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The ten-year cardiovascular disease risk prediction among primary healthcare workers using the office-based globorisk tool: A cross-sectional study.
Med J Malaysia
January 2025
Occupational and Safety Unit, Selangor State Health Department, Ministry of Health, Selangor, Malaysia.
Introduction: Cardiovascular diseases (CVDs) remain to be the leading cause of premature mortality worldwide, and healthcare workers (HCWs) are potentially at risk for developing CVDs. Office-based Globorisk is a ten-year risk prediction tool for CVDs risk scores. This present study aims to determine the prevalence of CVDs risk and risk factors associated with moderate-high CVDs risk among primary HCWs in government health clinics in Selangor.
View Article and Find Full Text PDFA comparison of some existing and novel methods for integrating historical models to improve estimation of coefficients in logistic regression.
J R Stat Soc Ser A Stat Soc
January 2025
Biostatistics, University of Michigan, 1415 Washington Heights, Michigan 48109, USA.
Model integration refers to the process of incorporating a fitted historical model into the estimation of a current study to increase statistical efficiency. Integration can be challenging when the current model includes new covariates, leading to potential model misspecification. We present and evaluate seven existing and novel model integration techniques, which employ both likelihood constraints and Bayesian informative priors.
View Article and Find Full Text PDFArtificial Intelligence in Predicting Postpartum Hemorrhage in Twin Pregnancies Undergoing Cesarean Section.
Twin Res Hum Genet
January 2025
Necmettin Erbakan University Medical School of Meram, Department of Obstetrics and Gynecology, Division of Fetal and Maternal Medicine, Konya, Turkey.
This study aimed to create a risk prediction model with artificial intelligence (AI) to identify patients at higher risk of postpartum hemorrhage using perinatal characteristics that may be associated with later postpartum hemorrhage (PPH) in twin pregnancies that underwent cesarean section. The study was planned as a retrospective cohort study at University Hospital. All twin cesarean deliveries were categorized into two groups: those with and without PPH.
View Article and Find Full Text PDFDevelopment of a machine learning predictive model for central venous catheter-associated thrombosis in patients undergoing abdominal surgery.
Nurs Crit Care
January 2025
Department of Nursing, the First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Background: Central venous catheters (CVCs) are placed where the vena cava meets the right atrium. Their common use raises the risk of catheter-related thrombosis (CRT), a potentially life-threatening complication.
Aim: This study leverages machine learning to develop a CRT predictive model for abdominal surgery patients, aiming to refine clinical decisions and elevate treatment quality.
Integrating the ADS Score with 24-Hour ASPECTS and red cell distribution width for enhanced prediction of stroke-associated pneumonia following intravenous thrombolysis: model development and internal validation.
Eur J Med Res
January 2025
Division of Radiology, Saraburi Hospital, Saraburi, Thailand.
Introduction: Stroke-associated pneumonia (SAP) is a major cause of mortality during the acute phase of stroke. The ADS score is widely used to predict SAP risk but does not include 24-h non-contrast computed tomography-Alberta Stroke Program Early CT Score (NCCT-ASPECTS) or red cell distribution width (RDW). We aim to evaluate the added prognostic value of incorporating 24-h NCCT-ASPECTS and RDW into the ADS score and to develop a novel prediction model for SAP following thrombolysis.
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