Key result
An XGBoost machine learning model using blood biomarkers predicted resting rate pressure product with a Pearson correlation of 0.377 (95% CI 0.375-0.378), outperforming standard linear regression.
Why the study?
Resting rate pressure product (rRPP) varies among individuals, but its biochemical and cellular phenotype remains unknown.
Does a machine learning model using standard blood biomarkers accurately predict resting rate pressure product compared to standard linear regression?
Cross-Sectional (n=55,730)
No
Does a machine learning model using standard blood biomarkers accurately predict resting rate pressure product compared to standard linear regression?
Machine learning models using standard blood biomarkers, particularly glucose, total protein, and neutrophils, can modestly predict resting cardiac workload.
Answers are grounded in this paper and cite their sources.
Modest accuracy precludes routine use; leaves open prospective validation of biomarker-based ML for cardiac workload.
Introduction . Rate pressure product (the product of heart rate and systolic blood pressure) is a measure of cardiac workload. Resting rate pressure product (rRPP) varies from one individual to the next, but its biochemical/cellular phenotype remains unknown. This study determined the degree to which an individual’s biochemical/cellular profile as characterized by a standard blood panel is predictive of rRPP, as well the importance of each blood biomarker in this prediction. Methods . We included data from 55,730 participants in this study with complete rRPP measurements and concurrently collected blood panel information from the Health Management Centre at the Affiliated Hospital of Hangzhou Normal University. We used the XGBoost machine learning algorithm to train a tree‐based model and then assessed its accuracy on an independent portion of the dataset and then compared its performance against a standard linear regression technique. We further determined the predictive importance of each feature in the blood panel. Results . We found a fair positive correlation (Pearson r ) of 0.377 (95% CI: 0.375‐0.378) between observed rRPP and rRPP predicted from blood biomarkers. By comparison, the performance for standard linear regression was 0.352 (95% CI: 0.351‐0.354). The top three predictors in this model were glucose concentration, total protein concentration, and neutrophil count. Discussion / Conclusion . Blood biomarkers predict resting RPP when modeled in combination with one another; such models are valuable for studying the complex interrelations between resting cardiac workload and one’s biochemical/cellular phenotype.
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Shou et al. (2021) conducted a cross-sectional in Cardiac workload (resting rate pressure product) (n=55,730). XGBoost machine learning algorithm using blood biomarkers vs. Standard linear regression was evaluated on Correlation between observed and predicted resting rate pressure product (Pearson r 0.377, 95% CI 0.375-0.378). An XGBoost machine learning model using blood biomarkers predicted resting rate pressure product with a Pearson correlation of 0.377 (95% CI 0.375-0.378), outperforming standard linear regression.
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