Investigation of Acoustic Cardiographic Parameters before and after Hemodialysis

The phonation test can distinguish the patient with Parkinson's disease via Bayes inference

Parkinson's disease (PD) is a neurodegenerative disease with various clinical manifestations caused by multiple risk factors. However, the effect of different factors and relationships between different features related to PD and the extent of those factors leading to the incidence of PD remains unclear. we employed Bayesian network to construct a prediction model. The prediction system was trained on the data of 35 patients and 26 controls. The structure learning and parameter learning of Bayesian Network was completed through the tree-augmented network (TAN) and Netica software, respectively. We employed four Bayesian Networks in terms of the syllable, including monosyllables, disyllables, multisyllables and unsegmented syllables. The area under the curve (AUC) of monosyllabic, disyllabic, multisyllabic, and unsegmented-syllable models were 0.95, 0.83, 0.80 and 0.84, respectively. In the monosyllabic tests, the best predictor of PD was duration, the posterior probability of which was 92.70%. Meanwhile, minimum f0 (61.60%) predicted best in the disyllabic tests and the variables that predicted best in multisyllables and unsegmented syllables were end f0 (59.40%) and maximum f0 (58.40%). In the cross-sectional comparison, the prediction effect of each variable in the monosyllabic tests was generally higher than that of other test groups. The monosyllabic models had the highest predicted performance of PD. Among acoustic parameters, duration was the strongest feature in predicting the prevalence of PD in monosyllabic tests. We believe that this network methodology will be a useful tool for the clinical prediction of Parkinson's disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10194-x.

Value of acoustic cardiography in the clinical diagnosis of coronary heart disease

Background

To investigate the clinical value of acoustic cardiography in the diagnosis of coronary artery disease (CAD) and post‐percutaneous coronary intervention (PCI) early asymptomatic left ventricular systolic dysfunction.

Methods

Inpatients in the department of cardiology were included in the research (n = 315); including 180 patients with angina pectoris and 135 patients with acute anterior wall myocardial infarction after emergency PCI did not present with signs and symptoms of heart failure. Color Doppler echocardiography, brain natriuretic peptide, acoustic cardiography examination were performed. The patients were divided into four groups: non‐CAD group (n = 60), CAD group (n = 120), MIREF group (EF% < 50%, n = 75), and MINEF group (EF% ≥ 50%, n = 60).

Results

Acoustic cardiography parameters EMATc, systolic dysfunction index, S3 strength and S4 strength in the MIREF group were higher than those in MINEF group (p < .05), and the MINEF group was higher than CAD group (p < .05). S3 strength (area under the curve [AUC] 0.67, 95% CI 0.585–0.755, p < .001) and S4 strength (AUC 0.617, 95% CI 0.536–0.698, p = .011) are useful in the diagnosis of CAD. S3 strength (AUC 0.942, 95% CI 0.807–0.978, p < .001) was superior to other indicators in the diagnosis of early left ventricular systolic dysfunction after myocardial infarction.

Conclusion

S4 combined with STT standard change can improve the diagnosis of CAD. Acoustic cardiography can be used as a non‐invasive, rapid, effective, and simple method for the diagnosis of asymptomatic left ventricular systolic dysfunction in the early stage after myocardial infarction.

Changes in acoustic cardiographic parameters before and after hemodialysis are associated with overall and cardiovascular mortality in hemodialysis patients

Acoustic cardiography can provide simultaneous electrocardiography and acoustic cardiac data to assess the electronic and mechanical heart functions. The aim of this study was to assess whether changes in acoustic cardiographic parameters (ACPs) before and after hemodialysis (HD) are associated with overall and cardiovascular (CV) mortality in HD patients. A total of 162 HD patients was enrolled and ACPs were measured before and after HD, including left ventricular systolic time (LVST), systolic dysfunction index (SDI), third (S3) and fourth (S4) heart sounds, and electromechanical activation time (EMAT). During a follow-up of 2.9 years, 25 deaths occurred with 16 from CV causes. Multivariate analysis showed that high △SDI (per 1; hazard ratio [HR], 2.178; 95% confidence interval [CI], 1.189–3.990), high △EMAT (per 1%; HR, 2.218; 95% CI 1.382–3.559), and low △LVST (per 1 ms; HR, 0.947; 95% CI 0.912–0.984) were independently associated with increased overall mortality. In addition, high △EMAT (per 1%; HR, 2.141; 95% CI 1.117–4.102), and low △LVST (per 1 ms; HR, 0.777; 95% CI 0.637–0.949) were associated with increased CV mortality. In conclusion, the changes in ACPs before and after HD may be a useful clinical marker and stronger prognostic marker of overall and CV mortality than ACPs before HD.