Asthma action plan (AAP) is an essential component of asthma education and self-management. AAPs provide patients with instructions on how to recognise loss of asthma control and the appropriate treatment steps. The use of AAP improves asthma-related quality of life and reduces the risk of asthma exacerbation. Despite its benefits, utilisation of AAP is disappointingly low both locally and worldwide. This review highlights the importance of AAP as part of an asthma care plan and provides practical information on the prescription of AAPs. We conclude by identifying possible barriers to AAP implementation and how these may be overcome.

INTRODUCTIONUnderstanding the burden of uncontrolled severe asthma is essential for disease-targeted healthcare planning. There is a scarcity of data regarding the proportion, healthcare utilisation and costs of patients with uncontrolled severe asthma in Asia. This study aimed to plug the knowledge gap in this area.

MATERIALS AND METHODSConsecutive patients with asthma managed in our respiratory specialist clinic were evaluated prospectively. Healthcare utilisation comprising unscheduled asthma-related primary care visits, emergency department (ED) visits and hospital admissions were obtained from the national health records system. We defined uncontrolled severe asthma as poor symptom control (Asthma Control Test score <20); 2 or more asthma exacerbations requiring ≥3 days of systemic corticosteroids in the previous year; 1 or more serious asthma exacerbation requiring hospitalisation in the previous year; or airflow limitation with pre-bronchodilator forced expiratory volume in 1 second (FEV) <80% predicted despite high dose inhaled corticosteroids and another controller medication.

RESULTSOf the 423 study participants, 49 (11.6%) had uncontrolled severe asthma. Compared to non-severe asthma, patients with uncontrolled severe asthma were older and more likely to be female and obese. They had a median of 2 (interquartile range: 0 to 3) exacerbations a year, with 51% having ≥2 exacerbations in the past 12 months. They were responsible for 43.9% of the hospital admissions experienced by the whole study cohort. Mean annual direct asthma costs per patient was S$2952 ± S$4225 in uncontrolled severe asthma vs S$841 ± S$815 in non-severe asthma.

CONCLUSIONApproximately 12% of patients with asthma managed in a hospital-based respiratory specialist clinic in Singapore have uncontrolled severe asthma. They account for a disproportionate amount of healthcare utilisation and costs. Healthcare strategies targeting these patients are urgently needed.

INTRODUCTION: Singapore's enhanced surveillance programme for COVID-19 identifies and isolates hospitalised patients with acute respiratory symptoms to prevent nosocomial spread. We developed risk prediction models to identify patients with low risk for COVID-19 from this cohort of hospitalised patients with acute respiratory symptoms. METHODS: This was a single-centre retrospective observational study. Patients admitted to our institution's respiratory surveillance wards from 10 February to 30 April 2020 contributed data for analysis. Prediction models for COVID-19 were derived from a training cohort using variables based on demographics, clinical symptoms, exposure risks and blood investigations fitted into logistic regression models. The derived prediction models were subsequently validated on a test cohort. RESULTS: Of the 1,228 patients analysed, 52 (4.2%) were diagnosed with COVID-19. Two prediction models were derived, the first based on age, presence of sore throat, dormitory residence, blood haemoglobin level (Hb), and total white blood cell counts (TW), and the second based on presence of headache, contact with infective patients, Hb and TW. Both models had good diagnostic performance with areas under the receiver operating characteristic curve of 0.934 and 0.866, respectively. Risk score cut-offs of 0.6 for Model 1 and 0.2 for Model 2 had 100% sensitivity, allowing identification of patients with low risk for COVID-19. Limiting COVID-19 screening to only elevated-risk patients reduced the number of isolation days for surveillance patients by up to 41.7% and COVID-19 swab testing by up to 41.0%. CONCLUSION Prediction models derived from our study were able to identify patients at low risk for COVID-19 and rationalise resource utilisation.
Humans ; COVID-19/epidemiology* ; SARS-CoV-2 ; Hospitalization ; Logistic Models ; Retrospective Studies ; Hemoglobins