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01.12.2012 | Research article | Ausgabe 1/2012 Open Access

BMC Medical Informatics and Decision Making 1/2012

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

Zeitschrift:
BMC Medical Informatics and Decision Making > Ausgabe 1/2012
Autoren:
Margrethe Smidth, Ineta Sokolowski, Lone Kærsvang, Peter Vedsted
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​1472-6947-12-38) contains supplementary material, which is available to authorized users.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

PV conceived the study idea, participated in its design and in developing the algorithm and he helped to draft the manuscript. IS participated in the study design and in developing the algorithm and performed the statistical analyses. LK carried out data collection related to the GPs' verification of the algorithm. MS coordinated the study, carried out the questionnaire survey, participated in the study design and drafted the manuscript. All authors have read and approved the final manuscript.

Abstract

Background

An important prerequisite for the Chronic Care Model is to be able to identify, in a valid, simple and inexpensive way, the population with a chronic condition that needs proactive and planned care. We investigated if a set of administrative data could be used to identify patients with Chronic Obstructive Pulmonary Disease in a Danish population.

Methods

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).

Results

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 %].

Conclusions

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 of the algorithm is needed.

Trial registration

www.clinicaltrials.gov (NCT01228708)
Zusatzmaterial
Additional file 1: The conditions for identification by the algorithm developed and validated in the study. (PDF 86 KB)
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Additional file 2: Descriptive data for patients identified by their GP to have COPD - population A. (PDF 82 KB)
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Additional file 3: Patient identified with COPD using different algorithms for population A. An inclusion prerequisite was to be aged 35 or above and alive at the time of identification in the registries. (PDF 83 KB)
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Additional file 4: The characteristics of algorithm-identified patients for whom the GP were asked to verify the COPD diagnosis - population B. The prevalence of COPD suggested by Hansen et al.(16)was used. (PDF 83 KB)
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Additional file 5: The characteristics of algorithm-identified patients whom were asked to verify their COPD diagnosis - population C. The patients were divided into ten year age groups. Data from Statistics Denmark were used for the population in 2007. The prevalence of COPD suggested by Hansen et al.(16)was used. (PDF 75 KB)
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Authors’ original file for figure 1
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Authors’ original file for figure 2
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Authors’ original file for figure 3
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Authors’ original file for figure 4
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Literatur
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