Challenges and Pitfalls of Using Repeat Spirometry Recordings in Routine Primary Care Data to Measure FEV1 Decline in a COPD Population.

BACKGROUND: Electronic healthcare records (EHR) are increasingly used in epidemiological studies but are often viewed as lacking quality compared to randomised control trials and prospective cohorts. Studies of patients with chronic obstructive pulmonary disease (COPD) often use the rate of forced expiratory volume in 1 second (FEV1) decline as an outcome; however, its definition and robustness in EHR have not been investigated. We aimed to investigate how the rate of FEV1 decline differs by the criteria used in an EHR database. METHODS: Clinical Practice Research Datalink and Hospital Episode Statistics were used. Patient populations were defined using 8 sets of criteria around repeated FEV1 measurements. At a minimum, patients had a diagnosis of COPD, were ≥35 years old, were current or ex-smokers, and had data recorded from 2004. FEV1 measurements recorded during follow-up were identified. Thereafter, eight populations were defined based on criteria around: i) the exclusion of patients or individual measurements with potential measurement error; ii) minimum number of FEV1 measurements; iii) minimum time interval between measurements; iv) specific timing of measurements; v) minimum follow-up time; and vi) the use of linked data. For each population, the rate of FEV1 decline was estimated using mixed linear regression. RESULTS: For 7/8 patient populations, rates of FEV1 decline (age and sex adjusted) were similar and ranged from -18.7mL/year (95% CI -19.2 to -18.2) to -16.5mL/year (95% CI -17.3 to -15.7). Rates of FEV1 decline in populations that excluded patients with potential measurement error ranged from -79.4mL/year (95% CI -80.7 to -78.2) to -46.8mL/year (95% CI -47.6 to -46.0). CONCLUSION: FEV1 decline remained similar in a COPD population regardless of number of FEV1 measurements, time intervals between measurements, follow-up period, exclusion of specific FEV1 measurements, and linkage to HES. However, exclusion of individuals with questionable data led to selection bias and faster rates of decline

Self-reported symptoms of chronic cough and breathlessness in working-age men in the city of Izhevsk, Russia: associations with cardiovascular disease risk factors and comorbidities.

INTRODUCTION: Very little is known about the prevalence of respiratory symptoms or their associations with other health conditions in Russia. METHODS: Between 2008 and 2010, a sample of 983 men resident in Izhevsk, Russia, took part in a cross-sectional survey. Presence of respiratory symptoms was determined from self-report of chronic productive cough and breathlessness assessed using the British Medical Research Council (MRC) breathlessness scale. Self-reported physical and mental health were measured using the 12-Item Short-Form Health Survey (SF-12). Hypertension was assessed from mean blood pressure measured at the health check and/or self-reported use of antihypertensive medication. Other comorbidities were assessed from self-report. Logistic regression models were fitted assessing the association between respiratory symptoms and comorbidities. Linear regression models were fitted to investigate the association between respiratory symptoms and self-reported health scores. All models were adjusted for age, education and smoking status. RESULTS: The age-standardised prevalence of cough and breathlessness was 20.9% (prevalence with breathlessness MRC grade 3 or above 3.7%). The majority of men with respiratory symptoms (87.3%) were current smokers. Cough and breathlessness were associated with substantially worse self-reported physical and mental health (test for trend with severity of breathlessness p<0.001). Those with chronic cough and grade 3 or above breathlessness had higher odds of having hypertension (OR 3.03; 95% CI 1.36 to 6.74), diabetes (OR 10.55; 95% CI 2.69 to 41.37), angina pectoris (OR 7.54; 95% CI 3.61 to 15.73), previous myocardial infarction (OR 7.61; 95% CI 2.10 to 27.4) and previous stroke (OR 6.61; 95% CI 1.75 to 23.34) compared with those without respiratory symptoms. CONCLUSIONS: The prevalence of respiratory symptoms was high. Strong associations were found between respiratory symptoms and cardiovascular comorbidities. These are of particular importance given the extremely high level of cardiovascular disease mortality in Russia

Impact of COVID-19 national lockdown on asthma exacerbations: interrupted time-series analysis of English primary care data

Background: The impact of Covid-19 and ensuing national lockdown on asthma exacerbations is unclear. Methods: We conducted an interrupted time-series (lockdown on 23rd March as point of interruption) analysis in asthma cohort identified using a validated algorithm from a national-level primary care database, the Optimum Patient Care Database (OPCRD). We derived asthma exacerbation rates for every week and compared exacerbation rates in the period: January-August 2020 with a pre-Covid-19 period; January-August 2016-2019). Exacerbations were defined as asthma-related hospital attendance/admission (including accident and emergency visit), or an acute course of oral corticosteroids with evidence of respiratory review, as recorded in primary care. We used a generalised least squares modelling approach and stratified the analyses by age, sex, English region, and healthcare setting. Results: From a database of 9,949,487 patients, there were 100,165 asthma patients who experienced at least one exacerbation during 2016-2020. Of 278,996 exacerbation episodes, 49,938 (17.1%) required hospital visit. Comparing pre-lockdown to post-lockdown period, we observed a statistically significant reduction in the level (-0.196 episodes per person-year; p-value<0.001; almost 20 episodes for every 100 asthma patients per year) of exacerbation rates across all patients. The reductions in level in stratified analyses were: 0.005-0.244 (healthcare setting, only those without hospital attendance/admission were significant), 0.210-0.277 (sex), 0.159-0.367 (age), 0.068-0.371 (region). Conclusions: There has been a significant reduction in attendance to primary care for asthma exacerbations during the pandemic. This reduction was observed in all age groups, both sexes, and across most regions in England

Prediction of five-year mortality after COPD diagnosis using primary care records

Accurate prognosis information after a diagnosis of chronic obstructive pulmonary disease (COPD) would facilitate earlier and better informed decisions about the use of prevention strategies and advanced care plans. We therefore aimed to develop and validate an accurate prognosis model for incident COPD cases using only information present in general practitioner (GP) records at the point of diagnosis. Incident COPD patients between 2004–2012 over the age of 35 were studied using records from 396 general practices in England. We developed a model to predict all-cause five-year mortality at the point of COPD diagnosis, using 47,964 English patients. Our model uses age, gender, smoking status, body mass index, forced expiratory volume in 1-second (FEV1) % predicted and 16 co-morbidities (the same number as the Charlson Co-morbidity Index). The performance of our chosen model was validated in all countries of the UK (N = 48,304). Our model performed well, and performed consistently in validation data. The validation area under the curves in each country varied between 0.783–0.809 and the calibration slopes between 0.911–1.04. Our model performed better in this context than models based on the Charlson Co-morbidity Index or Cambridge Multimorbidity Score. We have developed and validated a model that outperforms general multimorbidity scores at predicting five-year mortality after COPD diagnosis. Our model includes only data routinely collected before COPD diagnosis, allowing it to be readily translated into clinical practice, and has been made available through an online risk calculator (https://skiddle.shinyapps.io/incidentcopdsurvival/)

Evaluation of data processing pipelines on real-world electronic health records data for the purpose of measuring patient similarity

BACKGROUND: The ever-growing size, breadth, and availability of patient data allows for a wide variety of clinical features to serve as inputs for phenotype discovery using cluster analysis. Data of mixed types in particular are not straightforward to combine into a single feature vector, and techniques used to address this can be biased towards certain data types in ways that are not immediately obvious or intended. In this context, the process of constructing clinically meaningful patient representations from complex datasets has not been systematically evaluated. AIMS: Our aim was to a) outline and b) implement an analytical framework to evaluate distinct methods of constructing patient representations from routine electronic health record data for the purpose of measuring patient similarity. We applied the analysis on a patient cohort diagnosed with chronic obstructive pulmonary disease. METHODS: Using data from the CALIBER data resource, we extracted clinically relevant features for a cohort of patients diagnosed with chronic obstructive pulmonary disease. We used four different data processing pipelines to construct lower dimensional patient representations from which we calculated patient similarity scores. We described the resulting representations, ranked the influence of each individual feature on patient similarity and evaluated the effect of different pipelines on clustering outcomes. Experts evaluated the resulting representations by rating the clinical relevance of similar patient suggestions with regard to a reference patient. RESULTS: Each of the four pipelines resulted in similarity scores primarily driven by a unique set of features. It was demonstrated that data transformations according to each pipeline prior to clustering can result in a variation of clustering results of over 40%. The most appropriate pipeline was selected on the basis of feature ranking and clinical expertise. There was moderate agreement between clinicians as measured by Cohen's kappa coefficient. CONCLUSIONS: Data transformation has downstream and unforeseen consequences in cluster analysis. Rather than viewing this process as a black box, we have shown ways to quantitatively and qualitatively evaluate and select the appropriate preprocessing pipeline

Validation of asthma recording in electronic health records: protocol for a systematic review.

BACKGROUND: Asthma is a common, heterogeneous disease with significant morbidity and mortality worldwide. It can be difficult to define in epidemiological studies using electronic health records as the diagnosis is based on non-specific respiratory symptoms and spirometry, neither of which are routinely registered. Electronic health records can nonetheless be valuable to study the epidemiology, management, healthcare use and control of asthma. For health databases to be useful sources of information, asthma diagnoses should ideally be validated. The primary objectives are to provide an overview of the methods used to validate asthma diagnoses in electronic health records and summarise the results of the validation studies. METHODS: EMBASE and MEDLINE will be systematically searched for appropriate search terms. The searches will cover all studies in these databases up to October 2016 with no start date and will yield studies that have validated algorithms or codes for the diagnosis of asthma in electronic health records. At least one test validation measure (sensitivity, specificity, positive predictive value, negative predictive value or other) is necessary for inclusion. In addition, we require the validated algorithms to be compared with an external golden standard, such as a manual review, a questionnaire or an independent second database. We will summarise key data including author, year of publication, country, time period, date, data source, population, case characteristics, clinical events, algorithms, gold standard and validation statistics in a uniform table. ETHICS AND DISSEMINATION: This study is a synthesis of previously published studies and, therefore, no ethical approval is required. The results will be submitted to a peer-reviewed journal for publication. Results from this systematic review can be used to study outcome research on asthma and can be used to identify case definitions for asthma. PROSPERO REGISTRATION NUMBER: CRD42016041798