Wang Liming, Chen Lin, Ni Hongying, Deng Hongsheng, Chen Kun, Wang Huabin
Department of Intensive Care Unit, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua City, Zhejiang Province, People's Republic of China, 321000.
Central Laboratory, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua City, Zhejiang Province, People's Republic of China, 321000.
Heliyon. 2022 Dec 26;8(12):e12585. doi: 10.1016/j.heliyon.2022.e12585. eCollection 2022 Dec.
Some studies have reported to use some predictors before extracorporeal membrane oxygenation (ECMO) initiation to predict the acute kidney injury (AKI) risk. However, injury during the ECMO operation and the response of patients to ECMO may significantly influence the prognosis, and they are unpredictable before ECMO initiation. This study aims to develop a potential model based clinical characteristics at the 2-hour time point during ECMO for the early prediction of AKI in patients receiving ECMO.
139 patients who underwent ECMO were enrolled in this study. The clinical characteristics and the laboratory examinations at 2-hour time point during ECMO were recorded. The least absolute shrinkage and selection operator (LASSO) regression method was performed to select predictors, and logistic regression and a nomogram were used to establish the prediction model. The area under curve (AUC) of the receiver operating characteristic and calibration curve were used to analyze the discrimination and calibration of the model. K-fold cross-validation method was performed to validate the accuracy of this model.
Among the 139 patients receiving ECMO, 106 participants (76.26%) developed AKI. Four predictive variables including ECMO model, serum creatinine (Scr-2h), uric acid(UA-2h), and serum lactate (Lac-2h) at the 2-hour time point during ECMO were filtered from 39 clinical parameters by LASSO regression. These four predictors were incorporated to develop a model for predicting AKI risk using logistic regression. The AUC of the model was 0.905 (0.845-0.965), corresponding to 81.1% sensitivity, 90.9% specificity and 83.5% accuracy. Moreover, this model showed good consistency between observed and predicted probability based on the calibration curve (P > 0.05). The validation performed by K-fold cross-validation method showed that the accuracy was 0.874 ± 0.006 in training sets, 0.827 ± 0.053 in test sets, indicating a good capability for AKI risk prediction. Finally, a nomogram based on this model was constructed to facilitate its use in clinical practice.
The nomogram incorporating Scr-2h,Lac-2h, UA-2h, and ECMO model may facilitate the individualized prediction of the AKI risk among patients undergoing ECMO.
一些研究报道在体外膜肺氧合（ECMO）启动前使用某些预测指标来预测急性肾损伤（AKI）风险。然而，ECMO操作过程中的损伤以及患者对ECMO的反应可能会显著影响预后，且在ECMO启动前这些情况无法预测。本研究旨在基于ECMO期间2小时时间点的临床特征开发一个潜在模型，用于早期预测接受ECMO治疗患者的AKI。
本研究纳入了139例行ECMO的患者。记录ECMO期间2小时时间点的临床特征和实验室检查结果。采用最小绝对收缩和选择算子（LASSO）回归方法筛选预测指标，并使用逻辑回归和列线图建立预测模型。采用受试者工作特征曲线下面积（AUC）和校准曲线分析模型的区分度和校准度。采用K折交叉验证法验证该模型的准确性。
在139例接受ECMO治疗的患者中，106例（76.26%）发生了AKI。通过LASSO回归从39个临床参数中筛选出4个预测变量，包括ECMO模式、ECMO期间2小时时间点的血清肌酐（Scr-2h）、尿酸（UA-2h）和血清乳酸（Lac-2h）。将这4个预测指标纳入，采用逻辑回归建立AKI风险预测模型。该模型的AUC为0.905（0.845-0.965），对应灵敏度为81.1%，特异度为90.9%，准确率为83.5%。此外，基于校准曲线，该模型在观察概率和预测概率之间显示出良好的一致性（P>0.05）。采用K折交叉验证法进行的验证显示，训练集的准确率为0.874±0.006，测试集的准确率为0.827±0.053，表明该模型具有良好的AKI风险预测能力。最后，基于该模型构建了列线图，以方便其在临床实践中的应用。
纳入Scr-2h、Lac-2h、UA-2h和ECMO模式的列线图可能有助于对接受ECMO治疗的患者进行AKI风险的个体化预测。
