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In Vitro Modeling of Fat Deposition in Metabolic Dysfunction-Associated Steatotic Liver Disease
Published on: July 19, 2024
Cardiovascular Disease Risk Prediction in Patients With Metabolic Dysfunction-Associated Steatohepatitis
Joe Hollinghurst1, Margarida Augusto2, Fotis Tefos2
1Health Economics and Outcomes Research Ltd, Cardiff, UK.
New models predict cardiovascular disease (CVD) risk in patients with metabolic dysfunction-associated steatohepatitis (MASH). These models offer improved accuracy over existing algorithms for better MASH patient management.
Area of Science:
- Cardiology
- Hepatology
- Data Science
Background:
- Metabolic dysfunction-associated steatohepatitis (MASH) significantly elevates cardiovascular disease (CVD) risk.
- Increased CVD morbidity and mortality are observed in MASH patients.
Purpose of the Study:
- To develop novel prediction models for CVD risk specifically in a MASH patient cohort.
- To establish the first predictive tool for CVD risk tailored to individuals with MASH.
Main Methods:
- Retrospective cohort study utilizing the UK Clinical Practice Research Datalink (CPRD) database.
- Accelerated failure time (AFT) models were employed for CVD risk prediction in male and female MASH patients.
- Inclusion of QRisk3 algorithm covariates and assessment of model calibration and discrimination.
Main Results:
- Developed CVD risk prediction models for 10,461 MASH patients (5364 female, 5097 male) with moderate predictive power (C-statistics 0.7-0.72).
- Identified age, cholesterol ratio, type 2 diabetes, and chronic kidney disease (CKD) as key risk factors impacting time to CVD.
- AFT models demonstrated superior accuracy in predicting CVD risk compared to the QRisk3 algorithm within the MASH cohort.
Conclusions:
- Introduced a pioneering predictive model for assessing CVD risk in MASH patients.
- The developed model holds potential for enhancing the precision of treatment and management strategies for MASH populations.
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