The effect of electrolyte balance on the voice in hemodialysis patients

Clinical Utility of Machine Learning-Derived Vocal Biomarkers in the Management of Heart Failure

Background

This study aimed to systematically evaluate voice symptoms during heart failure (HF) treatments and to exploratorily extract HF-related vocal biomarkers.

Methods and Results

This single-center, prospective study longitudinally acquired 839 audio files from 59 patients with acute decompensated HF. Patients' voices were analyzed along with conventional HF indicators (New York Heart Association [NYHA] class, presence of pulmonary congestion and pleural effusion on chest X-ray, and B-type natriuretic peptide [BNP]) and GOKAN scores based on the assessment of a cardiologist. Machine-learning (ML) models to estimate HF conditions were created using a Light Gradient Boosting Machine. Voice analysis identified 27 acoustic features that correlated with conventional HF indicators and GOKAN scores. When creating ML models based on the acoustic features, there was a significant correlation between actual and ML-derived BNP levels (r=0.49; P<0.001). ML models also identified good diagnostic accuracies in determining HF conditions characterized by NYHA class ≥2, BNP ≥300 pg/mL, presence of pulmonary congestion or pleural effusion on chest X-ray, and decompensated HF (defined as NYHA class ≥2 and BNP levels ≥300 pg/mL; accuracy: 75.1%, 69.1%, 68.7%, 66.4%, and 80.4%, respectively).

Conclusions

The present study successfully extracted HF-related acoustic features that correlated with conventional HF indicators. Although the data are preliminary, ML models based on acoustic features (vocal biomarkers) have the potential to infer various HF conditions, which warrant future studies.

Remote Speech Analysis in the Evaluation of Hospitalized Patients With Acute Decompensated Heart Failure

OBJECTIVES

This study assessed the performance of an automated speech analysis technology in detecting pulmonary fluid overload in patients with acute decompensated heart failure (ADHF).

BACKGROUND

Pulmonary edema is the main cause of heart failure (HF)-related hospitalizations and a key predictor of poor postdischarge prognosis. Frequent monitoring is often recommended, but signs of decompensation are often missed. Voice and sound analysis technologies have been shown to successfully identify clinical conditions that affect vocal cord vibration mechanics.

METHODS

Adult patients with ADHF (n = 40) recorded 5 sentences, in 1 of 3 languages, using HearO, a proprietary speech processing and analysis application, upon admission (wet) to and discharge (dry) from the hospital. Recordings were analyzed for 5 distinct speech measures (SMs), each a distinct time, frequency resolution, and linear versus perceptual (ear) model; mean change from baseline SMs was calculated.

RESULTS

In total, 1,484 recordings were analyzed. Discharge recordings were successfully tagged as distinctly different from baseline (wet) in 94% of cases, with distinct differences shown for all 5 SMs in 87.5% of cases. The largest change from baseline was documented for SM2 (218%). Unsupervised, blinded clustering of untagged admission and discharge recordings of 9 patients was further demonstrated for all 5 SMs.

CONCLUSIONS

Automated speech analysis technology can identify voice alterations reflective of HF status. This platform is expected to provide a valuable contribution to in-person and remote follow-up of patients with HF, by alerting to imminent deterioration, thereby reducing hospitalization rates. (Clinical Evaluation of Cordio App in Adult Patients With CHF; NCT03266029).

Acoustic Characteristics of Voice in Patients with Chronic Kidney Disease

Purpose

To investigate the multifactorial effects of chronic kidney disease (CKD) and hemodialysis (HD) on subjects' voices by examining correlations between laboratory investigations, respiratory function, and acoustic voice parameters.

Methods

This case-control study was conducted on 60 participants aged 18-50 years, divided equally into three groups: controls (no health problems or voice disorders), cCKD (stage 3-5, no HD HD]), HD, and CKD stage 5. The study took 21 months. All participants underwent general and otolaryngological examinations, followed by laboratory investigations (hemoglobin, uric acid, HCO₃, estimated glomerular filtration rate, urea, urea-reduction ratio, and creatinine), respiratory function tests, and acoustic voice analysis.

Results

There were significant differences between the control and HD groups for jitter, shimmer, and harmonic:noise (HNR) ratio (P=0 and between the control and CKD groups for shimmer and HNR (P=0), with no significant difference between HD and CKD. There were statistically significant correlations between duration of HD and HNR, jitter percentage, and shimmer percentage (P=0.

Conclusion

Systemic effects of CKD and HD were found to impair the acoustic characteristics of voice in both groups. Regression analysis revealed that hemoglobin, uric acid, and expiratory time were the most significant predictors of impaired acoustic characteristics.

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.

Real-world evidence and optimization of vocal dysfunction in end-stage renal disease patients with secondary hyperparathyroidism

Patients with end-stage renal disease (ESRD) may demonstrate secondary hyperparathyroidism (SHPT), characterized by parathyroid hormone oversecretion in response to electrolyte imbalance (e.g., hypocalcemia and hyperphosphatemia). Moreover, this electrolyte imbalance may affect vocal cord muscle contraction and lead to voice change. Here, we explored the effects of SHPT on the voices of patients with ESRD. We used data of 147,026 patients with ESRD from the registry for catastrophic illness patients, a sub-database of Taiwan National Health Insurance Research Database. We divided these patients into 2 groups based on whether they had hyperparathyroidism (HPT) and compared vocal dysfunction (VD) incidence among them. We also prospectively included 60 ESRD patients with SHPT; 45 of them underwent parathyroidectomy. Preoperatively and postoperatively, voice analysis was used to investigate changes in vocal parameters. In the real-world database analysis, the presence of HPT significantly increased VD incidence in patients with ESRD (p = 0.003): Cox regression analysis results indicated that patients with ESRD had an approximately 1.6-fold increased VD risk (p = 0.003). In the clinical analysis, the “jitter” and “shimmer” factors improved significantly after operation, whereas the aerodynamic factors remained unchanged. In conclusion, SHPT was an independent risk factor for VD in patients with ESRD, mainly affecting their acoustic factors.