Clusters based on demography, disease phenotype, and autoantibody status predicts mortality in lupus: data from...

SLE is associated with significant mortality, and data from South Asia is limited. Thus, we analysed the causes and predictors of mortality and hierarchical cluster-based survival in the Indian SLE Inception cohort for Research (INSPIRE).

Data for patients with SLE was extracted from the INSPIRE database. Univariate analyses of associations between mortality and a number of disease variables were conducted. Agglomerative unsupervised hierarchical cluster analysis was undertaken using 25 variables defining the SLE phenotype. Survival rates across clusters were assessed using non-adjusted and adjusted Cox proportional-hazards models.

Among 2072 patients (with a median follow-up of 18 months), there were 170 deaths (49.2 deaths per 1000 patient-years) of which cause could be determined in 155 patients. 47.1% occurred in the first 6 months. Most of the mortality (n = 87) were due to SLE disease activity followed by coexisting disease activity and infection (n = 24), infections (n = 23), and 21 to other causes. Among the deaths in which infection played a role, 24 had pneumonia. Clustering identified four clusters, and the mean survival estimates were 39.26, 39.78, 37.69 and 35.86 months in clusters 1, 2, 3 and 4, respectively (P < 0.001). The adjusted hazard ratios (HRs) (95% CI) were significant for cluster 4 [2.19 (1.44, 3.31)], low socio-economic-status [1.69 (1.22, 2.35)], number of BILAG-A [1.5 (1.29, 1.73)] and BILAG-B [1.15 (1.01, 1.3)], and need for haemodialysis [4.63 (1.87,11.48)].

SLE in India has high early mortality, and the majority of deaths occur outside the health-care setting. Clustering using the clinically relevant variables at baseline may help identify individuals at high risk of mortality in SLE, even after adjusting for high disease activity.