Key result
The GA-KPLS machine learning model using gene expression data accurately predicted 3-year mortality risk in HFpEF patients, achieving an AUC of 0.955 and significantly outperforming conventional logistic regression.
Why the study?
HFpEF has high mortality, heterogeneity, and poor prognosis, making genomic classification a promising approach to identify high-risk groups for precision treatment.
Can machine learning models using gene expression data predict 3-year mortality risk in patients with HFpEF?
Observational (n=149)
Can machine learning models using gene expression data predict 3-year mortality risk in patients with HFpEF?
Machine learning models, particularly GA-KPLS, applied to gene expression data can effectively stratify 3-year mortality risk in patients with HFpEF.
May support gene expression-based risk stratification in HFpEF; leaves open prospective validation before clinical use.
Heart failure with preserved ejection fraction (HFpEF) has become a major health issue because of its high mortality, high heterogeneity, and poor prognosis. Using genomic data to classify patients into different risk groups is a promising method to facilitate the identification of high-risk groups for further precision treatment. Here, we applied six machine learning models, namely kernel partial least squares with the genetic algorithm (GA-KPLS), the least absolute shrinkage and selection operator (LASSO), random forest, ridge regression, support vector machine, and the conventional logistic regression model, to predict HFpEF risk and to identify subgroups at high risk of death based on gene expression data. The model performance was evaluated using various criteria. Our analysis was focused on 149 HFpEF patients from the Framingham Heart Study cohort who were classified into good-outcome and poor-outcome groups based on their 3-year survival outcome. The results showed that the GA-KPLS model exhibited the best performance in predicting patient risk. We further identified 116 differentially expressed genes (DEGs) between the two groups, thus providing novel therapeutic targets for HFpEF. Additionally, the DEGs were enriched in Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways related to HFpEF. The GA-KPLS-based HFpEF model is a powerful method for risk stratification of 3-year mortality in HFpEF patients.
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Zhou et al. (2021) conducted an observational in Heart Failure with Preserved Ejection Fraction (HFpEF) (n=149). GA-KPLS machine learning model using gene expression data vs. Conventional logistic regression and other machine learning models was evaluated on Area under the curve (AUC) for predicting 3-year cardiovascular mortality (p=<0.05). The GA-KPLS machine learning model using gene expression data accurately predicted 3-year mortality risk in HFpEF patients, achieving an AUC of 0.955 and significantly outperforming conventional logistic regression.
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