Evidence map›Paper›PMID 22616576›Full record

Trial reportBMC medical informatics and decision making2012

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

Margrethe Smidth, Ineta Sokolowski, Lone Kærsvang, Peter Vedsted

Registry-linked trialAbstract readClinical TrialRandomized Controlled Trial
In one paragraph

Trial report in BMC medical informatics and decision making, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01228708 (Effects of an Active Implementation of a Chronic Disease Management Programme for Patients With Chronic Obstructive Pulmonary Disease), which is not on this map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT01228708 nacompletednot on this map

Effects of an Active Implementation of a Chronic Disease Management Programme for Patients With Chronic Obstructive Pulmonary Disease (COPD)

TypeinterventionalSponsorUniversity of AarhusRan2009 to 2011Enrolled3,021ConditionsImplementation of a Chronic Disease Management Programme, COPDArmsActive implementation of a guideline for chronic disease
3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Trial
  4. Age-Specific Risk of Herpes Zoster in Adults Aged ≥18 Years With Comorbid Conditions-A Retrospective Cohort Study in the United States.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026
    Article
  5. Article
  6. Article
  7. Review
  8. Current Progress of COPD Early Detection: Key Points and Novel Strategies.International journal of chronic obstructive pulmonary disease · 2023
    Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Catalog of 199 register-based definitions of chronic conditions.Scandinavian journal of public health · 2016
    Article
  18. Article
  19. Observational
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Margrethe SmidthThe Research Unit for General Practice Aarhus, Aarhus University, Aarhus C, Denmark. m.smidth@alm.au.dk
Ineta Sokolowski
Lone Kærsvang
Peter Vedsted

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAn 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.

methodsSeven 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).

resultsIn 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 %].

conclusionsAn 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 registrationwww.clinicaltrials.gov (NCT01228708).

Indexed as

AlgorithmsAdultAgedAged, 80 and overAsthmaDatabases, FactualFemaleHumansMaleMiddle AgedPulmonary Disease, Chronic ObstructiveRespiratory Function Tests

Identifiers

PMID22616576
PMCPMC3444358

What OpenQuestion holds

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.