Effects of AST-120 on mortality in patients with chronic kidney disease modeled by artificial intelligence or traditional statistical analysis

作者：Chia-Lin Lee, Wei‐Ju Liu, Shang‐Feng Tsai · 发表于：Scientific Reports · 年份：2024 · DOI：10.1038/s41598-024-51498-6 · 被引用次数：8 · 研究领域：Artificial Intelligence in Healthcare and Education、Artificial Intelligence in Healthcare、Blood Pressure and Hypertension Studies

Chronic kidney disease (CKD) imposes a substantial burden, and patient prognosis remains grim. The impact of AST-120 (AST-120) on the survival of CKD patients lacks a consensus. This study aims to investigate the effects of AST-120 usage on the survival of CKD patients and explore the utility of artificial intelligence models for decision-making. We conducted a retrospective analysis of CKD patients receiving care in the pre-end-stage renal disease (ESRD) program at Taichung Veterans General Hospital from 2000 to 2019. We employed Cox regression models to evaluate the relationship between AST-120 use and patient survival, both before and after propensity score matching. Subsequently, we employed Deep Neural Network (DNN) and Extreme Gradient Boosting (XGBoost) models to assess their performance in predicting AST-120's impact on patient survival. Among the 2584 patients in our cohort, 2199 did not use AST-120, while 385 patients received AST-120. AST-120 users exhibited significantly lower mortality rates compared to non-AST-120 users (13.51% vs. 37.88%, p < 0.0001) and a reduced prevalence of ESRD (44.16% vs. 53.17%, p = 0.0005). Propensity score matching at 1:1 and 1:2 revealed no significant differences, except for dialysis and all-cause mortality, where AST-120 users exhibited significantly lower all-cause mortality (p < 0.0001), with a hazard ratio (HR) of 0.395 (95% CI = 0.295-0.522). This difference remained statistically significant even after propensity matching. In t...