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Objective: To develop and validate (both internally and externally) a prediction model examining a combination of risk factors in order to predict postpartum haemorrhage (PPH) in a general obstetric Irish population of singleton pregnancies.Study Design: We used data from the National Maternal and Newborn Clinical Management System (MN-CMS), including all singleton deliveries at Cork University Maternity Hospital (CUMH), Ireland during 2019. We defined PPH as an estimated blood loss of ≥ 1000 ml following the birth of the baby. Multivariable logistic regression with backward stepwise selection was used to develop the prediction model. Candidate predictors included maternal age, maternal body mass index, parity, previous caesarean section, assisted fertility, gestational age, fetal macrosomia, mode of delivery and history of PPH. Discrimination was assessed using the area under the receiver operating characteristic curve (ROC) C-statistic. We used bootstrapping for internal validation to assess overfitting, and conducted a temporal external validation using data from all singleton deliveries at CUMH during 2020.Results: Out of 6,077 women, 5,807 with complete data were included in the analyses, and there were 270 (4.65%) cases of PPH. Four variables were considered the best combined predictors of PPH, including parity (specifically nulliparous), macrosomia, mode of delivery (specifically operative vaginal delivery, emergency caesarean section and prelabour caesarean section), and history of PPH. These predictors were used to develop a nomogram to provide individualised risk assessment for PPH. The original apparent C-statistic was 0.751 (95% CI: 0.721, 0.779) suggesting good discriminative performance. There was minimal optimism adjustment to the C-statistic after bootstrapping, indicating good internal performance (optimism adjusted C-statistic: 0.748). Results of external validation were comparable with the development model suggesting good reproducibility.Conclusions: Four routinely collected variables (parity, fetal macrosomia, mode of delivery and history of PPH) were identified when predicting PPH in a general obstetric Irish population of singleton pregnancies. Use of our nomogram could potentially assist with individualised risk assessment of PPH and inform clinical decision-making allowing those at highest risk of PPH be actively managed. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To (1) evaluate an injury risk model that included modifiable and nonmodifiable factors into an arm injury risk prediction model in Minor League Baseball (MiLB) pitchers and (2) compare model performance separately for predicting the incidence of elbow and shoulder injuries. DESIGN: Prospective cohort. METHODS: A 10-year MiLB injury risk study was conducted. Pitchers were evaluated during preseason, and pitches and arm injuries were documented prospectively. Nonmodifiable variables included arm injury history, professional experience, arm dominance, year, and humeral torsion. Modifiable variables included BMI, pitch count, total range of motion, and horizontal adduction. We compared modifiable, nonmodifiable, and combined model performance by R², calibration (best = 1.00), and discrimination (area under the curve [AUC]; higher number is better). Sensitivity analysis included only arm injuries sustained in the first 90 days. RESULTS: In this study, 407 MiLB pitchers (141 arm injuries) were included. Arm injury incidence was 0.27 injuries per 1000 pitches. The arm injury model (calibration 1.05 [0.81-1.30]; AUC: 0.74 [0.69-0.80]) had improved performance compared to only using modifiable predictors (calibration: 0.91 [0.68-1.14]; AUC: 0.67 [0.62-0.73]) and only shoulder range of motion (calibration: 0.52 [0.29, 0.75]; AUC: 0.52 [0.46, 58]). Elbow injury model demonstrated improved performance (calibration: 1.03 [0.76-1.33]; AUC: 0.76 [0.69-0.83]) compared to the shoulder injury model (calibration: 0.46 [0.22-0.69]; AUC: 0.62 [95% CI: 0.55, 0.69]). The sensitivity analysis demonstrated improved model performance compared to the arm injury model. CONCLUSION: Arm injury risk is influenced by modifiable and nonmodifiable risk factors. The most accurate way to identify professional pitchers who are at risk for arm injury is to use a model that includes modifiable and nonmodifiable risk factors. [ABSTRACT FROM AUTHOR]

We performed an internal laboratory validation of the Precision ID GlobalFiler NGS STR panel v2 kit to assist the introduction of the technology into the routine forensic casework practice. The study was designed and evaluated based not only on the key validation standards like sensitivity, stability, reproducibility, repeatability, mixture, and concordance, but we also tested the effect of reduced input DNA, we measured and applied locus-specific analytical threshold values, tested two different PCR cycle conditions, sequence artifacts and stutters were also analysed. During the study we also tested the new method on real casework samples. The sensitivity study confirmed that adding 500 pg template DNA for library preparation can be optimal at base PCR cycle number (that was 24), because the measured average heterozygote balance was not lower than 0.82, and each allele was detected above the analytical threshold. However, contrary to previous communications, increasing the PCR cycle numbers up to 28 has not resulted the significant elevation of the heterozygote imbalance. According to our results, raised PCR cycle condition (i.e. 28) is appropriate at or below 150 pg total input DNA. For most loci, the calculated AT was lower than the manufacturer's recommended. Applying the newly established ATs with raised PCR cycle conditions the allele detection sensitivity and reliability increased. We observed allele dropouts only at the 15 pg template DNA experiments with 5 % frequency, that is better to previously published studies. This result indicates that this low amount of DNA (i.e. 15 pg) could be a minimum limit of template input for a potentially successful analysis. In the mixture study the minor contributor could be detected up to 1:19 mixture ratio. We detected minor alleles in all measurements and concentrations above the threshold if the template DNA were fixed and only SNP differences were observed between the same alleles of the contributors. To test concordance between the new method and traditional STR genotyping we analysed 58 Hungarian individual samples in parallel. Nearby the detected 248 different length-based alleles on the 31 loci in the sample pool we revealed additional 75 sequence variant alleles, that represent an approximately 23 % increase in the total number of observed alleles. The casework study confirmed that the Precision ID GlobalFiler NGS STR panel v2 kit is effective even in genotyping degraded samples with extremely low levels of DNA, if we apply elevated cycle number for library preparation and use locus-specific analytical thresholds. • GlobalFiler NGS STR Panel v2 kit applied with locus-specific analytical threshold. • 15 pg input DNA was the lower limit to generate a profile with elevated PCR-cycle. • Minor allelic components were recoverable with mixture ratio of 1:19. • Compatible profiles were obtained from degraded casework samples. • Obtained genotypes were >99 % in concordance with CE results. [ABSTRACT FROM AUTHOR]

The use of TDM in clinical practice to monitor the plasma levels of antibiotics administered to critically ill patients is a well-established approach that allows for optimization of the patient's response to drug therapy, considering the characteristics of the drug, the clinical and physiological status of the patient and any peculiar of the pathogen that caused the clinical picture. In our laboratory, we have developed a single LC-MS/MS analysis for dosing the serum concentration of an antibacterial panel composed of eight antibacterial and two selective inhibitors. The method presented used a certified material furnished by a commercial company and was internally validated using the EMA guidelines. The results have shown high sensitivity, precision, and accuracy, a lower matrix effect combined with simple sample preparation and a time-saving procedure. We have evaluated the recovery rate and matrix effect by testing serum samples without pathological index and serum pools obtained from haemolysed, icteric, or lipemic samples. The assay has shown a recovery range between 94% and 101%. • The tdm allows rapid adaptation of therapy in hospitalized subjects. • Personalized therapy is essential for the management of the critical patient. • Mass spectrometry is the pioneering technique in the correct determination of blood concentrations of drugs. • Validation of analytical methods is mandatory for the use of a method in diagnostics. [ABSTRACT FROM AUTHOR]

Background: Teachers with a teacher-centred perspective have difficulties applying student-centred approaches in Problem Based Learning (PBL) because they are inclined to show teacher-centred behaviours. The six aspects explained in Korthagen's Onion Model (environment, behaviour, competencies, beliefs, identity, and mission) are assumed to contribute to teachers' perspectives, showing that both the environment and personal characteristics influence behaviours. For teachers to function properly in PBL, those six aspects should reflect a student-centred perspective. Previous instruments to measure teaching perspectives focused on only a few of these relevant aspects. Therefore, we developed the Student-Centred Perspective of Teachers (SCPT) questionnaire with subscales for each aspect in the Onion Model. This study aimed to provide evidence for its internal and external validity. Methods: The SCPT was distributed in a survey to 795 teachers from 20 medical schools. For the internal validation, Confirmatory Factor Analysis was performed to analyse theoretical fit model validation, convergent validation, and discriminant validation. For the external validation, teachers' perspective scores were compared among three groups of amount of PBL training using Analysis of Variance (ANOVA) and post-hoc Least Significant Difference (LSD) tests. The p-value for all tests was set at.05. Results: A total of 543 out of 795 teachers (68.3%) participated. Confirmatory Factor Analysis showed the evidence of the SCPT's internal validation with acceptable fit for the six subscales measured by 19 items and the following Composite Reliability scores: environment (.72), behaviour (.74), competencies (.63), beliefs (.55), identity (.76), and mission (.60). All items' factors loadings reached a good standard (.5 or greater). Only the environment subscale had the Average Variance Extracted (AVE) score higher than.5 and the Maximum Shared Variance score lower than the AVE score. ANOVA and Post-hoc LSD tests showed that teachers who participated in more PBL training showed significantly higher student-centred perspectives, providing evidence for external validity. Conclusion: The SCPT is a reliable and valid instrument to measure teaching perspectives. Identifying aspects that do not represent the adoption of a student-centred perspective may provide valuable input for faculty development in the context of PBL. [ABSTRACT FROM AUTHOR]

The Philippines is frequently visited by strong and destructive typhoons, claiming hundreds of lives and extensive damage to the environment, and properties. To minimize negative impacts, understanding evacuee behavior for evacuation planning is essential. Determinants of household evacuation decision were investigated in this study using 164 valid observations obtained through face-to-face interviews with household heads in the affected area of Typhoon Hagupit in Barangay Maypangdan, Borongan City, Eastern Samar, Philippines. Correlation analysis and logistic regression were used to identify significant factors that affect household evacuation decisions. Results showed that the presence of children less than 10 years of age and elderly, house material, and house floor level affect the household evacuation decision. The insights from the results of this study are useful for policymakers and planners in preparing contingency plans for typhoon events at the barangay level. This may include ensuring the welfare of the vulnerable age groups, and strict implementation of building code for structural design and constructions. [ABSTRACT FROM AUTHOR]

BackgroundFor elderly patients with mild clinical symptoms of uncomplicated appendicitis(UA), non-surgical treatment has been shown to be feasible, whereas emergency surgical treatment is recommended in elderly patients with complicated appendicitis(CA), but it is still challenging to accurately distinguish CA and UA before treatment. This study aimed to develop a predictive model to assist clinicians to quickly determine the type of acute appendicitis.MethodsWe retrospectively studied the clinical data of elderly patients with acute appendicitis who visited the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from January 2012 to January 2022. The patients were divided into UA group and CA group, and the general conditions, medical history, physical examination, laboratory examination and imaging examination were compared between the two groups, and SPSS 26.0 and R 4.0.2 software were used to establish CA clinic. Predict the model, and validate it internally.ResultsThe clinical data of 441 elderly patients with acute appendicitis were collected, 119 patients were excluded due to incomplete clinical data or other diseases. Finally, 332 patients were included in the study and divided into UA group (n = 229) and CA group (n = 103). By analyzing the clinical data of the two groups of patients, the duration of abdominal pain [OR = 1.094, 95% CI (1.056–1.134)], peritonitis [OR = 8.486, 95% CI (2.017–35.703))] and total bilirubin [OR = 1.987, 95% CI (1.627–2.426)] were independent predictors of CA (all p

Background: Our aim was to create and validate a nomogram predicting cesarean delivery after induction of labor among nulliparous women at term.Methods: Data were obtained from medical records from Nanjing Drum Tower Hospital. Nulliparous women with singleton pregnancies undergoing induction of labor at term were involved. A total of 2950 patients from Jan. 2014 to Dec. 2015 were served as derivation cohort. A nomogram was constructed by multivariate logistic regression using maternal, fetal and pregnancy characteristics. The predictive accuracy and discriminative ability of the nomogram were internal validated by 1000-bootstrap resampling, followed by external validation of a new dataset from Jan. 2016 to Dec. 2016.Results: Logistic regression revealed nine predictors of cesarean delivery, including maternal height, age, uterine height, abdominal circumference, estimated fetal weight, indications for induction of labor, initial cervical consistency, cervical effacement and station. Nomogram was well calibrated and had an AUC of 0.73 (95% confidence interval [CI], 0.70-0.75) after bootstrap resampling for internal validation. The AUC in external validation reached 0.67, which was significantly higher than that of three models published previously (P<0.05).Conclusions: This validated nomogram, constructed by variables that were obtained form medical records, can help estimate risk of cesarean delivery before induction of labor. [ABSTRACT FROM AUTHOR]

Background: Despite advances in availability and access to antiretroviral therapy (ART), HIV still ranks as a major cause of global mortality. Hence, the aim of this study was to develop and internally validate a risk score capable of accurately predicting in‐hospital mortality in HIV‐positive patients requiring hospital admission. Methods: Consecutive HIV‐positive patients presenting to the Charlotte Maxeke Johannesburg Academic Hospital adult emergency department between 7 July 2017 and 18 October 2018 were prospectively enrolled. Multivariate logistic regression was used to determine parameters for inclusion in the final risk score. Discrimination and calibration were assessed by means of the area under the receiver operating curve (AUROC) and the Hosmer–Lemeshow goodness‐of‐fit test, respectively. Internal validation was conducted using the regular bootstrap technique. Results: The overall in‐hospital mortality rate was 13.6% (n = 166). Eight predictors were included in the final risk score: ART non‐adherence or not yet on ART, Glasgow Coma Scale < 15, respiratory rate > 20 breaths/min, oxygen saturation < 90%, white cell count < 4 × 109/L, creatinine > 120 μmol/L, lactate > 2 mmol/L and albumin < 35 g/L. After internal validation, the risk score maintained good discrimination [AUROC 0.83, 95% confidence interval (CI): 0.78–0.88] and calibration (Hosmer–Lemeshow χ2 = 2.26, p = 0.895). Conclusion: The HIV In‐hospital Mortality Prediction (HIV‐IMP) risk score has overall good discrimination and calibration and is relatively easy to use. Further studies should be aimed at externally validating the score in varying clinical settings. [ABSTRACT FROM AUTHOR]

Abstract Background Many colorectal cancer (CRC) survivors experience persisting health problems post-treatment that compromise their health-related quality of life (HRQoL). Prediction models are useful tools for identifying survivors at risk of low HRQoL in the future and for taking preventive action. Therefore, we developed prediction models for CRC survivors to estimate the 1-year risk of low HRQoL in multiple domains. Methods In 1458 CRC survivors, seven HRQoL domains (EORTC QLQ-C30: global QoL; cognitive, emotional, physical, role, social functioning; fatigue) were measured prospectively at study baseline and 1 year later. For each HRQoL domain, scores at 1-year follow-up were dichotomized into low versus normal/high. Separate multivariable logistic prediction models including biopsychosocial predictors measured at baseline were developed for the seven HRQoL domains, and internally validated using bootstrapping. Results Average time since diagnosis was 5 years at study baseline. Prediction models included both non-modifiable predictors (age, sex, socio-economic status, time since diagnosis, tumor stage, chemotherapy, radiotherapy, stoma, micturition, chemotherapy-related, stoma-related and gastrointestinal complaints, comorbidities, social inhibition/negative affectivity, and working status) and modifiable predictors (body mass index, physical activity, smoking, meat consumption, anxiety/depression, pain, and baseline fatigue and HRQoL scores). Internally validated models showed good calibration and discrimination (AUCs: 0.83–0.93). Conclusions The prediction models performed well for estimating 1-year risk of low HRQoL in seven domains. External validation is needed before models can be applied in practice.

Objective: The unique metastatic pattern of skip lateral lymph node metastasis (SLLNM) in PTC patients may lead to missed diagnosis of lateral cervical metastatic lymph nodes. Therefore, many different SLLNM prediction models were constructed. In this study, partially eligible models (Hu 2020, Wang 2020, and Zhao 2023 nomograms) were selected for external validation, and then new variables were incorporated for model reconstruction to extend clinical applicability., Methods: 576 PTC patients from our center were selected to evaluate the performance of the three nomograms using the receiver operating characteristic curve (ROC), calibration curves, and decision curve analyses (DCA). Three new variables were added to calibrate the model, including assessment of LN status on ultrasound (US-SLLNM), the distance from the tumor to the capsule (Capsular distance), and the number of central lymph node dissections (CLND number). Univariate and multivariate logistic regression analyses were used to screen independent predictors to reconstruct the model, and 1000 Bootstrap internal validations were performed., Results: SLLNM were present in 69/576 patients (12.0%). In external validation, the area under the ROC curves (AUCs) for Hu 2020, Wang 2020, and Zhao 2023 nomograms were 0.695 (95% CI:0.633-0.766), 0.792 (95% CI=0.73-0.845), and 0.769 (95% CI:0.713-0.824), respectively. The calibration curves for the three models were overall poorly fitted; DCA showed some net clinical benefit. Model differentiation and net clinical benefit improved by adding three new variables. Based on multivariate analysis, female, age, and maximum tumor diameter ≤ 10 mm, located at the upper pole, Capsular distance < 0mm, US-SLLNM, CLND number ≤ 5 were identified as independent predictors of SLLNM and were used to construct the new model. After 1000 Bootstrap internal validations, the mean AUC of the model was 0.870 (95% CI:0.839-0.901), the calibration curve was close to the ideal curve, and the net clinical benefit was significant., Conclusion: Overall, these nomograms were well differentiated and provided some net clinical benefit, but with varying degrees of underestimation or overestimation of the actual risk and high false-negative rates. New dynamic nomogram was constructed based on the addition of new variables and larger samples, showing better performance., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, Huang, Zou, Zhang and Xin.)

Clinical prediction models provide risks of health outcomes that can inform patients and support medical decisions. However, most models never make it to actual implementation in practice. A commonly heard reason for this lack of implementation is that prediction models are often not externally validated. While we generally encourage external validation, we argue that an external validation is often neither sufficient nor required as an essential step before implementation. As such, any available external validation should not be perceived as a license for model implementation. We clarify this argument by discussing 3 common misconceptions about external validation. We argue that there is not one type of recommended validation design, not always a necessity for external validation, and sometimes a need for multiple external validations. The insights from this paper can help readers to consider, design, interpret, and appreciate external validation studies., Competing Interests: Declaration of competing interest There are no competing interests for any author., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objective: To investigate the risk factors associated with prolonged hospitalization in patients diagnosed with diabetic foot ulcers (DFU), to develop a predictive model, and to conduct internal validation of the model., Methods: The clinical data of DFU patients admitted to West China Hospital, Sichuan University between January 2012 and December 2022 were retrospectively collected. The subjects were randomly assigned to a training cohort and a validation cohort at a ratio of 7 to 3. Hospital stays longer than 75th percentile were defined as prolonged length-of-stay. A thorough analysis of the risk factors was conducted using the training cohort, which enabled the development of an accurate risk prediction model. To ensure robustness, the model was internally validated using the validation cohort., Results: A total of 967 inpatients with DFU were included, among whom 245 patients were identified as having an extended length-of-stay. The training cohort consisted of 622 patients, while the validation cohort comprised 291 patients. Multivariate logistic regression analysis revealed that smoking history (odds ratio [OR]=1.67, 95% confidence interval [CI], 1.13 to 2.48, P =0.010), Wagner grade 3 or higher (OR=7.13, 95% CI, 3.68 to 13.83, P <0.001), midfoot ulcers (OR=1.99, 95% CI, 1.07 to 3.72, P =0.030), posterior foot ulcers (OR=3.68, 95% CI, 1.83 to 7.41, P <0.001), multisite ulcers (OR=2.91, 95% CI, 1.80 to 4.69, P <0.001), wound size≥3 cm 2 (OR=2.00, 95% CI, 1.28-3.11, P =0.002), and white blood cell count (OR=1.11, 95% CI, 1.05 to 1.18, P <0.001) were associated with an increased risk of prolonged length of stay. Additionally, a nomogram was constructed based on the identified risk factors. The areas under the receiver operating characteristic (ROC) curves for both the training cohort and the validation cohort were 0.782 (95% CI, 0.745 to 0.820) and 0.756 (95% CI, 0.694 to 0.818), respectively, indicating robust predictive performance. Furthermore, the calibration plot demonstrated optimal concordance between the predicted probabilities and the observed outcomes in both the training and the validation cohorts., Conclusion: Smoking history, Wagner grade≥3, midfoot ulcers, posterior foot ulcers, multisite ulcers, ulcer area≥3 cm 2 , and elevated white blood cell count are identified as independent predictors of prolonged hospitalization. Therefore, it is imperative that clinicians conduct a comprehensive patient evaluation and implement appropriate diagnostic and therapeutic strategies to effectively shorten the length of stay for DFU patients., Competing Interests: 利益冲突　本文作者冉兴无是本刊编委会编委。该文在编辑评审过程中所有流程严格按照期刊政策进行，且未经其本人经手处理。除此之外，所有作者声明不存在利益冲突。, (© 2024《四川大学学报（医学版）》编辑部 版权所有Copyright ©2024 Editorial Office of Journal of Sichuan University (Medical Sciences).)

Background: Post-ERCP pancreatitis (PEP) with trans-papillary approach remains a major issue, and the multi-factorial etiology can lead to the development of unpredictable PEP. Therefore, the early identification of PEP is highly desirable to assist with the health cost containment, the reduction in unnecessary admissions, earlier appropriate primary care, and intensive care for preventing progression of severe pancreatitis. This study aimed to establish a simplified predictive scoring system for PEP. Methods: Between January 1, 2012, and December 31, 2019, 3362 consecutive trans-papillary ERCP procedures were retrospectively analyzed. Significant risk factors were extracted by univariate, multivariate, and propensity score analyses, and the probability of PEP in the combinations of each factor were quantified using propensity score analysis. The results were internally validated using bootstrapping resampling. Results: In the scoring system with four stratifications using combinations of only five extracted risk factors, the very high-risk group showed 28.79% (95% confidence interval [CI], 18.30%–41.25%; P < 0.001) in the predicted incidence rate of PEP, and 9.09% (95% CI, 3.41%–18.74%; P < 0.001) in that of severe PEP; although the adjusted prevalence revealed 3.74% in PEP and 0.90% in severe PEP, respectively. The prediction model had an area under the curve of 0.86 (95% CI, 0.82–0.89) and the optimism-corrected model as an internal validation had an area under the curve of 0.81 (95% CI, 0.77–0.86). Conclusions: We established and validated a simplified predictive scoring system for PEP using five risk factors immediately after ERCP to assist with the early identification of PEP. [ABSTRACT FROM AUTHOR]

With the recent advances in next-generation sequencing (NGS), mitochondrial whole-genome sequencing has begun to be applied to the field of the forensic biology as an alternative to the traditional Sanger-type sequencing (STS). However, experimental workflows, commercial solutions, and output data analysis must be strictly validated before being implemented into the forensic laboratory. In this study, we performed an internal validation for an NGS-based typing of the entire mitochondrial genome using the Precision ID mtDNA Whole Genome Panel (Thermo Fisher Scientific) on the Ion S5 sequencer (Thermo Fisher Scientific). Concordance, repeatability, reproducibility, sensitivity, and heteroplasmy detection analyses were assessed using the 2800 M and 9947A standard control DNA as well as typical casework specimens, and results were compared with conventional Sanger sequencing and another NGS sequencer in a different laboratory. We discuss the strengths and limitations of this approach, highlighting some issues regarding noise thresholds and heteroplasmy detection, and suggesting solutions to mitigate these effects and improve overall data interpretation. Results confirmed that the Precision ID Whole mtDNA Genome Panel is highly reproducible and sensitive, yielding useful full mitochondrial DNA sequences also from challenging DNA specimens, thus providing further support for its use in forensic practice. [ABSTRACT FROM AUTHOR]

Some studies have suggested that early surgical treatment can effectively improve the prognosis of cervical spinal cord injury without radiological abnormality, but no research has focused on the development of a prognostic model of cervical spinal cord injury without radiological abnormality. This retrospective analysis included 43 patients with cervical spinal cord injury without radiological abnormality. Seven potential factors were assessed: age, sex, external force strength causing damage, duration of disease, degree of cervical spinal stenosis, Japanese Orthopaedic Association score, and physiological cervical curvature. A model was established using multiple binary logistic regression analysis. The model was evaluated by concordant profiling and the area under the receiver operating characteristic curve. Bootstrapping was used for internal validation. The prognostic model was as follows: logit(P) = −25.4545 + 21.2576VALUE + 1.2160SCORE − 3.4224TIME, where VALUE refers to the Pavlov ratio indicating the extent of cervical spinal stenosis, SCORE refers to the Japanese Orthopaedic Association score (0–17) after the operation, and TIME refers to the disease duration (from injury to operation). The area under the receiver operating characteristic curve for all patients was 0.8941 (95% confidence interval, 0.7930–0.9952). Three factors assessed in the predictive model were associated with patient outcomes: a great extent of cervical stenosis, a poor preoperative neurological status, and a long disease duration. These three factors could worsen patient outcomes. Moreover, the disease prognosis was considered good when logit(P) ≥ −2.5105. Overall, the model displayed a certain clinical value. This study was approved by the Biomedical Ethics Committee of the Second Affiliated Hospital of Xi’an Jiaotong University, China (approval number: 2018063) on May 8, 2018.

Overfitting is a common problem in the development of predictive models. It leads to an optimistic estimation of apparent model performance. Internal validation using bootstrapping techniques allows one to quantify the optimism of a predictive model and provide a more realistic estimate of its performance measures. Our objective is to build an easy-to-use command, bsvalidation, aimed to perform a bootstrap internal validation of a logistic regression model. [ABSTRACT FROM AUTHOR]

Abstract: Back pain is a leading cause of disability worldwide and is common in older adults. No clinical prediction models for poor long-term outcomes have been developed in older patients with back pain. This study aimed to develop and internally validate 3 clinical prediction models for nonrecovery in this population. A prospective cohort study in general practice was conducted (Back Complaints in the Elders, Netherlands), including 675 patients >55 years with a new episode of care for back pain. Three definitions of nonrecovery were used combining 6-month and 12-month follow-up data: (1) persistent back pain, (2) persistent disability, and (3) perceived nonrecovery. Sample size calculation resulted in a maximum of 14 candidate predictors that were selected from back pain prognostic literature and clinical experience. Multivariable logistic regression was used to develop the models (backward selection procedure). Models' performance was evaluated with explained variance (Nagelkerke's R2), calibration (Hosmer-Lemeshow test), and discrimination (area under the curve [AUC]) measures. The models were internally validated in 250 bootstrapped samples to correct for overoptimism. All 3 models displayed good overall performance during development and internal validation (ie, R2 > 30%; AUC > 0.77). The model predicting persistent disability performed best, showing good calibration, discrimination (AUC 0.86, 95% confidence interval 0.83-0.89; optimism-adjusted AUC 0.85), and explained variance (R2 49%, optimism-adjusted R2 46%). Common predictors in all models were: age, chronic duration, disability, a recent back pain episode, and patients' recovery expectations. Spinal morning stiffness and pain during spinal rotation were included in 2 of 3 models. These models should be externally validated before being used in a clinical primary care setting. [ABSTRACT FROM AUTHOR]

BackgroundMicrovascular invasion (MVI) is highly associated with poor prognosis in patients with liver cancer. Predicting MVI before surgery is helpful for surgeons to better make surgical plan. In this study, we aim at establishing a nomogram to preoperatively predict the occurrence of microvascular invasion in liver cancer.MethodA total of 405 patients with postoperative pathological reports who underwent curative hepatocellular carcinoma resection in the Third Affiliated Hospital of Sun Yat-sen University from 2013 to 2015 were collected in this study. Among these patients, 290 were randomly assigned to the development group while others were assigned to the validation group. The MVI predictive factors were selected by Lasso regression analysis. Nomogram was established to preoperatively predict the MVI risk in HCC based on these predictive factors. The discrimination, calibration, and effectiveness of nomogram were evaluated by internal validation.ResultsLasso regression analysis revealed that discomfort of right upper abdomen, vascular invasion, lymph node metastases, unclear tumor boundary, tumor necrosis, tumor size, higher alkaline phosphatase were predictive MVI factors in HCC. The nomogram was established with the value of AUROC 0.757 (0.716–0.809) and 0.768 (0.703–0.814) in the development and the validation groups. Well-fitted calibration was in both development and validation groups. Decision curve analysis confirmed that the predictive model provided more benefit than treat all or none patients. The predictive model demonstrated sensitivity of 58.7%, specificity of 80.7% at the cut-off value of 0.312.ConclusionNomogram was established for predicting preoperative risk of MVI in HCC. Better treatment plans can be formulated according to the predicted results.

Background: Microvascular invasion (MVI) is highly associated with poor prognosis in patients with liver cancer. Predicting MVI before surgery is helpful for surgeons to better make surgical plan. In this study, we aim at establishing a nomogram to preoperatively predict the occurrence of microvascular invasion in liver cancer. Method: A total of 405 patients with postoperative pathological reports who underwent curative hepatocellular carcinoma resection in the Third Affiliated Hospital of Sun Yat-sen University from 2013 to 2015 were collected in this study. Among these patients, 290 were randomly assigned to the development group while others were assigned to the validation group. The MVI predictive factors were selected by Lasso regression analysis. Nomogram was established to preoperatively predict the MVI risk in HCC based on these predictive factors. The discrimination, calibration, and effectiveness of nomogram were evaluated by internal validation. Results: Lasso regression analysis revealed that discomfort of right upper abdomen, vascular invasion, lymph node metastases, unclear tumor boundary, tumor necrosis, tumor size, higher alkaline phosphatase were predictive MVI factors in HCC. The nomogram was established with the value of AUROC 0.757 (0.716–0.809) and 0.768 (0.703–0.814) in the development and the validation groups. Well-fitted calibration was in both development and validation groups. Decision curve analysis confirmed that the predictive model provided more benefit than treat all or none patients. The predictive model demonstrated sensitivity of 58.7%, specificity of 80.7% at the cut-off value of 0.312. Conclusion: Nomogram was established for predicting preoperative risk of MVI in HCC. Better treatment plans can be formulated according to the predicted results. [ABSTRACT FROM AUTHOR]

Outcome misclassification occurs frequently in binary‐outcome studies and can result in biased estimation of quantities such as the incidence, prevalence, cause‐specific hazards, cumulative incidence functions, and so forth. A number of remedies have been proposed to address the potential misclassification of the outcomes in such data. The majority of these remedies lie in the estimation of misclassification probabilities, which are in turn used to adjust analyses for outcome misclassification. A number of authors advocate using a gold‐standard procedure on a sample internal to the study to learn about the extent of the misclassification. With this type of internal validation, the problem of quantifying the misclassification also becomes a missing data problem as, by design, the true outcomes are only ascertained on a subset of the entire study sample. Although, the process of estimating misclassification probabilities appears simple conceptually, the estimation methods proposed so far have several methodological and practical shortcomings. Most methods rely on missing outcome data to be missing completely at random (MCAR), a rather stringent assumption which is unlikely to hold in practice. Some of the existing methods also tend to be computationally‐intensive. To address these issues, we propose a computationally‐efficient, easy‐to‐implement, pseudo‐likelihood estimator of the misclassification probabilities under a missing at random (MAR) assumption, in studies with an available internal‐validation sample. We present the estimator through the lens of studies with competing‐risks outcomes, though the estimator extends beyond this setting. We describe the consistency and asymptotic distributional properties of the resulting estimator, and derive a closed‐form estimator of its variance. The finite‐sample performance of this estimator is evaluated via simulations. Using data from a real‐world study with competing‐risks outcomes, we illustrate how the proposed method can be used to estimate misclassification probabilities. We also show how the estimated misclassification probabilities can be used in an external study to adjust for possible misclassification bias when modeling cumulative incidence functions. [ABSTRACT FROM AUTHOR]

Study Design: Retrospective analysis.Objective: The aim of this study was to develop and validate a nomogram for the prediction of lung metastasis in patients with malignant primary spinal tumors.Summary Of Background Data: In patients with malignant primary spinal tumors, lung metastasis is usually found by computed tomography (CT) and is considered to be an essential factor affecting the prognosis and survival.Methods: We retrospectively collected 580 malignant primary osseous spinal neoplasms patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic analysis were used to identify independent factors. These prognostic factors were included in the nomograms. The nomograms were validated based on its calibration, discrimination, and clinical utility. The overall survival of the patients was analyzed using the Kaplan-Meier method and the survival differences were tested by the log-rank test.Results: We randomly divided all these patients (n = 580) into a training cohort (n = 408) and a validation cohort (n = 172). The results showed that the risk of lung metastasis was independently influenced by histologic type, use of surgery, clinical T stage, clinical N stage, and tumor extension (all P < 0.05). The nomogram consisted of five clinical features and provided good calibration and discrimination in the training and validation cohort, with an area under the curve of 0.858 and 0.811, respectively. Decision curve analysis showed that the nomogram was clinically useful. The Kaplan-Meier curves showed a significant difference between the higher and lower risk of lung metastasis groups (P < 0.001).Conclusion: Nomograms were developed to predict the risk of lung metastasis in patients with malignant primary spinal tumors. The nomogram showed favorable discrimination and calibration values, which may help optimize treatment decision-making for patients.Level Of Evidence: 4. [ABSTRACT FROM AUTHOR]

Background: Many colorectal cancer (CRC) survivors experience persisting health problems post-treatment that compromise their health-related quality of life (HRQoL). Prediction models are useful tools for identifying survivors at risk of low HRQoL in the future and for taking preventive action. Therefore, we developed prediction models for CRC survivors to estimate the 1-year risk of low HRQoL in multiple domains.Methods: In 1458 CRC survivors, seven HRQoL domains (EORTC QLQ-C30: global QoL; cognitive, emotional, physical, role, social functioning; fatigue) were measured prospectively at study baseline and 1 year later. For each HRQoL domain, scores at 1-year follow-up were dichotomized into low versus normal/high. Separate multivariable logistic prediction models including biopsychosocial predictors measured at baseline were developed for the seven HRQoL domains, and internally validated using bootstrapping.Results: Average time since diagnosis was 5 years at study baseline. Prediction models included both non-modifiable predictors (age, sex, socio-economic status, time since diagnosis, tumor stage, chemotherapy, radiotherapy, stoma, micturition, chemotherapy-related, stoma-related and gastrointestinal complaints, comorbidities, social inhibition/negative affectivity, and working status) and modifiable predictors (body mass index, physical activity, smoking, meat consumption, anxiety/depression, pain, and baseline fatigue and HRQoL scores). Internally validated models showed good calibration and discrimination (AUCs: 0.83-0.93).Conclusions: The prediction models performed well for estimating 1-year risk of low HRQoL in seven domains. External validation is needed before models can be applied in practice. [ABSTRACT FROM AUTHOR]

A new rapid DNA solution, the RapidHIT®ID, can accommodate two different sample cartridges, ACE, for the analysis of a single swab and EXT, for the analysis of DNA extracts. An efficient internal validation designed for low‐throughput rapid DNA is described. An evaluation of the EXT sample cartridge is also described. Each cartridge generated profiles with sufficient data quality to meet CODIS eligibility in fewer than 120 min. The results exhibited 100% correlation when compared to conventional DNA typing methods. Precision, reproducibility, stochastic, mixture, and contamination experiments produced expected results. Sensitivity of the ACE sample cartridge was acceptable for buccal swab analysis. The sensitivity of the EXT sample cartridge is discussed. The ACE validation and the EXT evaluation utilized a minimalist, cost‐saving, efficient design to generate a validated RapidHIT®ID instrument capable of producing genetic profiles from both extracted forensic DNA samples and buccal swab samples within 120 min. [ABSTRACT FROM AUTHOR]

A new rapid DNA solution, the RapidHIT®ID, can accommodate two different sample cartridges, ACE, for the analysis of a single swab and EXT, for the analysis of DNA extracts. An efficient internal validation designed for low‐throughput rapid DNA is described. An evaluation of the EXT sample cartridge is also described. Each cartridge generated profiles with sufficient data quality to meet CODIS eligibility in fewer than 120 min. The results exhibited 100% correlation when compared to conventional DNA typing methods. Precision, reproducibility, stochastic, mixture, and contamination experiments produced expected results. Sensitivity of the ACE sample cartridge was acceptable for buccal swab analysis. The sensitivity of the EXT sample cartridge is discussed. The ACE validation and the EXT evaluation utilized a minimalist, cost‐saving, efficient design to generate a validated RapidHIT®ID instrument capable of producing genetic profiles from both extracted forensic DNA samples and buccal swab samples within 120 min. [ABSTRACT FROM AUTHOR]

Abstract Background Missing values are a frequent issue in human studies. In many situations, multiple imputation (MI) is an appropriate missing data handling strategy, whereby missing values are imputed multiple times, the analysis is performed in every imputed data set, and the obtained estimates are pooled. If the aim is to estimate (added) predictive performance measures, such as (change in) the area under the receiver-operating characteristic curve (AUC), internal validation strategies become desirable in order to correct for optimism. It is not fully understood how internal validation should be combined with multiple imputation. Methods In a comprehensive simulation study and in a real data set based on blood markers as predictors for mortality, we compare three combination strategies: Val-MI, internal validation followed by MI on the training and test parts separately, MI-Val, MI on the full data set followed by internal validation, and MI(-y)-Val, MI on the full data set omitting the outcome followed by internal validation. Different validation strategies, including bootstrap und cross-validation, different (added) performance measures, and various data characteristics are considered, and the strategies are evaluated with regard to bias and mean squared error of the obtained performance estimates. In addition, we elaborate on the number of resamples and imputations to be used, and adopt a strategy for confidence interval construction to incomplete data. Results Internal validation is essential in order to avoid optimism, with the bootstrap 0.632+ estimate representing a reliable method to correct for optimism. While estimates obtained by MI-Val are optimistically biased, those obtained by MI(-y)-Val tend to be pessimistic in the presence of a true underlying effect. Val-MI provides largely unbiased estimates, with a slight pessimistic bias with increasing true effect size, number of covariates and decreasing sample size. In Val-MI, accuracy of the estimate is more strongly improved by increasing the number of bootstrap draws rather than the number of imputations. With a simple integrated approach, valid confidence intervals for performance estimates can be obtained. Conclusions When prognostic models are developed on incomplete data, Val-MI represents a valid strategy to obtain estimates of predictive performance measures.

Background: Perineal trauma is a common complication of childbirth and can have serious impacts on long-term health. Few studies have examined the combined effect of multiple risk factors. We developed and internally validated a risk prediction model to predict third and fourth degree perineal tears using data from a general obstetric population., Methods: Risk prediction model using data from all singleton vaginal deliveries at Cork University Maternity Hospital (CUMH), Ireland during 2019 and 2020. Third/fourth degree tears were diagnosed by an obstetrician or midwife at time of birth and defined as tears that extended into the anal sphincter complex or involved both the anal sphincter complex and anorectal mucosa. We used univariable and multivariable logistic regression with backward stepwise selection to develop the models. Candidate predictors included infant sex, maternal age, maternal body mass index, parity, mode of delivery, birthweight, post-term delivery, induction of labour and public/private antenatal care. We used the receiver operating characteristic (ROC) curve C-statistic to assess discrimination, and bootstrapping techniques were used to assess internal validation., Results: Of 8,403 singleton vaginal deliveries, 8,367 (99.54%) had complete data on predictors for model development. A total of 128 women (1.53%) had a third/fourth degree tear. Three variables remained in the final model: nulliparity, mode of delivery (specifically forceps delivery or ventouse delivery) and increasing birthweight (per 100 gram increase) (C-statistic: 0.75, 95% CI: 0.71, 0.79). We developed a nomogram to calculate individualised risk of third/fourth degree tears using these predictors. Bootstrapping indicated good internal performance., Conclusions: Use of our nomogram can provide an individualised risk assessment of third/fourth degree tears and potentially aid counselling of women on their potential risk., Competing Interests: No competing interests were disclosed., (Copyright: © 2023 Maher GM et al.)

Abstract Background It is important to find ways to minimize errors when children self-report food consumption. Objective The objective of this study was to investigate whether assistance given to children completing a self-administered 24-hour dietary recall instrument called SACANA (Self-Administered Child, Adolescent and Adult Nutrition Assessment) increased the repeatability and plausibility of energy intake (EI) estimates. Participants/setting The study was conducted between October 2013 and March 2016 in a convenience sample of 395 children, aged 8 to 17 years, from eight European countries participating in the I.Family study. Design SACANA was used to recall the previous day's food intake, twice in a day, once with and once without assistance. Main outcome measures The difference in EI between the first and second recalls was the main repeatability measure; the ratio of EI to basal metabolic rate was the plausibility measure. Statistical methods Generalized linear mixed models, adjusted for sex, age, and body mass index z -score, were used to assess whether assistance during the first vs second recall influenced repeatability and plausibility. Results The difference in estimated EI (EI from second recall minus EI from first recall) was significantly lower (P <0.001) in those assisted at first (median=–76 kcal) than those assisted at second recall (median=282 kcal). Modeling showed that EI at assisted first recall was 19% higher (95% CI 1.13 to 1.24) than in assisted second recall. Overall, 60% of recalls had a plausible EI. Modeling to estimate the simultaneous effects of second vs first recall and assistance vs no assistance on plausibility showed that those assisted at first recall had significantly higher odds of a plausible recall than those unassisted (odds ratio 3.64, 95% CI 2.20 to 6.01), with no significant difference in plausibility of second recall compared to the first (odds ratio 1.48, 95% CI 0.92 to 2.35). Conclusions When children are assisted at first recall, the plausibility and repeatability of the later unassisted recall improve. This improvement was evident for all ages. A future, adequately powered study is required to investigate the age range for which assistance is advisable. [ABSTRACT FROM AUTHOR]

Study Design: A case-control study; clinical prediction rule.Objective: The aim of this study was to construct and internally validate a clinical prediction rule to identify patients at high risk of developing heterotopic ossification (HO) after spinal cord injury (SCI).Summary Of Background Data: HO after SCI can lead to loss of joint mobility, loss of function, peripheral nerve entrapment, and pressure sores. HO is usually diagnosed on the basis of clinical symptoms, as no laboratory tests are yet available to identify patients with early HO formation. Risk factors include a complete SCI; patient age; spasticity; urinary tract infection (UTI); pneumonia; pelvic trauma; cervical or thoracic injury; and pressure sores.Methods: We analyzed a total of 558 patients with SCI, of whom 221 developed HO of the hip. A parametric survival model was fitted to estimate the probability of developing HO of the hip within 3 months of a SCI. Hazard ratios (HRs) calculation, internal validation, calibration, and model reduction were performed over 200 bootstrapped resamples. A risk score for clinical used was developed.Results: Risk factors contributing to the risk score were completeness of the injury, age, sex, UTI, spasticity, and pneumonia. The model demonstrated good discrimination (AUC = 0.72). According to the risk score quintiles, the risk of developing HO after SCI was 1.0 for persons with a score of 0 to 17 and increased 2.47-fold for persons with a score of 1 to 21, 4.75-fold for persons with a score of 22 to 27, 6.95-fold for persons with a score of 28 to 31, and 9.23-fold for persons with a score of 32-35.Conclusion: The risk score demonstrated good discrimination in predicting the occurrence of HO within 3 months of a SCI. Further development and validation of the model in other populations is warranted.Level Of Evidence: 3. [ABSTRACT FROM AUTHOR]