Feasibility Of Remote Speech Analysis In Evaluation Of Dynamic Fluid Overload In Heart Failure Patients Undergoing Haemodialysis Treatment

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.