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 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.

Influence of Multimorbidity on New Treatment Initiation and Achieving Target Disease Activity Thresholds in Active Rheumatoid Arthritis: A Cohort Study Using the Rheumatology Informatics System for Effectiveness Registry.

OBJECTIVE: To determine whether multimorbidity is associated with treatment changes and achieving target disease activity thresholds in patients with active rheumatoid arthritis (RA). METHODS: We conducted a retrospective cohort study of adults with active RA within the Rheumatology Informatics System for Effectiveness (RISE) registry. Multimorbidity was measured using RxRisk, a medication-based index of chronic disease. We used multivariable logistic regression models to assess the associations of multimorbidity with the odds of initiating a new disease-modifying antirheumatic drug (DMARD) in active RA, and among those initiating a new DMARD, the odds of achieving low disease activity or remission. RESULTS: We identified 15,626 patients using the Routine Assessment of Patient Index Data 3 (RAPID3) cohort and 5,733 patients using the Clinical Disease Activity Index (CDAI) cohort. All patients had active RA, of which 1,558 (RAPID3) and 834 (CDAI) initiated a new DMARD and had follow-up disease activity measures. Patients were middle aged, female, and predominantly White, and on average received medications from 6 to 7 RxRisk categories. Multimorbidity was not associated with new DMARD initiation in active RA. However, a greater burden of multimorbidity was associated with lower odds of achieving treatment targets (per 1-unit RxRisk: RAPID3 cohort odds ratio [OR] 0.95 [95% confidence interval (95% CI) 0.91, 0.98]; CDAI cohort OR 0.94 [95% CI 0.90, 0.99]). Those with the highest burden of multimorbidity had the lowest odds of achieving target RA disease activity (RAPID3 cohort OR 0.54 [95% CI 0.34, 0.85]; CDAI cohort OR 0.65 [95% CI 0.37, 1.15]). CONCLUSION: These findings from a large, real-world registry illustrate the potential impact of multimorbidity on treatment response and indicate that a more holistic management approach targeting multimorbidity may be needed to optimize RA disease control in these patients.

Treatment of very early rheumatoid arthritis with symptomatic therapy, disease-modifying antirheumatic drugs, or biologic agents: a cost-effectiveness analysis.

BACKGROUND: Long-term control or remission of rheumatoid arthritis (RA) may be possible with very early treatment. However, no optimal first therapeutic strategy has been determined. OBJECTIVE: To assess the potential cost-effectiveness of major therapeutic strategies for very early RA. DESIGN: Decision analytic model with probabilistic sensitivity analyses. DATA SOURCES: Published data, the National Data Bank for Rheumatic Diseases, and actual 2007 hospital costs. TARGET POPULATION: U.S. adults with very early RA (symptom duration

Biologic treatment of rheumatoid arthritis and the risk of malignancy: analyses from a large US observational study.

OBJECTIVE: Induction of malignancy is a major concern when rheumatoid arthritis (RA) is treated with biologic therapy. A meta-analysis of RA biologic clinical trials found a general increased risk of malignancy, but this risk was not found in a large observational study. We undertook this study to assess the risk of malignancy among biologic-treated patients in a large US observational database. METHODS: We studied incident cases of cancer among 13,001 patients during approximately 49,000 patient-years of observation in the years 1998-2005. Cancer rates were compared with population rates using the US National Cancer Institute SEER (Surveillance, Epidemiology, and End-Results) database. Assessment of the risk of biologic therapy utilized conditional logistic regression to calculate odds ratios (ORs) as estimates of the relative risk, further adjusted for 6 confounders: age, sex, education level, smoking history, RA severity, and prednisone use. RESULTS: Biologic exposure was 49%. There were 623 incident cases of nonmelanotic skin cancer and 537 other cancers. The standardized incidence ratios and 95% confidence intervals (95% CIs) compared with SEER data were as follows: all cancers 1.0 (1.0-1.1), breast 0.8 (0.6-0.9), colon 0.5 (0.4-0.6), lung 1.2 (1.0-1.4), lymphoma 1.7 (1.3-2.2). Biologics were associated with an increased risk of nonmelanotic skin cancer (OR 1.5, 95% CI 1.2-1.8) and melanoma (OR 2.3, 95% CI 0.9-5.4). No other malignancy was associated with biologic use; the OR (overall risk) of any cancer was 1.0 (95% CI 0.8-1.2). CONCLUSION: Biologic therapy is associated with increased risk for skin cancers, but not for solid tumors or lymphoproliferative malignancies. These associations were consistent across different biologic therapies.

The cost-effectiveness of therapy with teriparatide and alendronate in women with severe osteoporosis.

BACKGROUND: Teriparatide is a promising new agent for the treatment of osteoporosis. METHODS: The objective of this study was to evaluate the cost-effectiveness of teriparatide-based strategies compared with alendronate sodium for the first-line treatment of high-risk osteoporotic women. We developed a microsimulation with a societal perspective. Key data sources include the Study of Osteoporotic Fractures, the Fracture Intervention Trial, and the Fracture Prevention Trial. We evaluated postmenopausal white women with low bone density and prevalent vertebral fracture. The interventions were usual care (UC) (calcium or vitamin D supplementation) compared with 3 strategies: 5 years of alendronate therapy, 2 years of teriparatide therapy, and 2 years of teriparatide therapy followed by 5 years of alendronate therapy (sequential teriparatide/alendronate). The main outcome measure was cost per quality-adjusted life-year (QALY). RESULTS: For the base-case analysis, the cost of alendronate treatment was 11,600 dollars per QALY compared with UC. The cost of sequential teriparatide/alendronate therapy was 156,500 dollars per QALY compared with alendronate. Teriparatide treatment alone was more expensive and produced a smaller increase in QALYs than alendronate. For sensitivity analysis, teriparatide alone was less cost-effective than alendronate even if its efficacy lasted 15 years after treatment cessation. Sequential teriparatide/alendronate therapy was less cost-effective than alendronate even if fractures were eliminated during the alendronate phase, although its cost-effectiveness was less than 50,000 dollars per QALY if the price of teriparatide decreased 60%, if used in elderly women with T scores of -4.0 or less, or if 6 months of teriparatide therapy had comparable efficacy to 2 years of treatment. CONCLUSIONS: Alendronate compares favorably to interventions accepted as cost-effective. Therapy with teriparatide alone is more expensive and produces a smaller increase in QALYs than therapy with alendronate. Sequential teriparatide/alendronate therapy appear expensive but could become more cost-effective with reductions in teriparatide price, with restriction to use in exceptionally high-risk women, or if short courses of treatment have comparable efficacy to that observed in clinical trials.