Closed loop construction of hypoglycemia risk management for high risk neonates in mother infant rooming in settings: a retrospective study with an embedded clinical decision support system

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Objective To evaluate the effectiveness of an intelligent clinical decision support system (CDSS) for neonatal hypoglycemia management in mother-infant rooming-in settings, and to dissect the differential hypoglycemia risk conferred by individual high-risk factors and their specific combinations under standardized surveillance. Methods A multidisciplinary team developed a knowledge-driven CDSS grounded in national expert consensus, integrating automated maternal-neonatal risk identification, dynamic tiered monitoring reminders, and structured stratified management recommendations. Effectiveness was assessed using a pre-post self-controlled analysis (historical control: January–March 2024, n = 522; CDSS-implemented: April–June 2024, n = 417) and a concurrent parallel controlled analysis (non-CDSS wards: n = 389; CDSS wards: n = 352). Neonatal hypoglycemia was defined as blood glucose <2.2 mmol/L. Risk factor combination patterns were explored among 6,667 system-flagged high-risk neonates. Results CDSS implementation significantly reduced hypoglycemia incidence in both the pre-post (5.76% vs. 11.88%, P < 0.05) and parallel (5.40% vs. 9.25%, P < 0.05) analyses. Under CDSS-managed surveillance, the overall hypoglycemia incidence in the high-risk cohort was 6.3%. Marked heterogeneity was observed: preterm birth (15.4%) and low birth weight (25.0%) carried the highest independent risks, while risk escalated non-linearly with specific factor combinations, reaching 18.8% in neonates with five concurrent factors. Serial monitoring demonstrated a sharp decline in hypoglycemia incidence from 6.1% at first measurement to ≤0.4% thereafter. Conclusion The intelligent CDSS effectively reduces neonatal hypoglycemia in rooming-in settings. Hypoglycemia risk depends more on the specific types and combinations of high-risk factors than on their quantity alone, providing evidence for precise risk stratification. This closed-loop, guideline-driven workflow enhances clinical standardization and patient safety. Future multicenter studies incorporating machine learning and long-term neurodevelopmental follow-up are warranted.

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