A smartphone-based algorithm comprising cough analysis and patient-reported symptoms identifies acute exacerbations of asthma: a prospective, double blind, diagnostic accuracy study.

Objective: Early and accurate recognition of asthma exacerbations reduces the duration and risk of hospitalization. Current diagnostic methods depend upon patient recognition of symptoms, expert clinical examination, or measures of lung function. Here, we aimed to develop and test the accuracy of a smartphone-based diagnostic algorithm that analyses five cough events and five patient-reported features (age, fever, acute or productive cough and wheeze) to detect asthma exacerbations.Methods: We conducted a double-blind, prospective, diagnostic accuracy study comparing the algorithm with expert clinical opinion and formal lung function testing. Results: One hundred nineteen participants >12 years with a physician-diagnosed history of asthma were recruited from a hospital in Perth, Western Australia: 46 with clinically confirmed asthma exacerbations, 73 with controlled asthma. The groups were similar in median age (54yr versus 60yr, p=0.72) and sex (female 76% versus 70%, p=0.5). The algorithm's positive percent agreement (PPA) with the expert clinical diagnosis of asthma exacerbations was 89% [95% CI: 76%, 96%]. The negative percent agreement (NPA) was 84% [95% CI: 73%, 91%]. The algorithm's performance for asthma exacerbations diagnosis exceeded its performance as a detector of patient-reported wheeze (sensitivity, 63.7%). Patient-reported wheeze in isolation was an insensitive marker of asthma exacerbations (PPA=53.8%, NPA=49%). Conclusions: Our diagnostic algorithm accurately detected the presence of an asthma exacerbation as a point-of-care test without requiring clinical examination or lung function testing. This method could improve the accuracy of telehealth consultations and might be helpful in Asthma Action Plans and patient-initiated therapy.

Stratifying asthma severity in children using cough sound analytic technology.

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.AbbreviationsAMaccessory muscleBIbreathing indexCIconfidence intervalFEV1forced expiratory volume in one secondLRlogistic regressionPEFRpeak expiratory flow ratePSpulmonary scoreRRrespiratory rateSDstandard deviationSEstandard errorWAWestern Australia.

A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children.

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.

Diagnostic Errors Are Common in Acute Pediatric Respiratory Disease: A Prospective, Single-Blinded Multicenter Diagnostic Accuracy Study in Australian Emergency Departments.

Background: Diagnostic errors are a global health priority and a common cause of preventable harm. There is limited data available for the prevalence of misdiagnosis in pediatric acute-care settings. Respiratory illnesses, which are particularly challenging to diagnose, are the most frequent reason for presentation to pediatric emergency departments. Objective: To evaluate the diagnostic accuracy of emergency department clinicians in diagnosing acute childhood respiratory diseases, as compared with expert panel consensus (reference standard). Methods: Prospective, multicenter, single-blinded, diagnostic accuracy study in two well-resourced pediatric emergency departments in a large Australian city. Between September 2016 and August 2018, a convenience sample of children aged 29 days to 12 years who presented with respiratory symptoms was enrolled. The emergency department discharge diagnoses were reported by clinicians based upon standard clinical diagnostic definitions. These diagnoses were compared against consensus diagnoses given by an expert panel of pediatric specialists using standardized disease definitions after they reviewed all medical records. Results: For 620 participants, the sensitivity and specificity (%, [95% CI]) of the emergency department compared with the expert panel diagnoses were generally poor: isolated upper respiratory tract disease (64.9 [54.6, 74.4], 91.0 [88.2, 93.3]), croup (76.8 [66.2, 85.4], 97.9 [96.2, 98.9]), lower respiratory tract disease (86.6 [83.1, 89.6], 92.9 [87.6, 96.4]), bronchiolitis (66.9 [58.6, 74.5], 94.3 [80.8, 99.3]), asthma/reactive airway disease (91.0 [85.8, 94.8], 93.0 [90.1, 95.3]), clinical pneumonia (63·9 [50.6, 75·8], 95·0 [92·8, 96·7]), focal (consolidative) pneumonia (54·8 [38·7, 70·2], 86.2 [79.3, 91.5]). Only 59% of chest x-rays with consolidation were correctly identified. Between 6.9 and 14.5% of children were inappropriately prescribed based on their eventual diagnosis. Conclusion: In well-resourced emergency departments, we have identified a previously unrecognized high diagnostic error rate for acute childhood respiratory disorders, particularly in pneumonia and bronchiolitis. These errors lead to the potential of avoidable harm and the administration of inappropriate treatment.

Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis.

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are commonly encountered in the primary care setting, though the accurate and timely diagnosis is problematic. Using technology like that employed in speech recognition technology, we developed a smartphone-based algorithm for rapid and accurate diagnosis of AECOPD. The algorithm incorporates patient-reported features (age, fever, and new cough), audio data from five coughs and can be deployed by novice users. We compared the accuracy of the algorithm to expert clinical assessment. In patients with known COPD, the algorithm correctly identified the presence of AECOPD in 82.6% (95% CI: 72.9-89.9%) of subjects (n = 86). The absence of AECOPD was correctly identified in 91.0% (95% CI: 82.4-96.3%) of individuals (n = 78). The diagnostic agreement was maintained in milder cases of AECOPD (PPA: 79.2%, 95% CI: 68.0-87.8%), who typically comprise the cohort presenting to primary care. The algorithm may aid early identification of AECOPD and be incorporated in patient self-management plans.

Accuracy, Clinical Utility, and Usability of a Wireless Self-Guided Fetal Heart Rate Monitor.

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.

Diagnosing community-acquired pneumonia via a smartphone-based algorithm: a prospective cohort study in primary and acute-care consultations.

BACKGROUND: Community-acquired pneumonia (CAP) is an essential consideration in patients presenting to primary care with respiratory symptoms; however, accurate diagnosis is difficult when clinical and radiological examinations are not possible, such as during telehealth consultations. AIM: To develop and test a smartphone-based algorithm for diagnosing CAP without need for clinical examination or radiological inputs. DESIGN AND SETTING: A prospective cohort study using data from participants aged >12 years presenting with acute respiratory symptoms to a hospital in Western Australia. METHOD: Five cough audio-segments were recorded and four patient-reported symptoms (fever, acute cough, productive cough, and age) were analysed by the smartphone-based algorithm to generate an immediate diagnostic output for CAP. Independent cohorts were recruited to train and test the accuracy of the algorithm. Diagnostic agreement was calculated against the confirmed discharge diagnosis of CAP by specialist physicians. Specialist radiologists reported medical imaging. RESULTS: The smartphone-based algorithm had high percentage agreement (PA) with the clinical diagnosis of CAP in the total cohort (n = 322, positive PA [PPA] = 86.2%, negative PA [NPA] = 86.5%, area under the receiver operating characteristic curve [AUC] = 0.95); in participants 22-<65 years (n = 192, PPA = 85.7%, NPA = 87.0%, AUC = 0.94), and in participants aged ≥65 years (n = 86, PPA = 85.7%, NPA = 87.5%, AUC = 0.94). Agreement was preserved across CAP severity: 85.1% (n = 80/94) of participants with CRB-65 scores 1 or 2, and 87.7% (n = 57/65) with a score of 0, were correctly diagnosed by the algorithm. CONCLUSION: The algorithm provides rapid and accurate diagnosis of CAP. It offers improved accuracy over current protocols when clinical evaluation is difficult. It provides increased capabilities for primary and acute care, including telehealth services, required during the COVID-19 pandemic.

Diagnosing Chronic Obstructive Airway Disease on a Smartphone Using Patient-Reported Symptoms and Cough Analysis: Diagnostic Accuracy Study.

BACKGROUND: Rapid and accurate diagnosis of chronic obstructive pulmonary disease (COPD) is problematic in acute care settings, particularly in the presence of infective comorbidities. OBJECTIVE: The aim of this study was to develop a rapid smartphone-based algorithm for the detection of COPD in the presence or absence of acute respiratory infection and evaluate diagnostic accuracy on an independent validation set. METHODS: Participants aged 40 to 75 years with or without symptoms of respiratory disease who had no chronic respiratory condition apart from COPD, chronic bronchitis, or emphysema were recruited into the study. The algorithm analyzed 5 cough sounds and 4 patient-reported clinical symptoms, providing a diagnosis in less than 1 minute. Clinical diagnoses were determined by a specialist physician using all available case notes, including spirometry where available. RESULTS: The algorithm demonstrated high positive percent agreement (PPA) and negative percent agreement (NPA) with clinical diagnosis for COPD in the total cohort (N=252; PPA=93.8%, NPA=77.0%, area under the curve [AUC]=0.95), in participants with pneumonia or infective exacerbations of COPD (n=117; PPA=86.7%, NPA=80.5%, AUC=0.93), and in participants without an infective comorbidity (n=135; PPA=100.0%, NPA=74.0%, AUC=0.97). In those who had their COPD confirmed by spirometry (n=229), PPA was 100.0% and NPA was 77.0%, with an AUC of 0.97. CONCLUSIONS: The algorithm demonstrated high agreement with clinical diagnosis and rapidly detected COPD in participants presenting with or without other infective lung illnesses. The algorithm can be installed on a smartphone to provide bedside diagnosis of COPD in acute care settings, inform treatment regimens, and identify those at increased risk of mortality due to seasonal or other respiratory ailments. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12618001521213; http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375939.

Buccal DNA collection: comparison of buccal swabs with FTA cards.

Collection and analysis of DNA, most commonly from blood or buccal cells, is becoming more common in epidemiologic studies. Buccal samples, which are painless to take and relatively easily collected, are often the preferred source. There are several buccal cell collection methods: swabs, brushes, mouthwash, and treated cards, such as FTA or IsoCode cards. Few studies have systematically compared methods of buccal cell collection with respect to DNA yield and amplification success under similar conditions. We compared buccal DNA collection and amplification using buccal swabs and FTA cards in 122 control subjects from our Australian case-control study of childhood acute lymphoblastic leukaemia. Buccal DNA was quantified using a real-time PCR for beta-actin and genotyped at the loci of three polymorphisms (MTHFR 677C>T, ACE I/D, and XPD 1012G>A). PCR was successful with DNA from buccal swabs for 62% to 89% of subjects and from FTA cards for 83% to 100% of subjects, depending on the locus. The matched pair odds ratios (95% confidence interval) comparing success of FTA cards with buccal swabs are as follows: MTHFR 677C>T using PCR-RFLP, 12.5 (11.6-13.5) and using real-time PCR, 130.0 (113.1-152.8); ACE I/D using PCR-amplified fragment length polymorphism, 3.36 (3.2-3.5); XPD 1012G>A using real-time PCR, 150.0 (132.7-172.3). FTA cards are a robust DNA collection method and generally produce DNA suitable for PCR more reliably than buccal swabs. There are, however, technical challenges in handling discs punched from FTA cards that intending users should be aware of.

Experiences of expressing and storing colostrum antenatally: A qualitative study of mothers in regional Western Australia.

This qualitative study explored the experiences and breastfeeding outcomes of a group of mothers who expressed colostrum in the antenatal period. In-depth interviews were conducted over the telephone with 12 women who had attended a unique antenatal lactation clinic appointment at 37 weeks' gestation. Seven main response themes were identified. Most women reflected positively upon their attendance and reported that the experience of expressing colostrum allowed them to become familiar with their breasts and gave them a sense of security by having a supply of colostrum stored for possible use after birth. The main negative emotions reported were a sense of embarrassment at expressing the colostrum, particularly in front of another person, the difficulties with expressing colostrum and in one instance, the physical pain associated with the process. Antenatal expression of colostrum may improve maternal breastfeeding confidence. Further research using long-term records will determine whether this practice improves breastfeeding outcomes.

The Western Australian family connections genealogical project: detection of familial occurrences of single gene and chromosomal disorders.

AIM: To investigate using a Western Australian (WA) genealogical database for the identification of single gene and chromosome disorders among families. METHOD: Hospital admissions for single gene and chromosome disorders recorded during 2000-2006 were identified from the WA Hospital Morbidity Data System. The proportion of these conditions occurring in family groups was then identified using genealogical links created through the WA Family Connections Genealogical Project. RESULTS: There were 216 family clusters among 11,303 people who were recorded as having a genetic or chromosomal disorder on their hospital admission record. The most common single gene conditions found to occur in multiple family members included blood clotting disorders such as Factor VIII deficiency and Von Willebrand's disease, followed by cystic fibrosis, myotonic dystrophies, neurofibromatosis, tuberous sclerosis, and osteogenesis imperfecta. DISCUSSION: Single gene disorders most commonly occurring in multiple family members have been identified using the WA Family Connections Genealogical Project. These disorders reflect the most common single gene disorders requiring hospital admission, but which are not fatal before reproductive age and do not result in a loss of fertility. They are also restricted to disorders with earlier onset, as the WA Family Connections Genealogical Project currently covers 2-3 of the most recent generations. This study demonstrates the utility of record linkage genealogies to identify kindred with genetic disorders, offering a rich resource of information for focused genetic epidemiological research.