Data_Sheet_1_Prediction of Mortality Risk After Ischemic Acute Kidney Injury With a Novel Prognostic Model: A Multivariable Prediction Model Development and Validation Study.PDF

Background and Objectives:Acute kidney injury (AKI) that results from ischemia is a common clinical syndrome and correlates with high morbidity and mortality among hospitalized patients. However, a clinical tool to predict mortality risk of ischemic AKI is not available. In this study, we aimed to develop and validate models to predict the 30-day and 1-year mortality risk of hospitalized patients with ischemic AKI.MethodsA total of 1,836 admissions with ischemic AKI were recruited from 277,898 inpatients admitted to three affiliated tertiary general hospitals of Central South University in China between January 2015 and December 2015. Patients in the final analysis were followed up for 1 year. Study patients were randomly divided in a 7:3 ratio to form the training cohort and validation cohort. Multivariable regression analyses were used for developing mortality prediction models.ResultsHepatorenal syndrome, shock, central nervous system failure, Charlson comorbidity index (≥2 points), mechanical ventilation, renal function at discharge were independent risk factors for 30-day mortality after ischemic AKI, while malignancy, sepsis, heart failure, liver failure, Charlson comorbidity index (≥2 points), mechanical ventilation, and renal function at discharge were predictors for 1-year mortality. The area under the receiver operating characteristic curves (AUROCs) of 30-day prediction model were 0.878 (95% confidence interval (CI): 0.849-0.908) in the training cohort and 0.867 (95% CI: 0.820–0.913) in the validation cohort. The AUROCs of the 1-year mortality prediction in the training and validation cohort were 0.803 (95% CI: 0.772–0.834) and 0.788 (95% CI: 0.741–0.835), respectively.ConclusionOur easily applied prediction models can effectively identify individuals at high mortality risk within 30 days or 1 year in hospitalized patients with ischemic AKI. It can guide the optimal clinical management to minimize mortality after an episode of ischemic AKI.

背景与目标：由缺血引起的急性肾脏损伤（AKI）是一种常见的临床综合征，与住院患者的较高发病率和死亡率相关。然而，目前尚无可用于预测缺血性AKI死亡率风险的临床工具。在本研究中，我们旨在开发并验证预测住院患者缺血性AKI 30天和1年死亡率风险的模型。方法：本研究共纳入了2015年1月至2015年12月期间入住中国中南大学三所附属三级综合医院的1,836例缺血性AKI患者。最终分析的患者均进行了1年的随访。研究患者按7:3的比例随机分为训练组和验证组。采用多变量回归分析开发死亡率预测模型。结果：肝肾功能不全、休克、中枢神经系统衰竭、Charlson合并症指数（≥2分）、机械通气、出院时的肾功能是缺血性AKI后30天死亡率的独立风险因素，而恶性肿瘤、败血症、心力衰竭、肝衰竭、Charlson合并症指数（≥2分）、机械通气、出院时的肾功能是1年死亡率的预测因子。30天预测模型的受试者工作特征曲线下面积（AUROC）在训练组为0.878（95%置信区间（CI）：0.849-0.908），在验证组为0.867（95% CI：0.820–0.913）。训练组和验证组的1年死亡率预测AUROC分别为0.803（95% CI：0.772–0.834）和0.788（95% CI：0.741–0.835）。结论：我们的易于应用的预测模型能够有效识别缺血性AKI住院患者中30天或1年内死亡风险较高的人群，并可指导临床管理的优化，以降低缺血性AKI事件后的死亡率。