用机器学习预测术后呛咳风险，发现止痛药剂量和手术部位是关键因素。
What Causes Postoperative Aspiration?
- 基于医院数据训练机器学习模型，用术前信息预测术后呛咳
- 模型准确率AUC达0.86，高剂量止痛药和手术部位影响最大
- 男性患者用药多、呛咳风险高，提示需关注性别差异
背景：呛咳是手术患者常见并发症，显著增加发病率和死亡率。本研究构建机器学习（ML）模型以预测术后七天内呛咳风险，实现早期干预。方法：从超过40万次住院记录的MIMIC-IV数据库中，筛选出826名外科患者（平均年龄62岁，55.7%为男性），其在术后7天内发生呛咳，并匹配非呛咳对照组。使用XGBoost、多层感知机和随机森林三种机器学习模型，基于术前住院数据进行训练。通过增强逆概率加权法估算平均治疗效应（ATE），探究因果关系。结果：所提模型在独立测试集上达到AUROC 0.86，灵敏度77.3%。最重要预测因子为最大日剂量阿片类药物、住院时长和年龄。ATE分析显示：阿片类药物（0.25 ± 0.06）、颈部手术（0.20 ± 0.13）和头颈部手术（0.19 ± 0.13）具有显著因果影响。尽管手术率相同，男性呛咳风险是女性的1.5倍，且最大日剂量高出27%。结论：机器学习可有效预测术后呛咳风险，指导精准预防。最大阿片类药物剂量和手术部位是关键影响因素。男女在用药量与呛咳率上的差异值得深入研究，对优化术后护理和预防策略具有重要意义。
原文摘要 · Abstract (English)
Background: Aspiration, the inhalation of foreign material into the lungs, significantly impacts surgical patient morbidity and mortality. This study develops a machine learning (ML) model to predict postoperative aspiration, enabling timely preventative interventions. Methods: From the MIMIC-IV database of over 400,000 hospital admissions, we identified 826 surgical patients (mean age: 62, 55.7\% male) who experienced aspiration within seven days post-surgery, along with a matched non-aspiration cohort. Three ML models: XGBoost, Multilayer Perceptron, and Random Forest were trained using pre-surgical hospitalization data to predict postoperative aspiration. To investigate causation, we estimated Average Treatment Effects (ATE) using Augmented Inverse Probability Weighting. Results: Our ML model achieved an AUROC of 0.86 and 77.3\% sensitivity on a held-out test set. Maximum daily opioid dose, length of stay, and patient age emerged as the most important predictors. ATE analysis identified significant causative factors: opioids (0.25 +/- 0.06) and operative site (neck: 0.20 +/- 0.13, head: 0.19 +/- 0.13). Despite equal surgery rates across genders, men were 1.5 times more likely to aspirate and received 27\% higher maximum daily opioid dosages compared to women. Conclusion: ML models can effectively predict postoperative aspiration risk, enabling targeted preventative measures. Maximum daily opioid dosage and operative site significantly influence aspiration risk. The gender disparity in both opioid administration and aspiration rates warrants further investigation. These findings have important implications for improving postoperative care protocols and aspiration prevention strategies.
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