用机器学习提前预测重症患者使用万古霉素后的肾损伤风险
Prediction of Significant Creatinine Elevation in First ICU Stays with Vancomycin Use: A retrospective study through Catboost
- 基于10288例数据，用CatBoost模型结合15个关键指标预测肾损伤
- 模型准确率（AUROC）达0.818，对高危患者风险预估平均60.5%
- 可解释性强，磷酸盐、胆红素等是关键预警因子，适合临床决策支持
万古霉素是治疗重症患者革兰氏阳性菌感染的重要药物，但具有高肾毒性风险。本研究利用MIMIC-IV数据库中10,288名接受万古霉素的成年患者（18-80岁），基于常规ICU数据开发机器学习模型，预测万古霉素相关肌酐升高。肾损伤依据KDIGO标准定义：48小时内肌酐升高≥0.3 mg/dL，或7天内升高≥50%。通过SelectKBest和随机森林筛选出前30和最终15个特征，测试六种算法并采用5折交叉验证。结果表明，2,903例（28.2%）患者发生肌酐升高。CatBoost表现最优，AUROC为0.818（95%置信区间：0.801–0.834），敏感性0.800，特异性0.681，阴性预测值0.900。SHAP分析确认磷酸盐为主要风险因素，ALE显示剂量-效应关系，贝叶斯分析估计高风险人群平均风险为60.5%（95%可信区间：16.8–89.4%）。研究证明该模型能高效、可解释地预测万古霉素相关肾损伤，助力早期干预。
原文摘要 · Abstract (English)
Background: Vancomycin, a key antibiotic for severe Gram-positive infections in ICUs, poses a high nephrotoxicity risk. Early prediction of kidney injury in critically ill patients is challenging. This study aimed to develop a machine learning model to predict vancomycin-related creatinine elevation using routine ICU data. Methods: We analyzed 10,288 ICU patients (aged 18-80) from the MIMIC-IV database who received vancomycin. Kidney injury was defined by KDIGO criteria (creatinine rise >=0.3 mg/dL within 48h or >=50% within 7d). Features were selected via SelectKBest (top 30) and Random Forest ranking (final 15). Six algorithms were tested with 5-fold cross-validation. Interpretability was evaluated using SHAP, Accumulated Local Effects (ALE), and Bayesian posterior sampling. Results: Of 10,288 patients, 2,903 (28.2%) developed creatinine elevation. CatBoost performed best (AUROC 0.818 [95% CI: 0.801-0.834], sensitivity 0.800, specificity 0.681, negative predictive value 0.900). Key predictors were phosphate, total bilirubin, magnesium, Charlson index, and APSIII. SHAP confirmed phosphate as a major risk factor. ALE showed dose-response patterns. Bayesian analysis estimated mean risk 60.5% (95% credible interval: 16.8-89.4%) in high-risk cases. Conclusions: This machine learning model predicts vancomycin-associated creatinine elevation from routine ICU data with strong accuracy and interpretability, enabling early risk detection and supporting timely interventions in critical care.
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