An Algorithm for the Identification of Undiagnosed COPD Cases Using Administrative Claims Data

Mapel, Douglas W.; Frost, Floyd J.; Hurley, Judith S.; Petersen, Hans; Roberts, Melissa M.; Marton, J.; Shah, Hemal

doi:10.18553/jmcp.2006.12.6.458

Cited by 30 publications

(54 citation statements)

References 13 publications

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“…13 A diagnostic algorithm for COPD used in a UK Clinical Practice Research Datalink study was less sensitive and specific, 14 and algorithms derived from administrative data had lower sensitivity and PPV than those observed in our study. [15][16][17] Little is known about diagnostic algorithms for osteoarthritis, though validation results from a predictive algorithm used in a computerized, diagnostic database in the United States had outcomes similar to those of this study. 18 The CPCSSN case definition for dementia performed better than those constructed from Quebec billing data (sensitivity of 12.9%-39.7%) 19 and Canadian administrative hospital discharge data (sensitivity of 32.3%-66.9%, specificity of 100.0%).…”

Section: Overall Study Samplesupporting

confidence: 60%

Validating the 8 CPCSSN Case Definitions for Chronic Disease Surveillance in a Primary Care Database of Electronic Health Records

Williamson

Green

Birtwhistle

et al. 2014

The Annals of Family Medicine

198

228

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PURPOSEThe Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is Canada's first national chronic disease surveillance system based on electronic health record (EHR) data. The purpose of this study was to develop and validate case definitions and case-finding algorithms used to identify 8 common chronic conditions in primary care: chronic obstructive pulmonary disease (COPD), dementia, depression, diabetes, hypertension, osteoarthritis, parkinsonism, and epilepsy. METHODSUsing a cross-sectional data validation study design, regional and local CPCSSN networks from British Columbia, Alberta (2), Ontario, Nova Scotia, and Newfoundland participated in validating EHR case-finding algorithms. A random sample of EHR charts were reviewed, oversampling for patients older than 60 years and for those with epilepsy or parkinsonism. Charts were reviewed by trained research assistants and residents who were blinded to the algorithmic diagnosis. Sensitivity, specificity, and positive and negative predictive values (PPVs, NPVs) were calculated. RESULTSWe obtained data from 1,920 charts from 4 different EHR systems (Wolf, Med Access, Nightingale, and PS Suite). For the total sample, sensitivity ranged from 78% (osteoarthritis) to more than 95% (diabetes, epilepsy, and parkinsonism); specificity was greater than 94% for all diseases; PPV ranged from 72% (dementia) to 93% (hypertension); NPV ranged from 86% (hypertension) to greater than 99% (diabetes, dementia, epilepsy, and parkinsonism). CONCLUSIONSThe CPCSSN diagnostic algorithms showed excellent sensitivity and specificity for hypertension, diabetes, epilepsy, and parkinsonism and acceptable values for the other conditions. CPCSSN data are appropriate for use in public health surveillance, primary care, and health services research, as well as to inform policy for these diseases.

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Section: Overall Study Samplesupporting

confidence: 60%

Validating the 8 CPCSSN Case Definitions for Chronic Disease Surveillance in a Primary Care Database of Electronic Health Records

Williamson

Green

Birtwhistle

et al. 2014

The Annals of Family Medicine

198

228

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“…For example, one validation study used a 1:3 casecontrol design to assess the accuracy of COPD case-finding algorithms in administrative billing data. 23 The results from this study estimated a sensitivity of 61% and specificity of 82% (compared with our validation study, which indicated 41% sensitivity and 99% specificity). Another validation study for a diabetes diagnostic algorithm using administrative data yielded a sensitivity of 90% and specificity of 92%.…”

Section: Discussionmentioning

confidence: 56%

Validation of the Diagnostic Algorithms for 5 Chronic Conditions in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN): A Kingston Practice-based Research Network (PBRN) Report

Kadhim-Saleh

Green

Williamson

et al. 2013

The Journal of the American Board of Family Medicine

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Objective: The objective of this study was to assess the validity of electronic medical records-based diagnostic algorithms for 5 chronic conditions.Methods: A retrospective validation study using primary chart abstraction. A standardized abstraction form was developed to ascertain diagnoses of diabetes, hypertension, osteoarthritis, chronic obstructive pulmonary disease, and depression. Information about billing, laboratory tests, notes, specialist and hospital reports, and physiologic data was collected. An age-stratified random sample of 350 patient charts was selected from Kingston, Ontario, Canada. Approximately 90% of those charts were allocated to people aged >60 years.Results: Three hundred thirteen patient records were included in the study. Patients' mean age was 68 years and 52% were women. High interrater reliability was indicated by 92% complete agreement and a statistic of 89.3%. The sensitivities of algorithms were 100% (diabetes), 83% (hypertension), 45% (osteoarthritis), 41% (chronic obstructive pulmonary disease), and 39% (depression). The lowest specificity was 97%, for depression. The positive predictive value ranged from 79% (depression) to 100%, and the negative predictive value ranged from 68% (osteoarthritis) to 100%. Chronic diseases constitute a major burden of illness in Canada and around the world. Recent estimates suggest that 46% of adult Canadians suffer from one or more of 7 common chronic diseases. 1Of these conditions, 6 million Canadians are affected with hypertension, 2 2 million with diabetes, 3 1.2 million with major depression, 4 Ͼ750,000adults with chronic obstructive pulmonary disease (COPD), 5 and 3 million with osteoarthritis. 6Currently available information on chronic diseases at the national level is derived from databases such as hospital discharge summaries, disease-specific registries, and population health surveys. These sources have significant limitations, such as the inability to capture data on conditions that do not lead to hospitalizations and the unreliability of self-reported surveys.7 A large validation study of the Discharge At the Canadian provincial level, billing for physician services may provide a source of data, but it is limited in the depth of information because administrative data are created for financial management rather than research purposes.10 When compared against a clinical research database, administrative data had only 20% agreement. 11Primary care databases constitute another source of data on chronic conditions. For instance, people with one or more chronic conditions accounted for 51% of family physician encounters, 12 suggesting that comprehensive clinical records collected by primary care physicians could be a rich resource for researchers and policymakers. The benefit of using primary care databases is that they provide prospective and systematic collection of clinically verified data that can be comprehensive for studying a variety of important outcomes. 13The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is one...

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“…We also had no information on smoking and disease severity, which may lead to underestimate or overestimate the true economic burden of SAS in this population. Mapel and colleagues developed an algorithm based on medical and pharmacy claims records from a health maintenance organization to predict individuals at risk for having undiagnosed COPD [30]. Future research is needed to validate the algorithm to identify undiagnosed or uncoded COPD and asthma patients, especially in the Medicaid population in which obstructive lung disease is prevalent.…”

Section: Discussionmentioning

confidence: 99%

Elevated economic burden in obstructive lung disease patients with concomitant sleep apnea syndrome

et al. 2009

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An Algorithm for the Identification of Undiagnosed COPD Cases Using Administrative Claims Data

Cited by 30 publications

References 13 publications

Validating the 8 CPCSSN Case Definitions for Chronic Disease Surveillance in a Primary Care Database of Electronic Health Records

Validating the 8 CPCSSN Case Definitions for Chronic Disease Surveillance in a Primary Care Database of Electronic Health Records

Validation of the Diagnostic Algorithms for 5 Chronic Conditions in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN): A Kingston Practice-based Research Network (PBRN) Report

Elevated economic burden in obstructive lung disease patients with concomitant sleep apnea syndrome

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