Predicting Heart Rate at the Anaerobic Threshold Using a Machine Learning Model Based on a Large-Scale Population Dataset | JoVE Visualize

Area of Science:

Cardiology
Exercise Physiology
Machine Learning in Medicine

Background:

Determining target heart rate at the anaerobic threshold (AT-HR) is crucial for effective exercise prescription in cardiovascular disease (CVD) patients.
Cardiopulmonary exercise testing (CPET) is the standard method for AT-HR determination but can be resource-intensive.
Developing alternative methods to predict AT-HR from readily available clinical features is needed.

Purpose of the Study:

To develop and validate a machine learning (ML) model for predicting AT-HR using only non-exercise clinical features.
To compare the accuracy of the ML model against established guideline-recommended equations for AT-HR prediction.
To identify key clinical features that significantly contribute to AT-HR prediction.

Main Methods:

Utilized a dataset of 8228 participants (healthy and CVD patients) from 21,482 CPET cases.
Employed a gradient boosting ML model trained on 78 clinical features (e.g., demographics, vitals, blood tests, echocardiography).
Evaluated prediction accuracy using Mean Absolute Error (MAE) and compared ML model results with Karvonen and simpler formulas.

Main Results:

The ML model achieved a significantly lower MAE (7.7 ± 0.2 bpm) compared to guideline equations (e.g., Karvonen: 34.5 ± 0.3 bpm, 11.9 ± 0.2 bpm; simpler formulas: 15.9 ± 0.3 bpm, 9.7 ± 0.2 bpm).
Key predictors for AT-HR included resting heart rate, age, N-terminal pro-brain natriuretic peptide (NT-proBNP), resting systolic blood pressure, hsCRP, CVD diagnosis, and beta-blocker use.
High prediction accuracy was maintained using the top 10-20 features, indicating feature efficiency.

Conclusions:

An accurate ML-based prediction model for AT-HR from non-exercise clinical features has been successfully developed.
This model can potentially simplify and enhance exercise prescription for cardiac rehabilitation.
The study identified novel determinants of AT-HR, such as NT-proBNP and hsCRP, offering new insights into CVD pathophysiology.