TY - JOUR T1 - Predicting asthma-related crisis events using routine electronic healthcare data: a quantitative database analysis study JF - British Journal of General Practice JO - Br J Gen Pract DO - 10.3399/BJGP.2020.1042 SP - BJGP.2020.1042 AU - Michael Noble AU - Annie Burden AU - Susan Stirling AU - Allan B Clark AU - Stanley Musgrave AU - Mohammad A Alsallakh AU - David Price AU - Gwyneth A Davies AU - Hilary Pinnock AU - Martin Pond AU - Aziz Sheikh AU - Erika J Sims AU - Samantha Walker AU - Andrew M Wilson Y1 - 2021/06/15 UR - http://bjgp.org/content/early/2021/10/18/BJGP.2020.1042.abstract N2 - Background There is no published algorithm predicting asthma crisis events (accident and emergency [A&E] attendance, hospitalisation, or death) using routinely available electronic health record (EHR) data.Aim To develop an algorithm to identify individuals at high risk of an asthma crisis event.Design and setting Database analysis from primary care EHRs of people with asthma across England and Scotland.Method Multivariable logistic regression was applied to a dataset of 61 861 people with asthma from England and Scotland using the Clinical Practice Research Datalink. External validation was performed using the Secure Anonymised Information Linkage Databank of 174 240 patients from Wales. Outcomes were ≥1 hospitalisation (development dataset) and asthma-related hospitalisation, A&E attendance, or death (validation dataset) within a 12-month period.Results Risk factors for asthma-related crisis events included previous hospitalisation, older age, underweight, smoking, and blood eosinophilia. The prediction algorithm had acceptable predictive ability with a receiver operating characteristic of 0.71 (95% confidence interval [CI] = 0.70 to 0.72) in the validation dataset. Using a cut-point based on the 7% of the population at greatest risk results in a positive predictive value of 5.7% (95% CI = 5.3% to 6.1%) and a negative predictive value of 98.9% (95% CI = 98.9% to 99.0%), with sensitivity of 28.5% (95% CI = 26.7% to 30.3%) and specificity of 93.3% (95% CI = 93.2% to 93.4%); those individuals had an event risk of 6.0% compared with 1.1% for the remaining population. In total, 18 people would need to be followed to identify one admission.Conclusion This externally validated algorithm has acceptable predictive ability for identifying patients at high risk of asthma-related crisis events and excluding those not at high risk. ER -