Cui Xiao, Shi Yu, He Xinlei, Zhang Mingyuan, Zhang Hua, Yang Jianhong, Leng Yuxin
Department of Intensive Care Units, Peking University Third Hospital, Beijing, China.
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China.
Front Med (Lausanne). 2024 Apr 9;11:1338061. doi: 10.3389/fmed.2024.1338061. eCollection 2024.
Gastrointestinal (GI) function is critical for patients in intensive care units (ICUs). Whether and how much critically ill patients without GI primary diseases benefit from abdominal physical examinations remains unknown. No evidence from big data supports its possible additive value in outcome prediction.
We performed a big data analysis to confirm the value of abdominal physical examinations in ICU patients without GI primary diseases. Patients were selected from the Medical Information Mart for Intensive Care (MIMIC)-IV database and classified into two groups depending on whether they received abdominal palpation and auscultation. The primary outcome was the 28-day mortality. Statistical approaches included Cox regression, propensity score matching, and inverse probability of treatment weighting. Then, the abdominal physical examination group was randomly divided into the training and testing cohorts in an 8:2 ratio. And patients with GI primary diseases were selected as the validation group. Several machine learning algorithms, including Random Forest, Gradient Boosting Decision Tree, Adaboost, Extra Trees, Bagging, and Multi-Layer Perceptron, were used to develop in-hospital mortality predictive models.
Abdominal physical examinations were performed in 868 (2.63%) of 33,007 patients without primary GI diseases. A significant benefit in terms of 28-day mortality was observed among the abdominal physical examination group (HR 0.75, 95% CI 0.56-0.99; = 0.043), and a higher examination frequency was associated with improved outcomes (HR 0.62, 95%CI 0.40-0.98; = 0.042). Machine learning studies further revealed that abdominal physical examinations were valuable in predicting in-hospital mortality. Considering both model performance and storage space, the Multi-Layer Perceptron model performed the best in predicting mortality (AUC = 0.9548 in the testing set and AUC = 0.9833 in the validation set).
Conducting abdominal physical examinations improves outcomes in critically ill patients without GI primary diseases. The results can be used to predict in-hospital mortality using machine learning algorithms.
胃肠道（GI）功能对重症监护病房（ICU）患者至关重要。无胃肠道原发性疾病的重症患者是否能从腹部体格检查中获益以及获益程度尚不清楚。尚无大数据证据支持其在预后预测中的潜在附加价值。
我们进行了一项大数据分析，以证实腹部体格检查对无胃肠道原发性疾病的ICU患者的价值。患者选自重症监护医学信息数据库（MIMIC）-IV数据库，并根据是否接受腹部触诊和听诊分为两组。主要结局为28天死亡率。统计方法包括Cox回归、倾向评分匹配和逆概率处理加权。然后，腹部体格检查组以8:2的比例随机分为训练队列和测试队列。选择有胃肠道原发性疾病的患者作为验证组。使用包括随机森林、梯度提升决策树、Adaboost、极端随机树、装袋法和多层感知器在内的几种机器学习算法来建立院内死亡率预测模型。
在33007例无原发性胃肠道疾病的患者中，868例（2.63%）接受了腹部体格检查。腹部体格检查组在28天死亡率方面有显著获益（HR 0.75，95%CI 0.56-0.99；P = 0.043），且检查频率越高，结局越好（HR 0.62，95%CI 0.40-0.98；P = 0.042）。机器学习研究进一步表明，腹部体格检查在预测院内死亡率方面具有价值。综合考虑模型性能和存储空间，多层感知器模型在预测死亡率方面表现最佳（测试集中AUC = 0.9548，验证集中AUC = 0.9833）。
对无胃肠道原发性疾病的重症患者进行腹部体格检查可改善结局。这些结果可用于使用机器学习算法预测院内死亡率。
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