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

Comparison of Experiences in Two Birth Cohorts Comprising Young Families with Children under Four Years during the Initial COVID-19 Lockdown in Australia and the UK: A Qualitative Study.

This study aims to understand the experience and impact of the initial COVID-19 lockdown in young families with children aged below 4 years. Free text questions were administered to participants in the ORIGINS (Australia) and Born in Bradford (UK) cohort studies to collect qualitative information on worries, concerns and enjoyable experiences during the pandemic. A total of 903 (400 for ORIGINS and 503 for BiB) participants completed the two surveys during April 2020. Despite varying in geography, levels of socio-economic disadvantage and their situational context during the pandemic, respondents from both cohorts reported similar worries and challenges during the lockdown period, including: employment/finances, health anxiety, mental health and social isolation, caring for children and child development. Families across the globe experienced both positive and negative immediate impacts of COVID-19. Population-based data can be used to inform the development of support services, public health campaigns and universal interventions to assist families in future health crises.

Can Positive Mindsets Be Protective Against Stress and Isolation Experienced during the COVID-19 Pandemic? A Mixed Methods Approach to Understanding Emotional Health and Wellbeing Needs of Perinatal Women.

The aim of this study was to explore the relationship between emotional health and wellbeing and support needs of perinatal women during the COVID-19 pandemic, and to understand their experiences and need for support. This is a potentially vulnerable group and a critical developmental phase for women and infants. A mixed methods design was used to collect quantitative and qualitative data that provided a robust insight into their unique needs. A total of 174 women who were either pregnant or post-birth participated. The main findings demonstrated that women in this cohort experienced varying levels of stress and isolation but also positive experiences. Exploring the relationship between mental health (perceived stress and wellbeing) and resilience (mindfulness and self-compassion) revealed an association between positive mental health and higher levels of mindfulness and self-compassion. Positive mindsets may be protective against psychological distress for the mother and her child, suggesting that meditation-based or similar training might help support expectant and post-birth mothers during times of crisis, such as a pandemic. This information could be used to make recommendations for future planning for practitioners and policymakers in preparing for prospective infection waves, pandemics, or natural disasters, and could be used to develop targeted tools, support, and care.

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.