1. Artificial intelligence based real-time prediction of imminent heart failure hospitalisation in patients undergoing non-invasive telemedicine
- Author
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Nils Hinrichs, Alexander Meyer, Kerstin Koehler, Thomas Kaas, Meike Hiddemann, Sebastian Spethmann, Felix Balzer, Carsten Eickhoff, Volkmar Falk, Gerhard Hindricks, Nikolaos Dagres, and Friedrich Koehler
- Subjects
heart failure ,decision support (DS) ,telemedicine ,machine learning ,remote patient care ,risk stratification ,Diseases of the circulatory (Cardiovascular) system ,RC666-701 - Abstract
BackgroundRemote patient management may improve prognosis in heart failure. Daily review of transmitted data for early recognition of patients at risk requires substantial resources that represent a major barrier to wide implementation. An automated analysis of incoming data for detection of risk for imminent events would allow focusing on patients requiring prompt medical intervention.MethodsWe analysed data of the Telemedical Interventional Management in Heart Failure II (TIM-HF2) randomized trial that were collected during quarterly in-patient visits and daily transmissions from non-invasive monitoring devices. By application of machine learning, we developed and internally validated a risk score for heart failure hospitalisation within seven days following data transmission as estimate of short-term patient risk for adverse heart failure events. Score performance was assessed by the area under the receiver-operating characteristic (ROCAUC) and compared with a conventional algorithm, a heuristic rule set originally applied in the randomized trial.ResultsThe machine learning model significantly outperformed the conventional algorithm (ROCAUC 0.855 vs. 0.727, p
- Published
- 2024
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