Kari R Gillmeyer1, Eduardo R Nunez2, Seppo T Rinne2, Shirley X Qian3, Elizabeth S Klings4, Renda Soylemez Wiener2. 1. Center for Healthcare Organization & Implementation Research, Edith Nourse Rogers Veterans Hospital, Bedford, MA; VA Boston Healthcare System, Boston, MA; The Pulmonary Center, Boston University School of Medicine, Boston, MA. Electronic address: krgill@bu.edu. 2. Center for Healthcare Organization & Implementation Research, Edith Nourse Rogers Veterans Hospital, Bedford, MA; VA Boston Healthcare System, Boston, MA; The Pulmonary Center, Boston University School of Medicine, Boston, MA. 3. Center for Healthcare Organization & Implementation Research, Edith Nourse Rogers Veterans Hospital, Bedford, MA; VA Boston Healthcare System, Boston, MA. 4. The Pulmonary Center, Boston University School of Medicine, Boston, MA.
Abstract
BACKGROUND: Pulmonary arterial hypertension (PAH) is a rare disease, and much of our understanding stems from single-center studies, which are limited by sample size and generalizability. Administrative data offer an appealing opportunity to inform clinical, research, and quality improvement efforts for PAH. Yet, currently no standardized, validated method exists to distinguish PAH from other subgroups of pulmonary hypertension (PH) within this data source. RESEARCH QUESTION: Can a collection of algorithms be developed and validated to detect PAH in administrative data in two diverse settings: all Veterans Health Administration (VA) hospitals and Boston Medical Center (BMC), a PAH referral center. STUDY DESIGN AND METHODS: In each setting, we identified all adult patients with incident PH from 2006 through 2017 using International Classification of Diseases PH diagnosis codes. From this baseline cohort of all PH subgroups, we sequentially applied the following criteria: diagnosis codes for PAH-associated conditions, procedure codes for right heart catheterizations (RHCs), and pharmacy claims for PAH-specific therapy. We then validated each algorithm using a gold standard review of primary clinical data and calculated sensitivity, specificity, positive predictive values (PPVs), and negative predictive values. RESULTS: From our baseline cohort, we identified 12,012 PH patients in all VA hospitals and 503 patients in BMC. Sole use of PH diagnosis codes performed poorly in identifying PAH (PPV, 16.0% in VA hospitals and 36.0% in BMC). The addition of PAH-associated conditions to the algorithm modestly improved PPV. The best performing algorithm required ICD diagnosis codes, RHC codes, and PAH-specific therapy (VA hospitals: specificity, 97.1%; PPV, 70.0%; BMC: specificity, 95.0%; PPV, 86.0%). INTERPRETATION: This set of validated algorithms to identify PAH in administrative data can be used by the PAH scientific and clinical community to enhance the reliability and value of research findings, to inform quality improvement initiatives, and ultimately to improve health for PAH patients.
BACKGROUND: Pulmonary arterial hypertension (PAH) is a rare disease, and much of our understanding stems from single-center studies, which are limited by sample size and generalizability. Administrative data offer an appealing opportunity to inform clinical, research, and quality improvement efforts for PAH. Yet, currently no standardized, validated method exists to distinguish PAH from other subgroups of pulmonary hypertension (PH) within this data source. RESEARCH QUESTION: Can a collection of algorithms be developed and validated to detect PAH in administrative data in two diverse settings: all Veterans Health Administration (VA) hospitals and Boston Medical Center (BMC), a PAH referral center. STUDY DESIGN AND METHODS: In each setting, we identified all adult patients with incident PH from 2006 through 2017 using International Classification of Diseases PH diagnosis codes. From this baseline cohort of all PH subgroups, we sequentially applied the following criteria: diagnosis codes for PAH-associated conditions, procedure codes for right heart catheterizations (RHCs), and pharmacy claims for PAH-specific therapy. We then validated each algorithm using a gold standard review of primary clinical data and calculated sensitivity, specificity, positive predictive values (PPVs), and negative predictive values. RESULTS: From our baseline cohort, we identified 12,012 PH patients in all VA hospitals and 503 patients in BMC. Sole use of PH diagnosis codes performed poorly in identifying PAH (PPV, 16.0% in VA hospitals and 36.0% in BMC). The addition of PAH-associated conditions to the algorithm modestly improved PPV. The best performing algorithm required ICD diagnosis codes, RHC codes, and PAH-specific therapy (VA hospitals: specificity, 97.1%; PPV, 70.0%; BMC: specificity, 95.0%; PPV, 86.0%). INTERPRETATION: This set of validated algorithms to identify PAH in administrative data can be used by the PAH scientific and clinical community to enhance the reliability and value of research findings, to inform quality improvement initiatives, and ultimately to improve health for PAH patients.
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