ABSTRACT
Objective:To explore the effect of pediatric critical illness score (PCIS), pediatric risk of mortality Ⅲ score (PRISM Ⅲ), pediatric logistic organ dysfunction 2 (PELOD-2), pediatric sequential organ failure assessment (p-SOFA) score and Glasglow coma scale (GCS) in the prognosis evaluation of septic-associated encephalopathy (SAE).Methods:The data of children with SAE admitted to the Pediatric Intensive Care Unit (PICU), Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences from January 2010 to December 2020 were retrospectively analyzed. They were divided into the survival and death groups according to the clinical outcome on the 28th day after admission. The efficiency of PCIS, PRISM Ⅲ, PELOD-2, p-SOFA and GCS scores for predicting death were evaluated by the area under the ROC curve (AUC). The Hosmer-Lemeshow goodness-of-fit test assessed the calibration of each scoring system.Results:Up to 28 d after admission, 72 of 82 children with SAE survived and 10 died, with a mortality rate of 12.20%. Compared with the survival group, the death group had significantly lower GCS [7 (3, 12) vs. 12 (8, 14)] and PCIS scores [76 (64, 82) vs. 82 (78, 88)], and significantly higher PRISM Ⅲ [14 (12, 17) vs. 7 (3, 12)], PELOD-2 [8 (5, 13) vs. 4 (2, 7)] and p-SOFA scores [11 (5, 12) vs. 6 (3, 9)] ( P<0.05). The AUCs of PCIS, PRISM Ⅲ, PELOD-2, p-SOFA and GCS scores for predicting SAE prognosis were 0.773 ( P=0.012, AUC>0.7), 0.832 ( P=0.02, AUC>0.7), 0.767 ( P=0.014, AUC>0.7), 0.688 ( P=0.084, AUC<0.7), and 0.692 ( P=0.077,AUC<0.7), respectively. Hosmer-Lemeshow goodness-of-fit test showed that PCIS ( χ2=5.329, P=0.722) predicted the mortality and the actual mortality in the best fitting effect, while PRISM Ⅲ ( χ2=12.877, P=0.177), PELOD-2 ( χ2=8.487, P=0.205), p-SOFA ( χ2=9.048, P=0.338) and GCS ( χ2=3.780, P=0.848) had poor fitting effect. Conclusions:The PCIS, PRISM Ⅲ and PELOD-2 scores have good predictive ability assessing the prognosis of children with SAE, while the PCIS score can more accurately evaluate the fitting effect of SAE prognosis prediction.