# Consensus Framework for Cardiovascular Risk Prediction in a Clinical Setting

> **NIH NIH R21** · BRIGHAM AND WOMEN'S HOSPITAL · 2024 · $134,250

## Abstract

Project Summary/Abstract
Accurate assessment of cardiovascular risk in clinical settings is important for the appropriate management
and counseling of millions of patients. Current US treatment guidelines focus on a single risk score. This
approach has good overall performance but has limitations when applied to clinical setting. For example,
important new markers or specific missing patterns cannot be accommodated with established, guideline-
endorsed prediction scores. Scores also often over or under predict in new populations or subgroups. These
gaps call for exploring approaches to risk prediction that go beyond a single model paradigm and beyond
classical Cox proportional hazards and logistic regression methods. At the same time, recent publications
question the utility of uninterpretable models in prognostic settings especially in high-stakes situations in
healthcare. Therefore we need to strike a balance between the sophistication of a model and its interpretability
in order to avoid potentially tragic consequences for patients in the near future.The goal of this project is to
address these issues. We will evaluate the improvement in discrimination and calibration of existing consensus
models such as the Super Learner and eXtreme Gradient Boosting in clinical settings and develop a novel
method called the Consensus Framework. This novel method has the consensus property because it
combines multiple published and validated risk models to ensure not only good overall performance but also
good performance in important subgroups of patients. The Consensus Framework is adapted to clinical
practice because it can handle limited information or additional risk factors. We will also assess specific
properties of prognostic risk prediction and how they inform the selection of the most appropriate class of
models. This project is relevant to public health because 1) the interpretability of the consensus models that
we propose to use ensures transparency in the assessment of their quality and limitations, which is of
paramount importance in high-stakes decision making in healthcare, 2) their flexibility to accommodate missing
data or the availability of known risk factors will produce more personalized treatment decisions, 3) a better
understanding of the unique properties of prognostic risk prediction will dictate a more informative choice of
prognostic model. All these factors will lead to better informed treatment decisions for millions of patients.

## Key facts

- **NIH application ID:** 10788410
- **Project number:** 5R21HL167173-02
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Olga Demler
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $134,250
- **Award type:** 5
- **Project period:** 2023-02-15 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10788410

## Citation

> US National Institutes of Health, RePORTER application 10788410, Consensus Framework for Cardiovascular Risk Prediction in a Clinical Setting (5R21HL167173-02). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10788410. Licensed CC0.

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