Using Deep Learning to Identify High-Risk Patients with Heart Failure with Reduced Ejection Fraction

Abstract

Background: Deep Learning (DL) has not been well-established as a method to identify high-risk patients among patients with heart failure (HF). Objectives: This study aimed to use DL models to predict hospitalizations, worsening HF events, and 30-day and 90-day readmissions in patients with heart failure with reduced ejection fraction (HFrEF). Methods: We analyzed the data of adult HFrEF patients from the IBM® MarketScan® Commercial and Medicare Supplement databases between January 1, 2015 and December 31, 2017. A sequential model architecture based on bi-directional long short-term memory (Bi-LSTM) layers was utilized. For DL models to predict HF hospitalizations and worsening HF events, we utilized two study designs: with and without a buffer window. For comparison, we also tested multiple traditional machine learning models including logistic regression, random forest, and eXtreme Gradient Boosting (XGBoost). Model performance was assessed by area under the curve (AUC) values, precision, and recall on an independent testing dataset. Results: A total of 47 498 HFrEF patients were included; 9427 with at least one HF hospitalization. The best AUCs of DL models without a buffer window in predicting HF hospitalizations and worsening HF events in the total patient cohort were 0.977 and 0.972; with a 7-day buffer window the best AUCs were 0.573 and 0.608, respectively. The best AUCs in predicting 30- and 90-day readmissions in all adult patients were 0.597 and 0.614, respectively. An AUC of 0.861 was attained for prediction of 90-day readmission in patients aged 18-64. For all outcomes assessed, the DL approach outperformed traditional machine learning models. Discussion: The DL approach can automate feature engineering during the model learning, which can increase the clinical applicability and lead to comparable or better model performance. However, the lack of granular clinical data, and sample size and imbalance issues may have limited the model’s performance. Conclusions: A DL approach using Bi-LSTM was shown to be a feasible and useful tool to predict HF-related outcomes. This study can help inform the future development and deployment of predictive tools to identify high-risk HFrEF patients and ultimately facilitate targeted interventions in clinical practice.

Generic medical concept embedding and time decay for diverse patient outcome prediction tasks

“… Bipolar ( Lambert and Reps, 2020 ): this is a package for validation of models predicting bipolar disorder misdiagnosed as major depression disorder (MDD). Heart ( Wang et al., 2021 ): predicting 30-day (Heart-1) and 90-day (Heart-2) readmissions in hospitalized patients with heart failure with reduced ejection fraction (HFrEF). The cohort index date is the discharge date.…”

“…Heart ( Wang et al., 2021 ): predicting 30-day (Heart-1) and 90-day (Heart-2) readmissions in hospitalized patients with heart failure with reduced ejection fraction (HFrEF). The cohort index date is the discharge date.…”

Applications of Machine Learning in Cardiology

“…The supervised ML was superior to other ML models and had a mean C -statistic of 0.72 for predicting mortality (Brier score 0.17) and 0.76 for HF hospitalization (Bier score 0.19). Wang et al evaluated the role of multiple ML models for predicting hospitalization and readmission in patients with HF with reduced ejection fraction (HFrEF) [ 48 ]. The deep learning outperformed other ML models for predicting 30- and 90-day readmission rates for patients with HFrEF (AUC = 0.977 and 0.972).…”

Deep Learning-Enabled Technologies for Bioimage Analysis

“…However, bioimages exhibit a large variability due to the different possible combinations of imaging modalities and acquisition parameters, sample preparation protocols, and phenotypes of interest, resulting in time-consuming and error-prone analysis by human experts [1,15]. Employing deep learning (DL) techniques can facilitate interpretation of multi-spectral heterogeneous medical data by providing insight for clinicians, contributing to easier identification of high-risk patients with real-time analytics, timely decision making, and optimized care delivery [16,17]. Moreover, DL can support medical decisions made by clinicians, and improve targeted treatment as well as medical treatment surveillance by determination of deviation of the treatment process from the ideal condition [11,[18][19][20][21].…”