Frontiers in cellular and infection microbiology · 2026
The predictive value of aGAPSS score combined with clinical factors for thrombosis in systemic lupus erythematosus.
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Shi Juan, Ouyang Libo, Ma Qiong, Zheng Peiming
Аннотация
PURPOSE: This study evaluated the predictive value of antiphospholipid antibodies (aPLs) combined with the adjusted global antiphospholipid syndrome score (aGAPSS) for thrombosis in hospitalized systemic lupus erythematosus (SLE) patients.
METHODS: A retrospective analysis included 309 SLE patients (65 with in-hospital thrombosis, 244 without). Demographic, clinical, and serological data were collected. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression identified independent risk factors. A combined predictive model was constructed and internally validated using bootstrap.
RESULTS: Patients with thrombosis were older, had higher smoking prevalence, LA ratio, and aGAPSS (all P < 0.05). Disease activity was significantly higher in the thrombosis group, as reflected by a higher mean SLEDAI score (14.29 ± 2.88 vs. 12.45 ± 1.62, P < 0.001). Multivariate analysis identified age (OR = 1.053), smoking (OR = 2.945), SLEDAI (OR = 1.312), LA ratio (OR = 17.432), and aGAPSS (OR = 1.514) as independent predictors. The combined model (aGAPSS + clinical covariates) achieved an area under the curve (AUC) of 0.832, significantly outperforming aGAPSS alone (AUC = 0.693; ΔAUC = 0.139, P < 0.001), with NRI of 0.356 and IDI of 0.142.
CONCLUSION: Integrating aGAPSS with clinical factors provides good discriminative ability for in-hospital thrombosis risk stratification in hospitalized SLE patients, offering a practical tool that may inform surveillance intensity and prophylactic consideration.
METHODS: A retrospective analysis included 309 SLE patients (65 with in-hospital thrombosis, 244 without). Demographic, clinical, and serological data were collected. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression identified independent risk factors. A combined predictive model was constructed and internally validated using bootstrap.
RESULTS: Patients with thrombosis were older, had higher smoking prevalence, LA ratio, and aGAPSS (all P < 0.05). Disease activity was significantly higher in the thrombosis group, as reflected by a higher mean SLEDAI score (14.29 ± 2.88 vs. 12.45 ± 1.62, P < 0.001). Multivariate analysis identified age (OR = 1.053), smoking (OR = 2.945), SLEDAI (OR = 1.312), LA ratio (OR = 17.432), and aGAPSS (OR = 1.514) as independent predictors. The combined model (aGAPSS + clinical covariates) achieved an area under the curve (AUC) of 0.832, significantly outperforming aGAPSS alone (AUC = 0.693; ΔAUC = 0.139, P < 0.001), with NRI of 0.356 and IDI of 0.142.
CONCLUSION: Integrating aGAPSS with clinical factors provides good discriminative ability for in-hospital thrombosis risk stratification in hospitalized SLE patients, offering a practical tool that may inform surveillance intensity and prophylactic consideration.