A nomogram to predict 28-day mortality in neonates with sepsis: a retrospective study based on the MIMIC-III database

Abstract

Background Sepsis is the second-leading cause of death in neonates. We established a predictive nomogram to identify critically ill neonates early and reduce the time to treatment. Methods It is a retrospective case-control study based on the MIMIC-III database. The study population comprised 924 neonates diagnosed with sepsis. Results Neonates with sepsis included in the MIMIC-III database were enrolled, including 880 surviving neonates and 44 neonates who died. In the derivation dataset, stepwise regression and the Lasso algorithm were employed to select predictive variables, and the neonatal sequential organ failure assessment score (nSOFA) was calculated simultaneously. Bootstrap resampling was utilized to perform internal validation. The results indicated that the Lasso algorithm displayed superior discrimination, sensitivity, and specificity relative to stepwise regression and nSOFA scores. After 500 bootstrap resampling tests, the area under the receiver operating characteristic curve (AUC) of the Lasso algorithm was 0.912 (95% CI: 0.870–0.977). The nomogram based on the Lasso algorithm outperformed stepwise regression and nSOFA scores in terms of calibration and the clinical net benefit. This nomogram can assist in prognosticating neonatal severe sepsis and aid in guiding clinical practice while concurrently improving patient outcomes. Conclusions The established nomogram revealed that jaundice, corticosteroid use, weight, serum calcium, inotropes and base excess are all important predictors of 28-day mortality in neonates with sepsis. This nomogram can facilitate the early identification of neonates with severe sepsis. However, it still requires further modification and external validation to make it widely available.