Feasibility of remote speech analysis in evaluation of dynamic fluid overload in heart failure patients undergoing haemodialysis treatment.

Autor: Amir O; Department of Cardiology, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.; Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel., Anker SD; Department of Cardiology (CVK) and Berlin Institute of Health Center for Regenerative Therapies (BCRT), German Centre for Cardiovascular Research (DZHK) partner site Berlin, Charité-Universitätsmedizin Berlin, Augustenburger Platz, Berlin, D-13353, Germany., Gork I; Department of Cardiology, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel., Abraham WT; Division of Cardiovascular Medicine, The Ohio State University, Columbus, OH, USA., Pinney SP; University of Chicago, Chicago, IL, USA., Burkhoff D; Cardiovascular Research Foundation, New York, NY, USA., Shallom ID; Cordio Medical Ltd., Or Yehuda, Israel., Haviv R; Cordio Medical Ltd., Or Yehuda, Israel., Edelman ER; Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA., Lotan C; Department of Cardiology, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
Jazyk: angličtina
Zdroj: ESC heart failure [ESC Heart Fail] 2021 Aug; Vol. 8 (4), pp. 2467-2472. Date of Electronic Publication: 2021 May 05.
DOI: 10.1002/ehf2.13367
Abstrakt: Aims: This study aimed to assess the ability of a voice analysis application to discriminate between wet and dry states in chronic heart failure (CHF) patients undergoing regular scheduled haemodialysis treatment due to volume overload as a result of their chronic renal failure.
Methods and Results: In this single-centre, observational study, five patients with CHF, peripheral oedema of ≥2, and pulmonary congestion-related dyspnoea, undergoing haemodialysis three times per week, recorded five sentences into a standard smartphone/tablet before and after haemodialysis. Recordings were provided that same noon/early evening and the next morning and evening. Patient weight was measured at the hospital before and after each haemodialysis session. Recordings were analysed by a smartphone application (app) algorithm, to compare speech measures (SMs) of utterances collected over time. On average, patients provided recordings throughout 25.8 ± 3.9 dialysis treatment cycles, resulting in a total of 472 recordings. Weight changes of 1.95 ± 0.64 kg were documented during cycles. Median baseline SM prior to dialysis was 0.87 ± 0.17, and rose to 1.07 ± 0.15 following the end of the dialysis session, at noon (P = 0.0355), and remained at a similar level until the following morning (P = 0.007). By the evening of the day following dialysis, SMs returned to baseline levels (0.88 ± 0.19). Changes in patient weight immediately after dialysis positively correlated with SM changes, with the strongest correlation measured the evening of the dialysis day [slope: -0.40 ± 0.15 (95% confidence interval: -0.71 to -0.10), P = 0.0096].
Conclusions: The fluid-controlled haemodialysis model demonstrated the ability of the app algorithm to identify cyclic changes in SMs, which reflected bodily fluid levels. The voice analysis platform bears considerable potential as a harbinger of impending fluid overload in a range of clinical scenarios, which will enhance monitoring and triage efforts, ultimately optimizing remote CHF management.
(© 2021 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)
Databáze: MEDLINE
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