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      Validation of chronic obstructive pulmonary disease recording in the Clinical Practice Research Datalink (CPRD-GOLD)

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          Abstract

          Objectives

          The optimal method of identifying people with chronic obstructive pulmonary disease (COPD) from electronic primary care records is not known. We assessed the accuracy of different approaches using the Clinical Practice Research Datalink, a UK electronic health record database.

          Setting

          951 participants registered with a CPRD practice in the UK between 1 January 2004 and 31 December 2012. Individuals were selected for ≥1 of 8 algorithms to identify people with COPD. General practitioners were sent a brief questionnaire and additional evidence to support a COPD diagnosis was requested. All information received was reviewed independently by two respiratory physicians whose opinion was taken as the gold standard.

          Primary outcome measure

          The primary measure of accuracy was the positive predictive value (PPV), the proportion of people identified by each algorithm for whom COPD was confirmed.

          Results

          951 questionnaires were sent and 738 (78%) returned. After quality control, 696 (73.2%) patients were included in the final analysis. All four algorithms including a specific COPD diagnostic code performed well. Using a diagnostic code alone, the PPV was 86.5% (77.5–92.3%) while requiring a diagnosis plus spirometry plus specific medication; the PPV was slightly higher at 89.4% (80.7–94.5%) but reduced case numbers by 10%. Algorithms without specific diagnostic codes had low PPVs (range 12.2–44.4%).

          Conclusions

          Patients with COPD can be accurately identified from UK primary care records using specific diagnostic codes. Requiring spirometry or COPD medications only marginally improved accuracy. The high accuracy applies since the introduction of an incentivised disease register for COPD as part of Quality and Outcomes Framework in 2004.

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          Most cited references9

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            Prevalence, diagnosis and relation to tobacco dependence of chronic obstructive pulmonary disease in a nationally representative population sample.

            Chronic obstructive pulmonary disease (COPD) is the fourth most common cause of death worldwide. It is caused primarily by cigarette smoking. Given its importance, it is remarkable that reliable national prevalence data are lacking for most countries. This study provides estimates of the national prevalence of COPD in England, the extent of under-detection of the disorder, and patterns of cigarette smoking, dependence, and motivation to stop smoking in those with the disease. Data from 8215 adults over the age of 35 who participated in the Health Survey for England were analysed. Information was obtained on self-reported and cotinine validated smoking status, cigarette dependence, motivation to stop smoking, COPD defined by spirometry using joint American Thoracic Society and European Respiratory Society criteria, and self-reports of diagnosis with respiratory disorders. Spirometry-defined COPD was present in 13.3% (95% CI 12.6 to 14.0) of participants, over 80% of whom reported no respiratory diagnosis. Even among people with severe or very severe COPD by spirometric assessment, only 46.8% (95% CI 39.1 to 54.6) reported any diagnosed respiratory disease. A total of 34.9% (95% CI 32.1 to 37.8) of people with spirometry-defined COPD were smokers compared with 22.4% (95% CI 21.4 to 23.4) of those without, and smoking prevalence increased with disease severity. Smokers with spirometry-defined COPD were more cigarette dependent but had no greater desire to quit than other smokers. COPD is common among adults in England and is predominantly undiagnosed. In smokers it is associated with higher degrees of cigarette dependence but not with a greater motivation to stop smoking.
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              The validity of using ICD-9 codes and pharmacy records to identify patients with chronic obstructive pulmonary disease

              Background Administrative data is often used to identify patients with chronic obstructive pulmonary disease (COPD), yet the validity of this approach is unclear. We sought to develop a predictive model utilizing administrative data to accurately identify patients with COPD. Methods Sequential logistic regression models were constructed using 9573 patients with postbronchodilator spirometry at two Veterans Affairs medical centers (2003-2007). COPD was defined as: 1) FEV1/FVC <0.70, and 2) FEV1/FVC < lower limits of normal. Model inputs included age, outpatient or inpatient COPD-related ICD-9 codes, and the number of metered does inhalers (MDI) prescribed over the one year prior to and one year post spirometry. Model performance was assessed using standard criteria. Results 4564 of 9573 patients (47.7%) had an FEV1/FVC < 0.70. The presence of ≥1 outpatient COPD visit had a sensitivity of 76% and specificity of 67%; the AUC was 0.75 (95% CI 0.74-0.76). Adding the use of albuterol MDI increased the AUC of this model to 0.76 (95% CI 0.75-0.77) while the addition of ipratropium bromide MDI increased the AUC to 0.77 (95% CI 0.76-0.78). The best performing model included: ≥6 albuterol MDI, ≥3 ipratropium MDI, ≥1 outpatient ICD-9 code, ≥1 inpatient ICD-9 code, and age, achieving an AUC of 0.79 (95% CI 0.78-0.80). Conclusion Commonly used definitions of COPD in observational studies misclassify the majority of patients as having COPD. Using multiple diagnostic codes in combination with pharmacy data improves the ability to accurately identify patients with COPD.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2014
                22 July 2014
                : 4
                : 7
                : e005540
                Affiliations
                [1 ]Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine , London, UK
                [2 ]Department of Respiratory Epidemiology, GlaxoSmithKline R&D , Uxbridge, UK
                [3 ]Department of Respiratory Epidemiology, GlaxoSmithKline R&D , ResearchTriangle Park, North Carolina, USA
                [4 ]Clinical Practice Research Datalink Group, Medicines and Healthcare Products Regulatory Agency , London, UK
                [5 ]Department of UCL Respiratory Medicine, Royal Free Campus, University College London Medical School , London, UK
                Author notes
                [Correspondence to ] Dr Jennifer K Quint; Jennifer.quint@ 123456lshtm.ac.uk
                Article
                bmjopen-2014-005540
                10.1136/bmjopen-2014-005540
                4120321
                25056980
                24af96dd-4d55-4b6c-be7d-b1185c9d4d84
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

                This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/

                History
                : 23 April 2014
                : 20 June 2014
                : 23 June 2014
                Categories
                Respiratory Medicine
                Research
                1506
                1731
                1692

                Medicine
                respiratory medicine (see thoracic medicine),thoracic medicine
                Medicine
                respiratory medicine (see thoracic medicine), thoracic medicine

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