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Background: Asthma remains a common cause of hospital admissions across the life course. We estimated the contribution of key risk factors to asthma-related hospital and intensive care unit (ICU) admissions in children, adolescents and adults., Methods: This was a UK-based cohort study using linked primary care (Clinical Practice Research Datalink Aurum) and secondary care (Hospital Episode Statistics Admitted Patient Care) data. Patients were eligible if they were aged 5 years and older and had been diagnosed with asthma. This included 90 989 children aged 5-11 years, 114 927 adolescents aged 12-17 years and 1 179 410 adults aged 18 years or older. The primary outcome was asthma-related hospital admissions from 1 January 2017 to 31 December 2019. The secondary outcome was asthma-related ICU admissions. Incidence rate ratios adjusted for demographic and clinical risk factors were estimated using negative binomial models. Population attributable fraction (PAF) was estimated for modifiable risk factors., Results: Younger age groups, females and those from ethnic minority and lower socioeconomic backgrounds had an increased risk of asthma-related hospital admissions. Increasing medication burden, including excessive use of short-acting bronchodilators, was also strongly associated with the primary outcome. Similar risk factors were observed for asthma-related ICU admissions. The key potentially modifiable or treatable risk factors were smoking in adolescents and adults (PAF 6.8%, 95% CI 0.9% to 12.3% and 4.3%, 95% CI 3.0% to 5.7%, respectively), and obesity (PAF 23.3%, 95% CI 20.5% to 26.1%), depression (11.1%, 95% CI 9.1% to 13.1%), gastro-oesophageal reflux disease (2.3%, 95% CI 1.2% to 3.4%), anxiety (2.0%, 95% CI 0.5% to 3.6%) and chronic rhinosinusitis (0.8%, 95% CI 0.3% to 1.3%) in adults., Conclusions: There are significant sociodemographic inequalities in the rates of asthma-related hospital and ICU admissions. Treating age-specific modifiable risk factors should be considered an integral part of asthma management, which could potentially reduce the rate of avoidable hospital admissions., Competing Interests: Competing interests: SH reports receiving funding from NIHR and UKRI. KN has been awarded research grants from NIHR, UKRI/MRC, Kennedy Trust for Rheumatology Research, Health Data Research UK, Wellcome Trust, European Regional Development Fund, Institute for Global Innovation, Boehringer Ingelheim, Action Against Macular Degeneration Charity, Midlands Neuroscience Teaching and Development Funds, South Asian Health Foundation, Vifor Pharma, College of Police and CSL Behring, with all payments made to his academic institution; KN received consulting fees from BI, Sanofi, CEGEDIM, MSD and holds a leadership/fiduciary role with NICST, a charity and OpenClinical, a Social Enterprise. AHM received personal and institution funds for talks, consultancy fees, education or research grants from GSK, AZ, Sanofi, Teva, BI, Novartis, Chiesi. NJA reports receiving funding from NIHR outside the submitted work. PN reports receiving grants from NIHR and fees for educational talks and consultancy from Novartis, GSK and Astrazaneca., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: To develop and validate a clinical risk score to identify patients at risk of chronic obstructive pulmonary disease (COPD) using clinical factors routinely recorded in primary care., Design: Case-control study of patients containing one incident COPD case to two controls matched on age, sex and general practice. Candidate risk factors were included in a conditional logistic regression model to produce a clinical score. Accuracy of the score was estimated on a separate external validation sample derived from 20 purposively selected practices., Setting: UK general practices enrolled in the Clinical Practice Research Datalink (1 January 2000 to 31 March 2006)., Participants: Development sample included 340 practices containing 15 159 newly diagnosed COPD cases and 28 296 controls (mean age 70 years, 52% male). Validation sample included 2259 cases and 4196 controls (mean age 70 years, 50% male)., Main Outcome Measures: Area under the receiver operator characteristic curve (c statistic), sensitivity and specificity in the validation practices., Results: The model included four variables including smoking status, history of asthma, and lower respiratory tract infections and prescription of salbutamol in the previous 3 years. It had a high average c statistic of 0.85 (95% CI 0.83 to 0.86) and yielded a sensitivity of 63.2% (95% CI 63.1 to 63.3) and specificity 87.4% (95% CI 87.3 to 87.5)., Conclusions: Risk factors associated with COPD and routinely recorded in primary care have been used to develop and externally validate a new COPD risk score. This could be used to target patients for case finding.