From: Assessing clusters of comorbidities in rheumatoid arthritis: a machine learning approach
Baseline (N = 11,883) | At Two Years (N = 10,887) | |
---|---|---|
N (%) unless noted | ||
Age, years (SD) | 59.06 (11.96) | 60.78 (11.92) |
Female sex | 9142 (76.93) | 8354(76.73) |
Race/ethnicity | ||
White | 10,079 (84.82) | 9309 (85.51) |
Black | 654 (5.50) | 555 (5.10) |
Hispanic | 678 (5.71) | 606 (5.57) |
Asian | 184 (1.55) | 166 (1.52) |
Other | 288 (2.42) | 251 (2.31) |
Duration of RA, years (SD) | 10.78 (9.86) | 12.40 (9.86) |
Erosions | 3636 (30.60) | 3643 (33.46) |
Serologic status, positive | 5717 (76.21) (n = 7502) | 5483 (76.61) (n = 7157) |
CDAI (SD) | 11.28 (11.94) | 8.82 (10.00) |
HAQ-DI (SD) | 0.32 (0.42) | 0.31 (0.42) |
Medications—RA | ||
NSAIDs | 7405 (62.32) | 7288 (66.94) |
Glucocorticoids | 3765 (31.68) | 2534 (23.28) |
Methotrexate | 7682 (64.65) | 6813 (62.58) |
Leflunomide | 918 (7.73) | 1021 (9.38) |
Hydroxychloroquine | 2382 (20.05) | 2212 (20.32) |
Sulfasalazine | 599 (5.04) | 618 (5.68) |
TNF blocker | 5025 (42.29) | 4862 (44.66) |
IL6 blocker | 372 (3.13) | 561 (5.15) |
Abatacept | 862 (7.25) | 983 (9.03) |
JAK inhibitor | 1115 (9.38) | 1459 (13.40) |
Rituximab | 404 (3.40) | 502 (4.61) |
Medications—non-RAa | ||
Diabetes medications | 553 (4.65) | 1224 (11.24) |
Anti-hypertensive medications | 2088 (17.57) | 4978 (45.72) |
Osteoporosis medications | 460 (3.87) | 783 (7.19) |
Lipid-lowering medications | 3176 (26.73) | 3333 (30.61) |
Anti-depressant medications | 2554 (21.49) | 2971 (27.29) |
Opioid analgesics | 2231 (18.77) | 2781 (25.54) |