Development and validation of a claims-based model to identify patients at risk of chronic thromboembolic pulmonary hypertension following acute pulmonary embolism

Current Medical Research and Opinion, 2021


Chronic thromboembolic pulmonary hypertension (CTEPH) is a rare disease that often follows pulmonary embolism (PE). Screening for CTEPH is challenging, often delaying diagnosis and worsening prognosis. Predictive risk models for CTEPH could help identify at-risk patients, but existing models require multiple clinical inputs. We developed and validated a predictive risk model for CTEPH using health insurance claims that can be used by payers/quality-of-care organizations to screen patients post-PE.


Adult patients newly diagnosed with acute PE (index date) were identified from the Optum De-identified Clinformatics Extended DataMart (January 2007-March 2018; development set) and IBM MarketScan (January 2008-June 2019; validation set) databases. Predictors were identified 12 months before or on the index PE. Risk of "likely CTEPH" was assessed post-PE based on CTEPH-related diagnoses and procedures since the CTEPH diagnosis code (ICD-10-CM: I27.24) was not available until 1 October 2017. Stepwise variable selection was used to build the model using the development set; model validation was subsequently conducted using the validation set.


The development set included 93,428 patients, of whom 11,878 (12.7%) developed likely CTEPH. Older age (odds ratios [OR] = 1.16-1.49), female (OR = 1.09), unprovoked PE (i.e. without thrombotic factors; OR = 1.14), hypertension (OR = 1.07), osteoarthritis (OR = 1.08), diabetes (OR = 1.07), chronic obstructive pulmonary disease (OR = 1.11), obesity (OR = 1.21) were associated with higher odds of likely CTEPH, and oral anticoagulants with lower odds (OR= 0.50, all p < .01). C-statistic was 0.77 in the development and validation sets.


A claims-based risk model reliably predicted the risk of CTEPH post-PE and could be used to identify high-risk patients who may benefit from focused monitoring.

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Kanwar MK, Cole M, Gauthier-Loiselle M, Manceur AM, Tsang Y, Lefebvre P, Panjabi S, Benza RL