IF 2.7 Q4 Medicine
Critical care explorations Pub Date : 2025-10-03 eCollection Date: 2025-10-01 DOI:10.1097/CCE.0000000000001327
Orkun Baloglu, Xiaofeng Wang, Bradley S Marino, Ayse Morca, Izzet T Akbasli, Samir Q Latifi, Alex Klaben, Animesh Tandon
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引用次数: 0

摘要

目的:训练和测试有监督机器学习(ML)模型,用于预测新生儿心胸外科术后48小时内低心输出量综合征(LCOS)的发生。设计:回顾性观察性研究。开发了一种高效的基于树的梯度增强算法(LightGBM) ML模型,结合预测时间和前两个小时的数据,预测2、4、6和12小时的LCOS发生。特征重要性分析采用SHapley加性解释(SHAP)分析。设置:单中心,2012年1月至2023年4月。患者:28天或以下接受心胸外科手术的新生儿。干预措施:没有。测量和主要结果:共纳入181例新生儿,14.9%发生LCOS。使用术后前48小时记录的每小时临床和实验室变量构建多变量时间序列数据集。在2小时、4小时、6小时和12小时的LCOS预测中,LightGBM ML模型实现了接收器工作特征曲线下的面积范围为0.91 ~ 0.98,精确召回率曲线下的面积范围为0.60 ~ 0.80。SHAP分析发现,较高的血管活性肌力评分、较低的尿量和较高的血清乳酸是最具影响力的预测因素。结论:本研究表明,有监督机器学习模型可以准确预测新生儿LCOS,具有较高的可解释性。研究结果支持在多中心环境下进一步验证,并整合到临床工作流程中,以加强新生儿术后危重心脏护理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Supervised Machine Learning Models Predicting Postoperative Low Cardiac Output Syndrome In Neonates.

Objective: To train and test supervised machine learning (ML) models to predict low cardiac output syndrome (LCOS) within the first 48 postoperative hours in neonates undergoing cardiothoracic surgery.

Design: Retrospective observational study. An efficient tree-based gradient-boosting algorithm (LightGBM) ML models were developed to predict LCOS occurrence at 2-, 4-, 6-, and 12-hour forecasting horizons, incorporating data from the prediction time and the two preceding hours. SHapley Additive exPlanations (SHAP) analyses were used for feature importance analyses.

Setting: Single center, January 2012 to April 2023.

Patients: Neonates 28 days old or younger who underwent cardiothoracic surgery.

Interventions: None.

Measurements and main results: A total of 181 neonates were included, with 14.9% experiencing LCOS. A multivariate time-series dataset was constructed using hourly clinical and laboratory variables recorded during the first 48 postoperative hours. The LightGBM ML models achieved area under the receiver operating characteristic curve values ranging from 0.91 to 0.98 and area under the precision-recall curve values ranging from 0.60 to 0.80 for LCOS prediction across 2-, 4-, 6-, and 12-hour forecasting horizons. SHAP analyses identified higher vasoactive inotrope score, lower urine output, and higher serum lactate as the most influential predictors.

Conclusions: This study demonstrates that the supervised machine learning models can accurately predict LCOS in neonates, offering high interpretability. The findings support further validation in multicenter settings and integration into clinical workflows to enhance postoperative critical cardiac care neonates.

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CiteScore
5.70
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