Biomarker-based score for predicting in-hospital mortality of children admitted to the intensive care unit

This study aims to establish a new scoring system based on biomarkers for predicting in-hospital mortality of children admitted to the pediatric intensive care unit (PICU). The biomarkers were chosen using the least absolute shrinkage and selection operator (LASSO)-logistic regression in this observational case-control study. The performance of the new predictive model was evaluated by the area under the receiver operating characteristic curve (AUC). Calibration plot was established to validate the new score accompanied by the Hosmer-Lemeshow test. There were 8818 patients included in this study. Finally, six predictors were included in the LASSO-regression model. Albumin <40 g/L, lactate dehydrogenase >452 U/L, lactate >3.2 mmol/L, urea >5.6 mmol/L, arterial PH <7.3 and glucose >6.9 mmol/L were treated as risk factors for higher mortality. The new score ranged from 1 to 6 among all the included patients. In the training set, the AUC of the probability of in-hospital mortality for the new predictive model was 0.81 (95% CI 0.79 to 0.84), which is larger than for the Pediatric Critical Illness Score (PCIS) (0.69, 95% CI 0.66 to 0.72). Similarly, in the validating set, the AUC of the probability of in-hospital mortality was larger for the new score (0.80, 95% CI 0.77 to 0.84) than for PCIS (0.67, 95% CI 0.63 to 0.72). The calibration plot and Hosmer-Lemeshow test showed excellent calibration. The calculated ORs showed a trend that higher scores indicated higher risk of death (p value for trend <0.001). In summary, this study develops and validates a totally biomarker-based new score to predict in-hospital mortality for pediatric patients admitted to PICU. More attention and more positive care and treatment should be given to children with a higher score.