Validation of chronic obstructive pulmonary disease recording in the Clinical Practice Research Datalink (CPRD-GOLD)

Objectives The optimal method of identifying people with chronic obstructive pulmonary disease (COPD) from electronic primary care records is not known. We assessed the accuracy of different approaches using the Clinical Practice Research Datalink, a UK electronic health record database. Setting 951...

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Published inBMJ open Vol. 4; no. 7; p. e005540
Main Authors Quint, Jennifer K, Müllerova, Hana, DiSantostefano, Rachael L, Forbes, Harriet, Eaton, Susan, Hurst, John R, Davis, Kourtney, Smeeth, Liam
Format Journal Article
LanguageEnglish
Published England British Medical Journal Publishing Group 01.07.2014
BMJ Publishing Group LTD
BMJ Publishing Group
Subjects
Online AccessGet full text
ISSN2044-6055
2044-6055
DOI10.1136/bmjopen-2014-005540

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Abstract Objectives The optimal method of identifying people with chronic obstructive pulmonary disease (COPD) from electronic primary care records is not known. We assessed the accuracy of different approaches using the Clinical Practice Research Datalink, a UK electronic health record database. Setting 951 participants registered with a CPRD practice in the UK between 1 January 2004 and 31 December 2012. Individuals were selected for ≥1 of 8 algorithms to identify people with COPD. General practitioners were sent a brief questionnaire and additional evidence to support a COPD diagnosis was requested. All information received was reviewed independently by two respiratory physicians whose opinion was taken as the gold standard. Primary outcome measure The primary measure of accuracy was the positive predictive value (PPV), the proportion of people identified by each algorithm for whom COPD was confirmed. Results 951 questionnaires were sent and 738 (78%) returned. After quality control, 696 (73.2%) patients were included in the final analysis. All four algorithms including a specific COPD diagnostic code performed well. Using a diagnostic code alone, the PPV was 86.5% (77.5–92.3%) while requiring a diagnosis plus spirometry plus specific medication; the PPV was slightly higher at 89.4% (80.7–94.5%) but reduced case numbers by 10%. Algorithms without specific diagnostic codes had low PPVs (range 12.2–44.4%). Conclusions Patients with COPD can be accurately identified from UK primary care records using specific diagnostic codes. Requiring spirometry or COPD medications only marginally improved accuracy. The high accuracy applies since the introduction of an incentivised disease register for COPD as part of Quality and Outcomes Framework in 2004.
AbstractList Objectives The optimal method of identifying people with chronic obstructive pulmonary disease (COPD) from electronic primary care records is not known. We assessed the accuracy of different approaches using the Clinical Practice Research Datalink, a UK electronic health record database. Setting 951 participants registered with a CPRD practice in the UK between 1 January 2004 and 31 December 2012. Individuals were selected for ≥1 of 8 algorithms to identify people with COPD. General practitioners were sent a brief questionnaire and additional evidence to support a COPD diagnosis was requested. All information received was reviewed independently by two respiratory physicians whose opinion was taken as the gold standard. Primary outcome measure The primary measure of accuracy was the positive predictive value (PPV), the proportion of people identified by each algorithm for whom COPD was confirmed. Results 951 questionnaires were sent and 738 (78%) returned. After quality control, 696 (73.2%) patients were included in the final analysis. All four algorithms including a specific COPD diagnostic code performed well. Using a diagnostic code alone, the PPV was 86.5% (77.5–92.3%) while requiring a diagnosis plus spirometry plus specific medication; the PPV was slightly higher at 89.4% (80.7–94.5%) but reduced case numbers by 10%. Algorithms without specific diagnostic codes had low PPVs (range 12.2–44.4%). Conclusions Patients with COPD can be accurately identified from UK primary care records using specific diagnostic codes. Requiring spirometry or COPD medications only marginally improved accuracy. The high accuracy applies since the introduction of an incentivised disease register for COPD as part of Quality and Outcomes Framework in 2004.
ObjectivesThe optimal method of identifying people with chronic obstructive pulmonary disease (COPD) from electronic primary care records is not known. We assessed the accuracy of different approaches using the Clinical Practice Research Datalink, a UK electronic health record database.Setting951 participants registered with a CPRD practice in the UK between 1 January 2004 and 31 December 2012. Individuals were selected for ≥1 of 8 algorithms to identify people with COPD. General practitioners were sent a brief questionnaire and additional evidence to support a COPD diagnosis was requested. All information received was reviewed independently by two respiratory physicians whose opinion was taken as the gold standard.Primary outcome measureThe primary measure of accuracy was the positive predictive value (PPV), the proportion of people identified by each algorithm for whom COPD was confirmed.Results951 questionnaires were sent and 738 (78%) returned. After quality control, 696 (73.2%) patients were included in the final analysis. All four algorithms including a specific COPD diagnostic code performed well. Using a diagnostic code alone, the PPV was 86.5% (77.5–92.3%) while requiring a diagnosis plus spirometry plus specific medication; the PPV was slightly higher at 89.4% (80.7–94.5%) but reduced case numbers by 10%. Algorithms without specific diagnostic codes had low PPVs (range 12.2–44.4%).ConclusionsPatients with COPD can be accurately identified from UK primary care records using specific diagnostic codes. Requiring spirometry or COPD medications only marginally improved accuracy. The high accuracy applies since the introduction of an incentivised disease register for COPD as part of Quality and Outcomes Framework in 2004.
The optimal method of identifying people with chronic obstructive pulmonary disease (COPD) from electronic primary care records is not known. We assessed the accuracy of different approaches using the Clinical Practice Research Datalink, a UK electronic health record database.OBJECTIVESThe optimal method of identifying people with chronic obstructive pulmonary disease (COPD) from electronic primary care records is not known. We assessed the accuracy of different approaches using the Clinical Practice Research Datalink, a UK electronic health record database.951 participants registered with a CPRD practice in the UK between 1 January 2004 and 31 December 2012. Individuals were selected for ≥1 of 8 algorithms to identify people with COPD. General practitioners were sent a brief questionnaire and additional evidence to support a COPD diagnosis was requested. All information received was reviewed independently by two respiratory physicians whose opinion was taken as the gold standard.SETTING951 participants registered with a CPRD practice in the UK between 1 January 2004 and 31 December 2012. Individuals were selected for ≥1 of 8 algorithms to identify people with COPD. General practitioners were sent a brief questionnaire and additional evidence to support a COPD diagnosis was requested. All information received was reviewed independently by two respiratory physicians whose opinion was taken as the gold standard.The primary measure of accuracy was the positive predictive value (PPV), the proportion of people identified by each algorithm for whom COPD was confirmed.PRIMARY OUTCOME MEASUREThe primary measure of accuracy was the positive predictive value (PPV), the proportion of people identified by each algorithm for whom COPD was confirmed.951 questionnaires were sent and 738 (78%) returned. After quality control, 696 (73.2%) patients were included in the final analysis. All four algorithms including a specific COPD diagnostic code performed well. Using a diagnostic code alone, the PPV was 86.5% (77.5-92.3%) while requiring a diagnosis plus spirometry plus specific medication; the PPV was slightly higher at 89.4% (80.7-94.5%) but reduced case numbers by 10%. Algorithms without specific diagnostic codes had low PPVs (range 12.2-44.4%).RESULTS951 questionnaires were sent and 738 (78%) returned. After quality control, 696 (73.2%) patients were included in the final analysis. All four algorithms including a specific COPD diagnostic code performed well. Using a diagnostic code alone, the PPV was 86.5% (77.5-92.3%) while requiring a diagnosis plus spirometry plus specific medication; the PPV was slightly higher at 89.4% (80.7-94.5%) but reduced case numbers by 10%. Algorithms without specific diagnostic codes had low PPVs (range 12.2-44.4%).Patients with COPD can be accurately identified from UK primary care records using specific diagnostic codes. Requiring spirometry or COPD medications only marginally improved accuracy. The high accuracy applies since the introduction of an incentivised disease register for COPD as part of Quality and Outcomes Framework in 2004.CONCLUSIONSPatients with COPD can be accurately identified from UK primary care records using specific diagnostic codes. Requiring spirometry or COPD medications only marginally improved accuracy. The high accuracy applies since the introduction of an incentivised disease register for COPD as part of Quality and Outcomes Framework in 2004.
The optimal method of identifying people with chronic obstructive pulmonary disease (COPD) from electronic primary care records is not known. We assessed the accuracy of different approaches using the Clinical Practice Research Datalink, a UK electronic health record database. 951 participants registered with a CPRD practice in the UK between 1 January 2004 and 31 December 2012. Individuals were selected for ≥1 of 8 algorithms to identify people with COPD. General practitioners were sent a brief questionnaire and additional evidence to support a COPD diagnosis was requested. All information received was reviewed independently by two respiratory physicians whose opinion was taken as the gold standard. The primary measure of accuracy was the positive predictive value (PPV), the proportion of people identified by each algorithm for whom COPD was confirmed. 951 questionnaires were sent and 738 (78%) returned. After quality control, 696 (73.2%) patients were included in the final analysis. All four algorithms including a specific COPD diagnostic code performed well. Using a diagnostic code alone, the PPV was 86.5% (77.5-92.3%) while requiring a diagnosis plus spirometry plus specific medication; the PPV was slightly higher at 89.4% (80.7-94.5%) but reduced case numbers by 10%. Algorithms without specific diagnostic codes had low PPVs (range 12.2-44.4%). Patients with COPD can be accurately identified from UK primary care records using specific diagnostic codes. Requiring spirometry or COPD medications only marginally improved accuracy. The high accuracy applies since the introduction of an incentivised disease register for COPD as part of Quality and Outcomes Framework in 2004.
Author Eaton, Susan
DiSantostefano, Rachael L
Forbes, Harriet
Smeeth, Liam
Hurst, John R
Müllerova, Hana
Quint, Jennifer K
Davis, Kourtney
AuthorAffiliation 5 Department of UCL Respiratory Medicine , Royal Free Campus, University College London Medical School , London , UK
1 Department of Non-Communicable Disease Epidemiology , London School of Hygiene and Tropical Medicine , London , UK
4 Clinical Practice Research Datalink Group , Medicines and Healthcare Products Regulatory Agency , London , UK
2 Department of Respiratory Epidemiology , GlaxoSmithKline R&D , Uxbridge , UK
3 Department of Respiratory Epidemiology , GlaxoSmithKline R&D , ResearchTriangle Park, North Carolina , USA
AuthorAffiliation_xml – name: 5 Department of UCL Respiratory Medicine , Royal Free Campus, University College London Medical School , London , UK
– name: 4 Clinical Practice Research Datalink Group , Medicines and Healthcare Products Regulatory Agency , London , UK
– name: 2 Department of Respiratory Epidemiology , GlaxoSmithKline R&D , Uxbridge , UK
– name: 1 Department of Non-Communicable Disease Epidemiology , London School of Hygiene and Tropical Medicine , London , UK
– name: 3 Department of Respiratory Epidemiology , GlaxoSmithKline R&D , ResearchTriangle Park, North Carolina , USA
Author_xml – sequence: 1
  givenname: Jennifer K
  surname: Quint
  fullname: Quint, Jennifer K
  organization: Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
– sequence: 2
  givenname: Hana
  surname: Müllerova
  fullname: Müllerova, Hana
  organization: Department of Respiratory Epidemiology, GlaxoSmithKline R&D, Uxbridge, UK
– sequence: 3
  givenname: Rachael L
  surname: DiSantostefano
  fullname: DiSantostefano, Rachael L
  organization: Department of Respiratory Epidemiology, GlaxoSmithKline R&D, ResearchTriangle Park, North Carolina, USA
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  givenname: Harriet
  surname: Forbes
  fullname: Forbes, Harriet
  organization: Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
– sequence: 5
  givenname: Susan
  surname: Eaton
  fullname: Eaton, Susan
  organization: Clinical Practice Research Datalink Group, Medicines and Healthcare Products Regulatory Agency, London, UK
– sequence: 6
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  surname: Hurst
  fullname: Hurst, John R
  organization: Department of UCL Respiratory Medicine, Royal Free Campus, University College London Medical School, London, UK
– sequence: 7
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  surname: Davis
  fullname: Davis, Kourtney
  organization: Department of Respiratory Epidemiology, GlaxoSmithKline R&D, Uxbridge, UK
– sequence: 8
  givenname: Liam
  surname: Smeeth
  fullname: Smeeth, Liam
  organization: Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
BackLink https://www.ncbi.nlm.nih.gov/pubmed/25056980$$D View this record in MEDLINE/PubMed
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Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
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Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions 2014
Copyright_xml – notice: Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions
– notice: Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
– notice: Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions 2014 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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RESPIRATORY MEDICINE (see Thoracic Medicine)
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Snippet Objectives The optimal method of identifying people with chronic obstructive pulmonary disease (COPD) from electronic primary care records is not known. We...
The optimal method of identifying people with chronic obstructive pulmonary disease (COPD) from electronic primary care records is not known. We assessed the...
ObjectivesThe optimal method of identifying people with chronic obstructive pulmonary disease (COPD) from electronic primary care records is not known. We...
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SubjectTerms Accuracy
Aged
Algorithms
Biomedical Research
Bronchitis
Chronic obstructive pulmonary disease
Clinical medicine
Codes
Databases, Factual
Electronic Health Records
Female
Humans
Lung diseases
Male
Medical coding
Medical records
Middle Aged
Patients
Practice research
Primary care
Primary Health Care
Pulmonary Disease, Chronic Obstructive
Questionnaires
Respiratory Medicine
RESPIRATORY MEDICINE (see Thoracic Medicine)
Response rates
Software
Spirometry
THORACIC MEDICINE
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Title Validation of chronic obstructive pulmonary disease recording in the Clinical Practice Research Datalink (CPRD-GOLD)
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