We previously developed a predictive model to assess the risk of developing acute pancreatitis (AP) in patients with severe hypertriglyceridemia (HTG). In this study, we aimed to externally validate this model. The validation cohort included cross-sectional data between 2013 and 2017. Adult patients (≥18 years old) with triglyceride levels ≥1,000 mg/dL were identified. Based on our previous 4-factor predictive model (age, triglyceride [TG], excessive alcohol use, and gallstone disease), we estimated the probability of developing AP. Model performance was assessed using area under receiver operating characteristic curve (AUROC). In comparison to the original cohort, patients in the validation cohort had more prevalent acute pancreatitis (16.2% versus 9.2%; <.001) and gallstone disease (7.5% versus 2.1%; <.001). Other characteristics were comparable and not statistically significant. The AUROCs were almost identical: 0.8337 versus 0.8336 in the validation and the original cohorts, respectively. In univariable analyses, the highest increase in odds of AP was associated with HTG, followed by gallstones, excessive alcohol use, and younger age. This study externally validates the 4-factor predictive model to estimate the risk of AP in adult patients with severe HTG (TG ≥1,000 mg/dL). Younger age was confirmed to place patients at high risk of AP. The clinical risk categories suggested in this study may be useful to guide treatment options. = acute pancreatitis; = atherosclerotic cardiovascular disease; = area under the receiver operating characteristic curve; = fracture risk assessment tool; = hypertriglyceridemia; = odds ratio; = triglyceride level.
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http://dx.doi.org/10.4158/EP-2018-0599 | DOI Listing |
Environ Sci Technol
January 2025
State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
Air pollution is a leading contributor to the global disease burden. However, the complex nature of the chemicals to which humans are exposed through inhalation has obscured the identification of the key compounds responsible for diseases. Here, we develop a network topology-based framework to identify key toxic compounds in the airborne chemical exposome.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Behavioural and Implementation Science Group, School of Health Sciences, University of East Anglia, Norwich, United Kingdom.
Background: If the most evidence-based and effective smoking cessation apps are not selected by smokers wanting to quit, their potential to support cessation is limited.
Objective: This study sought to determine the attributes that influence smoking cessation app uptake and understand their relative importance to support future efforts to present evidence-based apps more effectively to maximize uptake.
Methods: Adult smokers from the United Kingdom were invited to participate in a discrete choice experiment.
Comput Methods Biomech Biomed Engin
February 2025
Zhejiang Weilian Technology Co., Ltd, Jiaxing, China.
Functional and esthetic results require accurate implant placement. We aimed to develop a predictive method for assessing dental implant accuracy, and to evaluate the cumulative system influence of surgical guides. A mathematical model was constructed to determine the influence of surface changes on a specific point, using Jacobian matrix expressions.
View Article and Find Full Text PDFCurr Opin Crit Care
January 2025
Department of Critical Care Medicine.
Purpose Of Review: Neuroprognostication after acute brain injury (ABI) is complex. In this review, we examine the threats to accurate neuroprognostication, discuss strategies to mitigate the self-fulfilling prophecy, and how to approach the indeterminate prognosis.
Recent Findings: The goal of neuroprognostication is to provide a timely and accurate prediction of a patient's neurologic outcome so treatment can proceed in accordance with a patient's values and preferences.
Brain Inform
January 2025
Department of Computing, Glasgow Caledonian University, Glasgow, G4 0BA, Scotland.
A digital twin is a virtual model of a real-world system that updates in real-time. In healthcare, digital twins are gaining popularity for monitoring activities like diet, physical activity, and sleep. However, their application in predicting serious conditions such as heart attacks, brain strokes and cancers remains under investigation, with current research showing limited accuracy in such predictions.
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