TY - JOUR
T1 - Identification of Multimorbidity Patterns in Rheumatoid Arthritis Through Machine Learning
AU - England, Bryant R.
AU - Yang, Yangyuna
AU - Roul, Punyasha
AU - Haas, Christian
AU - Najjar, Lotfollah
AU - Sayles, Harlan
AU - Yu, Fang
AU - Sauer, Brian C.
AU - Baker, Joshua F.
AU - Xie, Fenglong
AU - Michaud, Kaleb
AU - Curtis, Jeffrey R.
AU - Mikuls, Ted R.
N1 - Funding Information:
Dr. England's work was supported by the Rheumatology Research Foundation (Scientist Development Award), the Great Plains IDeA‐CTR from the NIH (National Institute of General Medical Sciences grant U54‐GM‐115458), and VA (Clinical Science Research and Development grant IK2 CX002203). Dr. Baker's work was supported by a VA Merit Award (CX001703). Dr. Curtis’ work was supported by the NIH (National Institute of Arthritis and Musculoskeletal and Skin Diseases grant 1P3‐0A‐R072583) and the Patient Centered Outcomes Research Institute. Dr. Mikuls’ work was supported by a VA Merit Award (BX004600), the Department of Defense (PR200793), and the NIH (National Institute of General Medical Sciences grant U54‐GM‐115458, National Institute on Alcohol Abuse and Alcoholism grant R25‐AA‐020818, and National Institute of Arthritis and Musculoskeletal and Skin Diseases grant 2P5‐0A‐R60772).
Publisher Copyright:
© 2022 American College of Rheumatology. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
PY - 2023/2
Y1 - 2023/2
N2 - 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.
AB - 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.
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U2 - 10.1002/acr.24956
DO - 10.1002/acr.24956
M3 - Article
C2 - 35588095
AN - SCOPUS:85138298984
SN - 2151-464X
VL - 75
SP - 220
EP - 230
JO - Arthritis care & research
JF - Arthritis care & research
IS - 2
ER -