Artificial intelligence based real-time prediction of imminent heart failure hospitalisation in patients undergoing non-invasive telemedicine.

Background

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.

Methods

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.

Results

The machine learning model significantly outperformed the conventional algorithm (ROCAUC 0.855 vs. 0.727,

Conclusions

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.

Informations de copyright

© 2024 Hinrichs, Meyer, Koehler, Kaas, Hiddemann, Spethmann, Balzer, Eickhoff, Falk, Hindricks, Dagres and Koehler.

Déclaration de conflit d'intérêts

AM is co-founder of x-cardiac GmbH, advisory board member of Kenkou GmbH, and reports consulting and/or speaker fees from Pfizer, Medtronic and Edwards, all outside the submitted work. CE is co-founder of Codiag AG and x-cardiac GmbH, and reports consulting fees from Abacus Health and speaker fees from Merck, all outside the submitted work. GH serves as Abbott steering committee member, outside the submitted work. FB has received funding from Medtronic; has received grants from the German Federal Ministry of Education and Research, the German Federal Ministry of Health, the Berlin Institute of Health, Hans Böckler Foundation, Einstein Foundation, and the Berlin University Alliance outside the submitted work; he has received personal fees from Elsevier Publishing; and has received other funding from the Robert Koch Institute, all outside the submitted work. VF has received educational grants (including travel support), fees for lectures and speeches, fees for professional consultation, and research and study funds from Medtronic GmbH, Biotronik SE & Co, Abbott GmbH & Co KG, Boston Scientific, Edwards Lifesciences, Berlin Heart, Novartis Pharma GmbH, JOTEC GmbH, and Zurich Heart, all outside the submitted work. FK has received a research grant from the German Federal Ministry of Education and Research for the TIM-HF2 trial and a research grant from the German Federal Ministry of Economic Affairs and Climate Action for the AI-based analysis of the TIM-HF2 data. He reports personal fees from Abbott, Sanofi-Aventis, Novartis, Roche, AstraZeneca, SHL, Medtronic, Biotronik; support for attending meetings and/or travel from the German Society of Internal Medicine and the European Society of Cardiology; co-ownership of a patent in cooperation with BRAHMS GmbH, all outside the submitted work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.