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

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 \u3e12 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

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

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

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.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.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%).The results indicate that this technology has a role as a high-level diagnostic aid in the assessment of common childhood respiratory disorders.Australian and New Zealand Clinical Trial Registry (retrospective) - ACTRN12618001521213 : 11.09.2018