POS0720 MORTALITY PATTERNS AND THEIR PROGNOSTIC VALUE IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS IN CUBA
Authors: H. Hernández-Negrín, Y. Roque-Dapresa
Journal: Annals of the Rheumatic Diseases (2021)

Background: There is an increase over time in studies adopting cluster analysis (CA) to identify causes, patterns and severity in rheumatic diseases. 1 However, CA has not been used to identify patterns of causes-of-death in patients with systemic lupus erythematosus (SLE). Objectives: 1) to identify homogeneous groups of patients according to their causes-of-death, 2) to identify homogeneous groups of cause-of-death combinations and 3) to determine the potential prognostic value of the identified patterns. Methods: This is a population-based study using the mortality register of Villa Clara (province of Cuba) between 1990 and 2019. Based on International Classification of Disease (ICD), Nine and Tenth Revision (ICD-9, code 710.0 and ICD-10, code M32), there were identified the SLE-related deaths (any mention on the death certificate) and their associated causes-of-death. Two hierarchical CA with Ward’s method was carried out, based on the associated causes-of-death: one to conglomerate patients and another one to conglomerate variables. To identify differences in socio-demographic characteristics and causes-of-death between each cluster, Chi-square test, Fisher’s exact test and Kruskal-Wallis test were used as appropriate. Multivariate Poisson regression model corrected for over-dispersion was used to determine the prognostic value of the clusters. Years of potentially life lost (YPLL) was defined as a dependent variable. To calculate YPLL, each decedent’s age at death was subtracted from a predetermined age of 75 years. Results: A total of 141 patients were analyzed, which were grouped into three clusters. The socio-demographic features and causes-of-death that characterized each cluster are summarized in Table 1. Figure 1 shows the cause-of-death patterns identified by clustering the variables. After adjusting for sex, skin color, and the presence of pregnancy in the last year, cluster 1 was significantly associated with the number of YPLL in the multivariate analysis (adjusted rate ratios= 1.32; 95% confidence interval= 1.03 to 1.70; p = 0.028). Table 1. Socio-demographic characteristics and top ten causes-of-death according to clusters. Variable Cluster 1 n=40 Cluster 2 n=71 Cluster 3 n=30 P value Age at death median (IR) 36(28.5 to 50.2) 46(30.0 to 56.0) 49(39.0 to 63.2) 0.022 Sex n(%) Male 4(28.6) 8(57.1) 2(14.3) 0.873 Female 36(28.3) 63(49.6) 28(22.0) Skin color n(%) White 18(21.4) 45(53.6) 21(25.0) 0.070 Non White 22(38.6) 26(45.6) 9(15.8) Associated causes-of-death n(%) Acute respiratory failure 2(11.8) 6(35.3) 9(52.9) 0.006 Chronic kidney disease 31(96.9) 0(0) 1(3.1) 0.000 Essential hypertension 2(22.2) 6(66.7) 1(11.1) 0.734 Heart failure 3(25.0) 9(75.0) 0(0) 0.093 Lupus nephritis 39(95.1) 2(4.9) 0(0) 0.000 Multiple organ failure 2(18.2) 5(45.5) 4(36.4) 0.496 Pericarditis 4(40.0) 5(50.0) 1(10.0) 0.569 Pneumonia 5(14.7) 6(17.6) 23(67.6) 0.000 Pulmonary embolism 4(20.0) 15(75.0) 1(5.0) 0.043 Septicemia 4(22.2) 0(0) 14(77.8) 0.000 YPLL: Years of potentially life lost. IR: interquartile rank. Conclusion: Three clusters of patients were identified, the one with higher prevalence of causes-of-death expressing renal damage presented higher mortality burden. The patterns of causes-of-death were mainly shaped by renal, cardiovascular and infectious causes. Better understanding of the association among causes of death, improves our comprehension of SLE and facilitate its clinical management. References: [1]Han L, Benseler SM, Tyrrell PN. Cluster and Multiple Correspondence Analyses in Rheumatology. Rheum Dis Clin N Am. 2018; 44:349-60. https://doi.org/10.1016/j.rdc.2018.01.013 Disclosure of Interests: None declared