Zhang Jiangming, Qi Minjun, Ma Lumei, Zhang Kaishuai, Liu Dong, Liu Dongmei
The First Clinical Medical College, Gansu University of Traditional Chinese Medicine, Lanzhou 730000, Gansu, China.
Department of Intensive Care Unit, the 940th Hospital of Joint Logistic Support Force of PLA, Lanzhou 730050, Gansu, China.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2024 Aug;36(8):801-807. doi: 10.3760/cma.j.cn121430-20240221-00150.
To construct and validate a nomogram model for predicting sepsis-associated acute kidney injury (SA-AKI) risk in intensive care unit (ICU) patients.
A retrospective cohort study was conducted. Adult sepsis patients admitted to the department of ICU of the 940th Hospital of Joint Logistic Support Force of PLA from January 2017 to December 2022 were enrolled. Demographic characteristics, clinical data within 24 hours after admission to ICU diagnosis, and clinical outcomes were collected. Patients were divided into training set and validation set according to a 7 : 3 ratio. According to the consensus report of the 28th Acute Disease Quality Initiative Working Group (ADQI 28), the data were analyzed with serum creatinine as the parameter and AKI occurrence 7 days after sepsis diagnosis as the outcome. Lasso regression analysis and univariate and multivariate Logistic regression analysis were performed to construct the nomogram prediction model for SA-AKI. The discrimination and accuracy of the model were evaluated by the Hosmer-Lemeshow test, receiver operator characteristic curve (ROC curve), decision curve analysis (DCA), and clinical impact curve (CIC).
A total of 247 sepsis patients were enrolled, 184 patients developed SA-AKI (74.49%). The number of AKI patients in the training and validation sets were 130 (75.58%) and 54 (72.00%), respectively. After Lasso regression analysis and univariate and multivariate Logistic regression analysis, four independent predictive factors related to the occurrence of SA-AKI were selected, namely procalcitonin (PCT), prothrombin activity (PTA), platelet distribution width (PDW), and uric acid (UA) were significantly associated with the onset of SA-AKI, the odds ratio (OR) and 95% confidence interval (95%CI) was 1.03 (1.01-1.05), 0.97 (0.55-0.99), 2.68 (1.21-5.96), 1.01 (1.00-1.01), all P < 0.05, respectively. A nomogram model was constructed using the above four variables. ROC curve analysis showed that the area under the curve (AUC) was 0.869 (95%CI was 0.870-0.930) in the training set and 0.710 (95%CI was 0.588-0.832) in the validation set. The P-values of the Hosmer-Lemeshow test were 0.384 and 0.294, respectively. In the training set, with an optimal cut-off value of 0.760, a sensitivity of 77.5% and specificity of 88.1% were achieved. Both DCA and CIC plots demonstrated the model's good clinical utility.
A nomogram model based on clinical indicators of sepsis patients admitted to the ICU within 24 hours could be used to predict the risk of SA-AKI, which would be beneficial for early identification and treatment on SA-AKI.
构建并验证一种用于预测重症监护病房（ICU）患者脓毒症相关急性肾损伤（SA-AKI）风险的列线图模型。
进行一项回顾性队列研究。纳入2017年1月至2022年12月在中国人民解放军联勤保障部队第九四〇医院ICU科收治的成年脓毒症患者。收集人口统计学特征、ICU诊断后24小时内的临床资料以及临床结局。患者按7:3的比例分为训练集和验证集。根据第28届急性疾病质量改进工作组（ADQI 28）的共识报告，以血清肌酐为参数、脓毒症诊断后7天内AKI发生情况为结局进行数据分析。进行Lasso回归分析以及单因素和多因素Logistic回归分析，以构建SA-AKI的列线图预测模型。通过Hosmer-Lemeshow检验、受试者工作特征曲线（ROC曲线）、决策曲线分析（DCA）和临床影响曲线（CIC）评估模型的区分度和准确性。
共纳入247例脓毒症患者，184例发生SA-AKI（74.49%）。训练集和验证集中AKI患者数量分别为130例（75.58%）和54例（72.00%）。经过Lasso回归分析以及单因素和多因素Logistic回归分析，筛选出4个与SA-AKI发生相关的独立预测因素，即降钙素原（PCT）、凝血酶原活动度（PTA）、血小板分布宽度（PDW）和尿酸（UA），它们与SA-AKI的发生显著相关，比值比（OR）及95%置信区间（95%CI）分别为1.03（1.01 - 1.05）、0.97（0.55 - 0.99）、2.68（1.21 - 5.96）、1.01（1.00 - 1.01），均P < 0.05。使用上述4个变量构建列线图模型。ROC曲线分析显示，训练集中曲线下面积（AUC）为0.869（95%CI为0.870 - 0.930），验证集中为0.710（95%CI为0.588 - 0.832）。Hosmer-Lemeshow检验的P值分别为0.384和0.294。在训练集中，最佳截断值为0.760时，灵敏度为77.5%，特异度为88.1%。DCA和CIC图均显示该模型具有良好的临床实用性。
基于ICU内24小时内脓毒症患者临床指标构建的列线图模型可用于预测SA-AKI风险，这将有助于SA-AKI的早期识别和治疗。
