO infection treated with colistin sulfate. [ABSTRACT FROM AUTHOR]

Purpose: Sleep disorders are becoming more prevalent in hospitalized patients with acute myocardial infarction (AMI). We aimed to investigate the risk factors for sleep disorders in hospitalized patients with AMI, then develop and validate a prediction nomogram for the risk of sleep disorders. Methods: Clinical data were collected from patients with AMI hospitalized in our hospital from January 2020 to June 2023. All patients were divided into the training group and the validation group with a ratio of 7:3 in sequential order. The LASSO regression analysis and multivariate logistic regression analysis were used to screen potential risk factors for sleep disorders. The concordance index (C-index), calibration curves, and decision curve analysis (DCA) were plotted. Results: A total of 256 hospitalized patients with AMI were enrolled. Patients were divided into the training group (180) and the validation group (76) according to a scale of 7:3. Of the 256 patients, 90 patients (35.16%) suffered from sleep disorders, and 33 patients (12.89%) needed hypnotics. The variables screened by LASSO regression included age, smoking, NYHA class, anxiety status at admission, depression status at admission, and strangeness of environment. A nomogram model was established by incorporating the risk factors selected. The C-index, calibration curve, and DCA showed good predictive performance. Conclusions: We identified six clinical characteristics as predictors of sleep disorders in hospitalized patients with AMI. It helps nurses make appropriate decisions in clinical practice. [ABSTRACT FROM AUTHOR]

Background: This dynamic nomogram model was developed to predict the probability of fetal loss in pregnant patients with systemic lupus erythematosus (SLE) with mild disease severity before conception. Methods: An analysis was conducted on 314 pregnancy records of patients with SLE who were hospitalized between January 2015 and January 2022 at Shenzhen People's Hospital, and the Longhua Branch of Shenzhen People's Hospital. Data from the Longhua Branch of the Shenzhen People's Hospital were utilized as an independent external validation cohort. The nomogram, a widely used statistical visualization tool to predict disease onset, progression, prognosis, and survival, was created after feature selection using multivariate logistic regression analysis. To evaluate the model prediction performance, we employed the receiver operating characteristic curve, calibration curve, and decision curve analysis. Results: Lupus nephritis, complement 3, immunoglobulin G, serum albumin, C-reactive protein, and hydroxychloroquine were all included in the nomogram model. The model demonstrated good calibration and discriminatory power, with an area under the curve of 0.867 (95% confidence interval: 0.787–0.947). According to decision curve analysis, the nomogram model exhibited clinical importance when the probability of fetal loss in patients with SLE ranged between 10 and 70%. The predictive ability of the model was demonstrated through external validation. Conclusion: The predictive nomogram approach may facilitate precise management of pregnant patients with SLE with mild disease severity before conception. [ABSTRACT FROM AUTHOR]

BackgroundPostpartum urinary incontinence (UI) is a serious condition that significantly affects the quality of life. Several studies have demonstrated that it is associated with pelvic floor dysfunction. This study aimed to develop and validate a UI risk prediction model to identify primiparas with singleton pregnancies at high risk.MethodsA multistage stratified random sampling process was used. UI was measured using the International Standard Consultation on Incontinence Questionnaire Form (a modified Bristol questionnaire, ICIQ-FLUTS). Records of 1,340 primiparas with singleton pregnancies were reviewed, and data were collected from January 2014 to December 2014 in multiple centers. A univariate logistic regression analysis was performed, followed by a multivariable logistic regression analysis of the data. Using bootstrap resampling, we constructed a nomogram to assess postpartum UI risk.ResultsA total of 1,340 patients were enrolled, including 345 with postpartum UI and 995 with non-postpartum UI. The occurrence of postpartum UI was significantly related to the mode of delivery, family history of UI, coffee or tea consumption, antenatal UI, and frequent cough. The nomogram exhibited good discriminatory ability with a C-index of 0.718 (95% confidence interval: 0.684–0.752) and a bootstrap-corrected C-index of 0.716. Additionally, the calibration curve demonstrated that the predicted outcomes aligned well with the actual observations. Ultimately, the decision curve analysis indicated that the nomogram exhibited favorable clinical applicability.ConclusionThe decision curve analysis suggests that the nomogram could provide clinical value. The clinician will then feel more confident about making clinical recommendations regarding postpartum UI screening for primiparous women with singleton pregnancies.

Abstract Background The rising number of women giving birth at advanced maternal age has posed significant challenges in obstetric care in recent years, resulting in increased incidence of neonatal transfer to the Neonatal Intensive Care Unit (NICU). Therefore, identifying fetuses requiring NICU transfer before delivery is essential for guiding targeted preventive measures. Objective This study aims to construct and validate a nomogram for predicting the prenatal risk of NICU admission in neonates born to mothers over 35 years of age. Study design Clinical data of 4218 mothers aged ≥ 35 years who gave birth at the Department of Obstetrics of the Second Hospital of Shandong University between January 1, 2017 and December 31, 2021 were reviewed. Independent predictors were identified by multivariable logistic regression, and a predictive nomogram was subsequently constructed for the risk of neonatal NICU admission. Results Multivariate logistic regression demonstrated that the method of prenatal screening, number of implanted embryos, preterm premature rupture of the membranes, preeclampsia, HELLP syndrome, fetal distress, premature birth, and cause of preterm birth are independent predictors of neonatal NICU admission. Analysis of the nomogram decision curve based on these 8 independent predictors showed that the prediction model has good net benefit and clinical utility. Conclusion The nomogram demonstrates favorable performance in predicting the risk of neonatal NICU transfer after delivery by mothers older than 35 years. The model serves as an accurate and effective tool for clinicians to predict NICU admission in a timely manner.

Abstract Newborns are as the primary recipients of blood transfusions. There is a possibility of an association between blood transfusion and unfavorable outcomes. Such complications not only imperil the lives of newborns but also cause long hospitalization. Our objective is to explore the predictor variables that may lead to extended hospital stays in neonatal intensive care unit (NICU) patients who have undergone blood transfusions and develop a predictive nomogram. A retrospective review of 539 neonates who underwent blood transfusion was conducted using median and interquartile ranges to describe their length of stay (LOS). Neonates with LOS above the 75th percentile (P75) were categorized as having a long LOS. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was employed to screen variables and construct a risk model for long LOS. A multiple logistic regression prediction model was then constructed using the selected variables from the LASSO regression model. The significance of the prediction model was evaluated by calculating the area under the ROC curve (AUC) and assessing the confidence interval around the AUC. The calibration curve is used to further validate the model’s calibration and predictability. The model’s clinical effectiveness was assessed through decision curve analysis. To evaluate the generalizability of the model, fivefold cross-validation was employed. Internal validation of the models was performed using bootstrap validation. Among the 539 infants who received blood transfusions, 398 infants (P75) had a length of stay (LOS) within the normal range of 34 days, according to the interquartile range. However, 141 infants (P75) experienced long LOS beyond the normal range. The predictive model included six variables: gestational age (GA) (

Background: The rising number of women giving birth at advanced maternal age has posed significant challenges in obstetric care in recent years, resulting in increased incidence of neonatal transfer to the Neonatal Intensive Care Unit (NICU). Therefore, identifying fetuses requiring NICU transfer before delivery is essential for guiding targeted preventive measures. Objective: This study aims to construct and validate a nomogram for predicting the prenatal risk of NICU admission in neonates born to mothers over 35 years of age. Study design: Clinical data of 4218 mothers aged ≥ 35 years who gave birth at the Department of Obstetrics of the Second Hospital of Shandong University between January 1, 2017 and December 31, 2021 were reviewed. Independent predictors were identified by multivariable logistic regression, and a predictive nomogram was subsequently constructed for the risk of neonatal NICU admission. Results: Multivariate logistic regression demonstrated that the method of prenatal screening, number of implanted embryos, preterm premature rupture of the membranes, preeclampsia, HELLP syndrome, fetal distress, premature birth, and cause of preterm birth are independent predictors of neonatal NICU admission. Analysis of the nomogram decision curve based on these 8 independent predictors showed that the prediction model has good net benefit and clinical utility. Conclusion: The nomogram demonstrates favorable performance in predicting the risk of neonatal NICU transfer after delivery by mothers older than 35 years. The model serves as an accurate and effective tool for clinicians to predict NICU admission in a timely manner. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to investigate the factors associated with pathologic node-negativity (ypN0) in patients who received neoadjuvant chemotherapy (NAC) to develop and validate an accurate prediction nomogram. Methods: The CSBrS-012 study (2010-2020) included female patients with primary breast cancer treated with NAC followed by breast and axillary surgery in 20 hospitals across China. In the present study, 7,711 eligible patients were included, comprising 6,428 patients in the primary cohort from 15 hospitals and 1,283 patients in the external validation cohort from five hospitals. The hospitals were randomly assigned. The primary cohort was randomized at a 3:1 ratio and divided into a training set and an internal validation set. Univariate and multivariate logistic regression analyses were performed on the training set, after which a nomogram was constructed and validated both internally and externally. Results: In total, 3,560 patients (46.2%) achieved ypN0, and 1,558 patients (20.3%) achieved pathologic complete response in the breast (bpCR). A nomogram was constructed based on the clinical nodal stage before NAC (cN), ER, PR, HER2, Ki67, NAC treatment cycle, and bpCR, which were independently associated with ypN0. The area under the receiver operating characteristic curve (AUC) for the training set was 0.80. The internal and external validation demonstrated good discrimination, with AUCs of 0.79 and 0.76, respectively. Conclusion: We present a real-world study based on nationwide large-sample data that can be used to effectively screen for ypN0 to provide better advice for the management of residual axillary disease in breast cancer patients undergoing NAC. [ABSTRACT FROM AUTHOR]

Background: The coronary no-reflow (NR) phenomenon is an independent predictor of major adverse cardiac events (MACEs). This study aimed to establish a clinical and comprehensive nomogram for predicting NR in acute myocardial infarction (AMI) patients after primary percutaneous coronary intervention (pPCI). Methods: The multivariable logistic regression analysis was performed to determine the NR-related factors. A nomogram was established via several clinical and biochemical factors, and the performance was evaluated via discrimination, calibration, and clinical factors. Results: The study consisted of 3041 AMI patients after pPCI, including 2129 patients in the training set (70%) and 912 patients in the validation set (30%). The NR event was 238 in the training set and 87 in the validation set. The level of N-terminal prohormone B-type natriuretic peptide (NT-proBNP), basophil count (BASO), neutrophil count (NEUBC), D-dimer, hemoglobin (Hb), and red blood cell distribution width (RDW.CV) in NR patients showed statistically significant differences. In the training set, the C-index was 0.712, 95% CI 0.677 to 0.748. In the validation set, the C-index was 0.663, 95% CI 0.604 to 0.722. Conclusions: A nomogram that may predict NR in AMI patients undergoing pPCI was established and validated. We hope this nomogram can be used for NR risk assessment and clinical decision-making and significantly prevent potentially impaired reperfusion associated with NR. [ABSTRACT FROM AUTHOR]

Newborns are as the primary recipients of blood transfusions. There is a possibility of an association between blood transfusion and unfavorable outcomes. Such complications not only imperil the lives of newborns but also cause long hospitalization. Our objective is to explore the predictor variables that may lead to extended hospital stays in neonatal intensive care unit (NICU) patients who have undergone blood transfusions and develop a predictive nomogram. A retrospective review of 539 neonates who underwent blood transfusion was conducted using median and interquartile ranges to describe their length of stay (LOS). Neonates with LOS above the 75th percentile (P75) were categorized as having a long LOS. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was employed to screen variables and construct a risk model for long LOS. A multiple logistic regression prediction model was then constructed using the selected variables from the LASSO regression model. The significance of the prediction model was evaluated by calculating the area under the ROC curve (AUC) and assessing the confidence interval around the AUC. The calibration curve is used to further validate the model's calibration and predictability. The model's clinical effectiveness was assessed through decision curve analysis. To evaluate the generalizability of the model, fivefold cross-validation was employed. Internal validation of the models was performed using bootstrap validation. Among the 539 infants who received blood transfusions, 398 infants (P75) had a length of stay (LOS) within the normal range of 34 days, according to the interquartile range. However, 141 infants (P75) experienced long LOS beyond the normal range. The predictive model included six variables: gestational age (GA) (< 28 weeks), birth weight (BW) (< 1000 g), type of respiratory support, umbilical venous catheter (UVC), sepsis, and resuscitation frequency. The area under the receiver operating characteristic (ROC) curve (AUC) for the training set was 0.851 (95% CI 0.805–0.891), and for the validation set, it was 0.859 (95% CI 0.789–0.920). Fivefold cross-validation indicates that the model has good generalization ability. The calibration curve demonstrated a strong correlation between the predicted risk and the observed actual risk, indicating good consistency. When the intervention threshold was set at 2%, the decision curve analysis indicated that the model had greater clinical utility. The results of our study have led to the development of a novel nomogram that can assist clinicians in predicting the probability of long hospitalization in blood transfused infants with reasonable accuracy. Our findings indicate that GA (< 28 weeks), BW(< 1000 g), type of respiratory support, UVC, sepsis, and resuscitation frequency are associated with a higher likelihood of extended hospital stays among newborns who have received blood transfusions. [ABSTRACT FROM AUTHOR]

Yue Liu, Shi Qi Xie, Xia Yang, Jing Lan Chen, Jian Rong Zhou School of Nursing, Chongqing Medical University, Chongqing, People’s Republic of ChinaCorrespondence: Jian Rong Zhou; Shi Qi Xie, School of Nursing, Chongqing Medical University, 1 Medical College Road, Yu Zhong District, Chongqing, 400016, People’s Republic of China, Tel +86-135 0830 0955 ; +86-156 0833 2043, Fax +86-23-63555767, Email 202028@cqmu.edu.cn; xie47@cqmu.edu.cnPurpose: The clinical presentation of Obstructive Sleep Apnea (OSA) in children is insidious and harmful. Early identification of children with OSA, particularly those at a higher risk for severe symptoms, is essential for making informed clinical decisions and improving long-term outcomes. Therefore, we developed and validated a risk prediction model for severity in Chinese children with OSA to effectively identify children with moderate-to-severe OSA in a clinical setting.Patients and Methods: From June 2023 to September 2023, we retrospectively analyzed the medical records of 367 Children diagnosed with OSA through portable bedside polysomnography (PSG). Predictor variables were screened using the least absolute shrinkage and selection operator (LASSO) and logistic regression techniques to construct nomogram to predict the severity of OSA. Receiver operating characteristic curve (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to determine the discrimination, calibration, and clinical usefulness of the nomogram.Results: A total of 367 children with a median age of 84 months were included in this study. Neck circumference, ANB, gender, learning problem, and level of obstruction were identified as independent risk factors for moderate-severe OSA. The consistency indices of the nomogram in the training and validation cohorts were 0.841 and 0.75, respectively. The nomogram demonstrated a strong concordance between the predicted probabilities and the observed probabilities for children diagnosed with moderate-severe OSA. With threshold probabilities ranging from 0.1 to 1.0, the predictive model demonstrated strong predictive efficacy and yielded improved net benefit for clinical decision-making. ROC analysis was employed to classify the children into high and low-risk groups, utilizing the Optimal Cutoff value of 0.39.Conclusion: A predictive model using LASSO regression was developed and validated for children with varying levels of OSA. This model identifies children at risk of developing OSA at an early stage.Keywords: obstructive sleep apnea, children, cephalometric, prediction nomogram, risk prediction model

Abstract Background Accurate prediction of acute exacerbation helps select patients with chronic obstructive pulmonary disease (COPD) for individualized therapy. The potential of lymphocyte subsets to function as clinical predictive factors for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) remains uncertain. Methods In this single-center prospective cohort study with a 2-year follow-up, 137 patients aged 51 to 79 with AECOPD were enrolled. We examined the prognostic indicators of AECOPD by analyzing lymphocyte subsets and baseline symptom score. Furthermore, a predictive model was constructed to anticipate the occurrence of respiratory failure in patients experiencing AECOPD. Results The COPD Assessment Test (CAT) score combined with home oxygen therapy and CD4+CD8+ T cells% to predict respiratory failure in AECOPD patients were the best (the area under the curves [AUC] = 0.77, 95% CI: 0.70–0.86, P

Abstract Aims Left ventricular (LV) remodelling after acute myocardial infarction (AMI) is associated with heart failure and increased mortality. There was no consensus on the definition of LV remodelling, and the prognostic value of LV remodelling with different definitions has not been compared. We aimed to find the optimal definition and develop a prediction nomogram as well as online calculator that can identify patients at risk of LV remodelling. Methods and results This prospective, observational study included 829 AMI patients undergoing percutaneous coronary intervention from January 2015 to January 2020. Echocardiography was performed within the 48 h of admission and at 6 months after infarction to evaluate LV remodelling, defined as a 20% increase in LV end‐diastolic volume (LVEDV), a 15% increase in LV end‐systolic volume (LVESV), or LV ejection fraction (LVEF)

PurposeThis study aimed to investigate the factors associated with pathologic node-negativity (ypN0) in patients who received neoadjuvant chemotherapy (NAC) to develop and validate an accurate prediction nomogram.MethodsThe CSBrS-012 study (2010–2020) included female patients with primary breast cancer treated with NAC followed by breast and axillary surgery in 20 hospitals across China. In the present study, 7,711 eligible patients were included, comprising 6,428 patients in the primary cohort from 15 hospitals and 1,283 patients in the external validation cohort from five hospitals. The hospitals were randomly assigned. The primary cohort was randomized at a 3:1 ratio and divided into a training set and an internal validation set. Univariate and multivariate logistic regression analyses were performed on the training set, after which a nomogram was constructed and validated both internally and externally.ResultsIn total, 3,560 patients (46.2%) achieved ypN0, and 1,558 patients (20.3%) achieved pathologic complete response in the breast (bpCR). A nomogram was constructed based on the clinical nodal stage before NAC (cN), ER, PR, HER2, Ki67, NAC treatment cycle, and bpCR, which were independently associated with ypN0. The area under the receiver operating characteristic curve (AUC) for the training set was 0.80. The internal and external validation demonstrated good discrimination, with AUCs of 0.79 and 0.76, respectively.ConclusionWe present a real-world study based on nationwide large-sample data that can be used to effectively screen for ypN0 to provide better advice for the management of residual axillary disease in breast cancer patients undergoing NAC.

Background Our goal was to create a simple risk‐prediction model for renal function decline after cardiac surgery to help focus renal follow‐up efforts on patients most likely to benefit. Methods and Results This single‐center retrospective cohort study enrolled 24 904 patients who underwent cardiac surgery from 2012 to 2019 at Fuwai Hospital, Beijing, China. An estimated glomerular filtration rate (eGFR) reduction of ≥30% 3 months after surgery was considered evidence of renal function decline. Relative to patients with eGFR 60 to 89 mL/min per 1.73 m2 (4.5% [531/11733]), those with eGFR ≥90 mL/min per 1.73 m2 (10.9% [1200/11042]) had a higher risk of renal function decline, whereas those with eGFR ≤59 mL/min per 1.73 m2 (5.8% [124/2129]) did not. Each eGFR stratum had a different strongest contributor to renal function decline: increased baseline eGFR levels for patients with eGFR ≥90 mL/min per 1.73 m2, transfusion of any blood type for patients with eGFR 60 to 89 mL/min per 1.73 m2, and no recovery of renal function at discharge for patients with eGFR ≤59 mL/min per 1.73 m2. Different nomograms were established for the different eGFR strata, which yielded a corrected C‐index value of 0.752 for eGFR ≥90 mL/min per 1.73 m2, 0.725 for eGFR 60–89 mL/min per 1.73 m2 and 0.791 for eGFR ≤59 mL/min per 1.73 m2. Conclusions Predictors of renal function decline over the follow‐up showed marked differences across the eGFR strata. The nomograms incorporated a small number of variables that are readily available in the routine cardiac surgical setting and can be used to predict renal function decline in patients stratified by baseline eGFR.

Background: Numerous of models have been developed to predict the bone metastasis (BM) risk; however, due to the variety of cancer types, it is difficult for clinicians to use these models efficiently. We aimed to perform the pan‐cancer analysis to create the cancer classification system for BM, and construct the nomogram for predicting the BM risk. Methods: Cancer patients diagnosed between 2010 and 2018 in the Surveillance, Epidemiology, and End Results (SEER) database were included. Unsupervised hierarchical clustering analysis was performed to create the BM prevalence‐based cancer classification system (BM‐CCS). Multivariable logistic regression was applied to investigate the possible associated factors for BM and construct a nomogram for BM risk prediction. The patients diagnosed between 2017 and 2018 were selected for validating the performance of the BM‐CCS and the nomogram, respectively. Results: A total of 50 cancer types with 2,438,680 patients were included in the construction model. Unsupervised hierarchical clustering analysis classified the 50 cancer types into three main phenotypes, namely, categories A, B, and C. The pooled BM prevalence in category A (17.7%; 95% CI: 17.5%–17.8%) was significantly higher than that in category B (5.0%; 95% CI: 4.5%–5.6%), and category C (1.2%; 95% CI: 1.1%–1.4%) (p < 0.001). Advanced age, male gender, race, poorly differentiated grade, higher T, N stage, and brain, lung, liver metastasis were significantly associated with BM risk, but the results were not consistent across all cancers. Based on these factors and BM‐CCS, we constructed a nomogram for predicting the BM risk. The nomogram showed good calibration and discrimination ability (AUC in validation cohort = 88%,95% CI: 87.4%–88.5%; AUC in construction cohort = 86.9%,95% CI: 86.8%–87.1%). The decision curve analysis also demonstrated the clinical usefulness. Conclusion: The classification system and prediction nomogram may guide the cancer management and individualized BM screening, thus allocating the medical resources to cancer patients. Moreover, it may also have important implications for studying the etiology of BM. [ABSTRACT FROM AUTHOR]

Autoimmunity is present in patients with stable chronic obstructive pulmonary disease (COPD), playing a role in indirect and direct ways. We aimed to explore whether autoimmunity could play a role in COPD exacerbations and construct autoimmunity-related prediction models. This prospective, longitudinal, observational cohort study enrolled 155 patients with acute COPD exacerbations (AECOPD) followed for at least two years. The laboratory parameters, including complete blood count, serum immunoglobulins G/A/M and complement C3/C4 levels, were collected at enrollment. We studied the demographic characteristics, clinical characteristics and laboratory parameters to identify independent risk factors and build predictive models. The results showed that lower lymphocyte count was associated with noninvasive ventilation (NIV) in patients with AECOPD (the odds ratio [OR] 0.25, the 95% confidence interval [CI]: 0.08–0.81, P = 0.02). Lymphocyte count performed well with an area under the curves (AUC) of 0.75 (P < 0.0001, sensitivity: 78.1%, specificity: 62.3%, cutoff value [Cov] ≤ 1.1). The C index, calibration plot, decision curve analysis (DCA) and bootstrap repetitions indicated that this clinical prediction model based on lymphocyte count for NIV in patients with AECOPD performed well. Having prior home oxygen therapy (OR: 2.82, 95% CI: 1.25–6.36, P = 0.013) and higher COPD Assessment Test (CAT) scores (OR: 1.14, 95% CI: 1.03–1.25, P = 0.011) were associated with the increased risk for respiratory failure. For predicting respiratory failure, CAT scores and home oxygen therapy combined had an AUC-ROC of 0.73 (P < 0.0001). This clinical prediction model based on lymphocyte count may help to assist in treatment decisions for NIV in patients with AECOPD. Lower complement C3 seems to be associated with worse outcomes in patients with AECOPD. [ABSTRACT FROM AUTHOR]

Developing and validating a clinical prediction nomogram of enteral feeding intolerance (NOFI) in critically ill patients. So as to help clinicians implement pre-intervention for patients with high risk of enteral feeding intolerance (FI), formulate individualized feeding strategies, and reduce the probability of FI occurrence. From March 2018 to April 2023, patients who met the inclusion criteria but did not meet the exclusion criteria constituted the development cohort for retrospective analysis, and NOFI was developed. Patients recruited consecutively between May 2023 and July 2023 constituted the validation cohort for the prospective analysis for independent external validation of NOFI. Initially, a backward stepwise method was employed to conduct a multivariate logistic regression analysis in the development cohort, aiming to identify the optimal-fit model. Subsequently, a nomogram was derived from this model. The validation of the nomogram was carried out in an independent external validation cohort, where discrimination and calibration were evaluated. Additionally, a decision curve analysis was conducted to assess the net benefit of utilizing the nomogram for decision-making. A total of 628 and 143 patients, 49.0 % and 51.7 % of patients occurred FI, were included in the development and validation cohort, respectively. We developed a NOFI in severely ill patients and the primary diagnosis, Acute gastrointestinal injury (AGI) grade, and APACHE II score were independent predictors of FI, with the OR of the primary diagnosis of circulatory disease being 2.281 (95 % CI, 1.364–3.816; P = 0.002); The OR of respiratory diseases was 0.424 (95 % CI, 0.259–0.594; P = 0.001); The OR of AGI grade was 4.920 (95 % CI, 3.773–6.416; P < 0.001), OR of APACHE II score was 1.100 (95 % CI, 1.059–1.143; P < 0.001). Independent external validation of the prediction model was performed. This model has good discrimination and calibration. The decision curve analysis of the nomogram provided better net benefit than the alternate options (full early enteral nutrition or delayed enteral nutrition). The prediction of enteral feeding intolerance can be conveniently facilitated by the NOFI that integrates primary diagnosis, AGI grade, and APACHE II score in critically ill patients. [ABSTRACT FROM AUTHOR]

Summary: Background: Essential thrombocythemia (ET), a myeloproliferative neoplasm (MPN), has a substantial risk of evolving into post-essential thrombocythemia myelofibrosis (post-ET MF). This study aims to establish a prediction nomogram for early prediction of post-ET MF in ET patients. Methods: The training cohort comprised 558 patients from 8 haematology centres between January 1, 2010, and May 1, 2023, while the external validation cohort consisted of 165 patients from 6 additional haematology centres between January 1, 2010, and May 1, 2023. Univariable and multivariable Cox regression analysis was performed to identified independent risk factors and establish a nomogram to predict the post-ET MF free survival. Both bias-corrected area under the curve (AUC), calibration curves and concordance index (C-index) were employed to assess the predictive accuracy of the nomogram. Findings: Multivariate Cox regression demonstrated that elevated red blood cell distribution width (RDW), elevated levels of lactate dehydrogenase (LDH) and the level of haemoglobin (Hb), a history of smoking and the presence of splenomegaly were independent risk factors for post-ET MF. The C-index displayed of the training and validation cohorts were 0.877 and 0.853. The 5 years, 10 years AUC values in training and external validation cohorts were 0.948, 0.769 and 0.978, 0.804 respectively. Bias-corrected curve is close to the ideal curve and revealed a strong consistency between actual observation and prediction. Interpretation: We developed a nomogram capable of predicting the post-ET MF free survival probability at 5 years and 10 years in ET patients. This tool helps doctors identify patients who need close monitoring and appropriate counselling. Funding: This research was funded by the Key R&D Program of Zhejiang (No. 2022C03137); the Public Technology Application Research Program of Zhejiang, China (No. LGF21H080003); and the Zhejiang Medical Association Clinical Medical Research special fund project (No. 2022ZYC-D09).

Background: Accurate prediction of acute exacerbation helps select patients with chronic obstructive pulmonary disease (COPD) for individualized therapy. The potential of lymphocyte subsets to function as clinical predictive factors for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) remains uncertain. Methods: In this single-center prospective cohort study with a 2-year follow-up, 137 patients aged 51 to 79 with AECOPD were enrolled. We examined the prognostic indicators of AECOPD by analyzing lymphocyte subsets and baseline symptom score. Furthermore, a predictive model was constructed to anticipate the occurrence of respiratory failure in patients experiencing AECOPD. Results: The COPD Assessment Test (CAT) score combined with home oxygen therapy and CD4+CD8+ T cells% to predict respiratory failure in AECOPD patients were the best (the area under the curves [AUC] = 0.77, 95% CI: 0.70–0.86, P < 0.0001, sensitivity: 60.4%, specificity: 86.8%). The nomogram model, the C index, calibration plot, decision curve analysis, and clinical impact curve all indicate the model's good predictive performance. The observed decrease in the proportions of CD4+CD8+ T cells appears to be correlated with more unfavorable outcomes. Conclusions: The nomogram model, developed to forecast respiratory failure in patients with AECOPD, utilizing variables such as home oxygen therapy, CAT score, and CD4+CD8+ T cells%, demonstrated a high level of practicality in clinical settings. CD4+CD8+ T cells serve as a reliable and readily accessible predictor of AECOPD, exhibiting greater stability compared to other indices. It is less susceptible to subjective influences from patients or physicians. This model facilitated personalized estimations, enabling healthcare professionals to make informed decisions regarding preventive interventions. [ABSTRACT FROM AUTHOR]

Aims: Left ventricular (LV) remodelling after acute myocardial infarction (AMI) is associated with heart failure and increased mortality. There was no consensus on the definition of LV remodelling, and the prognostic value of LV remodelling with different definitions has not been compared. We aimed to find the optimal definition and develop a prediction nomogram as well as online calculator that can identify patients at risk of LV remodelling. Methods and results: This prospective, observational study included 829 AMI patients undergoing percutaneous coronary intervention from January 2015 to January 2020. Echocardiography was performed within the 48 h of admission and at 6 months after infarction to evaluate LV remodelling, defined as a 20% increase in LV end‐diastolic volume (LVEDV), a 15% increase in LV end‐systolic volume (LVESV), or LV ejection fraction (LVEF) < 50% at 6 months. The impact of LV remodelling on long‐term outcomes was analysed. Lasso regression was performed to screen potential predictors, and multivariable logistic regression analysis was conducted to establish the prediction nomogram. The area under the curve, calibration curve and decision curve analyses were used to determine the discrimination, calibration and clinical usefulness of the remodelling nomogram. The incidences of LV remodelling defined by LVEDV, LVESV and LVEF were 24.85% (n = 206), 28.71% (n = 238) and 14.60% (n = 121), respectively. Multivariable Cox regression models demonstrated that different definitions of LV remodelling were independently associated with the composite endpoint. However, only remodelling defined by LVEF was significantly connected with long‐term mortality (hazard ratio = 2.78, 95% confidence interval 1.41–5.48, P = 0.003). Seven variables were selected to construct the remodelling nomogram, including diastolic blood pressure, heart rate, AMI type, stent length, N‐terminal pro brain natriuretic peptide, troponin I, and glucose. The prediction model had an area under the receiver operating characteristics curve of 0.766. The calibration curve and decision curve analysis indicated consistency and better net benefit in the prediction model. Conclusions: LV remodelling defined by LVEDV, LVESV and LVEF were independent predictors for long‐term mortality or heart failure hospitalization in AMI patients after percutaneous coronary intervention. However, only remodelling defined by LVEF was suitable for predicting all‐cause death. In addition, the nomogram can provide an accurate and effective tool for the prediction of postinfarct remodelling. [ABSTRACT FROM AUTHOR]

Background and Aim: Postoperative complications are important clinical outcomes for colon cancer patients. This study aimed to investigate the predictive value of inflammatory–nutritional indicators combined with computed tomography body composition on postoperative complications in patients with stage II–III colon cancer. Methods: We retrospectively collected data from patients with stage II–III colon cancer admitted to our hospital from 2017 to 2021, including 198 patients in the training cohort and 50 patients in the validation cohort. Inflammatory–nutritional indicators and body composition were included in the univariate and multivariate analyses. Binary regression was used to develop a nomogram and evaluate its predictive value. Results: In the multivariate analysis, the monocyte–lymphocyte ratio (MLR), systemic immune‐inflammation index (SII), nutritional risk score (NRS), skeletal muscle index (SMI), and visceral fat index (VFI) were independent risk factors for postoperative complications of stage II–III colon cancer. In the training cohort, the area under the receiver operating characteristic curve of the predictive model was 0.825 (95% confidence interval [CI] 0.764–0.886). In the validation cohort, it was 0.901 (95% CI 0.816–0.986). The calibration curve showed that the prediction results were in good agreement with the observational results. Decision curve analysis showed that colon cancer patients could benefit from the predictive model. Conclusions: A nomogram combining MLR, SII, NRS, SMI, and VFI with good accuracy and reliability in predicting postoperative complications in patients with stage II–III colon cancer was established, which can help guide treatment decisions. [ABSTRACT FROM AUTHOR]

Purpose: The prediction of axillary lymph node status after neoadjuvant chemotherapy (NAC) becoming critical because of the advocation of the de-escalation of axillary management. We investigate associated factors of axillary upstaging in clinical node-negative (cN0) breast cancer patients receiving NAC to develop and validate an accurate prediction nomogram. Methods: We retrospectively analyzed 1892 breast cancer patients with stage of cT1-3N0 treated by NAC and subsequent surgery between 2010 and 2020 in twenty hospitals across China. Patients randomly divided into a training set and validation set (3:1). Univariate and multivariate logistic regression analysis were performed, after which a nomogram was constructed and validated. Results: In total, pathologic node negativity (ypN0) achieved in 1406 (74.3%) patients and another 486 (25.7%) patients upstaged to pathologic node positive (ypN+). Breast pathologic complete response (bpCR) was achieved in 445 (23.5%) patients and non-bpCR in 1447 (76.5%) patients. A nomogram was established by ER, tumor histology, HER2 status, cycle of NAC treatment, and the bpCR, which were confirmed by multivariate logistic analysis as independent predictors of nodal upstaging in the training cohort (n = 1419). The area under the receiver operating characteristic curve (AUC) of the training cohort and validation cohort (n = 473) were 0.73 (95% CI 0.693–0.751) and 0.77 (95% CI 0.723–0.812) respectively. Conclusion: We present a nomogram with a nationwide large sample data which can effectively predict axillary upstaging after neoadjuvant chemotherapy to give better advice for individualized axillary lymph node management of breast cancer. [ABSTRACT FROM AUTHOR]

Abstract Background To develop and validate a predictive nomogram for tumor residue 3–6 months after treatment based on postradiotherapy plasma Epstein–Barr virus (EBV) deoxyribonucleic acid (DNA), clinical stage, and radiotherapy (RT) dose in patients with stage II–IVA nasopharyngeal carcinoma (NPC) treated with intensity-modulated radiation therapy (IMRT). Methods In this retrospective study, 1050 eligible patients with stage II–IVA NPC, who completed curative IMRT and underwent pretreatment and postradiotherapy (-7 to +28 days after IMRT) EBV DNA testing, were enrolled from 2012 to 2017. The prognostic value of the residue was explored using Cox regression analysis in patients (n=1050). A nomogram for predicting tumor residues after 3–6 months was developed using logistic regression analyses in the development cohort (n=736) and validated in an internal cohort (n=314). Results Tumor residue was an independent inferior prognostic factor for 5-year overall survival, progression-free survival, locoregional recurrence-free survival and distant metastasis-free survival (all P

Abstract Background The triglyceride glucose (TyG) index is a well-established biomarker for insulin resistance (IR) that shows correlation with poor outcomes in patients with coronary artery disease. We aimed to integrate the TyG index with clinical data in a prediction nomogram for the long-term prognosis of new onset ST-elevation myocardial infarction (STEMI) following primary percutaneous coronary intervention (PCI) . Methods This retrospective study included new-onset STEMI patients admitted at two heart centers for emergency PCI from December 2015 to March 2018 in development and independent validation cohorts. Potential risk factors were screened applying least absolute shrinkage and selection operator (LASSO) regression. Multiple Cox regression was employed to identify independent risk factors for prediction nomogram construction. Nomogram performance was assessed based on receiver operating characteristic curve analysis, calibration curves, Harrell’s C-index and decision curve analysis (DCA). Results In total, 404 patients were assigned to the development cohort and 169 to the independent validation cohort. The constructed nomogram included four clinical variables: age, diabetes mellitus, current smoking, and TyG index. The Harrell’s C-index values for the nomogram were 0.772 (95% confidence interval [CI]: 0.721–0.823) in the development cohort and 0.736 (95%CI: 0.656–0.816) in the independent validation cohort. Significant correlation was found between the predicted and actual outcomes in both cohorts, indicating that the nomogram is well calibrated. DCA confirmed the clinical value of the development prediction nomogram. Conclusions Our validated prediction nomogram based on the TyG index and electronic health records data was shown to provide accurate and reliable discrimination of new-onset STEMI patients at high- and low-risk for major adverse cardiac events at 2, 3 and 5 years following emergency PCI.

Key points The MRI-based nomogram could predict the SDS occurrence in patients with SOC. The relationship between the sigmoid colon/rectum and ovarian mass accounts is critical. A preoperative nomogram provides gynecologists with an accurate and effective tool.

Edy Suyanto,1 Ibnu Fajar,2 Tanto Hariyanto3 1Nursing Department, Poltekkes Kemenkes Malang, Malang, Indonesia; 2Nutrition Department, Poltekkes Kemenkes Malang, Malang, Indonesia; 3Blood Bank Technology Department, Poltekkes Kemenkes Malang, Malang, IndonesiaCorrespondence: Edy Suyanto, Poltekkes Kemenkes Malang, Jl. Ijen 77C, Malang, Indonesia, Email edy_suyanto@poltekkes-malang.ac.id

Background: To develop and validate a predictive nomogram for tumor residue 3–6 months after treatment based on postradiotherapy plasma Epstein–Barr virus (EBV) deoxyribonucleic acid (DNA), clinical stage, and radiotherapy (RT) dose in patients with stage II–IVA nasopharyngeal carcinoma (NPC) treated with intensity-modulated radiation therapy (IMRT). Methods: In this retrospective study, 1050 eligible patients with stage II–IVA NPC, who completed curative IMRT and underwent pretreatment and postradiotherapy (-7 to +28 days after IMRT) EBV DNA testing, were enrolled from 2012 to 2017. The prognostic value of the residue was explored using Cox regression analysis in patients (n=1050). A nomogram for predicting tumor residues after 3–6 months was developed using logistic regression analyses in the development cohort (n=736) and validated in an internal cohort (n=314). Results: Tumor residue was an independent inferior prognostic factor for 5-year overall survival, progression-free survival, locoregional recurrence-free survival and distant metastasis-free survival (all P<0.001). A prediction nomogram based on postradiotherapy plasma EBV DNA level (0 vs. 1–499 vs. ≥500 copies/ml), clinical stage (II vs. III vs. IVA), and RT dose (68.00–69.96 vs. 70.00–74.00 Gy) estimated the probability of residue development. The nomogram showed better discrimination (area under the curve (AUC): 0.752) than either the clinical stage (0.659) or postradiotherapy EBV DNA level (0.627) alone in the development and validation cohorts (AUC: 0.728). Conclusions: We developed and validated a nomogram model integrating clinical characteristics at the end of IMRT for predicting whether tumor will residue or not after 3–6 months. Thus, high-risk NPC patients who might benefit from immediate additional intervention could be identified by the model, and the probability of residue can be reduced in the future. [ABSTRACT FROM AUTHOR]

Background: The triglyceride glucose (TyG) index is a well-established biomarker for insulin resistance (IR) that shows correlation with poor outcomes in patients with coronary artery disease. We aimed to integrate the TyG index with clinical data in a prediction nomogram for the long-term prognosis of new onset ST-elevation myocardial infarction (STEMI) following primary percutaneous coronary intervention (PCI). Methods: This retrospective study included new-onset STEMI patients admitted at two heart centers for emergency PCI from December 2015 to March 2018 in development and independent validation cohorts. Potential risk factors were screened applying least absolute shrinkage and selection operator (LASSO) regression. Multiple Cox regression was employed to identify independent risk factors for prediction nomogram construction. Nomogram performance was assessed based on receiver operating characteristic curve analysis, calibration curves, Harrell's C-index and decision curve analysis (DCA). Results: In total, 404 patients were assigned to the development cohort and 169 to the independent validation cohort. The constructed nomogram included four clinical variables: age, diabetes mellitus, current smoking, and TyG index. The Harrell's C-index values for the nomogram were 0.772 (95% confidence interval [CI]: 0.721–0.823) in the development cohort and 0.736 (95%CI: 0.656–0.816) in the independent validation cohort. Significant correlation was found between the predicted and actual outcomes in both cohorts, indicating that the nomogram is well calibrated. DCA confirmed the clinical value of the development prediction nomogram. Conclusions: Our validated prediction nomogram based on the TyG index and electronic health records data was shown to provide accurate and reliable discrimination of new-onset STEMI patients at high- and low-risk for major adverse cardiac events at 2, 3 and 5 years following emergency PCI. [ABSTRACT FROM AUTHOR]