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  1. Home
  3. Cardiovascular Disease Risk Prediction In Patients With Metabolic Dysfunction-associated Steatohepatitis.
  1. Home
  3. Cardiovascular Disease Risk Prediction In Patients With Metabolic Dysfunction-associated Steatohepatitis.

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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.

Diabetes, Obesity & Metabolism
|May 31, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

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.

Keywords:
cardiovascular diseasesliver steatosismetabolic dysfunction‐associated steatohepatitismetabolic dysfunction‐associated steatotic liver diseasenon‐alcoholic fatty liver diseaseprediction algorithms real world evidencerisk factors steatotic liver disease

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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.