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Chronic disease
P2-141 Definition and validation of an algorithm to identify COPD patients from administrative databases
  1. L Bauleo,
  2. U Kirchmayer,
  3. V Belleudi,
  4. N Agabiti,
  5. L Pinnarelli,
  6. S Cascini,
  7. D Fusco,
  8. M Arcà,
  9. M Davoli
  1. Department of Epidemiology of the Regional Health Service, Lazio Region, Italy

Abstract

Introduction Administrative databases are increasingly used to identify patients with chronic conditions, however the optimal methodology for Chronic Obstructive Pulmonary Disease (COPD) is still debated.

Objective To develop and validate an algorithm to identify patients with COPD in Lazio (2 625 102 residents over 45) linking clinical and administrative data.

Methods From the regional hospitalisation, drug prescription and outpatient registries, through record linkage, we identified patterns of specific drug use (minimum 2 prescription during 12 months) and COPD hospitalisations during a 9-year period in 428 patients with COPD, who attended an outpatient clinic in 2006, and in 2140 people without COPD. Through a Bootstrap-Stepwise procedure we selected COPD associated factors. We validated the algorithm through internal (cross-validation-bootstrap, jack-knife) and external validation (comparison with external COPD patients with confirmed diagnosis).

Results A total of 205 611 (7.8%) COPD patients were identified. Factors associated with COPD were: prescription of β2-agonists, anticholinergics, corticosteroids, oxygen, and previous hospitalisation for COPD and respiratory failure. For each patient we estimated an expected probability to suffer from COPD. Depending on the cut-point of expected probability, sensibility (SE) ranged from 0.15 to 0.87 and specificity (SP) from 0.79 to 0.99. We defined a cut-point of 0.30 (SE=64%; SP=97%) to identify the COPD patients. Applying our algorithm on external COPD patients we succeeded to identify 86%.

Conclusion The algorithm showed good performance to identify COPD patients among those individuals registered in the regional healthcare system confirming the strength of administrative data for monitoring chronic diseases.

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