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Objectives: Development and validation of risk prediction models at mid-pregnancy and delivery to predict admission to the neonatal care unit., Methods: We used data from all singleton deliveries at Cork University Maternity Hospital (CUMH), Ireland during 2019. Admission to the neonatal care unit was assumed if length of stay in the unit was > 24 h. Multivariable logistic regression with backward stepwise selection was used to develop the models. Discrimination was assessed using the ROC curve C-statistic, and internal validation was assessed using bootstrapping techniques. We conducted temporal external validation using data from all singleton deliveries at CUMH during 2020., Results: Out of 6,077 women, 5,809 (95.6%) with complete data were included in the analyses. A total of 612 infants (10.54%) were admitted to the neonatal care unit for > 24 hours. Six variables were informative at mid-pregnancy: male infants, maternal smoking, advancing maternal age, maternal overweight/obesity, nulliparity and history of gestational diabetes (C-statistic: 0.600, 95% CI: 0.567, 0.614). Seven variables were informative at delivery: male infants, nulliparity, public antenatal care, gestational age < 39 weeks', non-spontaneous vaginal delivery, premature rupture of membranes and time of birth between 17:01-07.59 h (C-statistic: 0.738, 95% CI: 0.715, 0.760). Using these predictors, we developed nomograms to calculate individualised risk of neonatal care unit admission. Bootstrapping indicated good internal performance and external validation suggested good reproducibility., Discussion: Our nomograms allow the user to quickly estimate individualised risk of neonatal care unit admission. Future research should aim to improve accuracy in early pregnancy to better assist counselling of parents., Competing Interests: Declarations Ethics Approval This study uses anonymised data and has been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Ethical approval was obtained from the Clinical Research Ethics Committee of the Cork Teaching Hospitals (ECM4(v)09/04/2020). Consent to Participate Not applicable. Consent for Publication Not applicable. Conflicts of Interest The authors have no conflicts of interest to disclose, including financial interest., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective: The objective was to establish a machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS., Methods: 80% of sepsis patients selected randomly from the MIMIC-IV database, without prior pulmonary conditions and with nonpulmonary infection sites, were used to construct prediction models through machine learning techniques (including K-nearest neighbour, extreme gradient boosting, support vector machine, deep neural network, and decision tree methods). The remaining 20% of patients were utilized to validate the model's accuracy. Additionally, local data were employed for further model validation., Results: A total of 11,409 patients were included, with the most common type of infection being bloodstream infection. A total of 7,632 (66.9%) patients developed nonpulmonary sepsis-associated ARDS (NPS-ARDS). Patients with NPS-ARDS had significantly longer ICU stays (6.2 ± 5.2 days vs. 4.4 ± 3.7 days, p < 0.01) and higher 28-day mortality rates (19.5% vs. 14.9%, p < 0.01). Both internal and external validation demonstrated that the model constructed with the extreme gradient boosting method had high accuracy. In the internal validation, the model predicted NPS-ARDS and mortality in such patients with accuracies of 77.5% and 71.8%, respectively. In the external validation, the model predicted NPS-ARDS and mortality in these patients with accuracies of 78.0% and 81.4%, respectively., Conclusion: The model established via the extreme gradient boosting method can predict the occurrence and mortality of nonpulmonary sepsis-associated ARDS to a certain extent., Competing Interests: Declarations Ethics approval and consent to participate All methods were carried out in accordance with relevant guidelines and regulations. All the experimental protocols were approved by the Institutional Review Boards (IRBs) of Beth Israel Deaconess Medical Center and the Massachusetts Institute of Technology and Affiliated Nantong Hospital 3 of the Nantong University Ethics Committee. A waiver of informed consent for the use of MIMIC‑IV data was given by the Institutional Review Boards (IRBs) of Beth Israel Deaconess Medical Center and the Massachusetts Institute of Technology. The need for informed consent from the local subjects and/or their legal guardian(s) was waived by the Affiliated Nantong Hospital 3 of Nantong University Ethics Committee because of the retrospective nature of the study. Consent for publication Not applicable. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Background: Gestational diabetes mellitus (GDM) is a common complication of mid-to-late pregnancy. Here, we constructed a predictive model for GDM based on a combination of clinical characteristics and relevant serum markers., Methods: Data from full-term singleton vaginal deliveries from January 2022 to January 2023 were retrospectively collected from the obstetrics department. The data collected were segregated and assigned to training, validation, and external test sets. Maternal demographic characteristics, living and working habits, and haematological indicators, such as liver function and lipids were collected using a questionnaire designed for the study. The "rms" package in R was used to explore GDM-associated factors through stepwise regression at P < 0.05. A predictive model was developed based on the results of multifactorial logistic regression analysis. We then evaluated the differentiation of the column-line graphical model and performed internal and external validation. To assess the accuracy of the bar graphical model, we plotted calibration and decision curves., Results: Data from 265 pregnant women were included in the training and internal validation sets, and data from 113 pregnant women were included in the external validation set. The logistic regression algorithm screened 8 indicators as predictors. A prediction model was constructed with ALT, TBA, TC, and TG levels while considering whether GDM affects appetite, the husband- wife relationship, family history, and parental relationships as predictors. The Hosmer-Lemeshow goodness-of-fit test revealed that the chi-square values for the modelling, internal validation, and external validation groups (χ 2  = 5.964, 3.249, and 12.182, respectively) were all P > 0.05. The ROC curve AUCs for the three groups were 0.93 (95% CI: 0.89-0.97), 0.72 (95% CI: 0.62-0.81), and 0.68 (95% CI: 0.53-0.83), respectively., Conclusion: In this study, a GDM prediction model was constructed to achieve high performance in GDM risk prediction based on routine obstetric tests and information., (© 2024. The Author(s).)

Study Question: Can a simplified ovarian hyperstimulation syndrome (OHSS) risk assessment index be developed and validated with sufficient discrimination of moderate/severe OHSS from those without OHSS?, Summary Answer: This easy-to-use OHSS risk assessment index shows good discriminative power and high calibration accuracy in internal and external validation cohorts., What Is Known Already: An early alert and risk stratification is critical to prevent the occurrence of OHSS. We have previously developed a multi-stage smartphone app-based prediction model to evaluate the risk of OHSS, but app use might not be so convenient in many primary institutions. A simplified OHSS risk assessment index has been required., Study Design, Size, Duration: This training and internal validation of an OHSS risk assessment index used retrospective cohort data from January 2016 to December 2020. External validation was performed with a prospective cohort database from January 2021 to May 2022. There were 15 066 cycles in the training cohort, 6502 cycles in the internal validation cohort, and 8097 cycles in the external validation cohort., Participants/materials, Setting, Methods: This study was performed in the reproductive medicine center of a tertiary hospital. Infertile women who underwent ovarian stimulation were included. Data were extracted from the local database with detailed medical records. A multi-stage risk assessment index was constructed at multiple stages. The first stage was before the initiation of ovarian stimulation, the second was before the ovulation trigger, the third was after oocyte retrieval, and the last stage was on the embryo transfer day if fresh embryo transfer was scheduled., Main Results and the Role of Chance: We established a simplified multi-stage risk assessment index for moderate/severe OHSS, the performance of which was further evaluated with discrimination and calibration abilities in training and internal and external validation cohorts. The discrimination abilities of the OHSS risk assessment index were determined with C-statistics. C-statistics in training (Stages 1-4: 0.631, 0.692, 0.751, 0.788, respectively) and internal (Stages 1-4: 0.626, 0.642, 0.755, 0.771, respectively) and external validation (Stages 1-4: 0.668, 0.670, 0.754, 0.773, respectively) cohorts were all increased from Stage 1 to 3 with similar trends, and were comparable between Stages 3 and 4. Calibration plots showed high agreement between observed and predicted cases in all three cohorts. Incidences of OHSS based on diverse risk stratification (negligible risk, low risk, medium risk, and high risk) were 0%, 0.6%, 2.7%, and 8.3% in the training cohort, 0%, 0.6%, 3.3%, and 8.5% in the internal validation cohort, and 0.1%, 1.1%, 4.1%, and 7.2% in the external validation cohort., Limitations, Reasons for Caution: The influence from clinical interventions including cryopreservation of all embryos cannot be eliminated and thus certain risk factors like estrogen level on trigger day might be assigned with a lower risk score. Another weakness of the study is that several preventive treatments, for instance oral aspirin and letrozole, were not recorded and evaluated in the model. Despite the robust reliability of OHSS assessment index, this tool cannot be used directly for clinical decision-making or as a diagnostic tool. Its value lies in its capacity to evaluate the prognosis of various interventions and to facilitate clinician-patient communication. The combination of this tool and further symptoms and examinations should be all taken into consideration for accurate and personalized management of OHSS., Wider Implications of the Findings: The OHSS risk assessment index can be implemented to facilitate personalized counseling and management of OHSS., Study Funding/competing Interest(s): This study was supported by National Key R&D Program of China (2022YFC2702504), Medical Research Fund Guangdong Provincial (A2024003), and Xinjiang Support Rural Science and Technology (Special Correspondent) Program in Guangdong Province (KTPYJ 2023014). All authors had nothing to disclose., Trial Registration Number: N/A., (© The Author(s) 2024. Published by Oxford University Press on behalf of European Society of Human Reproduction and Embryology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Aims: Patients with heart valvular regurgitation is increasing; early screening of potential patients developing heart failure (HF) is crucial., Methods: From 1 November 2019 to 31 October 2023, a total of 509 patients with heart valvular regurgitation hospitalized in the Department of Cardiovascular Disease of the First Affiliated Hospital of Guangzhou University of Traditional Medicine were enrolled. Three hundred fifty-six cases were selected as the training set for modelling, and 153 cases were selected as the validation set for the internal validation of the model., Results: A predictive model of heart failure with the following nine risk factors was developed: atrial fibrillation (AF), pulmonary infection (PI), coronary artery disease (CAD), creatinine (CREA), low-density lipoprotein cholesterol (LDL-C), d-dimer (DDi), left ventricular end-diastolic diameter (LVEDd), mitral regurgitation (MR) and aortic regurgitation (AR). The model was evaluated by the C-index [the training set: area under curve (AUC) 0.937, 95% confidence interval (CI) 0.911-0.963; the validation set: AUC 0.928, 95% CI 0.890-0.967]. Hosmer-Lemeshow test (the training set: χ 2 10.908, P = 0.207; the validation set: χ 2 4.896, P = 0.769) revealed that both the training and validation sets performed well in terms of model differentiation and calibration. Decision curve analysis showed that both the training and validation sets have higher net benefits, indicating that the model has good utility. Ten-fold cross-validation showed that the training set has high similarities with the validation set, which means that the model has good stability., Conclusions: The occurrence of heart failure in patients with valvular regurgitation has a significant correlation with AF, PI, CAD, CREA, LDL-C, DDi, LVEDd, MR and AR. Based on these risk factors, a prediction model for heart failure was developed and validated, which showed good differentiation and utility, high accuracy and stability, providing a method for predicting heart failure., (© 2024 The Author(s). ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

Background: Accurate and interpretable models are essential for clinical decision-making, where predictions can directly impact patient care. Machine learning (ML) survival methods can handle complex multidimensional data and achieve high accuracy but require post-hoc explanations. Traditional models such as the Cox Proportional Hazards Model (Cox-PH) are less flexible, but fast, stable, and intrinsically transparent. Moreover, ML does not always outperform Cox-PH in clinical settings, warranting a diligent model validation. We aimed to develop a set of R functions to help explore the limits of Cox-PH compared to the tree-based and deep learning survival models for clinical prediction modelling, employing ensemble learning and nested cross-validation., Methods: We developed a set of R functions, publicly available as the package "survcompare". It supports Cox-PH and Cox-Lasso, and Survival Random Forest (SRF) and DeepHit are the ML alternatives, along with the ensemble methods integrating Cox-PH with SRF or DeepHit designed to isolate the marginal value of ML. The package performs a repeated nested cross-validation and tests for statistical significance of the ML's superiority using the survival-specific performance metrics, the concordance index, time-dependent AUC-ROC and calibration slope. To get practical insights, we applied this methodology to clinical and simulated datasets with varying complexities and sizes., Results: In simulated data with non-linearities or interactions, ML models outperformed Cox-PH at sample sizes ≥ 500. ML superiority was also observed in imaging and high-dimensional clinical data. However, for tabular clinical data, the performance gains of ML were minimal; in some cases, regularised Cox-Lasso recovered much of the ML's performance advantage with significantly faster computations. Ensemble methods combining Cox-PH and ML predictions were instrumental in quantifying Cox-PH's limits and improving ML calibration. Traditional models like Cox-PH or Cox-Lasso should not be overlooked while developing clinical predictive models from tabular data or data of limited size., Conclusion: Our package offers researchers a framework and practical tool for evaluating the accuracy-interpretability trade-off, helping make informed decisions about model selection., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Diana Shamsutdinova reports financial support was provided by National Institute for Health Research Maudsley Biomedical Research Centre. Daniel Stahl reports financial support was provided by National Institute for Health Research Maudsley Biomedical Research Centre. Daniel Stamate reports financial support was provided by Alzheimer’s Research UK. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no competing interests., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

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).)

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%., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: People with opioid use disorder have substantially higher standardised mortality rates compared to the general population; however, lack of clear individual prognostic information presents challenges to prioritise or target interventions within drug treatment services. Previous prognostic models have been developed to estimate the risk of developing opioid use disorder and opioid-related overdose in people routinely prescribed opioids but, to our knowledge, none have been developed to estimate mortality risk in people accessing drug services with opioid use disorder. Initial presentation to drug services is a pragmatic time to evaluate mortality risk given the contemporaneous routine collection of prognostic indicators and as a decision point for appropriate service prioritisation and targeted intervention delivery. This study aims to develop and internally validate a model to estimate 6-month mortality risk for people with opioid use disorder from prognostic indicators recorded at initial assessment in drug services in England., Methods: An English national dataset containing records from individuals presenting to drug services between 1 April 2013 and 1 April 2023 (n > 800,000) (the National Drug Treatment Monitoring System (NDTMS)) linked to their lifetime hospitalisation and death records (Hospital Episode Statistics-Office of National Statistics (HES-ONS)). Twelve candidate prognostic indicator variables were identified based on literature review of demographic and clinical features associated with increased mortality for people in treatment for opioid use disorder. Variables will be extracted at initial presentation to drug services with mortality measured at 6 months. Two multivariable Cox regression models will be developed one for 6-month all-cause mortality and one for 6-month drug-related mortality using backward elimination with a fractional polynomial approach for continuous variables. Internal validation will be undertaken using bootstrapping methods. Discrimination of both models will be reported using Harrel's c and d-statistics. Calibration curves and slopes will be presented comparing expected and observed event rates., Discussion: The models developed and internally validated in this study aim to improve clinical assessment of mortality risk for people with opioid use disorder presenting to drug services in England. External validation in different populations will be required to develop the model into a tool to assist future clinical decision-making., (© 2024. The Author(s).)

Background: Most surveillance systems for catheter-related bloodstream infections (CRBSI) and central line-associated bloodstream infections (CLABSI) are based on manual chart review. Our objective was to validate a fully automated algorithm for CRBSI and CLABSI surveillance in intensive care units (ICU)., Methods: We developed a fully automated algorithm to detect CRBSI, CLABSI and ICU-onset bloodstream infections (ICU-BSI) in patients admitted to the ICU of a tertiary care hospital in Switzerland. The parameters included in the algorithm were based on a recently performed systematic review. Structured data on demographics, administrative data, central vascular catheter and microbiological results (blood cultures and other clinical cultures) obtained from the hospital's data warehouse were processed by the algorithm. Validation for CRBSI was performed by comparing results with prospective manual BSI surveillance data over a 6-year period. CLABSI were retrospectively assessed over a 2-year period., Results: From January 2016 to December 2021, 854 positive blood cultures were identified in 346 ICU patients. The median age was 61.7 years [IQR 50-70]; 205 (24%) positive samples were collected from female patients. The algorithm detected 5 CRBSI, 109 CLABSI and 280 ICU-BSI. The overall CRBSI and CLABSI incidence rates determined by automated surveillance for the period 2016 to 2021 were 0.18/1000 catheter-days (95% CI 0.06-0.41) and 3.86/1000 catheter days (95% CI: 3.17-4.65). The sensitivity, specificity, positive predictive and negative predictive values of the algorithm for CRBSI, were 83% (95% CI 43.7-96.9), 100% (95% CI 99.5-100), 100% (95% CI 56.5-100), and 99.9% (95% CI 99.2-100), respectively. One CRBSI was misclassified as an ICU-BSI by the algorithm because the same bacterium was identified in the blood culture and in a lower respiratory tract specimen. Manual review of CLABSI from January 2020 to December 2021 (n = 51) did not identify any errors in the algorithm., Conclusions: A fully automated algorithm for CRBSI and CLABSI detection in critically-ill patients using only structured data provided valid results. The next step will be to assess the feasibility and external validity of implementing it in several hospitals with different electronic health record systems., (© 2024. The Author(s).)

Background Context: With an increasing number of web-based calculators designed to provide the probabilities of an individual achieving improvement after lumbar spine surgery, there is a need to determine the accuracy of these models., Purpose: To perform an internal and external validation study of the reduced Quality Outcomes Database web-based Calculator (QOD-Calc)., Study Design: Observational longitudinal cohort., Patient Sample: Patients enrolled study-wide in Quality Outcomes Database (QOD) and patients enrolled in DaneSpine at a single institution who had elective lumbar spine surgery with baseline data to complete QOD-Calc and 12-month postoperative data., Outcome Measures: Oswestry Disability Index (ODI), Numeric Rating Scales (NRS) for back and leg pain, EuroQOL-5D (EQ-5D)., Methods: Baseline data elements were entered into QOD-Calc to determine the probability for each patient having Any Improvement and 30% Improvement in NRS leg pain, back pain, EQ-5D and ODI. These probabilities were compared with the actual 12-month postop data for each of the QOD and DaneSpine cases. Receiver-operating characteristics analyses were performed and calibration plots created to assess model performance., Results: 24,755 QOD cases and 8,105 DaneSpine lumbar cases were included in the analysis. QOD-Calc had acceptable to outstanding ability (AUC: 0.694-0.874) to predict Any Improvement in the QOD cohort and moderate to acceptable ability (AUC: 0.658-0.747) to predict 30% Improvement. QOD-Calc had acceptable to exceptional ability (AUC: 0.669-0.734) to predict Any improvement and moderate to exceptional ability (AUC: 0.619-0.862) to predict 30% Improvement in the DaneSpine cohort. AUCs for the DaneSpine cohort was consistently lower that the AUCs for the QOD validation cohort., Conclusion: QOD-Calc performs well in predicting outcomes in a patient population that is similar to the patients that was used to develop it. Although still acceptable, model performance was slightly worse in a distinct population, despite the fact that the sample was more homogenous. Model performance may also be attributed to the low discrimination threshold, with close to 90% of cases reporting Any Improvement in outcome. Prediction models may need to be developed that are highly specific to the characteristics of the population., Competing Interests: Declaration of competing interest One or more of the authors declare financial or professional relationships on ICMJE-TSJ disclosure forms., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: The coronavirus disease 2019 (COVID-19) pandemic has found the whole world unprepared for its correct management. Italy was the first European country to experience the spread of the SARS-CoV-2 virus at the end of February 2020. As a result of hospital overcrowding, the quality of care delivered was not always optimal. A substantial number of patients admitted to non-ICU units could have been treated at home. It would have been extremely useful to have a score that, based on personal and clinical characteristics and simple blood tests, could have predicted with sufficient reliability the probability that a patient had or did not have a disease that could have led to their death. This study aims to develop a scoring system to identify which patients with COVID-19 are at high mortality risk upon hospital admission, to expedite and enhance clinical decision making. Methods : A retrospective analysis was performed to develop a multivariable prognostic prediction model. Results : Derivation and external validation cohorts were obtained from two Italian University Hospital databases, including 388 (10.31% deceased) and 1357 (7.68% deceased) patients with confirmed COVID-19, respectively. A multivariable logistic model was used to select seven variables associated with in-hospital death (age, baseline oxygen saturation, hemoglobin value, white blood cell count, percentage of neutrophils, platelet count, and creatinine value). Calibration and discrimination were satisfactory with a cumulative AUC for prediction mortality of 0.924 (95% CI: 0.893-0.944) in derivation cohorts and 0.808 (95% CI: 0.886-0.828) in external validation cohorts. The risk score obtained was compared with the ISARIC 4C Mortality Score, and with all the other most important scores considered so far, to evaluate the risk of death of patients with COVID-19. It performed better than all the above scores to evaluate the predictability of dying. Its sensitivity, specificity, and AUC were higher than the other COVID-19 scoring systems when the latter were calculated for the 388 patients in our derivation cohort. Conclusions : In conclusion, the CZ-COVID-19 Score may help all physicians by identifying those COVID-19 patients who require more attention to provide better therapeutic regimens or, on the contrary, by identifying those patients for whom hospitalization is not necessary and who could therefore be sent home without overcrowding healthcare facilities. We developed and validated a new risk score based on seven variables for upon-hospital admission of COVID-19 patients. It is very simple to calculate and performs better than all the other similar scores to evaluate the predictability of dying.

Purpose: To investigate the risk factors associated with preeclampsia in hyperglycemic pregnancies and develop a predictive model based on routine pregnancy care., Patients and Methods: The retrospective collection of clinical data was performed on 951 pregnant women with hyperglycemia, including those diagnosed with diabetes in pregnancy (DIP) and gestational diabetes mellitus (GDM), who delivered after 34 weeks of gestation at the Maternal and Child Health Hospital Affiliated to Anhui Medical University between January 2017 and December 2019. Observation indicators included liver and kidney function factors testing at 24-29 +6 weeks gestation, maternal age, and basal blood pressure. The indicators were screened univariately, and the "rms" package in R language was applied to explore the factors associated with PE in HIP pregnancy by stepwise regression. Multivariable logistic regression analysis was used to develop the prediction model. Based on the above results, a nomogram was constructed to predict the risk of PE occurrence in pregnant women with HIP. Then, the model was evaluated from three aspects: discrimination, calibration, and clinical utility. The internal validation was performed using the bootstrap procedure., Results: Multivariate logistic regression analysis showed that cystatin C, uric acid, glutamyl aminotransferase, blood urea nitrogen, and basal systolic blood pressure as predictors of PE in pregnancy with HIP. The predictive model yielded an area under curve (AUC) value of 0.8031 (95% CI: 0.7383-0.8679), with an optimal threshold of 0.0805, at which point the sensitivity was 0.8307 and specificity of 0.6604. Hosmer-Lemeshow test values were P = 0.3736, Brier score value was 0.0461. After 1000 Bootstrap re-samplings for internal validation, the AUC was 0.7886, the Brier score was 0.0478 and the predicted probability of the calibration curve was similar to the actual probability. A nomogram was constructed based on the above to visualize the model., Conclusion: This study developed a model for predicting PE in pregnant women with HIP, achieving high predictive performance of PE risk through the information of routine pregnancy care., Competing Interests: Dr Bing Shen is now affiliated to Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macao SAR, People’s Respublic of China. The authors declare that they have no known competing financial interests or non-financial interests that could have appeared to influence the work reported in this paper., (© 2024 Fang et al.)

The GA118-24B Genetic Analyzer (hereafter, "GA118-24B") is an independently developed capillary electrophoresis instrument. In the present research, we designed a series of validation experiments to test its performance at detecting DNA fragments compared to the Applied Biosystems 3500 Genetic Analyzer (hereafter, "3500"). Three commercially available autosomal short tandem repeat multiplex kits were used in this validation. The results showed that GA118-24B had acceptable spectral calibration for three kits. The results of accuracy and concordance studies were also satisfactory. GA118-24B showed excellent precision, with a standard deviation of less than 0.1 bp. Sensitivity and mixture studies indicated that GA118-24B could detect low-template DNA and complex mixtures as well as the results generated by 3500 in parallel experiments. Based on the experimental results, we set specific analytical and stochastic thresholds. Besides, GA118-24B showed superiority than 3500 within certain size ranges in the resolution study. Instead of conventional commercial multiplex kits, GA118-24B performed stably on a self-developed eight-dye multiplex system, which were not performed on 3500 Genetic Analyzer. We compared our validation results with those of previous research and found our results to be convincing. Overall, we conclude that GA118-24B is a stable and reliable genetic analyzer for forensic DNA identification., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The onus of proof in criminal cases is beyond any reasonable doubt, and the issue on the lack of complete internal validation data can be manipulated when it comes to justifying the validity and reliability of the X-chromosomal short tandem repeats analysis for court representation. Therefore, this research evaluated the efficiency of the optimized 60% reduced volumes for polymerase chain reaction (PCR) amplification using the Qiagen Investigator® Argus X-12 QS Kit, as well as the capillary electrophoresis (CE) sample preparation for blood samples on Flinder's Technology Associates (FTA) cards. Good-quality DNA profile (3000-12,000 RFU) from the purified blood sample on FTA card (1.2 mm) were obtained using the optimized PCR (10.0 μL of PCR reaction volume and 21 cycles) and CE (9.0 μL Hi-Di™ Formamide and 0.3 μL DNA Size Standard 550 [BTO] and 27 s injection time) conditions. The analytical and stochastic thresholds were 100 and 200 RFU, respectively. Hence, the internal validation data supported the use of the optimized 60% reduced PCR amplification reaction volume of the Qiagen Investigator® Argus X-12 QS Kit as well as the CE sample preparation for producing reliable DNA profiles that comply with the quality assurance standards for forensic DNA testing laboratories, while optimizing the analytical cost., (© 2023 American Academy of Forensic Sciences.)

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.)

Background: Topic models are a class of unsupervised machine learning models, which facilitate summarization, browsing and retrieval from large unstructured document collections. This study reviews several methods for assessing the quality of unsupervised topic models estimated using non-negative matrix factorization. Techniques for topic model validation have been developed across disparate fields. We synthesize this literature, discuss the advantages and disadvantages of different techniques for topic model validation, and illustrate their usefulness for guiding model selection on a large clinical text corpus., Design, Setting and Data: Using a retrospective cohort design, we curated a text corpus containing 382,666 clinical notes collected between 01/01/2017 through 12/31/2020 from primary care electronic medical records in Toronto Canada., Methods: Several topic model quality metrics have been proposed to assess different aspects of model fit. We explored the following metrics: reconstruction error, topic coherence, rank biased overlap, Kendall's weighted tau, partition coefficient, partition entropy and the Xie-Beni statistic. Depending on context, cross-validation and/or bootstrap stability analysis were used to estimate these metrics on our corpus., Results: Cross-validated reconstruction error favored large topic models (K ≥ 100 topics) on our corpus. Stability analysis using topic coherence and the Xie-Beni statistic also favored large models (K = 100 topics). Rank biased overlap and Kendall's weighted tau favored small models (K = 5 topics). Few model evaluation metrics suggested mid-sized topic models (25 ≤ K ≤ 75) as being optimal. However, human judgement suggested that mid-sized topic models produced expressive low-dimensional summarizations of the corpus., Conclusions: Topic model quality indices are transparent quantitative tools for guiding model selection and evaluation. Our empirical illustration demonstrated that different topic model quality indices favor models of different complexity; and may not select models aligning with human judgment. This suggests that different metrics capture different aspects of model goodness of fit. A combination of topic model quality indices, coupled with human validation, may be useful in appraising unsupervised topic models., (© 2023. The Author(s).)

Background: In recent years, there has been a surge in machine learning-based models for diagnosis and prognostication of outcomes in oncology. However, there are concerns relating to the model's reproducibility and generalizability to a separate patient cohort (i.e., external validation)., Objectives: This study primarily provides a validation study for a recently introduced and publicly available machine learning (ML) web-based prognostic tool (ProgTOOL) for overall survival risk stratification of oropharyngeal squamous cell carcinoma (OPSCC). Additionally, we reviewed the published studies that have utilized ML for outcome prognostication in OPSCC to examine how many of these models were externally validated, type of external validation, characteristics of the external dataset, and diagnostic performance characteristics on the internal validation (IV) and external validation (EV) datasets were extracted and compared., Methods: We used a total of 163 OPSCC patients obtained from the Helsinki University Hospital to externally validate the ProgTOOL for generalizability. In addition, PubMed, OvidMedline, Scopus, and Web of Science databases were systematically searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines., Results: The ProgTOOL produced a predictive performance of 86.5% balanced accuracy, Mathew's correlation coefficient of 0.78, Net Benefit (0.7) and Brier score (0.06) for overall survival stratification of OPSCC patients as either low-chance or high-chance. In addition, out of a total of 31 studies found to have used ML for the prognostication of outcomes in OPSCC, only seven (22.6%) reported a form of EV. Three studies (42.9%) each used either temporal EV or geographical EV while only one study (14.2%) used expert as a form of EV. Most of the studies reported a reduction in performance when externally validated., Conclusion: The performance of the model in this validation study indicates that it may be generalized, therefore, bringing recommendations of the model for clinical evaluation closer to reality. However, the number of externally validated ML-based models for OPSCC is still relatively small. This significantly limits the transfer of these models for clinical evaluation and subsequently reduces the likelihood of the use of these models in daily clinical practice. As a gold standard, we recommend the use of geographical EV and validation studies to reveal biases and overfitting of these models. These recommendations are poised to facilitate the implementation of these models in clinical practice., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Complications from preterm birth (PTB) are the leading cause of death and disability in those under five years. Whilst the role of omega-3 (n-3) supplementation in reducing PTB is well-established, growing evidence suggests supplementation use in those replete may increase the risk of early PTB., Aim: To develop a non-invasive tool to identify individuals with total n-3 serum levels above 4.3% of total fatty acids in early pregnancy., Methods: We conducted a prospective observational study recruiting 331 participants from three clinical sites in Newcastle, Australia. Eligible participants (n = 307) had a singleton pregnancy between 8 and 20 weeks' gestation at recruitment. Data on factors associated with n-3 serum levels were collected using an electronic questionnaire; these included estimated intake of n-3 (including food type, portion size, frequency of consumption), n-3 supplementation, and sociodemographic factors. The optimal cut-point of estimated n-3 intake that predicted mothers with total serum n-3 levels likely above 4.3% was developed using multivariate logistic regression, adjusting for maternal age, body mass index, socioeconomic status, and n-3 supplementation use. Total serum n-3 levels above 4.3% was selected as previous research has demonstrated that mothers with these levels are at increased risk of early PTB if they take additional n-3 supplementation during pregnancy. Models were evaluated using various performance metrics including sensitivity, specificity, area under receiver operator characteristic (AUROC) curve, true positive rate (TPR) at 10% false positive rate (FPR), Youden Index, Closest to (0,1) Criteria, Concordance Probability, and Index of Union. Internal validation was performed using 1000-bootstraps to generate 95% confidence intervals for performance metrics generated., Results: Of 307 eligible participants included for analysis, 58.6% had total n-3 serum levels above 4.3%. The optimal model had a moderate discriminative ability (AUROC 0.744, 95% CI 0.742-0.746) with 84.7% sensitivity, 54.7% specificity and 37.6% TPR at 10% FPR., Conclusions: Our non-invasive tool was a moderate predictor of pregnant women with total serum n-3 levels above 4.3%; however, its performance is not yet adequate for clinical use., Trial Registration: This trial was approved by the Hunter New England Human Research Ethics Committee of the Hunter New England Local Health District (Reference 2020/ETH00498 on 07/05/2020 and 2020/ETH02881 on 08/12/2020)., (© 2023. The Author(s).)

Objectives: Risk stratification and prognosis prediction are critical for appropriate management of anti-neutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV). Herein, we aim to develop and internally validate a prediction model specifically for long-term survival of patients with AAV., Methods: We thoroughly reviewed the medical charts of patients with AAV admitted to Peking Union Medical College Hospital from January 1999 to July 2019. The Least Absolute Shrinkage and Selection Operator method and the COX proportional hazard regression was used to develop the prediction model. The Harrell's concordance index (C-index), calibration curves and Brier scores were calculated to evaluate the model performance. The model was internally validated by bootstrap resampling methods., Results: A total of 653 patients were included in the study, including 303 patients with microscopic polyangiitis, 245 patients with granulomatosis with polyangiitis and 105 patients with eosinophilic granulomatosis with polyangiitis, respectively. During a median follow-up of 33 months (interquartile range 15-60 months), 120 deaths occurred. Age at admission, chest and cardiovascular involvement, serum creatinine grade, hemoglobin levels at baseline and AAV sub-types were selected as predictive parameters in the final model. The optimism-corrected C-index and integrated Brier score of our prediction model were 0.728 and 0.109. The calibration plots showed fine agreement between observed and predicted probability of all-cause death. The decision curve analysis (DCA) showed that in a wide range of threshold probabilities, our prediction model had higher net benefits compared with the revised five factor score (rFFSand) and the birmingham vasculitis activity score (BVAS) system., Conclusion: Our model performs well in predicting outcomes of AAV patients. Patients with moderate-to-high probability of death should be followed closely and personalized monitoring plan should be scheduled., Competing Interests: Conflict of Interest 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. Meanwhile, Xinping Tian is the Executive Editor-in-Chief of of the journal, The article was subject to the journal's standard procedures, with peer review handled independently of the editor and the affiliated research groups., (© 2023 Zhe Chen et a., published by De Gruyter.)

Background Context: Postoperative recovery after adult spinal deformity (ASD) operations is arduous, fraught with complications, and often requires extended hospital stays. A need exists for a method to rapidly predict patients at risk for extended length of stay (eLOS) in the preoperative setting., Purpose: To develop a machine learning model to preoperatively estimate the likelihood of eLOS following elective multi-level lumbar/thoracolumbar spinal instrumented fusions (≥3 segments) for ASD., Study Design/setting: Retrospectively from a state-level inpatient database hosted by the Health care cost and Utilization Project., Patient Sample: Of 8,866 patients of age ≥50 with ASD undergoing elective lumbar or thoracolumbar multilevel instrumented fusions., Outcome Measures: The primary outcome was eLOS (>7 days)., Methods: Predictive variables consisted of demographics, comorbidities, and operative information. Significant variables from univariate and multivariate analyses were used to develop a logistic regression-based predictive model that use six predictors. Model accuracy was assessed through area under the curve (AUC), sensitivity, and specificity., Results: Of 8,866 patients met inclusion criteria. A saturated logistic model with all significant variables from multivariate analysis was developed (AUC=0.77), followed by generation of a simplified logistic model through stepwise logistic regression (AUC=0.76). Peak AUC was reached with inclusion of six selected predictors (combined anterior and posterior approach, surgery to both lumbar and thoracic regions, ≥8 level fusion, malnutrition, congestive heart failure, and academic institution). A cutoff of 0.18 for eLOS yielded a sensitivity of 77% and specificity of 68%., Conclusions: This predictive model can facilitate identification of adults at risk for eLOS following elective multilevel lumbar/thoracolumbar spinal instrumented fusions for ASD. With a fair diagnostic accuracy, the predictive calculator will ideally enable clinicians to improve preoperative planning, guide patient expectations, enable optimization of modifiable risk factors, facilitate appropriate discharge planning, stratify financial risk, and accurately identify patients who may represent high-cost outliers. Future prospective studies that validate this risk assessment tool on external datasets would be valuable., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Clinical prediction models should be validated before implementation in clinical practice. But is favorable performance at internal validation or one external validation sufficient to claim that a prediction model works well in the intended clinical context?, Main Body: We argue to the contrary because (1) patient populations vary, (2) measurement procedures vary, and (3) populations and measurements change over time. Hence, we have to expect heterogeneity in model performance between locations and settings, and across time. It follows that prediction models are never truly validated. This does not imply that validation is not important. Rather, the current focus on developing new models should shift to a focus on more extensive, well-conducted, and well-reported validation studies of promising models., Conclusion: Principled validation strategies are needed to understand and quantify heterogeneity, monitor performance over time, and update prediction models when appropriate. Such strategies will help to ensure that prediction models stay up-to-date and safe to support clinical decision-making., (© 2023. The Author(s).)

Background: Acute kidney injury (AKI) is a common complication after cardiac surgery. This study aims to develop and validate a risk model for predicting AKI after cardiac valve replacement surgery., Methods: Data from patients undergoing surgical valve replacement between January 2015 and December 2018 in our hospital were retrospectively analyzed. The subjects were randomly divided into a derivation cohort and a validation cohort at a ratio of 7:3. The primary outcome was defined as AKI within 7 days after surgery. Logistic regression analysis was conducted to select risk predictors for developing the prediction model. Receiver operator characteristic curve (ROC), calibration plot and clinical decision curve analysis (DCA) will be used to evaluate the discrimination, precision and clinical benefit of the prediction model., Results: A total of 1159 patients were involved in this study. The prevalence of AKI following surgery was 37.0% (429/1159). Logistic regression analysis showed that age, hemoglobin, fibrinogen, serum uric acid, cystatin C, bicarbonate, and cardiopulmonary bypass time were independent risk factors associated with AKI after surgical valve replacement (all P < 0.05). The areas under the ROC curves (AUCs) in the derivation cohort and the validation cohort were 0.777 (95% CI 0.744-0.810) and 0.760 (95% CI 0.706-0.813), respectively. The calibration plots indicated excellent consistency between the prediction probability and actual probability. DCA demonstrated great clinical benefit of the prediction model., Conclusions: We developed a prediction model for predicting AKI after cardiac valve replacement surgery that was internally validated to have good discrimination, calibration, and clinical practicability., Competing Interests: Declaration of Competing Interest 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 © 2022 Elsevier B.V. All rights reserved.)

Background: The currently available mortality prediction models (MPM) have suboptimal performance when predicting early mortality (30-days) following transcatheter aortic valve implantation (TAVI) on various external populations. We developed and validated a new TAVI-MPM based on a large number of predictors with recent data from a national heart registry., Methods: We included all TAVI-patients treated in the Netherlands between 2013 and 2018, from the Netherlands Heart Registration. We used logistic-regression analysis based on the Akaike Information Criterion for variable selection. We multiply imputed missing values, but excluded variables with &gt;30% missing values. For internal validation, we used ten-fold cross-validation. For temporal (prospective) validation, we used the 2018-data set for testing. We assessed discrimination by the c-statistic, predicted probability accuracy by the Brier score, and calibration by calibration graphs, and calibration-intercept and calibration slope. We compared our new model to the updated ACC-TAVI and IRRMA MPMs on our population., Results: We included 9144 TAVI-patients. The observed early mortality was 4.0%. The final MPM had 10 variables, including: critical-preoperative state, procedure-acuteness, body surface area, serum creatinine, and diabetes-mellitus status. The median c-statistic was 0.69 (interquartile range [IQR] 0.646-0.75). The median Brier score was 0.038 (IQR 0.038-0.040). No signs of miscalibration were observed. The c-statistic's temporal-validation was 0.71 (95% confidence intervals 0.64-0.78). Our model outperformed the updated currently available MPMs ACC-TAVI and IRRMA (p value &lt; 0.05)., Conclusion: The new TAVI-model used additional variables and showed fair discrimination and good calibration. It outperformed the updated currently available TAVI-models on our population. The model's good calibration benefits preprocedural risk-assessment and patient counseling., (© 2022 The Authors. Catheterization and Cardiovascular Interventions published by Wiley Periodicals LLC.)

We investigated the relevance of the validation principles on the Quantitative Structure Activity Relationship models issued by Organization for Economic and Co-operation and Development. We checked the goodness-of-fit, robustness and predictivity categories in linear and nonlinear models using benchmark datasets. Most of our conclusions are drawn using the sample size dependence of the different validation parameters. We found that the goodness-of-fit parameters misleadingly overestimate the models on small samples. In the case of neural network and support vector models, the feasibility of the goodness-of-fit parameters often might be questioned. We propose to use the simplest y-scrambling method to estimate chance correlation. We found that the leave-one-out and leave-many-out cross-validation parameters can be rescaled to each other in all models and the computationally feasible method should be chosen depending on the model type. We assessed the interdependence of the validation parameters by calculating their rank correlations. Goodness of fit and robustness correlate quite well over a sample size for linear models and one of the approaches might be redundant. In the rank correlation between internal and external validation parameters, we found that the assignment of good and bad modellable data to the training or the test causes negative correlations., (© 2022 The Authors. Molecular Informatics published by Wiley-VCH GmbH.)

Aim: Clinical prediction models need to be validated. In this study, we used simulation data to compare various internal and external validation approaches to validate models., Methods: Data of 500 patients were simulated using distributions of metabolic tumor volume, standardized uptake value, the maximal distance between the largest lesion and another lesion, WHO performance status and age of 296 diffuse large B cell lymphoma patients. These data were used to predict progression after 2 years based on an existing logistic regression model. Using the simulated data, we applied cross-validation, bootstrapping and holdout (n = 100). We simulated new external datasets (n = 100, n = 200, n = 500) and simulated stage-specific external datasets (1), varied the cut-off for high-risk patients (2) and the false positive and false negative rates (3) and simulated a dataset with EARL2 characteristics (4). All internal and external simulations were repeated 100 times. Model performance was expressed as the cross-validated area under the curve (CV-AUC ± SD) and calibration slope., Results: The cross-validation (0.71 ± 0.06) and holdout (0.70 ± 0.07) resulted in comparable model performances, but the model had a higher uncertainty using a holdout set. Bootstrapping resulted in a CV-AUC of 0.67 ± 0.02. The calibration slope was comparable for these internal validation approaches. Increasing the size of the test set resulted in more precise CV-AUC estimates and smaller SD for the calibration slope. For test datasets with different stages, the CV-AUC increased as Ann Arbor stages increased. As expected, changing the cut-off for high risk and false positive- and negative rates influenced the model performance, which is clearly shown by the low calibration slope. The EARL2 dataset resulted in similar model performance and precision, but calibration slope indicated overfitting., Conclusion: In case of small datasets, it is not advisable to use a holdout or a very small external dataset with similar characteristics. A single small testing dataset suffers from a large uncertainty. Therefore, repeated CV using the full training dataset is preferred instead. Our simulations also demonstrated that it is important to consider the impact of differences in patient population between training and test data, which may ask for adjustment or stratification of relevant variables., (© 2022. The Author(s).)

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., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

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. J Orthop Sports Phys Ther 2022;52(9):630-640. Epub: 9 July 2022. doi:10.2519/jospt.2022.11072 .

Objective: To internally and externally validate our diagnostic criteria of early stage knee osteoarthritis (OA) in the CHECK and OAI cohorts., Design: We applied two previously developed diagnostic models to all knees in CHECK and OAI cohorts to calculate probabilities of early stage knee OA at baseline. Knees were categorized into three groups based on probability: 'no OA' (probability ≤ 30%), 'uncertain' (probability between 30% and 70%) and 'early stage OA' (probability ≥ 70%). To validate the diagnosis, we obtained OA related outcome measures at 10-year follow-up in the CHECK cohort, and at 8-9-year follow-up in the OAI cohort. We compared outcome measures between 'no OA' and 'early stage OA' knees, and between 'no OA' and 'uncertain' knees using generalized estimating equations., Results: In CHECK (n = 1042 knees) both models showed 'early stage OA' knees presented with significant and clinically relevant higher WOMAC scores, higher Kellgren & Lawrence (KL) grade, and higher rates of joint space narrowing (JSN) progression after 10 years, compared to 'no OA' knees. In OAI (n = 2937 knees) both models showed 'early stage OA' knees presented with significant and clinically relevant higher WOMAC scores, higher KL grade, and higher rates of KL and JSN progression after 8-9 years, compared to 'no OA' knees. Smaller, but still significant differences between 'uncertain' and 'no OA' knees were observed in both cohorts., Conclusions: These results support internal and external validity of the two sets of diagnostic criteria for early stage knee OA., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

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., (© 2022. The Author(s).)

Background: For 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., Methods: We 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., Results: The 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  < 0.01). The model's Area Under Curve(AUC) = 0.985 (95% CI, 0.975-0.994). After internal verification by Bootstrap method, the model still has high discriminative ability (AUC = 0.983), and its predicted CA curve is still in good agreement with the actual clinical CA curve., Conclusion: We found that a clinical prediction model based on abdominal pain duration, peritonitis, and total bilirubin can help clinicians quickly and effectively identify UA or CA before treatment of acute appendicitis in the elderly, so as to make more scientific clinical decisions., 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 © 2022 Feng, Yu, Wang, Yang, Yu, Wei, Zheng, Peng and Zhang.)

Purpose: The current study aimed to develop a convenient and accurate prognostic dynamic nomogram model for the risk of all-cause death in acute heart failure (AHF) patients that incorporates clinical characteristics including N-terminal pro-brain natriuretic peptide (NT-pro BNP) and growth stimulation expresses gene 2 protein (ST2)., Patients and Methods: We prospectively studied 537 consecutive AHF patients and derived a clinical prediction model. The least absolute shrinkage and selection operator regression model combined with clinical characteristics were used for dimensional reduction and feature selection. Multivariate Cox proportional hazard analysis and "Dynnom" package were used to build the dynamic nomogram for prediction of 1-,2-,and 5-year overall survival for AHF. With bootstrap validation, the time-dependent concordance index (C-index) and calibration curves were used to assess predictive discrimination and accuracy. The contributions of NT-pro BNP and ST2 to the nomogram were evaluated using integrated discrimination improvement (IDI) and net reclassification improvement (NRI), while decision curve analysis (DCA) was used to assess clinical value., Results: Patients were randomly divided into derivation (74.9%, n=402) and validation (25.1%, n=135) cohorts. Optimal independent prognostic factors for 1-,2-, and 5-year all-cause mortality were BS-ACMR (B: NT-pro BNP; S: ST2; A: age; C: complete right bundle branch block; M: mean arterial pressure; and R: red cell distribution width >14.5%); these were incorporated into the dynamic nomogram (https://bs-acmr-nom.shinyapps.io/dynnomapp/) with bootstrap validation. The C-indexes in the derivation (0.793) and validation (0.782) cohorts were consistent with comparable performance parameters. The calibration curve showed good agreement between the nomogram-predicted and actual survival. Adding NT-pro BNP and ST2 provided a significant net benefit and improved performance over other less adequate schemes in terms of DCA of survival probability compared to those neglecting either of these two factors., Conclusion: The study constructed a dynamic BS-ACMR nomogram, which is a convenient, practical and effective clinical decision-making tool for providing accurate prognosis in AHF patients., Competing Interests: The authors report no conflicts of interest in this work., (© 2022 Yin et al.)

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., (© 2022. The Author(s).)

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 × 10 9 /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 χ 2 = 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., (© 2021 British HIV Association.)

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., (© 2020. Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© 2021. The Author(s).)

Objective: A traumatic axonal injury (TAI) diagnosis has traditionally been based on conventional MRI, especially on those sequences with a higher sensitivity to edema and blood degradation products. A more recent technique, diffusion tensor imaging (DTI), can infer the microstructure of white matter (WM) due to the restricted diffusion of water in organized tissues. However, there is little information regarding the correlation of the findings obtained by both methods and their use for outcome prognosis. The main objectives of this study were threefold: 1) study the correlation between DTI metrics and conventional MRI findings; 2) evaluate whether the prognostic information provided by the two techniques is supplementary or complementary; and 3) determine the incremental value of the addition of these variables compared to a traditional prognostic model., Methods: The authors studied 185 patients with moderate to severe traumatic brain injury (TBI) who underwent MRI with DTI study during the subacute stage. The number and volume of lesions in hemispheric subcortical WM, corpus callosum (CC), basal ganglia, thalamus, and brainstem in at least four conventional MRI sequences (T1-weighted, T2-weighted, FLAIR, T2* gradient recalled echo, susceptibility-weighted imaging, and diffusion-weighted imaging) were determined. Fractional anisotropy (FA) was measured in 28 WM bundles using the region of interest method. Nonparametric tests were used to evaluate the colocalization of macroscopic lesions and FA. A multivariate logistic regression analysis was performed to assess the independent prognostic value of each neuroimaging modality after adjustment for relevant clinical covariates, and the internal validation of the model was evaluated in a contemporary cohort of 92 patients., Results: Differences in the lesion load between patients according to their severity and outcome were found. Colocalization of macroscopic nonhemorrhagic TAI lesions (not microbleeds) and lower FA was limited to the internal and external capsule, corona radiata, inferior frontooccipital fasciculus, CC, and brainstem. However, a significant association between the FA value and the identification of macroscopic lesions in distant brain regions was also detected. Specifically, lower values of FA of some hemispheric WM bundles and the splenium of the CC were related to a higher number and volume of hyperintensities in the brainstem. The regression analysis revealed that age, motor score, hypoxia, FA of the genu of the CC, characterization of TAI lesions in the CC, and the presence of thalamic/basal ganglia lesions were independent prognostic factors. The performance of the proposed model was higher than that of the IMPACT (International Mission on Prognosis and Analysis of Clinical Trials in TBI) model in the validation cohort., Conclusions: Very limited colocalization of hyperintensities (none for microbleeds) with FA values was discovered. DTI and conventional MRI provide complementary prognostic information, and their combination can improve the performance of traditional prognostic models.

Background: Acute kidney injury (AKI) is commonly encountered in clinical practice and is associated with poor patient outcomes and increased health care costs. Despite it posing significant challenges for clinicians, effective measures for AKI prediction and prevention are lacking. Previously published AKI prediction models mostly have a simple design without external validation. Furthermore, little is known about the process of linking model output and clinical decisions due to the black-box nature of neural network models., Objective: We aimed to present an externally validated recurrent neural network (RNN)-based continuous prediction model for in-hospital AKI and show applicable model interpretations in relation to clinical decision support., Methods: Study populations were all patients aged 18 years or older who were hospitalized for more than 48 hours between 2013 and 2017 in 2 tertiary hospitals in Korea (Seoul National University Bundang Hospital and Seoul National University Hospital). All demographic data, laboratory values, vital signs, and clinical conditions of patients were obtained from electronic health records of each hospital. We developed 2-stage hierarchical prediction models (model 1 and model 2) using RNN algorithms. The outcome variable for model 1 was the occurrence of AKI within 7 days from the present. Model 2 predicted the future trajectory of creatinine values up to 72 hours. The performance of each developed model was evaluated using the internal and external validation data sets. For the explainability of our models, different model-agnostic interpretation methods were used, including Shapley Additive Explanations, partial dependence plots, individual conditional expectation, and accumulated local effects plots., Results: We included 69,081 patients in the training, 7675 in the internal validation, and 72,352 in the external validation cohorts for model development after excluding cases with missing data and those with an estimated glomerular filtration rate less than 15 mL/min/1.73 m2 or end-stage kidney disease. Model 1 predicted any AKI development with an area under the receiver operating characteristic curve (AUC) of 0.88 (internal validation) and 0.84 (external validation), and stage 2 or higher AKI development with an AUC of 0.93 (internal validation) and 0.90 (external validation). Model 2 predicted the future creatinine values within 3 days with mean-squared errors of 0.04-0.09 for patients with higher risks of AKI and 0.03-0.08 for those with lower risks. Based on the developed models, we showed AKI probability according to feature values in total patients and each individual with partial dependence, accumulated local effects, and individual conditional expectation plots. We also estimated the effects of feature modifications such as nephrotoxic drug discontinuation on future creatinine levels., Conclusions: We developed and externally validated a continuous AKI prediction model using RNN algorithms. Our model could provide real-time assessment of future AKI occurrences and individualized risk factors for AKI in general inpatient cohorts; thus, we suggest approaches to support clinical decisions based on prediction models for in-hospital AKI., (©Kipyo Kim, Hyeonsik Yang, Jinyeong Yi, Hyung-Eun Son, Ji-Young Ryu, Yong Chul Kim, Jong Cheol Jeong, Ho Jun Chin, Ki Young Na, Dong-Wan Chae, Seung Seok Han, Sejoong Kim. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.04.2021.)

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., 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 © 2021 Yang, Zhu, Yong, Xia, Qian, Yang, Hu, Li, Wang, Peng, Zhang, Deng and Pan.)

Baijiu is a traditional Chinese spirit with an extraordinarily rich pattern of volatile compounds resulting from the microflora involved in fermentation, as well as the complexity of technological process. Comprehensive two-dimensional gas chromatography in the conventional and reversed column setups (non-polar - polar and polar - non-polar columns) was tested for its ability to differentiate Baijiu samples in terms of their aroma and origin (region). A total of 65 Baijiu samples were used for the study and volatile compounds were extracted by SPME with a subsequent analysis by GC×GC-TOFMS. Orthogonality of both setups was compared, so was the number of compounds identified using each setup. Repeatability of compound groups for the conventional and reversed column setups was compared; moreover, multiblock orthogonal component analysis (MOCA) was used to visualize data sets. OPLS-DA was used for Baijiu classification. Both column setups provided excellent discrimination of the Light, Soy sauce, Feng and Herbal aromas. A better classification result for the Strong and Jian aromas was recorded for the conventional column setup. Within the Strong aroma using OPLS-DA both column setups provided perfect abilities to discriminate Baijiu from the Sichuan, Heilongjiang and Jiangsu regions. Two validation methods were applied in the classification models - all the predictive abilities evaluated by the internal validation were further confirmed by the external validation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:, (Copyright © 2020. Published by Elsevier B.V.)

Boosting methods are among the best general-purpose and off-the-shelf machine learning approaches, gaining widespread popularity. In this paper, we seek to develop a boosting method that yields comparable accuracy to popular AdaBoost and gradient boosting methods, yet is faster computationally and whose solution is more interpretable. We achieve this by developing MP-Boost, an algorithm loosely based on AdaBoost that learns by adaptively selecting small subsets of instances and features, or what we term minipatches (MP), at each iteration. By sequentially learning on tiny subsets of the data, our approach is computationally faster than other classic boosting algorithms. Also as it progresses, MP-Boost adaptively learns a probability distribution on the features and instances that upweight the most important features and challenging instances, hence adaptively selecting the most relevant minipatches for learning. These learned probability distributions also aid in interpretation of our method. We empirically demonstrate the interpretability, comparative accuracy, and computational time of our approach on a variety of binary classification tasks.

Flash flooding is the natural hazard provoking the largest number of casualties, so adequately characterizing vulnerability is key to improve flood risk analysis and management. Developing composite indices is the most widely used methodology in vulnerability analysis. However, very few studies have so far assessed vulnerability in urban areas prone to flash flooding and the resulting research presents two main drawbacks: i) a fragmented approach is often pursued, i.e. without jointly considering the vulnerability components (exposure, sensitivity and resilience) and the two most influential dimensions in urban environments (social and economic); and ii) vulnerability indices are not usually validated because an ancillary dataset is not generally available and flash flooding events do not happen simultaneously in all urban areas of a particular region. Considering the above gaps, this paper describes the construction of an Integrated Socio-Economic Vulnerability Index (ISEVI) at the regional scale, which considers all vulnerability components and social and economic dimensions. ISEVI was subsequently validated through an uncertainty and sensitivity analysis using the Monte Carlo method. Further, regional spatial patterns of vulnerability were identified implementing a Latent Class Cluster Analysis. Uncertainty analysis reveals the high stability of vulnerability categories of the ISEVI and sensitivity analysis shows that the type and the conservation state of buildings are the vulnerability factors that cause a greater variability in ISEVI scores. The method deployed here may allow specific strategies for vulnerability reduction to be developed based on disaggregating the validated ISEVI into dimensions and components and using the regional spatial patterns characterized., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

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., (© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Objective: To analyze vascular damage-related risk factors for ED in patients with type 2 diabetes mellitus (DM) and develop a nomogram for the prediction of the factors., Methods: A total of 181 patients with type 2 DM were included for sexual function assessment, and the clinical data on vascular damage were retrieved from the patients system. After preprocessing, the data were described by the number and percentage of different types of cases and subjected to statistical analysis with the R software. The Lasso regression model was used to optimize feature selection. On the premise of the sample size required for logistic regression analysis according to the number of events per variable, multivariable logistic regression analysis was performed on the selected variables and a nomogram was developed for diabetes-induced erectile dysfunction (DIED). Then, the performance of the nomogram was evaluated with respect to its calibration, discrimination and clinical utility using Harrell's concordance index (C-index), the calibration plot and decision curve analysis, as well as bootstrapping for internal validation., Results: ED was diagnosed in 90 (49.7%) of the 181 patients. The risk factors subjected to logistic regression analysis included the duration of DM (OR = 4.440, 95% CI: 1.594－13.105; OR = 7.667, 95% CI: 1.444－48.733), status of carotid intima-media thickness (c-IMT) (OR = 3.767, 95% CI: 1.194－12.691), diabetic retinopathy (DR) (OR = 5.382, 95% CI: 1.373－28.301), diabetic kidney disease (DKD) (OR = 4.959, 95% CI: 1.156－27.728), low-density lipoprotein cholesterol (LDL-C) (OR = 8.210, 95% CI: 2.027－43.507), red blood cell distribution width (RDW) (OR = 2.418, 95% CI: 1.021－5.826), and plasma fibrinogen (Fbg) (OR = 4.649, 95% CI: 2.001－11.339). The C-index of the DIED model was 0.911 (95% CI: 0.869－0.954). The curve representing the performance of the nomogram fit in well with that representing a perfect prediction by the calibration plot. Decision curve analysis indicated that the nomogram was clinically useful for predicting DIED in the type 2 DM patients at the possibility threshold of 6% to 93%., Conclusions: A nomogram was preliminarily developed for predicting the risk of DIED in type 2 DM patients with respect to the seven independent influencing factors, including the duration of DM, status of c-IMT, DR, DKD, LDL-C, RDW, and Fbg.

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.

Study Design: Prognostic study and validation using prospective clinical trial data., Objective: To derive and validate a model predicting curve progression to ≥45° before skeletal maturity in untreated patients with adolescent idiopathic scoliosis (AIS)., Summary of Background Data: Studies have linked the natural history of AIS with characteristics such as sex, skeletal maturity, curve magnitude, and pattern. The Simplified Skeletal Maturity Scoring System may be of particular prognostic utility for the study of curve progression. The reliability of the system has been addressed; however, its value as a prognostic marker for the outcomes of AIS has not. The BrAIST trial followed a sample of untreated AIS patients from enrollment to skeletal maturity, providing a rare source of prospective data for prognostic modeling., Methods: The development sample included 115 untreated BrAIST participants. Logistic regression was used to predict curve progression to ≥45° (or surgery) before skeletal maturity. Predictors included the Cobb angle, age, sex, curve type, triradiate cartilage, and skeletal maturity stage (SMS). Internal and external validity was evaluated using jackknifed samples of the BrAIST data set and an independent cohort (n = 152). Indices of discrimination and calibration were estimated. A risk classification was created and the accuracy evaluated via the positive (PPV) and negative predictive values (NPV)., Results: The final model included the SMS, Cobb angle, and curve type. The model demonstrated strong discrimination (c-statistics 0.89-0.91) and calibration in all data sets. The classification system resulted in PPVs of 0.71-0.72 and NPVs of 0.85-0.93., Conclusions: This study provides the first rigorously validated model predicting a short-term outcome of untreated AIS. The resultant estimates can serve two important functions: 1) setting benchmarks for comparative effectiveness studies and 2) most importantly, providing clinicians and families with individual risk estimates to guide treatment decisions., Level of Evidence: Level 1, prognostic., (Copyright © 2019 Scoliosis Research Society. Published by Elsevier Inc. All rights reserved.)