Ali Duarte-Garcia1, Maria Stevens2, Herbert Heien2, Gabriel Figueroa Parra1, Jose A Meade-Aguilar1, Molly M. Jeffery2, Uma Thanarajasingam1, Cynthia Crowson3 and Rozalina McCoy2, 1Mayo Clinic, Rochester, MN, 2Mayo Clinic, Rochester, 3Mayo Clinic, Eyota, MN
Background/Purpose: The presence of multiple chronic conditions (multimorbidity) is associated with disability and premature death. We determined the trajectory of multimorbidity in SLE compared to the general population.
Methods: An SLE cohort was assembled using OptumLabs Datawarehouse (OLDW) from 1/2006-9/2015. SLE cases were identified using > 3 SLE ICD-9 codes separated by > 30 days; the date of the third SLE code was considered the index date. Incident SLE was identified by requiring 12 months without SLE diagnostic codes. Patients with SLE were matched to non-SLE comparators on age, sex, race, region, and enrollment date. Diagnosis codes from the period between enrollment and the end of follow-up (disenrollment or 9/30/2015) were used to determine the presence of comorbidities. We assembled 57 chronic condition categories based on previously described 44 categories (England, B. ARD 2020). The 13 additional categories were added based on the SLICC/ACR damage index (SDI) or otherwise considered relevant to SLE. Two or more ICD-9 codes at least 30 days apart were used to define a comorbidity. We defined multimorbidity as the presence of ≥2 comorbidities (excluding SLE). Conditional logistic regression models were used to compare the prevalence of multimorbidity between cohorts. The trajectory of multimorbidity in people with SLE (vs without SLE) was estimated utilizing generalized estimating equations. We looked at overall trajectory after index date, expanding the observation time one year before index date, and excluding silent conditions to mitigate surveillance bias (hypertension, hypothyroidism, etc.) Odds ratios (OR) and estimates of the linear coefficient and 95% confidence intervals (CI) were reported.
Results: A total of 34,893 SLE patients were matched to 34,893 non-SLE comparators. Of these, 13,531 were incident cases. The mean age was 48 (SD 14.2) years, and 90.6% were female. 66.4% were White, 18.4% Black, 3.4% Asian, and 18.4% Hispanic. From enrollment to the index date, the mean observation time was 2.3 years (SD: 2.4) and 4.4 years (SD: 2.6) for the incident cohort. Multimorbidity was present in 72% of SLE vs. 47% of non-SLE subjects (OR 4.3; 95%CI 4.1-4.5). Patients with SLE had 4.5 comorbidities compared to 2.4 for non-SLE subjects (OR 1.91; 95%CI 1.89-1.94). Compared to baseline, multimorbidity increased among the incident cases, multimorbidity frequency was higher in incident SLE (vs non-SLE) throughout the follow-up compared to baseline (β: 1.85, 95%CI 1.79-1.91). The rate of accrual of chronic conditions was significantly higher in SLE than in non-SLE (Figure 1A; β: 0.63; 95%CI: 0.60-0.65). Patients with SLE had accelerated multimorbidity accrual after excluding silent conditions (Figure 1B). Patients with SLE had increased multimorbidity even one year before SLE onset (Figure 1C).
Conclusion: In this nationwide commercial database insurance study, patients with SLE were four times more likely to suffer from multimorbidity than the general population. Trajectory analysis shows that multimorbidity progresses more rapidly in patients with SLE than those without SLE and may begin before SLE onset. Figure 1: Predicted burden of multimorbidity in incident SLE compared with patients without SLE after diagnosis. Panel A, primary analytical approach requiring 1 year in the data set without SLE diagnostic codes chronic conditions. Panel B, similar analytical approach removing silent conditions. Panel C, restricting the population to individuals with at least 2 years of data prior to index date and beginning follow-up at 1 year before the index date.
Disclosures: A. Duarte-Garcia, None; M. Stevens, None; H. Heien, None; G. Figueroa Parra, None; J. Meade-Aguilar, None; M. Jeffery, None; U. Thanarajasingam, None; C. Crowson, None; R. McCoy, None.