RT Journal Article
SR Electronic
T1 Detecting undiagnosed COPD: Using routine primary care data to select and validate candidate variables for COPD risk prediction
JF European Respiratory Journal
JO Eur Respir J
FD European Respiratory Society
SP P2399
VO 42
IS Suppl 57
A1 Shamil Haroon
A1 Rachel Jordan
A1 Richard Riley
A1 Robert Lancashire
A1 Tom Marshall
A1 Peymane Adab
YR 2013
UL http://erj.ersjournals.com/content/42/Suppl_57/P2399.abstract
AB AimTo select and internally validate candidate variables to risk predict COPD in primary care.BackgroundCOPD is under-diagnosed in primary care. A validated algorithm could be used to identify patients who might benefit from spirometry.MethodWe used data on 17,719 COPD patients and 35,944 age/sex-matched controls, randomised in a 2:1 ratio to form derivation and validation samples. Candidate predictors were selected and adjusted ORs were estimated from a random intercept model. The AUC was estimated in the validation sample.ResultsMean age in the derivation sample was 69.7 years and 51.8% were male. Smoking status, salbutamol use and dyspnoea were the strongest predictors. The model had an AUC of 0.875 (95% CI 0.87 to 0.88). A cutpoint of 0.3 yielded 86.6% sensitivity and 70.1% specificity.View this table:Risk prediction modelConclusionsOur interim model appears to be highly predictive of incident COPD and will be further developed and externally validated in a large screening trial (TargetCOPD).