Department of Nursing, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, Anhui, China.
Department of Cardiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, China, Hefei, 230001, Anhui, China.
BMC Cardiovasc Disord. 2024 Aug 23;24(1):440. doi: 10.1186/s12872-024-04110-8.
This study aims to construct a clinical prediction model and create a visual line chart depicting the risk of acute kidney injury (AKI) following resuscitation in cardiac arrest (CA) patients. Additionally, the study aims to validate the clinical predictive accuracy of the developed model.
Data were retrieved from the Dryad database, and publicly shared data were downloaded. This retrospective cohort study included 347 successfully resuscitated patients post-cardiac arrest from the Dryad database. Demographic and clinical data of patients in the database, along with their renal function during hospitalization, were included. Through data analysis, the study aimed to explore the relevant influencing factors of acute kidney injury (AKI) in patients after cardiopulmonary resuscitation. The study constructed a line chart prediction model using multivariate logistic regression analysis with post-resuscitation shock status (Post-resuscitation shock refers to the condition where, following successful cardiopulmonary resuscitation after cardiac arrest, some patients develop cardiogenic shock.), C reactive protein (CRP), Lactate dehydrogenase (LDH), and Alkaline phosphatase (ALP) identified as predictive factors. The predictive efficiency of the fitted model was evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.
Multivariate logistic regression analysis showed that post-resuscitation shock status, CRP, LDH, and PAL were the influencing factors of AKI after resuscitation in CA patients. The calibration curve test indicated that the prediction model was well-calibrated, and the results of the Decision Curve Analysis (DCA) demonstrated the clinical utility of the model constructed in this study.
Post-resuscitation shock status, CRP, LDH, and ALPare the influencing factors for AKI after resuscitation in CA patients. The clinical prediction model constructed based on the above indicators has good clinical discriminability and practicality.
本研究旨在构建一个临床预测模型，并创建一个直观的折线图，以描绘心脏骤停（CA）患者复苏后发生急性肾损伤（AKI）的风险。此外，本研究旨在验证所开发模型的临床预测准确性。
从 Dryad 数据库中检索数据，并下载公开共享的数据。这项回顾性队列研究纳入了来自 Dryad 数据库的 347 例心脏骤停后成功复苏的患者。数据库中患者的人口统计学和临床数据以及住院期间的肾功能均包含在内。通过数据分析，本研究旨在探讨心肺复苏后患者急性肾损伤（AKI）的相关影响因素。本研究使用多变量逻辑回归分析构建了一个折线图预测模型，其中复苏后休克状态（复苏后休克是指在心脏骤停后成功心肺复苏后，一些患者出现心源性休克。）、C 反应蛋白（CRP）、乳酸脱氢酶（LDH）和碱性磷酸酶（ALP）被确定为预测因素。通过受试者工作特征（ROC）曲线下面积（AUC）评估拟合模型的预测效率。
多变量逻辑回归分析显示，复苏后休克状态、CRP、LDH 和 ALP 是 CA 患者复苏后 AKI 的影响因素。校准曲线测试表明预测模型具有良好的校准能力，决策曲线分析（DCA）的结果表明本研究构建的模型具有临床实用性。
复苏后休克状态、CRP、LDH 和 ALP 是 CA 患者复苏后 AKI 的影响因素。基于上述指标构建的临床预测模型具有良好的临床区分度和实用性。
