Key result
A Random Forest model detected low right ventricular function from clinical and 2D-ECHO parameters with an AUC of 0.80, outperforming general linear regression (AUC 0.62).
Why the study?
Recognizing low RV function from 2D-ECHO is challenging when parameters are contradictory, prompting the development of a model integrating 2D-ECHO parameters in reference to CMR.
Does a machine learning algorithm integrating 2D-ECHO parameters and clinical features improve the prediction of low RV function compared to general linear regression?
Observational (n=155)
Does a machine learning algorithm integrating 2D-ECHO parameters and clinical features improve the prediction of low RV function compared to general linear regression?
Effect estimate: AUC 0.80
A Random Forest machine learning model integrating 2D-ECHO and clinical parameters can effectively detect low RV function, outperforming traditional linear regression.
BACKGROUND: Recognizing low right ventricular (RV) function from 2-dimentiontial echocardiography (2D-ECHO) is challenging when parameters are contradictory. We aim to develop a model to predict low RV function integrating the various 2D-ECHO parameters in reference to cardiac magnetic resonance (CMR)-the gold standard. METHODS: ) that are classically used to assess RV function. We have assessed the ability of 2D-ECHO derived parameters and clinical features to predict RV function measured by the gold standard CMR. We compared outcomes from four machine learning algorithms, widely used in the biomedical community to solve classification problems. RESULTS: One hundred fifty-five patients were identified and included in our study. Average age was 43±17.1 years old and 52/156 (33.3%) were females. According to CMR, 21 patients were identified to have RV dysfunction, with an RVEF of 34.7%±6.4%, as opposed to 54.7%±6.7% in the normal RV population (P<0.0001). The Random Forest model was able to detect low RV function with an AUC =0.80, while general linear regression performed poorly in our population with an AUC of 0.62. CONCLUSIONS: In this study, we trained and validated an ML-based algorithm that could detect low RV function from clinical and 2D-ECHO parameters. The algorithm has two advantages: first, it performed better than general linear regression, and second, it integrated the various 2D-ECHO parameters.
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Ahmad et al. (2020) conducted an observational in Depressed right ventricular function (n=155). Random Forest model vs. General linear regression was evaluated on Detection of low RV function (AUC 0.80). A Random Forest model detected low right ventricular function from clinical and 2D-ECHO parameters with an AUC of 0.80, outperforming general linear regression (AUC 0.62).
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