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Early Detection of Worsening Heart Failure in Patients at Home Using a New Telemonitoring System of Respiratory Stability

Authors :
Mika Sakoda
Hidetsugu Asanoi
Tomohito Ohtani
Kei Nakamoto
Daisuke Harada
Takahisa Noto
Junya Takagawa
Osamu Wada
Eisaku Nakane
Moriaki Inoko
Hiroyuki Kurakami
Tomomi Yamada
Yasushi Sakata
Yoshiki Sawa
Shigeru Miyagawa
Source :
Circulation Journal. 86:1081-1091
Publication Year :
2022
Publisher :
Japanese Circulation Society, 2022.

Abstract

Early detection of worsening heart failure (HF) with a telemonitoring system crucially depends on monitoring parameters. The present study aimed to examine whether a serial follow up of all-night respiratory stability time (RST) built into a telemonitoring system could faithfully reflect ongoing deterioration in HF patients at home and detect early signs of worsening HF in a multicenter, prospective study.Methods and Results: Seventeen subjects with New York Heart Association class II or III were followed up for a mean of 9 months using a newly developed telemonitoring system equipped with non-attached sensor technologies and automatic RST analysis. Signals from the home sensor were transferred to a cloud server, where all-night RSTs were calculated every morning and traced by the monitoring center. During the follow up, 9 episodes of admission due to worsening HF and 1 episode of sudden death were preceded by progressive declines of RST. The receiver operating characteristic curve demonstrated that the progressive or sustained reduction of RST below 20 s during 28 days before hospital admission achieved the highest sensitivity of 90.0% and specificity of 81.7% to subsequent hospitalization, with an area under the curve of 0.85.RST could serve as a sensitive and specific indicator of worsening HF and allow the detection of an early sign of clinical deterioration in the telemedical management of HF.

Details

ISSN :
13474820 and 13469843
Volume :
86
Database :
OpenAIRE
Journal :
Circulation Journal
Accession number :
edsair.doi.dedup.....600df400d59f51afb8ac56f6b66d37e6