Characterizing patients with rare mucormycosis infections using real-world data

Area of Science:

• Medical Mycology
• Epidemiology
• Health Informatics

Background:

• Invasive mucormycosis (IM) is a rare, life-threatening fungal infection with limited clinical and epidemiological data.
• Electronic health records (EHR) offer a valuable resource for studying large patient populations with IM.
• This study utilized EHR data to describe patients diagnosed with IM.

Purpose of the Study:

• To descriptively assess the characteristics of patients with invasive mucormycosis (IM) using a large electronic health record (EHR) dataset.
• To identify demographic, comorbidity, and treatment patterns in patients with IM, particularly those with hematologic malignancies (HM).
• To highlight the utility of EHR data in understanding rare diseases like IM.

Main Methods:

• Analysis of deidentified US patient data from the Optum® EHR dataset (2007-2019).
• Identification of patients with IM using ICD-9 (117.7) and ICD-10 (B46) codes.
• Selection of patients with hematologic malignancies (HM) and analysis of their demographics, comorbidities, and treatments.

Main Results:

• 1133 patients with HM and IM were identified, predominantly Caucasian, aged 40-64, from the Midwest.
• Essential primary hypertension was the most common comorbidity (50.31%).
• Only 33.72% of patients received antifungal treatment; fluconazole (24.27%), posaconazole (16.33%), and AmB (15.62%) were most common.

Conclusions:

• Large-scale EHR data analysis can effectively identify and characterize patient populations with rare diseases like IM.
• Findings underscore potential gaps in the diagnosis and treatment of IM in high-risk HM patients.
• EHR data analysis is a powerful tool for advancing the understanding of rare disease epidemiology and management.