Evidence map›Paper›PMID 31640678›Full record

SynthesisBMC health services research2019

Algorithms to identify COPD in health systems with and without access to ICD coding: a systematic review.

Holger Gothe, Sasa Rajsic, Djurdja Vukicevic, Tonio Schoenfelder, Beate Jahn, Sabine Geiger-Gritsch, Diana Brixner, Niki Popper, Gottfried Endel, Uwe Siebert

Abstract readSystematic Review
In one paragraph

Synthesis in BMC health services research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed.

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  17. Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.Clinical research in cardiology : official journal of the German Cardiac Society · 2024
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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

10 authors.

Holger GotheDepartment of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Eduard Wallnoefer Zentrum 1, A-6060, Hall i.T., Austria. holger.gothe@mailbox.tu-dresden.de.
Sasa RajsicDepartment of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Eduard Wallnoefer Zentrum 1, A-6060, Hall i.T., Austria.
Djurdja VukicevicDepartment of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Eduard Wallnoefer Zentrum 1, A-6060, Hall i.T., Austria.
Tonio SchoenfelderMedical Faculty "Carl Gustav Carus", Technical University Dresden, Loescherstrasse 18, D-01307, Dresden, Germany.
Beate JahnDepartment of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Eduard Wallnoefer Zentrum 1, A-6060, Hall i.T., Austria.
Sabine Geiger-GritschDepartment of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Eduard Wallnoefer Zentrum 1, A-6060, Hall i.T., Austria.
Diana BrixnerUniversity of Utah, School of Medicine, Salt Lake City, UT, 84132, USA.
Niki PopperDepartment of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Eduard Wallnoefer Zentrum 1, A-6060, Hall i.T., Austria.
Gottfried EndelEvidence-Based Medicine and Health Technology Assessment, Main Association of Austrian Social Insurance Institutions, Kundmanngasse 21, A-1031, Vienna, Austria.
Uwe SiebertDepartment of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Eduard Wallnoefer Zentrum 1, A-6060, Hall i.T., Austria.

Funding

European Commission - Erasmus Mundus Western Balkans (ERAWEB) none
6 · The paper itself

Abstract

backgroundChronic obstructive pulmonary disease (COPD) causes significant morbidity and mortality worldwide. Estimation of incidence, prevalence and disease burden through routine insurance data is challenging because of under-diagnosis and under-treatment, particularly for early stage disease in health care systems where outpatient International Classification of Diseases (ICD) diagnoses are not collected. This poses the question of which criteria are commonly applied to identify COPD patients in claims datasets in the absence of ICD diagnoses, and which information can be used as a substitute. The aim of this systematic review is to summarize previously reported methodological approaches for the identification of COPD patients through routine data and to compile potential criteria for the identification of COPD patients if ICD codes are not available.

methodsA systematic literature review was performed in Medline via PubMed and Google Scholar from January 2000 through October 2018, followed by a manual review of the included studies by at least two independent raters. Study characteristics and all identifying criteria used in the studies were systematically extracted from the publications, categorized, and compiled in evidence tables.

resultsIn total, the systematic search yielded 151 publications. After title and abstract screening, 38 publications were included into the systematic assessment. In these studies, the most frequently used (22/38) criteria set to identify COPD patients included ICD codes, hospitalization, and ambulatory visits. Only four out of 38 studies used methods other than ICD coding. In a significant proportion of studies, the age range of the target population (33/38) and hospitalization (30/38) were provided. Ambulatory data were included in 24, physician claims in 22, and pharmaceutical data in 18 studies. Only five studies used spirometry, two used surgery and one used oxygen therapy.

conclusionsA variety of different criteria is used for the identification of COPD from routine data. The most promising criteria set in data environments where ambulatory diagnosis codes are lacking is the consideration of additional illness-related information with special attention to pharmacotherapy data. Further health services research should focus on the application of more systematic internal and/or external validation approaches.

Indexed as

AlgorithmsInternational Classification of DiseasesClinical CodingDelivery of Health CareFemaleHumansMaleMiddle AgedPulmonary Disease, Chronic ObstructiveAdministrative dataCase findingChronic obstructive pulmonary diseaseClaims dataCOPDEpidemiologyICD codeIncidencePatient identificationPrevalenceRoutine dataSecondary data

Identifiers

PMID31640678
PMCPMC6805625

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Registered trials

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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.