BackgroundFew studies have examined the characteristics of a general asthma population; most have focused on more severe patients or severe exacerbations.MethodsThis population-based cohort study, from April 2007 to September 2015, used linked primary and secondary care electronic healthcare records (Clinical Practice Research Datalink, Hospital Episode Statistics). Characteristics of four age cohorts, ‘Under 5s’, ‘5 to 17s’, ‘18 to 54s’, ‘55+’, were described. Exacerbation risk factors, including asthma severity (measured by the British Thoracic Society (BTS) stepwise approach), were assessed using Poisson regression.Results424 326 patients with current asthma were eligible (n, median follow-up: ‘Under 5s’=17 320, 1 year; ‘5 to 17s’=82 707, 3.3 years; ‘18 to 54s’=210 724, 4 years; ‘55+’=113 575, 5.1 years). Over 60% of the total study population had mild asthma (BTS steps 1/2). There were differences between the cohort’s characteristics, including by gender, disease severity and exacerbation pattern. The rate of exacerbations was highest in the oldest cohort and lowest in the ‘5 to 17s’ cohort (rate per 10 person-years (95% CI), ‘Under 5s’=4.27 (4.18 to 4.38), ‘5 to 17s’=1.48 (1.47 to 1.50), ‘18 to 54s’=3.22 (3.21 to 3.24), ‘55+’=9.40 (9.37 to 9.42)). In all cohorts, exacerbation rates increased with increasing asthma severity, after adjusting for confounders including gender, socioeconomic status, smoking, body mass index, atopy, rhinitis, gastro-oesophageal reflux, anxiety, depression and COPD.ConclusionThe majority of UK patients with asthma had mild asthma and did not experience an exacerbation during follow-up. Patients aged ≥55 years had the lowest proportion with mild asthma and highest rate of exacerbations; the opposite was found in patients aged between 5 and 18 years.

ObjectivesThe optimal method of identifying people with asthma from electronic health records in primary care is not known. The aim of this study is to determine the positive predictive value (PPV) of different algorithms using clinical codes and prescription data to identify people with asthma in the United Kingdom Clinical Practice Research Datalink (CPRD).Methods684 participants registered with a general practitioner (GP) practice contributing to CPRD between 1 December 2013 and 30 November 2015 were selected according to one of eight predefined potential asthma identification algorithms. A questionnaire was sent to the GPs to confirm asthma status and provide additional information to support an asthma diagnosis. Two study physicians independently reviewed and adjudicated the questionnaires and additional information to form a gold standard for asthma diagnosis. The PPV was calculated for each algorithm.Results684 questionnaires were sent, of which 494 (72%) were returned and 475 (69%) were complete and analysed. All five algorithms including a specific Read code indicating asthma or non-specific Read code accompanied by additional conditions performed well. The PPV for asthma diagnosis using only a specific asthma code was 86.4% (95% CI 77.4% to 95.4%). Extra information on asthma medication prescription (PPV 83.3%), evidence of reversibility testing (PPV 86.0%) or a combination of all three selection criteria (PPV 86.4%) did not result in a higher PPV. The algorithm using non-specific asthma codes, information on reversibility testing and respiratory medication use scored highest (PPV 90.7%, 95% CI (82.8% to 98.7%), but had a much lower identifiable population. Algorithms based on asthma symptom codes had low PPVs (43.1% to 57.8%)%).ConclusionsPeople with asthma can be accurately identified from UK primary care records using specific Read codes. The inclusion of spirometry or asthma medications in the algorithm did not clearly improve accuracy.Ethics and disseminationThe protocol for this research was approved by the Independent Scientific Advisory Committee (ISAC) for MHRA Database Research (protocol number15_257) and the approved protocol was made available to the journal and reviewers during peer review. Generic ethical approval for observational research using the CPRD with approval from ISAC has been granted by a Health Research Authority Research Ethics Committee (East Midlands—Derby, REC reference number 05/MRE04/87).The results will be submitted for publication and will be disseminated through research conferences and peer-reviewed journals.

Background COPD is a highly heterogeneous disease composed of different phenotypes with different aetiological and prognostic profiles and current classification systems do not fully capture this heterogeneity. In this study we sought to discover, describe and validate COPD subtypes using cluster analysis on data derived from electronic health records. Methods We applied two unsupervised learning algorithms (k-means and hierarchical clustering) in 30,961 current and former smokers diagnosed with COPD, using linked national structured electronic health records in England available through the CALIBER resource. We used 15 clinical features, including risk factors and comorbidities and performed dimensionality reduction using multiple correspondence analysis. We compared the association between cluster membership and COPD exacerbations and respiratory and cardiovascular death with 10,736 deaths recorded over 146,466 person-years of follow-up. We also implemented and tested a process to assign unseen patients into clusters using a decision tree classifier. Results We identified and characterized five COPD patient clusters with distinct patient characteristics with respect to demographics, comorbidities, risk of death and exacerbations. The four subgroups were associated with 1) anxiety/depression; 2) severe airflow obstruction and frailty; 3) cardiovascular disease and diabetes and 4) obesity/atopy. A fifth cluster was associated with low prevalence of most comorbid conditions. Conclusions COPD patients can be sub-classified into groups with differing risk factors, comorbidities, and prognosis, based on data included in their primary care records. The identified clusters confirm findings of previous clustering studies and draw attention to anxiety and depression as important drivers of the disease in young, female patients. Electronic supplementary material The online version of this article (10.1186/s12911-019-0805-0) contains supplementary material, which is available to authorized users.