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Estimation of life's essential 8 score with incomplete data of individual metrics

Yi Zheng1, Tianyi Huang1,2, Marta Guasch-Ferre3,4,5

  • 1Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.

Frontiers in Cardiovascular Medicine
|August 11, 2023
PubMed

Insights

Estimating cardiovascular health (CVH) using the American Heart Association's Life's Essential 8 (LE8) is challenging. Routinely collected health factors can accurately predict LE8 scores when all metrics are unavailable.

Area of Science:

  • Cardiovascular Health
  • Health Metrics Assessment
  • Predictive Modeling

Background:

  • The American Heart Association's Life's Essential 8 (LE8) is a comprehensive cardiovascular health (CVH) assessment.
  • Measuring all eight LE8 metrics simultaneously is often impractical in research and clinical settings.
  • This limitation hinders the assessment of long-term CVH trajectories.

Purpose of the Study:

  • To develop and validate a predictive model for estimating LE8-based CVH scores.
  • To determine if routinely collected health factors can accurately approximate complete LE8 measurements.
  • To facilitate the assessment of CVH over time even with incomplete data.

Main Methods:

  • Utilized data from the Nurses' Health Studies (NHS, NHSII), Health Professionals Follow-up Study (HPFS), and National Health and Nutrition Examination Survey (NHANES).
  • Trained gradient boosting decision tree models using routinely collected factors (demographics, BMI, smoking, hypertension, hypercholesterolemia, diabetes) and less frequent factors (physical activity, diet, blood pressure, sleep health).
  • Validated model performance using root mean squared error (RMSE) for internal and external datasets.

Main Results:

  • Base models trained on NHS, NHSII, and HPFS demonstrated validated RMSEs of 8.06 (internal) and 16.72 (external).
  • Inclusion of additional predictors improved model performance.
  • Consistent results were observed in models trained using NHANES data, indicating reliable prediction of CVH scores.

Conclusions:

  • Routinely measurable CVH-related factors can effectively estimate overall CVH when LE8 metrics are incomplete.
  • This approach enhances the feasibility of assessing CVH trajectories in diverse settings.
  • The predictive model offers a practical solution for tracking cardiovascular health.
Abstract

Keywords:
cardiovascular healthlife’s essential 8life’s simple 7machine learningprediction model

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