Four-factor nomogram for early-onset sepsis in preterm neonates: Development and internal validation of a stewardship tool - Li Guo; Zhiyang Zhang; Chunhui Zhao; Cuncun Shen; Haotian Zhao; Huifen Chen (PLoS ONE, 2025)
Background Early-onset sepsis (EOS) remains a leading cause of mortality and neurodevelopmental injury in preterm infants, yet widely used tools (e.g., Kaiser EOS Calculator) are not designed for <35–37 weeks’ gestation. Objective To develop and internally validate a concise, clinically interpretable nomogram for EOS risk stratification in preterm neonates and to evaluate its potential for antibiotic stewardship. Methods We performed a single-center retrospective cohort study (July 2023–June 2024) including 1,059 preterm infants admitted within 72 h of birth, randomly split 7:3 into training (n = 742) and validation (n = 317). Forty-five maternal and neonatal candidates were screened (univariable tests, LASSO), followed by multivariable logistic regression to build the final model and nomogram. Discrimination (AUC), calibration (Brier score, calibration curve, Hosmer–Lemeshow), and decision-curve analysis (DCA) were assessed; two biologically plausible interactions were prespecified. Results Four routinely available variables—gestational age, birth weight, umbilical cord abnormality, and mechanical ventilation within 72 h—composed the final model. In the validation cohort, AUC was 0.818 (95% CI, 0.767–0.868), Brier score 0.158, and Hosmer–Lemeshow P = 0.71; DCA showed net benefit across 5–65% risk thresholds. Using a ≥ 0.70 treatment threshold, the model identified 88% of EOS cases while recommending antibiotics for ~10% of infants. A culture-proven-only sensitivity analysis yielded comparable discrimination (AUC 0.819) with a Brier score 0.041. Conclusions A four-factor nomogram using EMR-available variables accurately stratifies EOS risk in preterm infants and may support risk-based antibiotic decisions while limiting overtreatment. Prospective multicenter external validation is warranted to confirm generalizability and guide implementation.