Journal of cardiovascular electrophysiology · 2026
Deep Learning-Based Prediction of Heart Failure Readmission Using Longitudinal Cardiac Device Data.
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Ramos Veronica, Sarkar Shantanu, Koehler Jodi, Ma Connie, Gonzalez Daniel Joseph, Devathu Rahul Anand, Godinez Erick, Chatterjee Kushal, Fazal Muhammad, Haddad Tarek, Cho Yong, Butler Javed, Sandhu Alexander T, Baykaner Tina
Аннотация
BACKGROUND: Heart failure (HF) is a leading cause of hospitalization and readmission. Cardiac implantable electronic devices (CIEDs) continuously capture physiologic diagnostics that change during and after HF decompensation and may enable early post-discharge risk stratification.
OBJECTIVES: To predict 60-day HF readmission using deep learning models trained on post-discharge temporal behavior of CIED diagnostic parameters.
METHODS: We performed a retrospective analysis of patients with implantable cardioverter defibrillators (ICDs) or cardiac resynchronization therapy defibrillators (CRT-Ds) devices enrolled in remote monitoring (2007-2021) with HF hospitalizations identified from linked electronic health record and remote monitoring databases. Five daily device diagnostics (activity, impedance, heart rate variability, and night/day heart rates) were aligned to the index HF discharge (day 0) and analyzed over the subsequent 30 days. Patients readmitted within 7 days and those with substantial missing device data were excluded; remaining recovery windows were labeled by the occurrence of HF readmission 8-60 days post-discharge. We trained and evaluated two architectures of neural networks.
RESULTS: The cohort included 5734 patients with 12 369 HF hospitalization events; 2489 index hospitalizations were followed by HF readmission within 60 days. Across the 30-day recovery window of device parameters, temporal characteristics of CIED cardiac compass parameters differed significantly between readmitted and non-readmitted patients (p < 0.001), consistent with slower physiologic recovery among readmitted events. On an independent data set, the 1-dimensional convolutional neural network architecture (1D CNN) achieved 0.77 sensitivity, 0.98 specificity, and area under the ROC curve (AUROC) of 0.89.
CONCLUSION: Deep learning models leveraging post-discharge trajectories of routinely captured CIED diagnostics can identify patients at higher risk of 60-day HF readmission and may support targeted early follow-up after HF hospitalization.
OBJECTIVES: To predict 60-day HF readmission using deep learning models trained on post-discharge temporal behavior of CIED diagnostic parameters.
METHODS: We performed a retrospective analysis of patients with implantable cardioverter defibrillators (ICDs) or cardiac resynchronization therapy defibrillators (CRT-Ds) devices enrolled in remote monitoring (2007-2021) with HF hospitalizations identified from linked electronic health record and remote monitoring databases. Five daily device diagnostics (activity, impedance, heart rate variability, and night/day heart rates) were aligned to the index HF discharge (day 0) and analyzed over the subsequent 30 days. Patients readmitted within 7 days and those with substantial missing device data were excluded; remaining recovery windows were labeled by the occurrence of HF readmission 8-60 days post-discharge. We trained and evaluated two architectures of neural networks.
RESULTS: The cohort included 5734 patients with 12 369 HF hospitalization events; 2489 index hospitalizations were followed by HF readmission within 60 days. Across the 30-day recovery window of device parameters, temporal characteristics of CIED cardiac compass parameters differed significantly between readmitted and non-readmitted patients (p < 0.001), consistent with slower physiologic recovery among readmitted events. On an independent data set, the 1-dimensional convolutional neural network architecture (1D CNN) achieved 0.77 sensitivity, 0.98 specificity, and area under the ROC curve (AUROC) of 0.89.
CONCLUSION: Deep learning models leveraging post-discharge trajectories of routinely captured CIED diagnostics can identify patients at higher risk of 60-day HF readmission and may support targeted early follow-up after HF hospitalization.
Предметные рубрики MeSH
HumansPatient ReadmissionDeep LearningHeart FailureDefibrillators, ImplantablePredictive Learning ModelsRetrospective StudiesPredictive Value of TestsTime FactorsRisk AssessmentRisk FactorsCardiac Resynchronization Therapy DevicesElectric CountershockFemaleAgedMalePrediction AlgorithmsRemote Patient MonitoringTreatment OutcomeHeart RateRemote Sensing TechnologyConvolutional Neural NetworksDatabases, FactualDecision Support Techniques