Characteristics and Comorbidities Influencing Mortality Risk Among Hereditary Angioedema Patients

TL;DR

This study identifies factors like age, race, and comorbidities that increase mortality risk in patients with hereditary angioedema using a Bayesian network model.

Contribution

The novel use of Bayesian network analysis to identify high-risk subgroups among hereditary angioedema patients.

Findings

Older HA patients and those with autoimmune conditions, hypertension, or low income face higher mortality risk.

Older Black patients from the Midwest had the highest estimated mortality risk.

Bayesian network model showed strong predictive performance for identifying high-risk subgroups.

Abstract

Background: Patients with hereditary angioedema (HA) face a heightened mortality risk due to multiple factors. Objective: The purpose of this study was to identify patient demographics or comorbidities associated with higher mortality risk using Bayesian network analysis. Methods: Data from the 2021 Nationwide Inpatient Sample were used to identify hospitalized patients with HA. Patient demographics, comorbidities, and severity measures were analyzed, and a Bayesian network model was developed to assess factors contributing to mortality risk. Structure learning was performed using a directed acyclic graph and probability estimating using Bayesian inference. Model performance was validated using a 70/30 training-testing split and assessed via area under the curve. Results: Older HA patients and those with autoimmune conditions, hypertension, or low income were at higher risk of mortality. Elevated risk was also observed across certain racial groups, insurance types, and income levels. Notably, older Black patients from the Midwest exhibited the highest estimated mortality risk. Conclusion: The Bayesian network demonstrated strong predictive performance, highlighting its potential for identifying high-risk subgroups and supporting targeted clinical interventions.

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