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Identification of Multimorbidity Patterns in Rheumatoid Arthritis Through Machine Learning.

Authors :
England, Bryant R.
Yang, Yangyuna
Roul, Punyasha
Haas, Christian
Najjar, Lotfollah
Sayles, Harlan
Yu, Fang
Sauer, Brian C.
Baker, Joshua F.
Xie, Fenglong
Michaud, Kaleb
Curtis, Jeffrey R.
Mikuls, Ted R.
Source :
Arthritis Care & Research; Feb2023, Vol. 75 Issue 2, p220-230, 11p
Publication Year :
2023

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 sex‐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, female and male patients, and patients in MarketScan and the VHA. RA patients had higher odds of each multimorbidity pattern (odds ratios [ORs] 1.17–2.96), with mental health and chronic pain disorders being the multimorbidity pattern most strongly associated with RA (ORs 2.07–2.96). Conclusion: Cardiopulmonary, cardiometabolic, and mental health and chronic pain disorders represent predominant multimorbidity patterns, each of which is overrepresented 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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
2151464X
Volume :
75
Issue :
2
Database :
Complementary Index
Journal :
Arthritis Care & Research
Publication Type :
Academic Journal
Accession number :
161788570
Full Text :
https://doi.org/10.1002/acr.24956