May 13, 2021Open Access
Machine Learning for Predicting Heart Failure Progression in Hypertrophic Cardiomyopathy
Key Result
An ensemble machine learning model using 17 clinical and imaging variables accurately predicted the 5-year risk of progressive heart failure in hypertrophic cardiomyopathy patients with an AUC of 0.81.
Study Design
Type
Observational (n=1,427)
Multicenter
No
Structured PICO
Can a machine learning-based model accurately predict the progression to advanced heart failure in patients with hypertrophic cardiomyopathy?
P
Population
1,427 patients with hypertrophic cardiomyopathy (HCM) evaluated at the Tufts HCM Institute from 2001 to 2018. Key inclusion: New York Heart Association (NYHA) functional class I/II and LV ejection fraction (LVEF) by echocardiography >35%.
I
Intervention
Machine learning (ML)-based risk stratification model using 17 clinical and imaging variables
O
Outcome
Composite of progression to NYHA class III/IV, drop in LVEF below 35%, septal reduction procedure, and/or heart transplantationcomposite
A machine learning model utilizing 17 clinical and imaging variables can accurately predict the 5-year risk of progressive heart failure in patients with hypertrophic cardiomyopathy.
Main Result
Effect estimate: AUC 0.81 (95% CI 0.76-0.86)
Limitations
- Model is designed to accommodate a typical clinical protocol implemented by a single medical center and is not tested using data acquired using different protocols.
- Novel potential risk markers, such as serum biomarkers or mechanical deformation parameters like global longitudinal strain, were not available.
- Not every patient was followed for the full 5-year term.
- Patients who did not develop HF symptoms during the follow-up period were treated as not having the outcome of interest, which could bias the model.
Abstract
Background: Development of advanced heart failure (HF) symptoms is the most common adverse pathway in hypertrophic cardiomyopathy (HCM) patients. Currently, there is a limited ability to identify HCM patients at risk of HF. Objectives: In this study, we present a machine learning (ML)-based model to identify individual HCM patients who are at high risk of developing advanced HF symptoms. Methods: From a consecutive cohort of HCM patients evaluated at the Tufts HCM Institute from 2001 to 2018, we extracted a set of 64 potential risk factors measured at baseline. Only patients with New York Heart Association (NYHA) functional class I/II and LV ejection fraction (LVEF) by echocardiography 35% were included. The study cohort ( n = 1,427 patients) was split into three disjoint subsets: development (50%), model selection (10%), and independent validation (40%). The least absolute shrinkage and selection operator was used to select the most influential clinical variables. An ensemble of ML classifiers, including logistic regression, was used to identify patients with high risk of developing a HF outcome. Study outcomes were defined as progression to NYHA class III/IV, drop in LVEF below 35%, septal reduction procedure, and/or heart transplantation. Results: During a mean follow-up of 4.7 ± 3.7 years, advanced HF occurred in 283 (20% out of 1,427) patients. The model features included patients' sex, NYHA class (I or II), HCM type (i.e., obstructive or not), LV wall thickness, LVEF, presence of HF symptoms (e.g., dyspnea, presyncope), comorbidities (atrial fibrillation, hypertension, mitral regurgitation, and systolic anterior motion), and type of cardiac medications. The developed risk stratification model showed strong differentiation power to identify patients at advanced HF risk in the testing dataset (c-statistics = 0.81; 95% confidence interval CI: 0.76, 0.86). The model allowed correct identification of high-risk patients with accuracy 74% (CI: 0.70, 0.78), sensitivity 80% (CI: 0.77, 0.83), and specificity 72% (CI: 0.68, 0.76). The model performance was comparable among different sex and age groups. Conclusions: A 5-year risk prediction of progressive HF in HCM patients can be accurately estimated using ML analysis of patients' clinical and imaging parameters. A set of 17 clinical and imaging variables were identified as the most important predictors of progressive HF in HCM.
Cite This Study
Fahmy et al. (Thu,) conducted a observational in Hypertrophic Cardiomyopathy (n=1,427). Machine learning risk stratification model was evaluated on Progression to advanced heart failure (NYHA class III/IV, LVEF <35%, septal reduction procedure, or heart transplantation) (AUC 0.81, 95% CI 0.76-0.86). An ensemble machine learning model using 17 clinical and imaging variables accurately predicted the 5-year risk of progressive heart failure in hypertrophic cardiomyopathy patients with an AUC of 0.81.