Predicting risk of undiagnosed COPD: development and validation of the TargetCOPD score

Shamil Haroon, Peymane Adab, Richard D Riley, David Fitzmaurice, Rachel Jordan

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21 Citations (Scopus)
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COPD is greatly underdiagnosed worldwide and more efficient methods of case-finding are required. We developed and externally validated a risk score to identify undiagnosed COPD using primary care records.

Retrospective cohort analysis of a pragmatic cluster case finding RCT in the West Midlands, UK. Participants aged 40-79 years with no prior diagnosis of COPD received a postal or opportunistic screening questionnaire. Those reporting chronic respiratory symptoms were assessed with spirometry. COPD was defined as presence of relevant symptoms with a post-bronchodilator FEV1/FVC below the lower limit of normal. A risk score was developed using logistic regression with variables available from electronic health records (EHRs) for 2398 participants who returned a postal questionnaire. This was externally validated among 1097 participants who returned an opportunistic questionnaire to derive the c-statistic, and sensitivity and specificity of cut-points.

A risk score containing age, smoking status, dyspnoea, prescriptions of salbutamol, and prescriptions of antibiotics discriminated between patients with and without undiagnosed COPD (c-statistic 0.74 [95% CI 0.68 to 0.80]). A cut-point of ≥7.5% predicted risk had a sensitivity of 68.8% (95% CI 57.3 to 78.9%) and a specificity of 68.8% (95% CI 65.8.1 to 71.6%).

A novel risk score using routine data from primary care EHRs can identify patients at high risk for undiagnosed symptomatic COPD. This score could be integrated with clinical information systems to help primary care clinicians target patients for case finding.
Original languageEnglish
Article number1602191
Number of pages13
JournalEuropean Respiratory Journal
Issue number6
Publication statusPublished - 22 Jun 2017


  • Chronic Obstructive Pulmonary Disease
  • Risk Factors
  • Primary Health Care
  • Diagnosis
  • Logistic Models


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