Search

Objective: To evaluate the accuracy, clinical utility, and usability of a wireless fetal and maternal heartbeat monitor to monitor fetal heart rate (FHR).Methods: We conducted a prospective, single-center study of a convenience sample of women aged 18 years or older with a singleton pregnancy of at least 12 weeks of gestation. Fetal heart rate recordings were performed using both the heartbeat monitor and cardiotocography to evaluate accuracy. Clinicians used the heartbeat monitor in the clinic. Women used the device, unassisted, during a clinic visit or at home. Obstetricians assessed the clinical utility of FHR traces. Women rated the heartbeat monitor using the System Usability Scale.Results: A total of 81 participants provided 126 recordings. The accuracy of the heartbeat monitor was excellent compared with cardiotocography, with limits of agreement (95%) for mean FHR between -1.6 (CI -2.0 to 1.3) and +1.0 (CI 0.7-1.4) beats per minute (bpm), mean difference -0.3 bpm, intraclass coefficient 0.99. The FHR was detected on all occasions. Clinicians took a median (interquartile range) of 0.5 (0.2-1.2) minutes to detect the FHR, obtaining a continuous trace of longer than 1 minute in 95% (39/41) of occasions. Home users took a median of 0.5 (0.2-2.0) minutes to detect the FHR, obtaining a continuous trace of longer than 1 minute in 92% (24/26) of occasions, with a median total trace time of 4.6 (4.4-4.8) minutes. The traces were deemed clinically useful in 100% (55/55) of clinician and 97% (31/32) of home recordings. The heartbeat monitor ranked in the 96-100th percentile for usability and learnability.Conclusion: The heartbeat monitor was accurate and easy for clinicians and participants to use. Data recorded at home were equivalent to those obtained using current assessment protocols for low-risk pregnancies, potentially allowing the device to be used in telehealth consultations.Clinical Trial Registration: Australian New Zealand Clinical Trial Registry, ACTRN12620000739910.Funding Sources: The HeraBEAT devices used in this study were loaned by HeraMED Pty Ltd (HeraMED, Netanya, ISRAEL). The study was supported by PHI Research Group (not-for-profit), which was responsible for Statistician fees and Research Assistants' salaries. Joondalup Health Campus provided infrastructure support, and IT services in-kind to the PHI research group. [ABSTRACT FROM AUTHOR]

Introduction: Asthma is a common childhood respiratory disorder characterized by wheeze, cough and respiratory distress responsive to bronchodilator therapy. Asthma severity can be determined by subjective, manual scoring systems such as the Pulmonary Score (PS). These systems require significant medical training and expertise to rate clinical findings such as wheeze characteristics, and work of breathing. In this study, we report the development of an objective method of assessing acute asthma severity based on the automated analysis of cough sounds. Methods: We collected a cough sound dataset from 224 children; 103 without acute asthma and 121 with acute asthma. Using this database coupled with clinical diagnoses and PS determined by a clinical panel, we developed a machine classifier algorithm to characterize the severity of airway constriction. The performance of our algorithm was then evaluated against the PS from a separate set of patients, independent of the training set. Results: The cough-only model discriminated no/mild disease (PS 0–1) from severe disease (PS 5,6) but required a modified respiratory rate calculation to separate very severe disease (PS > 6). Asymptomatic children (PS 0) were separated from moderate asthma (PS 2–4) by the cough-only model without the need for clinical inputs. Conclusions: The PS provides information in managing childhood asthma but is not readily usable by non-medical personnel. Our method offers an objective measurement of asthma severity which does not rely on clinician-dependent inputs. It holds potential for use in clinical settings including improving the performance of existing asthma-rating scales and in community-management programs. AM accessory muscle BI breathing index CI confidence interval FEV1 forced expiratory volume in one second LR logistic regression PEFR peak expiratory flow rate PS pulmonary score RR respiratory rate SD standard deviation SE standard error WA Western Australia [ABSTRACT FROM AUTHOR]

Background: The differential diagnosis of paediatric respiratory conditions is difficult and suboptimal. Existing diagnostic algorithms are associated with significant error rates, resulting in misdiagnoses, inappropriate use of antibiotics and unacceptable morbidity and mortality. Recent advances in acoustic engineering and artificial intelligence have shown promise in the identification of respiratory conditions based on sound analysis, reducing dependence on diagnostic support services and clinical expertise. We present the results of a diagnostic accuracy study for paediatric respiratory disease using an automated cough-sound analyser.Methods: We recorded cough sounds in typical clinical environments and the first five coughs were used in analyses. Analyses were performed using cough data and up to five-symptom input derived from patient/parent-reported history. Comparison was made between the automated cough analyser diagnoses and consensus clinical diagnoses reached by a panel of paediatricians after review of hospital charts and all available investigations.Results: A total of 585 subjects aged 29 days to 12 years were included for analysis. The Positive Percent and Negative Percent Agreement values between the automated analyser and the clinical reference were as follows: asthma (97, 91%); pneumonia (87, 85%); lower respiratory tract disease (83, 82%); croup (85, 82%); bronchiolitis (84, 81%).Conclusion: The results indicate that this technology has a role as a high-level diagnostic aid in the assessment of common childhood respiratory disorders.Trial Registration: Australian and New Zealand Clinical Trial Registry (retrospective) - ACTRN12618001521213 : 11.09.2018. [ABSTRACT FROM AUTHOR]