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Developing an algorithm to identify people with Chronic Obstructive Pulmonary Disease (COPD) using administrative data

DOI: 10.1186/1472-6947-12-38

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Abstract:

Seven general practices were asked to identify patients with known Chronic Obstructive Pulmonary Disease in their practices. For the 266 patients (population A), we used administrative data on hospital admissions for lung-related diagnoses, redeemed prescriptions for lung-diseases drugs and lung- function tests combined to develop an algorithm that identified the highest proportion of patients with Chronic Obstructive Pulmonary Disease with the fewest criteria involved. We tested nine different algorithms combining two to four criteria. The simplest algorithm with highest positive predictive value identified 532 patients (population B); with possible diagnosis of Chronic Obstructive Pulmonary Disease in five general practices. The doctors were asked to confirm the diagnosis. The same algorithm identified 2,895 patients whom were asked to confirm their diagnosis (population C).In population A the chosen algorithm had a positive predictive value of 72.2?% and three criteria: a) discharged patients with a chronic lung-disease diagnosis at least once during the preceding 5?years; or b) redeemed prescription of lung-medication at least twice during the preceding 12?months; or c) at least two spirometries performed at different dates during the preceding 12?months. In population B the positive predictive value was 65.0?% [60.8;69.1?%] and the sensitivity 44.8?% [41.3;48.4?%)] when the “uncertain” were added to where doctors agreed with the diagnosis. For the 1,984 respondents in population C, the positive predictive value was 72.9?% [70.8;74.8?%] and the sensitivity 29.7?% [28.4;31.0?%].An algorithm based on administrative data has been developed and validated with sufficient positive predictive value to be used as a tool for identifying patients with Chronic Obstructive Pulmonary Disease. Some of the identified patients had other chronic lung-diseases (asthma). The algorithm should mostly be regarded as a tool for identifying chronic lung-disease and further development

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