Liu Liang, Liu Dashuang, He Ting, Liang Bo, Zhao Jinghong
Department of Nephrology, The Key Laboratory for the Prevention and Treatment of Chronic Kidney Disease of Chongqing, Chongqing Clinical Research Center of Kidney and Urology Diseases, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, China,
Department of Nephrology, The Key Laboratory for the Prevention and Treatment of Chronic Kidney Disease of Chongqing, Chongqing Clinical Research Center of Kidney and Urology Diseases, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Blood Purif. 2024;53(11-12):916-927. doi: 10.1159/000540695. Epub 2024 Aug 12.
Continuous renal replacement therapy (CRRT) is a prolonged continuous extracorporeal blood purification therapy to replace impaired renal function. Typically, CRRT therapy requires routine anticoagulation, but for patients at risk of bleeding and with contraindications to sodium citrate, anticoagulant-free dialysis therapy is necessary. However, this approach increases the risk of CRRT circuit coagulation, leading to treatment interruption and increased resource consumption. In this study, we utilized artificial intelligence machine learning methods to predict the risk of CRRT circuit coagulation based on pre-CRRT treatment metrics.
We retrospectively analyzed 212 patients who underwent anticoagulant-free CRRT from October 2022 to October 2023. Patients were categorized into high-risk and low-risk groups based on CRRT circuit coagulation within 24 h. We employed eight machine learning methods to predict the risk of circuit coagulation. The performance of the model was evaluated using the area under the curve (AUC) of the receiver operating characteristic. 5-fold cross-validation was used to validate the machine learning models. Feature importance and SHAP plots were used to interpret the model's performance and key drivers.
We identified 88 patients (41.51%) at high risk of circuit coagulation within 24 h of CRRT. Our machine learning models showed excellent predictive performance, with ensemble learning achieving an AUC of 0.863 (95% CI: 0.860-0.868), outperforming individual algorithms. Random forest was the best single-algorithm model, with an AUC of 0.819 (95% CI: 0.814-0.823). The top three features identified as most important by the SHAP summary plot and feature importance graph are platelet, filtration fraction (FF), and triglycerides.
We created a model using machine learning to predict the risk of circuit coagulation during anticoagulant-free CRRT therapy. Our model performs well (AUC 0.863) and identifies key factors like platelets, FF, and triglycerides. This facilitates the development of personalized treatment strategies by clinicians aimed at reducing circuit coagulation risk, thereby enhancing patient outcomes and reducing healthcare expenses.
连续性肾脏替代治疗（CRRT）是一种延长的连续性体外血液净化治疗，用于替代受损的肾功能。通常，CRRT治疗需要常规抗凝，但对于有出血风险且有枸橼酸钠禁忌证的患者，无抗凝剂透析治疗是必要的。然而，这种方法会增加CRRT回路凝血的风险，导致治疗中断和资源消耗增加。在本研究中，我们利用人工智能机器学习方法，根据CRRT治疗前的指标预测CRRT回路凝血的风险。
我们回顾性分析了2022年10月至2023年10月期间接受无抗凝剂CRRT治疗的212例患者。根据24小时内CRRT回路凝血情况将患者分为高风险组和低风险组。我们采用了八种机器学习方法来预测回路凝血的风险。使用受试者工作特征曲线下面积（AUC）评估模型的性能。采用5折交叉验证来验证机器学习模型。使用特征重要性和SHAP图来解释模型的性能和关键驱动因素。
我们确定了88例（41.51%）在CRRT治疗24小时内回路凝血风险高的患者。我们的机器学习模型显示出优异的预测性能，集成学习的AUC为0.863（95%CI：0.860-0.868），优于单个算法。随机森林是最佳的单算法模型，AUC为0.819（95%CI：0.814-0.823）。SHAP总结图和特征重要性图确定的最重要的前三个特征是血小板、滤过分数（FF）和甘油三酯。
我们使用机器学习创建了一个模型，以预测无抗凝剂CRRT治疗期间回路凝血的风险。我们的模型性能良好（AUC 0.863），并识别出血小板、FF和甘油三酯等关键因素。这有助于临床医生制定个性化治疗策略，旨在降低回路凝血风险，从而改善患者预后并降低医疗费用。
