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Personalized prediction of adverse heart and kidney events using baseline and longitudinal data from SPRINT and
Gal Dinstag1, David Amar2, Erik Ingelsson2,3
1Blavatnik School of Computer Science, Tel-Aviv University, Tel Aviv, Israel.
Insights
New hypertension guidelines may increase medication use and adverse effects. This study developed models using patient data to predict cardiovascular and adverse events, aiding personalized treatment recommendations for hypertension management.
Area of Science:
- Cardiology
- Clinical Pharmacology
- Health Informatics
Background:
- The 2017 American College of Cardiology/American Heart Association guidelines redefined hypertension, potentially increasing diagnoses and medication use.
- Increased medication for hypertension may lead to a higher incidence of adverse effects.
- Developing predictive models is crucial for managing potential adverse events associated with expanded hypertension treatment.
Purpose of the Study:
- To develop predictive models for cardiovascular and adverse events in hypertension.
- To assess the impact of patient-specific data and treatment choices on event prediction.
- To support personalized treatment decisions in hypertension management.
Main Methods:
- Utilized data from the SPRINT trial, incorporating baseline and longitudinal patient characteristics.
- Developed predictive models for cardiovascular and kidney outcomes.
- Validated the cardiovascular predictor model using an independent cohort from the ACCORD trial.
Main Results:
- Achieved an AUC of 0.765 for the cardiovascular predictor in the SPRINT trial.
- Demonstrated strong performance of the cardiovascular predictor in the independent ACCORD trial cohort.
- Successfully produced patient-specific predictions for adverse cardiovascular or kidney outcomes.
Conclusions:
- Longitudinal data is essential for accurate personalized risk assessment in hypertension.
- The developed models offer a means to recommend personalized treatment strategies.
- This approach supports individualized patient care in managing hypertension and its associated risks.
Background:
The 2017 guidelines of the American College of Cardiology and the American Heart Association propose substantial changes to hypertension management. The guidelines lower the blood pressure threshold defining hypertension and promote more aggressive treatments. Thus, more individuals are now classified as hypertensive and as a result, medication usage may become more extensive. An inevitable byproduct of greater medication use is higher incidence of adverse effects. Here, we examined these issues by developing models that predict both cardiovascular events and other adverse events based on the treatment chosen and other patient's data.
Methods And Results:
We used data from the SPRINT trial to produce patient-specific predictions of the risks for adverse cardiovascular or kidney outcomes. Unlike prior models, we used both the baseline characteristics collected upon recruitment and the longitudinal data during the follow-up. Importantly, our cardiovascular predictor outperformed extant models on SPRINT participants, achieving AUC = 0.765, and was validated with good performance in an independent cohort of the ACCORD trial.
Conclusions:
Our study illustrates the importance of including longitudinal data for assessing personalized risk and provides means for recommending personalized treatment decisions.
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