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ORIGINAL RESEARCH article
Front. Cardiovasc. Med.
Sec. Heart Failure and Transplantation
Volume 11 - 2024 |
doi: 10.3389/fcvm.2024.1457995
This article is part of the Research Topic Contemporary Applications of Machine Learning and Artificial Intelligence for the Management of Heart Failure View all articles
Artificial intelligence based real-time prediction of imminent heart failure hospitalisation in patients undergoing non-invasive telemedicine
Provisionally accepted- 1 Charité University Medicine Berlin, Berlin, Germany
- 2 Deutsches Herzzentrum der Charité, Berlin, Germany
- 3 Berlin Institute of Health, Charité Medical University of Berlin, Berlin, Baden-Württemberg, Germany
- 4 Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
- 5 Partner site Berlin, German Center for Cardiovascular Research (DZHK), Berlin, Berlin, Germany
- 6 University of Tübingen, Tübingen, Baden-Württemberg, Germany
- 7 ETH Zürich, Zurich, Zürich, Switzerland
Remote 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.We 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.The machine learning model significantly outperformed the conventional algorithm (ROCAUC 0.855 vs. 0.727, p<0.001). On average, the machine learning risk score increased continuously in the three weeks preceding heart failure hospitalisations, indicating potential for early detection of risk. In a simulated one-year scenario, daily review of only the one third of patients with the highest machine learning risk score would have led to detection of 95 % of HF hospitalisations occurring within the following seven days.A machine learning model allowed automated analysis of incoming remote monitoring data and reliable identification of patients at risk of heart failure hospitalisation requiring immediate medical intervention. This approach may significantly reduce the need for manual data review.
Keywords: Heart Failure, Decision Support (DS), Telemedicine, machine learning, remote patient care, risk stratification
Received: 01 Jul 2024; Accepted: 09 Sep 2024.
Copyright: © 2024 Hinrichs, Meyer, Koehler, Kaas, Hiddemann, Spethmann, Balzer, Eickhoff, Falk, Hindricks, Dagres and Koehler. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Alexander Meyer, Charité University Medicine Berlin, Berlin, Germany
Kerstin Koehler, Deutsches Herzzentrum der Charité, Berlin, Germany
Thomas Kaas, Deutsches Herzzentrum der Charité, Berlin, Germany
Meike Hiddemann, Deutsches Herzzentrum der Charité, Berlin, Germany
Carsten Eickhoff, University of Tübingen, Tübingen, 72074, Baden-Württemberg, Germany
Volkmar Falk, Deutsches Herzzentrum der Charité, Berlin, Germany
Gerhard Hindricks, Deutsches Herzzentrum der Charité, Berlin, Germany
Nikolaos Dagres, Deutsches Herzzentrum der Charité, Berlin, Germany
Friedrich Koehler, Deutsches Herzzentrum der Charité, Berlin, Germany
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