Clinical Decision Support for Improved Neonatal Care: The Development of a Machine Learning Model for the Prediction of Late-onset Sepsis and Necrotizing Enterocolitis

医学 坏死性小肠结肠炎 新生儿重症监护室 回顾性队列研究 胎龄 儿科 重症监护 败血症 队列 警报 急诊医学 机器学习 重症监护医学 外科 怀孕 内科学 计算机科学 材料科学 生物 复合材料 遗传学
作者
M.T.H. Meeus,Charlie Beirnaert,Ludo Mahieu,Kris Laukens,Pieter Meysman,Antonius Mulder,David Van Laere
出处
期刊:The Journal of Pediatrics [Elsevier BV]
卷期号:266: 113869-113869 被引量:4
标识
DOI:10.1016/j.jpeds.2023.113869
摘要

Objective

To develop an artificial intelligence-based software system for predicting late-onset sepsis (LOS) and necrotizing enterocolitis (NEC) in infants admitted to the neonatal intensive care unit (NICU).

Study design

Single-center, retrospective cohort study, conducted in the NICU of the Antwerp University Hospital. Continuous monitoring data of 865 preterm infants born at <32 weeks gestational age, admitted to the NICU in the first week of life, were used to train an XGBoost machine learning (ML) algorithm for LOS and NEC prediction in a cross-validated setup. Afterward, the model's performance was assessed on an independent test set of 148 patients (internal validation).

Results

The ML model delivered hourly risk predictions with an overall sensitivity of 69% (142/206) for all LOS/NEC episodes and 81% (67/83) for severe LOS/NEC episodes. The model showed a median time gain of ≤10 hours (IQR, 3.1-21.0 hours), compared with historical clinical diagnosis. On the complete retrospective dataset, the ML model made 721 069 predictions, of which 9805 (1.3%) depicted a LOS/NEC probability of ≥0.15, resulting in a total alarm rate of <1 patient alarm-day per week. The model reached a similar performance on the internal validation set.

Conclusions

Artificial intelligence technology can assist clinicians in the early detection of LOS and NEC in the NICU, which potentially can result in clinical and socioeconomic benefits. Additional studies are required to quantify further the effect of combining artificial and human intelligence on patient outcomes in the NICU.
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