Quotation England, Bryant, Yang, Yangyuna, Roul, Punyasha, Haas, Christian, Najjar, Lotfollah, Sayles, Harlan, Yu, Fang, Sauer, Brian, Baker, Joshua, Xie, Fenglong, Michaud, Kaleb, Curtis, Jeffrey, Mikuls, Ted. 2022. Identification of Multimorbidity Patterns in Rheumatoid Arthritis through Machine Learning. Arthritis Care & Research.


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Abstract

Objective: Recognizing that the interrelationships between chronic conditions that complicate rheumatoid arthritis (RA) are poorly understood, we aimed to identify patterns of multimorbidity and to define their prevalence in RA through machine learning. Methods: We constructed RA and age- and gender-matched (1:1) non-RA cohorts within a large commercial insurance database (MarketScan®) and the Veterans Health Administration (VHA). Chronic conditions (n=44) were identified from diagnosis codes from outpatient and inpatient encounters. Exploratory factor analysis was performed separately in both databases, stratified by RA diagnosis and sex, to identify multimorbidity patterns. The association of RA with different multimorbidity patterns was determined using conditional logistic regression. Results: We studied 226,850 patients in MarketScan® (76% female) and 120,780 patients in the VHA (89% male). The primary multimorbidity patterns identified were characterized by the presence of cardiopulmonary, cardiometabolic, and mental health and chronic pain disorders. Multimorbidity patterns were similar between RA and non-RA patients, females and males, MarketScan® and the VHA. RA patients had higher odds of each multimorbidity pattern (odds ratios [ORs] 1.17 to 2.96), with mental health and chronic pain disorders being the multimorbidity pattern most strongly associated with RA (ORs 2.07 to 2.96). Conclusions: Cardiopulmonary, cardiometabolic, and mental health and chronic pain disorders represent predominant multimorbidity patterns, each of which are over-represented in RA. The identification of multimorbidity patterns occurring more frequently in RA is an important first step in progressing toward a holistic approach to RA management and warrants assessment of their clinical and predictive utility.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Arthritis Care & Research
Citation Index SCI
Language English
Title Identification of Multimorbidity Patterns in Rheumatoid Arthritis through Machine Learning
Year 2022
DOI https://doi.org/10.1002/acr.24956
Open Access N

Associations

People
Haas, Christian (Details)
External
Baker, Joshua (University of Pennsylvania, United States/USA)
Curtis, Jeffrey (University of Alabama Birmingham, United States/USA)
England, Bryant (University of Nebraska Medical Center, United States/USA)
Michaud, Kaleb (University of Nebraska Medical Center, United States/USA)
Mikuls, Ted (University of Nebraska Medical Center, United States/USA)
Najjar, Lotfollah (University of Nebraska Omaha, United States/USA)
Roul, Punyasha (University of Nebraska Medical Center, United States/USA)
Sauer, Brian (University of Utah, United States/USA)
Sayles, Harlan (University of Nebraska Medical Center, United States/USA)
Xie, Fenglong (University of Alabama Birmingham, United States/USA)
Yang, Yangyuna (University of Nebraska Medical Center, United States/USA)
Yu, Fang (University of Nebraska Medical Center, United States/USA)
Organization
Institute for Corporate Governance IN (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
1122 Artificial intelligence (Details)
1127 Information science (Details)
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