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Identification of Multimorbidity Patterns in Rheumatoid Arthritis through Machine Learning.

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.