Feasibility of remote speech analysis in evaluation of dynamic fluid overload in heart failure patients undergoing haemodialysis treatment
Abstract 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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-08-01
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| Series: | ESC Heart Failure |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ehf2.13367 |
| _version_ | 1819138869751185408 |
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| author | Offer Amir Stefan D. Anker Ittamar Gork William T. Abraham Sean P. Pinney Daniel Burkhoff Ilan D. Shallom Ronit Haviv Elazer R. Edelman Chaim Lotan |
| author_facet | Offer Amir Stefan D. Anker Ittamar Gork William T. Abraham Sean P. Pinney Daniel Burkhoff Ilan D. Shallom Ronit Haviv Elazer R. Edelman Chaim Lotan |
| author_sort | Offer Amir |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-22T11:13:38Z |
| format | Article |
| id | doaj.art-fa84d52ff90f4f9ea36560cc005d7b07 |
| institution | Directory Open Access Journal |
| issn | 2055-5822 |
| language | English |
| last_indexed | 2024-12-22T11:13:38Z |
| publishDate | 2021-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | ESC Heart Failure |
| spelling | doaj.art-fa84d52ff90f4f9ea36560cc005d7b072022-12-21T18:28:05ZengWileyESC Heart Failure2055-58222021-08-01842467247210.1002/ehf2.13367Feasibility of remote speech analysis in evaluation of dynamic fluid overload in heart failure patients undergoing haemodialysis treatmentOffer Amir0Stefan D. Anker1Ittamar Gork2William T. Abraham3Sean P. Pinney4Daniel Burkhoff5Ilan D. Shallom6Ronit Haviv7Elazer R. Edelman8Chaim Lotan9Department of Cardiology Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem Jerusalem IsraelDepartment 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 GermanyDepartment of Cardiology Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem Jerusalem IsraelDivision of Cardiovascular Medicine The Ohio State University Columbus OH USAUniversity of Chicago Chicago IL USACardiovascular Research Foundation New York NY USACordio Medical Ltd. Or Yehuda IsraelCordio Medical Ltd. Or Yehuda IsraelInstitute for Medical Engineering and Science MIT Cambridge MA USADepartment of Cardiology Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem Jerusalem IsraelAbstract 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.https://doi.org/10.1002/ehf2.13367DialysisAcute heart failure (AHF)Remote voice analysisSpeech measure (SM) |
| spellingShingle | Offer Amir Stefan D. Anker Ittamar Gork William T. Abraham Sean P. Pinney Daniel Burkhoff Ilan D. Shallom Ronit Haviv Elazer R. Edelman Chaim Lotan Feasibility of remote speech analysis in evaluation of dynamic fluid overload in heart failure patients undergoing haemodialysis treatment ESC Heart Failure Dialysis Acute heart failure (AHF) Remote voice analysis Speech measure (SM) |
| title | Feasibility of remote speech analysis in evaluation of dynamic fluid overload in heart failure patients undergoing haemodialysis treatment |
| title_full | Feasibility of remote speech analysis in evaluation of dynamic fluid overload in heart failure patients undergoing haemodialysis treatment |
| title_fullStr | Feasibility of remote speech analysis in evaluation of dynamic fluid overload in heart failure patients undergoing haemodialysis treatment |
| title_full_unstemmed | Feasibility of remote speech analysis in evaluation of dynamic fluid overload in heart failure patients undergoing haemodialysis treatment |
| title_short | Feasibility of remote speech analysis in evaluation of dynamic fluid overload in heart failure patients undergoing haemodialysis treatment |
| title_sort | feasibility of remote speech analysis in evaluation of dynamic fluid overload in heart failure patients undergoing haemodialysis treatment |
| topic | Dialysis Acute heart failure (AHF) Remote voice analysis Speech measure (SM) |
| url | https://doi.org/10.1002/ehf2.13367 |
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