Idiopathic Pulmonary Fibrosis in United States Automated Claims. Incidence, Prevalence, and Algorithm Validation

Estimates of idiopathic pulmonary fibrosis (IPF) incidence and prevalence from electronic databases without case validation may be inaccurate. Develop claims algorithms to identify IPF and assess their positive predictive value (PPV) to estimate incidence and prevalence in the United States. We deve...

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Published in:American journal of respiratory and critical care medicine Vol. 192; no. 10; pp. 1200 - 1207
Main Authors: Esposito, Daina B., Lanes, Stephan, Donneyong, Macarius, Holick, Crystal N., Lasky, Joseph A., Lederer, David, Nathan, Steven D., O’Quinn, Sean, Parker, Joseph, Tran, Trung N.
Format: Journal Article
Language:English
Published: United States American Thoracic Society 15.11.2015
Subjects:
Age Distribution
Aged
Aged, 80 and over
Algorithms
Comorbidity
Databases, Factual
Female
Humans
Idiopathic Pulmonary Fibrosis - epidemiology
Incidence
Insurance Claim Review
Logistic Models
Male
Medical Records - statistics & numerical data
Middle Aged
Predictive Value of Tests
Prevalence
Reproducibility of Results
Retrospective Studies
Sex Distribution
United States - epidemiology
United States
epidemiology
idiopathic pulmonary fibrosis
interstitial lung disease
validation
ISSN:1073-449X, 1535-4970, 1535-4970
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Abstract Estimates of idiopathic pulmonary fibrosis (IPF) incidence and prevalence from electronic databases without case validation may be inaccurate. Develop claims algorithms to identify IPF and assess their positive predictive value (PPV) to estimate incidence and prevalence in the United States. We developed three algorithms to identify IPF cases in the HealthCore Integrated Research Database. Sensitive and specific algorithms were developed based on literature review and consultation with clinical experts. PPVs were assessed using medical records. A third algorithm used logistic regression modeling to generate an IPF score and was validated using a separate set of medical records. We estimated incidence and prevalence of IPF using the sensitive algorithm corrected for the PPV. We identified 4,598 patients using the sensitive algorithm and 2,052 patients using the specific algorithm. After medical record review, the PPVs of these algorithms using the treating clinician's diagnosis were 44.4 and 61.7%, respectively. For the IPF score, the PPV was 76.2%. Using the clinical adjudicator's diagnosis, the PPVs were 54 and 57.6%, respectively, and for the IPF score, the PPV was 83.3%. The incidence and period prevalences of IPF, corrected for the PPV, were 14.6 per 100,000 person-years and 58.7 per 100,000 persons, respectively. Sensitive algorithms without correction for false positive errors overestimated incidence and prevalence of IPF. An IPF score offered the greatest PPV, but it requires further validation.
AbstractList Estimates of idiopathic pulmonary fibrosis (IPF) incidence and prevalence from electronic databases without case validation may be inaccurate. Develop claims algorithms to identify IPF and assess their positive predictive value (PPV) to estimate incidence and prevalence in the United States. We developed three algorithms to identify IPF cases in the HealthCore Integrated Research Database. Sensitive and specific algorithms were developed based on literature review and consultation with clinical experts. PPVs were assessed using medical records. A third algorithm used logistic regression modeling to generate an IPF score and was validated using a separate set of medical records. We estimated incidence and prevalence of IPF using the sensitive algorithm corrected for the PPV. We identified 4,598 patients using the sensitive algorithm and 2,052 patients using the specific algorithm. After medical record review, the PPVs of these algorithms using the treating clinician's diagnosis were 44.4 and 61.7%, respectively. For the IPF score, the PPV was 76.2%. Using the clinical adjudicator's diagnosis, the PPVs were 54 and 57.6%, respectively, and for the IPF score, the PPV was 83.3%. The incidence and period prevalences of IPF, corrected for the PPV, were 14.6 per 100,000 person-years and 58.7 per 100,000 persons, respectively. Sensitive algorithms without correction for false positive errors overestimated incidence and prevalence of IPF. An IPF score offered the greatest PPV, but it requires further validation.
Estimates of idiopathic pulmonary fibrosis (IPF) incidence and prevalence from electronic databases without case validation may be inaccurate.RATIONALEEstimates of idiopathic pulmonary fibrosis (IPF) incidence and prevalence from electronic databases without case validation may be inaccurate.Develop claims algorithms to identify IPF and assess their positive predictive value (PPV) to estimate incidence and prevalence in the United States.OBJECTIVESDevelop claims algorithms to identify IPF and assess their positive predictive value (PPV) to estimate incidence and prevalence in the United States.We developed three algorithms to identify IPF cases in the HealthCore Integrated Research Database. Sensitive and specific algorithms were developed based on literature review and consultation with clinical experts. PPVs were assessed using medical records. A third algorithm used logistic regression modeling to generate an IPF score and was validated using a separate set of medical records. We estimated incidence and prevalence of IPF using the sensitive algorithm corrected for the PPV.METHODSWe developed three algorithms to identify IPF cases in the HealthCore Integrated Research Database. Sensitive and specific algorithms were developed based on literature review and consultation with clinical experts. PPVs were assessed using medical records. A third algorithm used logistic regression modeling to generate an IPF score and was validated using a separate set of medical records. We estimated incidence and prevalence of IPF using the sensitive algorithm corrected for the PPV.We identified 4,598 patients using the sensitive algorithm and 2,052 patients using the specific algorithm. After medical record review, the PPVs of these algorithms using the treating clinician's diagnosis were 44.4 and 61.7%, respectively. For the IPF score, the PPV was 76.2%. Using the clinical adjudicator's diagnosis, the PPVs were 54 and 57.6%, respectively, and for the IPF score, the PPV was 83.3%. The incidence and period prevalences of IPF, corrected for the PPV, were 14.6 per 100,000 person-years and 58.7 per 100,000 persons, respectively.MEASUREMENTS AND MAIN RESULTSWe identified 4,598 patients using the sensitive algorithm and 2,052 patients using the specific algorithm. After medical record review, the PPVs of these algorithms using the treating clinician's diagnosis were 44.4 and 61.7%, respectively. For the IPF score, the PPV was 76.2%. Using the clinical adjudicator's diagnosis, the PPVs were 54 and 57.6%, respectively, and for the IPF score, the PPV was 83.3%. The incidence and period prevalences of IPF, corrected for the PPV, were 14.6 per 100,000 person-years and 58.7 per 100,000 persons, respectively.Sensitive algorithms without correction for false positive errors overestimated incidence and prevalence of IPF. An IPF score offered the greatest PPV, but it requires further validation.CONCLUSIONSSensitive algorithms without correction for false positive errors overestimated incidence and prevalence of IPF. An IPF score offered the greatest PPV, but it requires further validation.
Author Lanes, Stephan
Tran, Trung N.
Esposito, Daina B.
Lederer, David
Parker, Joseph
Donneyong, Macarius
Nathan, Steven D.
Lasky, Joseph A.
Holick, Crystal N.
O’Quinn, Sean
Author_xml – sequence: 1
  givenname: Daina B.
  surname: Esposito
  fullname: Esposito, Daina B.
  organization: Safety and Epidemiology, HealthCore, Inc, Andover, Massachusetts
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  givenname: Stephan
  surname: Lanes
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  organization: Safety and Epidemiology, HealthCore, Inc, Andover, Massachusetts
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  surname: Donneyong
  fullname: Donneyong, Macarius
  organization: Safety and Epidemiology, HealthCore, Inc, Andover, Massachusetts
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  givenname: Crystal N.
  surname: Holick
  fullname: Holick, Crystal N.
  organization: Safety and Epidemiology, HealthCore, Inc, Andover, Massachusetts
– sequence: 5
  givenname: Joseph A.
  surname: Lasky
  fullname: Lasky, Joseph A.
  organization: Pulmonary and Critical Care, Tulane University School of Medicine, New Orleans, Louisiana
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  givenname: David
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  organization: Division of Pulmonary, Allergy and Critical Care Medicine, Columbia University Medical Center, New York, New York
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  surname: Nathan
  fullname: Nathan, Steven D.
  organization: Lung Transplant and Advanced Lung Disease Programs, Inova Fairfax Hospital, Falls Church, Virginia
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  surname: O’Quinn
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  organization: Patient-Reported Outcomes and
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  givenname: Joseph
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  organization: Clinical Development, MedImmune, Gaithersburg, Maryland
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  givenname: Trung N.
  surname: Tran
  fullname: Tran, Trung N.
  organization: Observational Research Center, AstraZeneca, Gaithersburg, Maryland; and
BackLink https://www.ncbi.nlm.nih.gov/pubmed/26241562$$D View this record in MEDLINE/PubMed
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  doi: 10.1183/09059180.00002512
– ident: bib3
  doi: 10.1183/09031936.00005209
– reference: 26568236 - Am J Respir Crit Care Med. 2015 Nov 15;192(10):1147-8
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Snippet Estimates of idiopathic pulmonary fibrosis (IPF) incidence and prevalence from electronic databases without case validation may be inaccurate. Develop claims...
Estimates of idiopathic pulmonary fibrosis (IPF) incidence and prevalence from electronic databases without case validation may be...
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StartPage 1200
SubjectTerms Age Distribution
Aged
Aged, 80 and over
Algorithms
Comorbidity
Databases, Factual
Female
Humans
Idiopathic Pulmonary Fibrosis - epidemiology
Incidence
Insurance Claim Review
Logistic Models
Male
Medical Records - statistics & numerical data
Middle Aged
Predictive Value of Tests
Prevalence
Reproducibility of Results
Retrospective Studies
Sex Distribution
United States - epidemiology
Title Idiopathic Pulmonary Fibrosis in United States Automated Claims. Incidence, Prevalence, and Algorithm Validation
URI https://www.ncbi.nlm.nih.gov/pubmed/26241562
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Volume 192
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