日间头面部手术患儿术后疼痛预测模型的构建及效果评估

doi:10.19438/j.cjoms.2025.05.009

摘要/Abstract

摘要： 目的: 基于机器学习算法,构建日间头面部手术患儿术后疼痛的预测模型,并评估其预测效果。方法: 回顾性纳入893例全麻下日间头面部手术患儿,收集人口学特征和围术期资料。根据手术后全麻恢复期FLACC疼痛评估量表评分,将患儿分为疼痛组（FLACC评分≥4）和非疼痛组（FLACC评分<4）,采用最小绝对收缩和选择算子（LASSO）回归算法五倍交叉验证筛选变量,使用6种不同机器学习算法（Logistics回归模型、K最近邻模型、支持向量机模型、决策树模型、双向递归模型和神经网络模型）将受试者按照7∶3比例分为训练集和测试集,采用受试者工作特征曲线下面积（AUROC）展示训练集和测试集对术后疼痛的预测效果。结果: 日间头面部手术患儿术后疼痛的总发生率为23.63%,通过LASSO 回归筛选的危险因素包括患儿的改良耶鲁术前焦虑量表（m-YPAS）评分、家长术前的状态焦虑问卷（SAI）评分、手术史、手术类型、患儿年龄、性别、诱导期使用舒芬太尼和氯胺酮8项。6种不同机器学习算法中,4种（Logistics回归模型,支持向量机模型、双向递归模型和神经网络模型）能够在测试集中维持较好的表现（AUROC>0.70）。测试集中,Logistic回归模型的预测结果最好（AUROC=0.78,95%CI：0.72~0.84）。结论: 基于机器学习得到的预测模型,可有效预测日间头面部手术患儿的术后疼痛。

关键词: 头面部日间手术, 全身麻醉, 儿童, 术后疼痛, 风险预测模型

Abstract: PURPOSE: To develop and validate a machine learning-based prediction model for postoperative pain in pediatric patients undergoing head and facial day surgery. METHODS: A total of 893 children who underwent head and facial day surgery under general anesthesia were retrospectively included, the patients were categorized into pain (FLACC score ≥4) or non-pain (FLACC score <4) group based on assessments in the postanesthesia care unit (PACU). Variables were selected via the least absolute shrinkage and selection operator (LASSO) regression with fivefold cross-validation. Six machine learning algorithms—including logistic regression (LR), K-nearest neighbor(KNN), support vector machine (SVM), decision tree (DT), bidirectional recurrent neural network (BRNN), and artificial neural network (ANN)—were trained on a 7∶3 split dataset. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). RESULTS: The incidence of postoperative pain was 23.63%. Key predictors included preoperative m-YPAS scores, parental SAI scores, surgical history, procedure type, age, gender and induction with sufentanil/ketamine. Four out of 6 different machine learning algorithms (LR, SVM, BRNN, and ANN) were able to maintain good performance in the test set (AUROC>0.70). Among the models, logistic regression achieved the highest AUROC (0.78, 95%CI: 0.72-0.84) in the test set. CONCLUSIONS: The prediction model based on machine learning in this study can effectively predict postoperative pain in pediatric patients undergoing head and facial day surgery.

Key words: Head and facial day surgery, General anesthesia, Pediatric patients, Postoperative pain, Risk prediction model

中图分类号:

R782.05

沈晓敏, 杨美, 李静洁, 郑永超, 王旭. 日间头面部手术患儿术后疼痛预测模型的构建及效果评估[J]. 中国口腔颌面外科杂志, 2025, 23(5): 483-490.

Shen Xiaomin, Yang Mei, Li Jingjie, Zheng Yongchao, Wang Xu. Construction and effect evaluation of postoperative pain prediction model for children undergoing head and facial day surgery[J]. China J Oral Maxillofac Surg, 2025, 23(5): 483-490.

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