Yu Xiaoyuan, Xin Qi, Hao Yun, Zhang Jin, Ma Tiantian
Department of Hematology, The Affiliated Hospital of Northwest University, Xi'an No. 3 Hospital, Shaanxi, Xi'an, China.
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Front Med (Lausanne). 2024 Feb 26;10:1327036. doi: 10.3389/fmed.2023.1327036. eCollection 2023.
In sepsis patients, kidney damage is among the most dangerous complications, with a high mortality rate. In addition, major adverse kidney events within 30 days (MAKE30) served as a comprehensive and unbiased clinical outcome measure for sepsis patients due to the recent shift toward targeting patient-centered renal outcomes in clinical research. However, the underlying predictive model for the prediction of MAKE30 in sepsis patients has not been reported in any study.
A cohort of 2,849 sepsis patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database was selected and subsequently allocated into a training set ( = 2,137, 75%) and a validation set ( = 712, 25%) through randomization. In addition, 142 sepsis patients from the Xi'An No. 3 Hospital as an external validation group. Univariate and multivariate logistic regression analyses were conducted to ascertain the independent predictors of MAKE30. Subsequently, a nomogram was developed utilizing these predictors, with an area under curve (AUC) above 0.6. The performance of nomogram was assessed through calibration curve, receiver operating characteristics (ROC) curve, and decision curve analysis (DCA). The secondary outcome was 30-day mortality, persistent renal dysfunction (PRD), and new renal replacement therapy (RRT). MAKE30 were a composite of death, PRD, new RRT.
The construction of the nomogram was based on several independent predictors (AUC above 0.6), including age, respiratory rate (RR), PaO2, lactate, and blood urea nitrogen (BUN). The predictive model demonstrated satisfactory discrimination for MAKE30, with an AUC of 0.740, 0.753, and 0.821 in the training, internal validation, and external validation cohorts, respectively. Furthermore, the simple prediction model exhibited superior predictive value compared to the SOFA model in both the training (AUC = 0.710) and validation (AUC = 0.692) cohorts. The nomogram demonstrated satisfactory calibration and clinical utility as evidenced by the calibration curve and DCA. Additionally, the predictive model exhibited excellent accuracy in forecasting 30-day mortality (AUC = 0.737), PRD (AUC = 0.639), and new RRT (AUC = 0.846) within the training dataset. Additionally, the model displayed predictive power for 30-day mortality (AUC = 0.765), PRD (AUC = 0.667), and new RRT (AUC = 0.783) in the validation set.
The proposed nomogram holds the potential to estimate the risk of MAKE30 promptly and efficiently in sepsis patients within the initial 24 h of admission, thereby equipping healthcare professionals with valuable insights to facilitate personalized interventions.
在脓毒症患者中，肾损伤是最危险的并发症之一，死亡率很高。此外，由于临床研究最近转向以患者为中心的肾脏结局为目标，30天内的主要不良肾脏事件（MAKE30）成为脓毒症患者全面且无偏倚的临床结局指标。然而，尚未有任何研究报道脓毒症患者MAKE30预测的潜在预测模型。
从重症监护医学信息数据库（MIMIC）-IV中选取2849例脓毒症患者队列，随后通过随机化将其分为训练集（n = 2137，75%）和验证集（n = 712，25%）。此外，选取西安市第三医院的142例脓毒症患者作为外部验证组。进行单因素和多因素逻辑回归分析以确定MAKE30的独立预测因素。随后，利用这些预测因素绘制列线图，曲线下面积（AUC）大于0.6。通过校准曲线、受试者工作特征（ROC）曲线和决策曲线分析（DCA）评估列线图的性能。次要结局为30天死亡率、持续性肾功能障碍（PRD）和新的肾脏替代治疗（RRT）。MAKE30是死亡、PRD、新RRT的综合指标。
列线图的构建基于几个独立预测因素（AUC大于0.6），包括年龄、呼吸频率（RR）、动脉血氧分压（PaO₂）、乳酸和血尿素氮（BUN）。该预测模型对MAKE30显示出良好的区分度，在训练队列、内部验证队列和外部验证队列中的AUC分别为0.740、0.753和0.821。此外，在训练队列（AUC = 0.710）和验证队列（AUC = 0.692）中，简单预测模型比序贯器官衰竭评估（SOFA）模型具有更高的预测价值。校准曲线和DCA表明列线图具有良好的校准度和临床实用性。此外，预测模型在预测训练数据集中的30天死亡率（AUC = 0.737）、PRD（AUC = 0.639）和新RRT（AUC = 0.846）方面表现出优异的准确性。此外，该模型在验证集中对30天死亡率（AUC = 0.765）、PRD（AUC = 0.667）和新RRT（AUC = 0.783）也显示出预测能力。
所提出的列线图有可能在脓毒症患者入院后的最初24小时内迅速有效地估计MAKE30的风险，从而为医护人员提供有价值的见解以促进个性化干预。
