Related Experiment Video

Updated: Sep 9, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K

Predicting Risk of Cardiovascular Disease Events (PREVENT) score accuracy among people with HIV in the multicenter

Matthew S Durstenfeld1, Robin M Nance2, Raymond Jones3

  • 1University of California, San Francisco, CA.

AIDS (London, England)
|September 4, 2025
PubMed

Insights

New PREVENT equations underestimate cardiovascular risk in people with HIV (PWH). Observed risks were more than double predicted, indicating poor calibration for this high-risk group.

Area of Science:

  • Cardiovascular epidemiology
  • HIV medicine
  • Risk prediction modeling

Background:

  • People with HIV (PWH) face increased cardiovascular disease (CVD) risk.
  • Existing CVD risk calculators, like the Pooled Cohort Equations (PCE), show suboptimal calibration in PWH.
  • The American Heart Association's PREVENT equations offer a potential replacement for PCE but require validation in PWH.

Purpose of the Study:

  • To validate the PREVENT 10-year atherosclerotic cardiovascular disease (ASCVD) base equation in a cohort of PWH.
  • To compare the calibration and discrimination of PREVENT equations against PCE in PWH.
  • To assess the accuracy of cardiovascular risk prediction in PWH using both PREVENT and PCE.

Main Methods:

  • Utilized the Center for AIDS Research Network of Integrated Clinical Systems (CNICS) cohort (2001-2021).
  • Included 13,135 individuals aged 40-75 without prior myocardial infarction (MI) or stroke.
  • Calculated 10-year ASCVD risk using PREVENT and PCE; adjudicated MI and stroke events; assessed discrimination (C-index) and calibration (Greenwood-Nam-D'Agostino tests).

Main Results:

  • PREVENT equations demonstrated improved discrimination (C-index 0.722) compared to PCE (C-index 0.708).
  • Both PREVENT and PCE significantly underestimated risk in PWH (Observed-to-Expected ratios: 2.69 for PREVENT, 1.35 for PCE).
  • PREVENT equations showed poor calibration (slope 1.998), while PCE had a slope closer to 1 (0.932), but both underpredicted risk.

Conclusions:

  • The PREVENT 10-year ASCVD equations are poorly calibrated and underestimate cardiovascular risk in people with HIV.
  • Observed MI and stroke events were more than double the predicted risk using PREVENT equations in this PWH cohort.
  • Current cardiovascular risk prediction tools, including PREVENT, require further refinement for accurate risk assessment in PWH.
Abstract

Keywords:
HIVPREVENT equationscardiovascular risklongitudinal cohort studypredictionstatins

More Related Videos

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

523
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

372

Related Concept Videos

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
116
Hazard Ratio01:12

Hazard Ratio

The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
240
Relative Risk01:12

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
334
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
651
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
173
Coronary Artery Disease IV: Preventive Measures01:26

Coronary Artery Disease IV: Preventive Measures

Effective preventive measures for coronary artery disease (CAD) focus on controlling modifiable risk factors, including cholesterol abnormalities and lifestyle changes.Cholesterol ManagementFirst, the Mediterranean diet and the American Heart Association advocate for maintaining low-density lipoprotein (LDL) cholesterol levels below 100 mg/dL, with a more stringent recommendation of below 70 mg/dL for individuals at high risk. LDL cholesterol, often termed "bad cholesterol," can lead to the...
32