用临床数据训练可解释模型，提前预测肝硬化重症患者急性肾损伤风险。
Interpretable Machine Learning Model for Early Prediction of Acute Kidney Injury in Critically Ill Patients with Cirrhosis: A Retrospective Study
- 基于前48小时临床指标，用LASSO和SMOTE构建LightGBM模型。
- 模型AUROC达0.808，阴性预测值0.911，能有效识别低风险患者。
- 结果可解释，适合集成到ICU电子病历系统辅助临床决策。
背景：肝硬化是进展性肝病，死亡率高且常伴发急性肾损伤（AKI），住院患者中发生率高达50%，显著恶化预后。AKI源于复杂的血流动力学、炎症与代谢紊乱，早期识别至关重要。现有预测工具多存在准确率低、可解释性差及不契合ICU工作流程等问题。本研究开发了一种针对肝硬化重症患者的可解释机器学习模型以实现早期AKI预测。方法：回顾性分析MIMIC-IV v2.2数据库，筛选出1240名成人肝硬化重症患者，排除入住ICU不足48小时或关键数据缺失者。提取入院后前48小时的实验室与生理变量，经过预处理、缺失值过滤、LASSO特征选择及SMOTE类别平衡。采用六种算法（LightGBM、CatBoost、XGBoost、逻辑回归、朴素贝叶斯、神经网络）进行训练与评估，使用AUROC、准确率、F1分数、敏感性、特异性及预测值等指标。结果：LightGBM表现最佳（AUROC 0.808，95%置信区间0.741–0.856；准确率0.704；阴性预测值NPV 0.911）。关键预测因子包括凝血酶原时间延长、未在外院建立20G静脉通路、低pH值及动脉氧分压异常，符合肝硬化相关AKI机制，提示可干预靶点。结论：基于LightGBM的模型可利用常规临床变量实现对肝硬化重症患者早期AKI风险的精准分层。其高阴性预测值支持对低风险患者安全降级管理，可解释性有助于提升临床信任并指导针对性预防。需开展外部验证，并推动其与电子健康记录系统集成。
原文摘要 · Abstract (English)
Background: Cirrhosis is a progressive liver disease with high mortality and frequent complications, notably acute kidney injury (AKI), which occurs in up to 50% of hospitalized patients and worsens outcomes. AKI stems from complex hemodynamic, inflammatory, and metabolic changes, making early detection essential. Many predictive tools lack accuracy, interpretability, and alignment with intensive care unit (ICU) workflows. This study developed an interpretable machine learning model for early AKI prediction in critically ill patients with cirrhosis. Methods: We conducted a retrospective analysis of the MIMIC-IV v2.2 database, identifying 1240 adult ICU patients with cirrhosis and excluding those with ICU stays under 48 hours or missing key data. Laboratory and physiological variables from the first 48 hours were extracted. The pipeline included preprocessing, missingness filtering, LASSO feature selection, and SMOTE class balancing. Six algorithms-LightGBM, CatBoost, XGBoost, logistic regression, naive Bayes, and neural networks-were trained and evaluated using AUROC, accuracy, F1-score, sensitivity, specificity, and predictive values. Results: LightGBM achieved the best performance (AUROC 0.808, 95% CI 0.741-0.856; accuracy 0.704; NPV 0.911). Key predictors included prolonged partial thromboplastin time, absence of outside-facility 20G placement, low pH, and altered pO2, consistent with known cirrhosis-AKI mechanisms and suggesting actionable targets. Conclusion: The LightGBM-based model enables accurate early AKI risk stratification in ICU patients with cirrhosis using routine clinical variables. Its high negative predictive value supports safe de-escalation for low-risk patients, and interpretability fosters clinician trust and targeted prevention. External validation and integration into electronic health record systems are warranted.
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