■建立并评估接受连续性肾脏替代治疗(CRRT)的急性肾损伤(AKI)危重患者院内死亡率的预测模型,基于九种机器学习(ML)算法。
该研究回顾性地纳入了在美国初次住院期间使用重症监护医疗信息集市(MIMIC)数据库IV(2.0版)进行CRRT的AKI患者,以及湖州市中心医院重症监护室(ICU)。使用MIMIC数据库中的患者作为训练队列来构建模型(从2008年到2019年,n=1068)。湖州市中心医院的患者作为外部验证队列评估模型(2019年6月至2022年12月,n=327)。在训练组中,采用交叉验证的最小绝对收缩和选择算子(LASSO)回归来选择构建模型的特征,随后建立了9个ML预测模型.根据受试者工作特征曲线下面积(AUROC)对这9个模型在外部验证队列数据集上的表现进行综合评价,选择最优模型。提出了静态列线图和基于网络的动态列线图,从歧视(AUROC)的角度进行综合评估,校准(校准曲线)和临床实用性(DCA曲线)。
■最后,纳入了1395名符合条件的患者,包括训练队列中的1068例患者和外部验证队列中的327例患者。在训练组中,采用交叉验证的LASSO回归来选择特征,并分别构建了9个模型。与其他八种型号相比,Lasso正则化逻辑回归(Lasso-LR)模型显示出最高的AUROC(0.756)和最佳的校准曲线。DCA曲线表明在预测接受CRRT的AKI危重患者的院内死亡率方面具有一定的临床实用性。因此,Lasso-LR模型是最佳模型,它被可视化为通用列线图(静态列线图)和基于Web的动态列线图(https://chsyh2006。shinyapps.io/dynomapp/)。歧视,校准和DCA曲线用于评估列线图的性能.列线图模型中训练和外部验证队列的AUROC为0.771(95CI:0.743,0.799)和0.756(95CI:0.702,0.809),分别。训练队列的校准斜率和Brier评分分别为1.000和0.195,而对于外部验证队列,分别为0.849和0.197。DCA表明该模型具有一定的临床应用价值。
■我们的研究选择了最佳模型,并将其可视化为整合临床预测因子的静态和动态列线图,以便临床医生能够个性化预测ICU后接受CRRT的AKI危重患者的院内转归。
UNASSIGNED: To construct and evaluate a predictive model for in-hospital mortality among critically ill patients with acute kidney injury (AKI) undergoing continuous renal replacement therapy (CRRT), based on nine machine learning (ML) algorithm.
UNASSIGNED: The study retrospectively included patients with AKI who underwent CRRT during their initial hospitalization in the United States using the medical information mart for intensive care (MIMIC) database IV (version 2.0), as well as in the intensive care unit (ICU) of Huzhou Central Hospital. Patients from the MIMIC database were used as the training cohort to construct the models (from 2008 to 2019, n = 1068). Patients from Huzhou Central Hospital were utilized as the external validation cohort to evaluate the models (from June 2019 to December 2022, n = 327). In the training cohort, least absolute shrinkage and selection operator (LASSO) regression with cross-validation was employed to select features for constructing the model and subsequently established nine ML predictive models. The performance of these nine models on the external validation cohort dataset was comprehensively evaluated based on the area under the receiver operating characteristic curve (AUROC) and the optimal model was selected. A static nomogram and a web-based dynamic nomogram were presented, with a comprehensive evaluation from the perspectives of discrimination (AUROC), calibration (calibration curve) and clinical practicability (DCA curves).
UNASSIGNED: Finally, 1395 eligible patients were enrolled, including 1068 patients in the training cohort and 327 patients in the external validation cohort. In the training cohort, LASSO regression with cross-validation was employed to select features and nine models were individually constructed. Compared to the other eight models, the Lasso regularized logistic regression (Lasso-LR) model exhibited the highest AUROC (0.756) and the optimal calibration curve. The DCA curve suggested a certain clinical utility in predicting in-hospital mortality among critically ill patients with AKI undergoing CRRT. Consequently, the Lasso-LR model was the optimal model and it was visualized as a common nomogram (static nomogram) and a web-based dynamic nomogram (https://chsyh2006.shinyapps.io/dynnomapp/). Discrimination, calibration and DCA curves were employed to assess the performance of the nomogram. The AUROC for the training and external validation cohorts in the nomogram model was 0.771 (95%CI: 0.743, 0.799) and 0.756 (95%CI: 0.702, 0.809), respectively. The calibration slope and Brier score for the training cohort were 1.000 and 0.195, while for the external validation cohort, they were 0.849 and 0.197, respectively. The DCA indicated that the model had a certain clinical application value.
UNASSIGNED: Our study selected the optimal model and visualized it as a static and dynamic nomogram integrating clinical predictors, so that clinicians can personalized predict the in-hospital outcome of critically ill patients with AKI undergoing CRRT upon ICU admission.