Repeat Prostate-specific Antigen Testing Improves Risk-based Selection of Men for Prostate Biopsy After Magnetic Resonance Imaging.

Background and objective: The aim of our study was to investigate whether repeat prostate-specific antigen (PSA) testing as currently recommended improves risk stratification for men undergoing magnetic resonance imaging (MRI) and targeted biopsy for suspected prostate cancer (PCa). Methods: Consecutive men undergoing MRI and prostate biopsy who had at least two PSA tests before prostate biopsy were retrospectively registered and assigned to a development cohort (n = 427) or a validation (n = 174) cohort. Change in PSA level was assessed as a predictor of clinically significant PCa (csPCa; Gleason score ≥3 + 4, grade group ≥2) by multivariable logistic regression analysis. We developed a multivariable prediction model (MRI-RC) and a dichotomous biopsy decision strategy incorporating the PSA change. The performance of the MRI-RC model and dichotomous decision strategy was assessed in the validation cohort and compared to prediction models and decision strategies not including PSA change in terms of discriminative ability and decision curve analysis. Results: Men who had a decrease on repeat PSA testing had significantly lower risk of csPCa than men without a decrease (odds ratio [OR] 0.3, 95% confidence interval [CI] 0.16-0.54; p < 0.001). Men with an increased repeat PSA had a significantly higher risk of csPCa than men without an increase (OR 2.97, 95% CI 1.62-5.45; p < 0.001). Risk stratification using both the MRI-RC model and the dichotomous decision strategy was improved by incorporating change in PSA as a parameter. Conclusions and clinical implications: Repeat PSA testing gives predictive information regarding men undergoing MRI and targeted prostate biopsy. Inclusion of PSA change as a parameter in an MRI-RC model and a dichotomous biopsy decision strategy improves their predictive performance and clinical utility without requiring additional investigations. Patient summary: For men with a suspicion of prostate cancer, repeat PSA (prostate-specific antigen) testing after an MRI (magnetic resonance imaging) scan can help in identifying patients who can safely avoid prostate biopsy.

Preoperative and postoperative predictive models of early recurrence for colorectal liver metastases following chemotherapy and curative-intent one-stage hepatectomy.

INTRODUCTION: Accurate prediction of patients at risk for early recurrence (ER) among patients with colorectal liver metastases (CRLM) following preoperative chemotherapy and hepatectomy remains limited. METHODS: Patients with CRLM who received chemotherapy prior to undergoing curative-intent resection between 2000 and 2020 were identified from an international multi-institutional database. Multivariable Cox regression analysis was used to assess clinicopathological factors associated with ER, and an online calculator was developed and validated. RESULTS: Among 768 patients undergoing preoperative chemotherapy and curative-intent resection, 128 (16.7 %) patients had ER. Multivariable Cox analysis demonstrated that Eastern Cooperative Oncology Group Performance status ≥1 (HR 2.09, 95%CI 1.46-2.98), rectal cancer (HR 1.95, 95%CI 1.35-2.83), lymph node metastases (HR 2.39, 95%CI 1.60-3.56), mutated Kirsten rat sarcoma oncogene status (HR 1.95, 95%CI 1.25-3.02), increase in tumor burden score during chemotherapy (HR 1.51, 95%CI 1.03-2.24), and bilateral metastases (HR 1.94, 95%CI 1.35-2.79) were independent predictors of ER in the preoperative setting. In the postoperative model, in addition to the aforementioned factors, tumor regression grade was associated with higher hazards of ER (HR 1.91, 95%CI 1.32-2.75), while receipt of adjuvant chemotherapy was associated with lower likelihood of ER (HR 0.44, 95%CI 0.30-0.63). The discriminative accuracy of the preoperative (training: c-index: 0.77, 95%CI 0.72-0.81; internal validation: c-index: 0.79, 95%CI 0.75-0.82) and postoperative (training: c-index: 0.79, 95%CI 0.75-0.83; internal validation: c-index: 0.81, 95%CI 0.77-0.84) models was favorable (https://junkawashima.shinyapps.io/CRLMfollwingchemotherapy/). CONCLUSIONS: Patient-, tumor- and treatment-related characteristics in the preoperative and postoperative setting were utilized to develop an online, easy-to-use risk calculator for ER following resection of CRLM.

A nomogram and online calculator for predicting depression risk in obese Americans.

Background: Obese patients with depression face higher risks of adverse events. However, depression is often misdiagnosed and undertreated in this group. This study aimed to identify predictors of depression and create a nomogram and calculator to assess depression risk in obese Americans. Methods: This cross-sectional study included 2674 patients from the National Health and Nutrition Examination Survey database (NHANES). These participants were randomly classified into the training and validation groups in a 7:3 ratio. Predictors were selected by LASSO and multivariate logistic regression analysis to create the nomogram. C-statistics, calibration plots, and decision curve analysis (DCA) were used to test the nomogram's discriminative ability, calibration quality, and clinical value. Internal validation with bootstrap resampling and external validation with the validation group were also conducted. Results: The training and validation group consists of 1871 and 803 participants. Depression was presented in 11.4 % (203/2674) of these participants. Seven predictors were found, including gender, hypertension, weekday sleep duration, poverty to income ratio, history of seeing mental health doctor, diabetes, and feeling sleepy during the day. The nomogram showed good discrimination, with the area under the receiver operating characteristic curve (AUC) of 0.817 (95 % CI: 0.786-0.848) (0.806 through internal validation and 0.772 through external validation) and good calibration (P = 0.536). The DCA further confirmed the nomogram's clinical usefulness. Conclusion: The nomogram and calculator effectively predict depression risk in obese Americans and can be used as auxiliary tools for early screening in primary care.

External validation of the barcelona magnetic resonance imaging predictive model for detecting significant prostate cancer including men receiving 5-alpha reductase inhibitors.

PURPOSE: To validate the Barcelona-magnetic resonance imaging predictive model (BCN-MRI PM) for clinically significant prostate cancer (csPCa) in Catalonia, a Spanish region with 7.9 million inhabitants. Additionally, the BCN-MRI PM is validated in men receiving 5-alpha reductase inhibitors (5-ARI). MATERIALS AND METHODS: A population of 2,212 men with prostate-specific antigen serum level > 3.0 ng/ml and/or a suspicious digital rectal examination who underwent multiparametric MRI and targeted and/or systematic biopsies in the year 2022, at ten participant centers of the Catalonian csPCa early detection program, were selected. 120 individuals (5.7%) were identified as receiving 5-ARI treatment for longer than a year. The risk of csPCa was retrospectively assessed with the Barcelona-risk calculator 2 (BCN-RC 2). Men undergoing 5-ARI treatment for less than a year were excluded. CsPCa was defined when the grade group was ≥ 2. RESULTS: The area under the curve of the BCN-MRI PM in 5-ARI naïve men was 0.824 (95% CI 0.783-0.842) and 0.849 (0.806-0.916) in those receiving 5-ARI treatment, p 0.475. Specificities at 100, 97.5, and 95% sensitivity thresholds were to 2.7, 29.3, and 39% in 5-ARI naïve men, while 43.5, 46.4, and 47.8%, respectively in 5-ARI users. The application of BCN-MRI PM would result in a reduction of 23.8% of prostate biopsies missing 5% of csPCa in 5-ARI naïve men, while reducing 25% of prostate biopsies without missing csPCa in 5-ARI users. CONCLUSIONS: The BCN-MRI PM has achieved successful validation in Catalonia and, notably, for the first time, in men undergoing 5-ARI treatment.

Protocol for the development and validation of a Polypharmacy Assessment Score.

BACKGROUND: An increasing number of people are using multiple medications each day, named polypharmacy. This is driven by an ageing population, increasing multimorbidity, and single disease-focussed guidelines. Medications carry obvious benefits, yet polypharmacy is also linked to adverse consequences including adverse drug events, drug-drug and drug-disease interactions, poor patient experience and wasted resources. Problematic polypharmacy is 'the prescribing of multiple medicines inappropriately, or where the intended benefits are not realised'. Identifying people with problematic polypharmacy is complex, as multiple medicines can be suitable for people with several chronic conditions requiring more treatment. Hence, polypharmacy is often potentially problematic, rather than always inappropriate, dependent on clinical context and individual benefit vs risk. There is a need to improve how we identify and evaluate these patients by extending beyond simple counts of medicines to include individual factors and long-term conditions. AIM: To produce a Polypharmacy Assessment Score to identify a population with unusual levels of prescribing who may be at risk of potentially problematic polypharmacy. METHODS: Analyses will be performed in three parts: 1. A prediction model will be constructed using observed medications count as the dependent variable, with age, gender and long-term conditions as independent variables. A 'Polypharmacy Assessment Score' will then be constructed through calculating the differences between the observed and expected count of prescribed medications, thereby highlighting people that have unexpected levels of prescribing. Parts 2 and 3 will examine different aspects of validity of the Polypharmacy Assessment Score: 2. To assess 'construct validity', cross-sectional analyses will evaluate high-risk prescribing within populations defined by a range of Polypharmacy Assessment Scores, using both explicit (STOPP/START criteria) and implicit (Medication Appropriateness Index) measures of inappropriate prescribing. 3. To assess 'predictive validity', a retrospective cohort study will explore differences in clinical outcomes (adverse drug reactions, unplanned hospitalisation and all-cause mortality) between differing scores. DISCUSSION: Developing a cross-cutting measure of polypharmacy may allow healthcare professionals to prioritise and risk stratify patients with polypharmacy using unusual levels of prescribing. This would be an improvement from current approaches of either using simple cutoffs or narrow prescribing criteria.

Predicting Risk of Post-Operative Morbidity and Mortality following Gynaecological Oncology Surgery (PROMEGO): A Global Gynaecological Oncology Surgical Outcomes Collaborative Led Study.

The medical complexity of surgical patients is increasing, and surgical risk calculators are crucial in providing high-value, patient-centered surgical care. However, pre-existing models are not validated to accurately predict risk for major gynecological oncology surgeries, and many are not generalizable to low- and middle-income country settings (LMICs). The international GO SOAR database dataset was used to develop a novel predictive surgical risk calculator for post-operative morbidity and mortality following gynecological surgery. Fifteen candidate features readily available pre-operatively across both high-income countries (HICs) and LMICs were selected. Predictive modeling analyses using machine learning methods and linear regression were performed. The area-under-the-receiver-operating characteristic curve (AUROC) was calculated to assess overall discriminatory performance. Neural networks (AUROC 0.94) significantly outperformed other models (p < 0.001) for evaluating the accuracy of prediction across three groups, i.e., minor morbidity (Clavien-Dindo I-II), major morbidity (Clavien-Dindo III-V), and no morbidity. Logistic-regression modeling outperformed the clinically established SORT model in predicting mortality (AUROC 0.66 versus 0.61, p < 0.001). The GO SOAR surgical risk prediction model is the first that is validated for use in patients undergoing gynecological surgery. Accurate surgical risk predictions are vital within the context of major cytoreduction surgery, where surgery and its associated complications can diminish quality-of-life and affect long-term cancer survival. A model that requires readily available pre-operative data, irrespective of resource setting, is crucial to reducing global surgical disparities.

Machine Learning and External Validation of the IDENTIFY Risk Calculator for Patients with Haematuria Referred to Secondary Care for Suspected Urinary Tract Cancer.

BACKGROUND: The IDENTIFY study developed a model to predict urinary tract cancer using patient characteristics from a large multicentre, international cohort of patients referred with haematuria. In addition to calculating an individual's cancer risk, it proposes thresholds to stratify them into very-low-risk (<1%), low-risk (1-<5%), intermediate-risk (5-<20%), and high-risk (≥20%) groups. OBJECTIVE: To externally validate the IDENTIFY haematuria risk calculator and compare traditional regression with machine learning algorithms. DESIGN, SETTING, AND PARTICIPANTS: Prospective data were collected on patients referred to secondary care with new haematuria. Data were collected for patient variables included in the IDENTIFY risk calculator, cancer outcome, and TNM staging. Machine learning methods were used to evaluate whether better models than those developed with traditional regression methods existed. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The area under the receiver operating characteristic curve (AUC) for the detection of urinary tract cancer, calibration coefficient, calibration in the large (CITL), and Brier score were determined. RESULTS AND LIMITATIONS: There were 3582 patients in the validation cohort. The development and validation cohorts were well matched. The AUC of the IDENTIFY risk calculator on the validation cohort was 0.78. This improved to 0.80 on a subanalysis of urothelial cancer prevalent countries alone, with a calibration slope of 1.04, CITL of 0.24, and Brier score of 0.14. The best machine learning model was Random Forest, which achieved an AUC of 0.76 on the validation cohort. There were no cancers stratified to the very-low-risk group in the validation cohort. Most cancers were stratified to the intermediate- and high-risk groups, with more aggressive cancers in higher-risk groups. CONCLUSIONS: The IDENTIFY risk calculator performed well at predicting cancer in patients referred with haematuria on external validation. This tool can be used by urologists to better counsel patients on their cancer risks, to prioritise diagnostic resources on appropriate patients, and to avoid unnecessary invasive procedures in those with a very low risk of cancer. PATIENT SUMMARY: We previously developed a calculator that predicts patients' risk of cancer when they have blood in their urine, based on their personal characteristics. We have validated this risk calculator, by testing it on a separate group of patients to ensure that it works as expected. Most patients found to have cancer tended to be in the higher-risk groups and had more aggressive types of cancer with a higher risk. This tool can be used by clinicians to fast-track high-risk patients based on the calculator and investigate them more thoroughly.

Mobile calculator application for estimating human erythrocyte antigen frequency in Korea.

OBJECTIVES: This study aimed to establish a comprehensive human erythrocyte antigen (HEA) frequency data set for Koreans. It also sought to develop a mobile app that facilitates the calculation of the frequencies of specific antigen-negative red blood cell units and the average number of units required for antigen typing. METHODS: Human erythrocyte antigen frequencies were compiled from large-scale blood donor data and 5 previous papers. Based on the collected data, we developed a mobile calculator app for HEA frequency and evaluated its usability. RESULTS: Human erythrocyte antigen frequency data for 20 blood group systems, including the ABO, Rh, MNS, Duffy, Kidd, and Diego systems, were established. The app was designed to enable users to select the desired phenotype from a drop-down menu and display the calculated frequency at the bottom. The number of units required for antigen typing to find 1 compatible red blood cell unit was also displayed. Five users participated in app evaluation and rated the functionality and information categories highly. In quizzes prompting users to calculate frequencies using the app, all participants provided correct answers, confirming the app's user-friendly functionality. CONCLUSIONS: This app, which encompasses comprehensive HEA frequency data, is expected to find multiple uses in transfusion medicine, including optimizing blood bank workflow and defining rare blood groups in Korea.

Prediction of hepatic metastasis in esophageal cancer based on machine learning.

This study aimed to establish a machine learning (ML) model for predicting hepatic metastasis in esophageal cancer. We retrospectively analyzed patients with esophageal cancer recorded in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2020. We identified 11 indicators associated with the risk of liver metastasis through univariate and multivariate logistic regression. Subsequently, these indicators were incorporated into six ML classifiers to build corresponding predictive models. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. A total of 17,800 patients diagnosed with esophageal cancer were included in this study. Age, primary site, histology, tumor grade, T stage, N stage, surgical intervention, radiotherapy, chemotherapy, bone metastasis, and lung metastasis were independent risk factors for hepatic metastasis in esophageal cancer patients. Among the six models developed, the ML model constructed using the GBM algorithm exhibited the highest performance during internal validation of the dataset, with AUC, accuracy, sensitivity, and specificity of 0.885, 0.868, 0.667, and 0.888, respectively. Based on the GBM algorithm, we developed an accessible web-based prediction tool (accessible at https://project2-dngisws9d7xkygjcvnue8u.streamlit.app/ ) for predicting the risk of hepatic metastasis in esophageal cancer.

Nomogram and calculator for predicting the prognosis of patients with giant hepatocellular carcinoma.

OBJECTIVES: This study aimed to explore the factors affecting the overall survival (OS) of giant hepatocellular carcinoma (G-HCC) patients and establish a nomogram and an Internet-based OS calculator for evaluating the OS of G-HCC patients. RESEARCH DESIGN AND METHODS: A total of 2445 G-HCC patients were searched in the SEER database. The independent variables affecting OS of G-HCC patients were determined by univariate and multivariate analyses, and a nomogram and Internet-based OS calculator were established. The accuracy of the nomogram was evaluated by the C-index, the AUC curve, and calibration curve. RESULTS: Grade, surgery, radiotherapy, chemotherapy, T-staging, M-staging, AFP, and fibrosis were identified as independent variables affecting OS. These variables were included in the nomogram model and Internet-based OS calculator to evaluate OS in G-HCC patients. The C-indices and AUC of the nomogram are better than AJCC-staging system. Similarly, the calibration curves revealed that the actual survival was consistent with nomogram-based survival. CONCLUSION: The nomogram and Internet-based OS calculator are superior to the traditional AJCC-staging system in the reliability and convenience of prognosis assessment for G-HCC patients, which is more conducive for clinicians to predict the survival of G-HCC patients and make the best treatment strategy.

CD34+ cell yield among healthy donors: Large-scale model development and validation.

BACKGROUND: Successful engraftment in hematopoietic stem cell transplantation necessitates the collection of an adequate dose of CD34+ cells. Thus, the precise estimation of CD34+ cells harvested via apheresis is critical. Current CD34+ cell yield prediction models have limited reproducibility. This study aims to develop a more reliable and universally applicable model by utilizing a large dataset, enhancing yield predictions, optimizing the collection process, and improving clinical outcomes. MATERIALS AND METHODS: A secondary analysis was conducted using the Center for International Blood and Marrow Transplant Research database, involving data from over 17 000 healthy donors who underwent filgrastim-mobilized hematopoietic progenitor cell apheresis. Linear regression, gradient boosting regressor, and logistic regression classification models were employed to predict CD34+ cell yield. RESULTS: Key predictors identified include pre-apheresis CD34+ cell count, weight, age, sex, and blood volume processed. The linear regression model achieved a coefficient of determination (R2) value of 0.66 and a correlation coefficient (r) of 0.81. The gradient boosting regressor model demonstrated marginally improved results with an R2 value of 0.67 and an r value of 0.82. The logistic regression classification model achieved a predictive accuracy of 96% at the 200 × 106 CD34+ cell count threshold. At thresholds of 400, 600, 800, and 1000 × 106 CD34+ cell count, the accuracies were 88%, 83%, 83%, and 88%, respectively. The model demonstrated a high area under the receiver operator curve scores ranging from 0.90 to 0.93. CONCLUSION: This study introduces advanced predictive models for estimating CD34+ cell yield, with the logistic regression classification model demonstrating remarkable accuracy and practical utility.

Normative Data Study of Verbal Fluency and Naming Tests in Elderly People in Costa Rica.

OBJECTIVE: To generate normative data for the Verbal Fluency Test (VFT) and the Boston Naming Test (BNT) in the Costa Rican population. METHOD: The sample consisted of 563 healthy older people (aged 59-90 years). Polynomial multiple regression analyses were run to evaluate the effects of the age, sex, and education variables on VFT and BNT scores. RESULTS: The results showed a significant linear effect of education on the four-letter VF scores and an effect of sex on the letter P score, with females performing better than males. The explained variance ranged from 20.9% to 28.3%. A linear effect of age and education was also found for the four semantic VF scores, with scores decreasing with increasing age and lower education. The sex variable was significant for all semantic categories, with females performing better than males except in the animal category. The explained variance ranged from 21.7% to 30.9%. In the BNT, a linear effect of education was found, so that the more education, the better the score. In addition, a sex effect was also found, with males having higher scores than females. The predictors of the model explained 9.6% of the variance. CONCLUSIONS: This is the first study that generates normative data for the VF and BNT in the Costa Rican population over 59 years of age based on demographic variables. The use of these normative data will help clinicians in Costa Rica to better understand language functioning in the elderly, allowing for better classification and diagnosis in the future.

Precision in IOL Calculation for Cataract Patients with Prior History of Combined RK and LASIK Histories.

Purpose: This study aimed to evaluate the accuracy of 12 intraocular lens (IOL) power calculation formulae for eyes that have undergone both radial keratotomy (RK) and laser assisted in situ keratomileusis (LASIK) surgery to determine the efficacy of various IOL calculations for this unique patient group. Currently, research on this surgical topic is limited. Methods: In this retrospective study, 11 eyes from 7 individuals with a history of RK and LASIK who underwent cataract surgery at Hoopes Vision were analyzed. Preoperative biometric and corneal topographic measurements were performed. Subjective refraction was obtained postoperatively. Twelve different intraocular lens (IOL) power calculations were used: Barrett True K No History, Barrett True K (prior LASIK, Prior RK history), Barrett Universal 2, Camellin-Calossi-Camellin (3C), Double K-Modified Holladay, Haigis-L, Galilei, OCT, PEARL-DGS, Potvin-Hill, Panacea, and Shammas. Results: The rankings of mean arithmetic error (MAE), from least to greatest, were as follows: 3C (0.088), Haigis-L-L (-0.508), Shammas (-0.516), OCT Average (-0.538), Barrett True K (-0.557), OCT RK (-0.563), Galilei (-0.570), IOL Master (-0.571), OCT LASIK (-0.583), Barrett True K No History (-0.597), Pearl-DGS (-0.606), Potvin-Hill SF (-0.770), Potvin-Hill TNP (-0.778), Panacea (-0.876), and Barrett Universal 2 (-1.522). The 3C formula achieved the greatest percentage of eyes within ±0.25 D of target range (91%), while Haigis-L, Shammas, Galilei, Potvin Hill, Barrett True K, IOL Master, PEARL-DGS, and OCT formulae performed similarly, achieving 45% of eyes within ±0.75D of target refraction. Conclusion: This study demonstrates the accuracy of the lesser known 3C formula in IOL calculation, particularly for patients who have undergone both RK and LASIK. Well-known formulae, such as Haigis-L, Shammas, and Galilei, which are used by the American Society of Cataract and Refractive Surgery (ASCRS), are viable options, although 3C formulae should be considered in this patient population. Furthermore, larger studies can confirm the best IOL power formulas for post-RK and LASIK cataract patients.

Development and validation of a 30-day point-scoring risk calculator for small bowel obstruction surgery.

Small bowel obstruction (SBO) is one of the most frequent causes of general emergency surgery. The 30-day mortality rate post-surgery ranges widely from 2 to 30%, contingent upon the patient population, which renders risk assessment tools helpful. this study aimed to develop a 30-day point-scoring risk calculator designed for patients undergoing SBO surgery. Patients who underwent SBO surgery were identified in the ACS-NSQIP database from 2005 to 2021. Patients were randomly sampled into an experimental (2/3) and a validation (1/3) group. A weighted point scoring system was developed for the risk of 30-day mortality, utilizing multivariable regression on preoperative risk variables based on Sullivan's method. The risk scores underwent both internal and external validation. Furthermore, the efficacy of the risk score was evaluated in 30-day major surgical complications. A total of 93,517 patients were identified, with 63,521 and 29,996 assigned to the experimental and validation groups, respectively. The risk calculator is structured to assign points based on age (> 85 years, 4 points; 75-85 years, 3 points; 65-75 years, 2 points; 55-65 years, 1 point), disseminated cancer (2 points), American Society of Anesthesiology (ASA) score of 4 or 5 (1 point), preoperative sepsis (1 point), hypoalbuminemia (1 point), and fully dependent functional status (1 point). The risk calculator showed strong discrimination (c-statistic = 0.825, 95% CI 0.818-0.831) and good calibration (Brier score = 0.043) in the experimental group. The point scoring system was successfully translated from individual preoperative variables (c-statistic = 0.840, 95% CI 0.834-0.847) and was externally validated in ACS-NSQIP (c-statistic = 0.827, 95% = CI 0.834-0.847, Brier score = 0.043). The SBO risk score can effectively discriminate major surgical complications including major adverse cardiovascular events (c-statistic = 0.734), cardiac complications (c-statistic = 0.732), stroke (c-statistic = 0.725), pulmonary complications (c-statistic = 0.727), renal complications (c-statistic = 0.692), bleeding (c-statistic 0.674), sepsis (c-statistic = 0.670), with high predictive accuracy (all Brier scores < 0.1). This study developed and validated a concise yet robust 10-point risk scoring system for patients undergoing SBO surgery. It can be informative to determine treatment plans and to prepare for potential perioperative complications in patients undergoing SBO surgery.

Identifying and preventing human error in the sugar production process: A multi-stage approach using HTA, HEC and PHEA techniques.

This article discusses the importance of identifying and preventing human error in industrial environments, specifically in the sugar production process. The article emphasizes the importance of choosing the right technique for risk assessment studies resulting from human errors. A cross-sectional study was conducted using a multi-stage approach - Hierarchical Task Analysis (HTA), Human Error Calculator (HEC), and Predictive Human Error Analysis (PHEA) - to identify potential human errors in the sugar production process. The HTA, HEC, and PHEA techniques were employed to evaluate each stage of the process for potential human errors. The results of the HTA technique identified 35 tasks and 83 sub-tasks in 14 units of the sugar production process. According to HEC technique 4 tasks with 80 % probability of human error and 2 tasks with 50 % probability of human error had the highest calculated error probabilities. The factors of individual skill, task repetition and importance were the most important factors of human error in the present study. The analysis of PHEA worksheets showed that the number of human errors identified in the tasks with highest probability were 8 errors, of which 50 % were action errors, 25 % checking errors, 13 % selection errors, and 12 % retrieval errors. To mitigate the consequences of human error, it was recommended training courses, raising operator awareness of error consequences, and installing instructions in the sugar production process. Based on the findings, the article concludes that the HEC and PHEA techniques are applicable and effective in identifying and analyzing human errors in process and food industries.

Evaluation of the American College of Surgeons risk calculator in hepatectomy for metastatic colorectal cancer in a Southeast Asian population.

PURPOSE: This study evaluated the accuracy of the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) calculator in predicting outcomes after hepatectomy for colorectal cancer (CRC) liver metastasis in a Southeast Asian population. METHODS: Predicted and actual outcomes were compared for 166 patients undergoing hepatectomy for CRC liver metastasis identified between 2017 and 2022, using receiver operating characteristic curves with area under the curve (AUC) and Brier score. RESULTS: The ACS-NSQIP calculator accurately predicted most postoperative complications (AUC > 0.70), except for surgical site infection (AUC = 0.678, Brier score = 0.045). It also exhibited satisfactory performance for readmission (AUC = 0.818, Brier score = 0.011), reoperation (AUC = 0.945, Brier score = 0.002), and length of stay (LOS, AUC = 0.909). The predicted LOS was close to the actual LOS (5.9 vs. 5.0 days, P = 0.985). CONCLUSION: The ACS-NSQIP calculator demonstrated generally accurate predictions for 30-day postoperative outcomes after hepatectomy for CRC liver metastasis in our patient population.

[Advances in clinical management of neonatal sepsis]. / 新生儿败血症的临床管理进展.

Neonatal sepsis, as a significant cause of various complications and adverse outcomes in neonates, remains a serious health burden both domestically and internationally. Strategies such as antibiotic prophylaxis during delivery, the utilization of early-onset sepsis risk calculators, and quality improvement initiatives in neonatal wards are beneficial in alleviating the disease burden of neonatal sepsis. This paper provides a review of the epidemiology, risk factors, and recent advances in clinical management of neonatal sepsis.

Assessing the generalisability of the psychosis metabolic risk calculator (PsyMetRiC) for young people with first-episode psychosis with validation in a Hong Kong Chinese Han population: a 4-year follow-up study.

Background: Metabolic syndrome (MetS) is common following first-episode psychosis (FEP), contributing to substantial morbidity and mortality. The Psychosis Metabolic Risk Calculator (PsyMetRiC), a risk prediction algorithm for MetS following a FEP diagnosis, was developed in the United Kingdom and has been validated in other European populations. However, the predictive accuracy of PsyMetRiC in Chinese populations is unknown. Methods: FEP patients aged 15-35 y, first presented to the Early Assessment Service for Young People with Early Psychosis (EASY) Programme in Hong Kong (HK) between 2012 and 2021 were included. A binary MetS outcome was determined based on the latest available follow-up clinical information between 1 and 12 years after baseline assessment. The PsyMetRiC Full and Partial algorithms were assessed for discrimination, calibration and clinical utility in the HK sample, and logistic calibration was conducted to account for population differences. Sensitivity analysis was performed in patients aged >35 years and using Chinese MetS criteria. Findings: The main analysis included 416 FEP patients (mean age = 23.8 y, male sex = 40.4%, 22.4% MetS prevalence at follow-up). PsyMetRiC showed adequate discriminative performance (full-model C = 0.76, 95% C.I. = 0.69-0.81; partial-model: C = 0.73, 95% C.I. = 0.65-0.8). Systematic risk underestimation in both models was corrected using logistic calibration to refine PsyMetRiC for HK Chinese FEP population (PsyMetRiC-HK). PsyMetRiC-HK provided a greater net benefit than competing strategies. Results remained robust with a Chinese MetS definition, but worse for the older age group. Interpretation: With good predictive performance for incident MetS, PsyMetRiC-HK presents a step forward for personalized preventative strategies of cardiometabolic morbidity and mortality in young Hong Kong Chinese FEP patients. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Synthetic minority over-sampling technique-enhanced machine learning models for predicting recurrence of postoperative chronic subdural hematoma.

Objective: Chronic subdural hematoma (CSDH) is a neurological condition with high recurrence rates, primarily observed in the elderly population. Although several risk factors have been identified, predicting CSDH recurrence remains a challenge. Given the potential of machine learning (ML) to extract meaningful insights from complex data sets, our study aims to develop and validate ML models capable of accurately predicting postoperative CSDH recurrence. Methods: Data from 447 CSDH patients treated with consecutive burr-hole irrigations at Wenzhou Medical University's First Affiliated Hospital (December 2014-April 2019) were studied. 312 patients formed the development cohort, while 135 comprised the test cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) method was employed to select crucial features associated with recurrence. Eight machine learning algorithms were used to construct prediction models for hematoma recurrence, using demographic, laboratory, and radiological features. The Border-line Synthetic Minority Over-sampling Technique (SMOTE) was applied to address data imbalance, and Shapley Additive Explanation (SHAP) analysis was utilized to improve model visualization and interpretability. Model performance was assessed using metrics such as AUROC, sensitivity, specificity, F1 score, calibration plots, and decision curve analysis (DCA). Results: Our optimized ML models exhibited prediction accuracies ranging from 61.0% to 86.2% for hematoma recurrence in the validation set. Notably, the Random Forest (RF) model surpassed other algorithms, achieving an accuracy of 86.2%. SHAP analysis confirmed these results, highlighting key clinical predictors for CSDH recurrence risk, including age, alanine aminotransferase level, fibrinogen level, thrombin time, and maximum hematoma diameter. The RF model yielded an accuracy of 92.6% with an AUC value of 0.834 in the test dataset. Conclusion: Our findings underscore the efficacy of machine learning algorithms, notably the integration of the RF model with SMOTE, in forecasting the recurrence of postoperative chronic subdural hematoma. Leveraging the RF model, we devised an online calculator that may serve as a pivotal instrument in tailoring therapeutic strategies and implementing timely preventive interventions for high-risk patients.