A machine learning model combining cardiovascular magnetic resonance markers and electronic health information accurately predicted heart failure hospitalization, identifying high-risk patients who experienced a 19% event rate at 90 days compared to 0.6% in low-risk patients.
Cohort (n=1,775)
No
Does a machine learning model combining CMR phenotypes, patient-reported health status, and electronic health information accurately predict time to heart failure hospitalization in patients with chronic systolic heart failure?
Combining routinely reported CMR phenotypic markers with patient-reported and electronic health information using machine learning provides robust, personalized predictions of heart failure hospitalization.
Absolute Event Rate: 19% vs 0.6%
p-value: p=<0.0001
Background: Heart failure (HF) hospitalization is a dominant contributor of morbidity and healthcare expenditures in patients with systolic HF. Cardiovascular magnetic resonance (CMR) imaging is increasingly employed for the evaluation of HF given capacity to provide highly reproducible phenotypic markers of disease. The combined value of CMR phenotypic markers and patient health information to deliver predictions of future HF events has not been explored. We sought to develop and validate a novel risk model for the patient-specific prediction of time to HF hospitalization using routinely reported CMR variables, patient-reported health status, and electronic health information. Methods: Standardized data capture was performed for 1,775 consecutive patients with chronic systolic HF referred for CMR imaging. Patient demographics, symptoms, Health-related Quality of Life, pharmacy, and routinely reported CMR features were provided to both machine learning (ML) and competing risk Fine-Gray-based models (FGM) for the prediction of time to HF hospitalization. Results: The mean age was 59 years with a mean LVEF of 36 ± 11%. The population was evenly distributed between ischemic (52%) and idiopathic non-ischemic cardiomyopathy (48%). Over a median follow-up of 2.79 years (IQR: 1.59-4.04) 333 patients (19%) experienced HF related hospitalization. Both ML and competing risk FGM based models achieved robust performance for the prediction of time to HF hospitalization. Respective 90-day, 1 and 2-year AUC values were 0.87, 0.83, and 0.80 for the ML model, and 0.89, 0.84, and 0.80 for the competing risk FGM-based model in a holdout validation cohort. Patients classified as high-risk by the ML model experienced a 34-fold higher occurrence of HF hospitalization at 90 days vs. the low-risk group. Conclusion: In this study we demonstrated capacity for routinely reported CMR phenotypic markers and patient health information to be combined for the delivery of patient-specific predictions of time to HF hospitalization. This work supports an evolving migration toward multi-domain data collection for the delivery of personalized risk prediction at time of diagnostic imaging.
Cornhill et al. (Thu,) conducted a cohort in Chronic systolic heart failure (n=1,775). Machine learning risk model (Random Survival Forest) high-risk classification vs. Low-risk classification was evaluated on Heart failure hospitalization at 90 days (p=<0.0001). A machine learning model combining cardiovascular magnetic resonance markers and electronic health information accurately predicted heart failure hospitalization, identifying high-risk patients who experienced a 19% event rate at 90 days compared to 0.6% in low-risk patients.
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