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

Abstract

Objective

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.

Methods

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.

Results

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.

Conclusion

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.

Keywords:

• Chronic thromboembolic pulmonary hypertension
• pulmonary embolism
• predictive risk model
• validation
• health insurance claims

Transparency

Declaration of funding

This work was supported by Actelion Pharmaceuticals US, a Janssen Pharmaceutical Company of Johnson & Johnson.

Declaration of financial/other relationships

MK received honoraria from Bayer Inc: Speakers Bureau and Medical Advisory Board. MC, YT, and SP are employees of Actelion Pharmaceuticals US, Inc. and own stocks. MGL, AMM, and PL are employees of Analysis Group, Inc. which has received consultancy fees from Actelion Pharmaceuticals US, Inc. for the conduct of this study. RB received honoraria from Actelion Pharmaceuticals US, Inc., Bayer Inc, and Abbott; Actelion Pharmaceuticals US, Inc, United Therapeutics, Abbott, Liquidia, and Bayer Inc granted support to RB’s institution.

The study sponsor was involved in all aspects of the research, including the collection of data, its analysis and interpretation, and approval of the final manuscript for publication.

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Authors’ contributions

All authors participated in the design of the study, data interpretation, and critically revised the intellectual content of this manuscript. MGL, AMM, and PL participated in the data analysis. All authors approved the final manuscript.

Acknowledgements

Medical writing assistance was provided by Samuel Rochette, an employee of Analysis Group, Inc. Funding for this assistance was provided by Actelion Pharmaceuticals US, Inc.

The authors would like to thank Cassandra A. Lickert and William Drake III, who are former employees of Actelion Pharmaceuticals US, Inc., and Morris Greenberg, who is a former employee of Analysis Group, Inc., for their contribution to the model development.

Data availability statement

The data that support the findings of this study are available from Optum, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Any researchers interested in obtaining the data used in this study can access database through Optum, under a license agreement, including the payment of appropriate license fee.

Notes

i Clinformatics is a registered trademark of OptumInsight Life Sciences, Inc., Eden Prairie, MN, USA.

ii IBM MarketScan is a registered trademark of International Business Machines Corporation, Pittsburgh, PA, USA.