Acute kidney injury (AKI) is a common and serious complication that is closely correlated to a poor short-term or long-term prognosis in stroke patients. Therefore, it is necessary to develop a specific AKI screening tool to early identify patients at high risk of AKI.

To construct and verify a risk prediction model of post-stroke AKI and to develop a simple post-stroke AKI risk assessment scale.

Stroke inpatients with complete medical records were selected from the Second Affiliated Hospital Zhejiang University School of Medicine by use of convenience sampling, including 760 from neurology department treated during January to September 2021 (model group, 140 with AKI, and 620 without), and 310 treated during October to December 2021 (validation group, 53 with AKI and 257 without). Multivariate Logistic regression was used to identify factors associated with post-stroke AKI, then these factors were used to develop a risk prediction model. The Hosmer-Lemeshow test and receiver operating characteristic analysis were performed to assess the accuracy of fit and prediction value of the model, respectively. Then the model was verified in validation group, and based on the validation results, a simple post-stroke AKI risk assessment scale was developed.

The prevalence of post-stroke AKI in the model group was 18.42% (P<0.05). Multivariate Logistic regression analysis showed that sex, history of hypertension, NIHSS score, history of use of loop diuretics, history of mechanical thrombectomy, serum levels of β₂-MG, urea nitrogen, and sCysC were independently associated with post-stroke AKI (P<0.05). The post-stroke AKI risk prediction model constructed is y=1/ (1+e^-a), in which a=-4.047+1.222× male + 1.386 × hypertension history + 1.716 × NIHSS score + 1.098 ×history of use of loop diuretics + 0.830 × mechanical thrombectomy history + 1.739 × β₂-MG+1.202 × urea nitrogen + 2.160 × sCysC. The fit of the model was χ²=6.523, P=0.367. The AUC of the model for predicting post-stroke AKI in model group was 0.916 〔95%CI (0.891, 0.940) 〕, with 0.857 sensitivity, 0.832 specificity, and 0.689 Youden index when the optimal cut-off value was chosen as 12.8%. And the AUC of the model in predicting post-stroke AKI in the verification group was 0.906 〔95%CI (0.853, 0.960) 〕. The coefficients (β) derived from multivariate Logistic regression were rounded to the nearest integral value and weighted, then used to compile a simple scale with a total points of 11, whose AUC in predicting post-stroke AKI risk was 0.900〔95%CI (0.843, 0.957), P<0.001〕when the optimal cut-off value was determined as 4, and the accuracy rate of which in practical applications was 88.39%.

Our risk prediction model could effectively predict the risk of post-stroke AKI with high sensitivity and specificity, and the risk assessment scale compiled based on the model is a simple, feasible, objective, and quantitative tool for identifying high-risk patients, and the assessment result may be a reference for doctors and nurses to take interventions to early prevent AKI in stroke patients.

急性肾损伤（AKI）是脑卒中患者常见的严重并发症之一，与患者不良的短期和长期预后密切相关，因此需要探索特异性的AKI筛查工具，以便早期识别AKI的高危人群。

构建并验证脑卒中后发生AKI风险预测模型，并编制简易风险评分量表。

采用便利抽样法选取2021年1—9月在浙江大学医学院附属第二医院神经内科住院治疗且病历资料完整的760例脑卒中患者为建模组，根据是否发生AKI将其分为AKI亚组和非AKI亚组。采用多因素Logistic回归分析进行影响因素分析，并构建脑卒中后发生AKI风险预测模型，采用Hosmer-Lemeshow和受试者工作特征曲线（ROC曲线）检验模型的拟合优度及预测效果。采用便利抽样法选取2021年10—12月在本院治疗的脑卒中患者310例作为模型外部验证组，其中AKI者53例、非AKI者257例。将预测模型在验证组人群进行验证，并编制脑卒中后发生AKI简易风险评分量表。

建模组760例脑卒中患者中发生AKI 140例，AKI发生率为18.42%。多因素Logistic回归分析结果显示，性别、高血压史、美国国立卫生研究院脑卒中量表（NIHSS）评分、袢利尿剂用药史、机械取栓史、血清β₂微球蛋白（β₂-MG）、尿素氮、血清胱抑素C（sCysC）是脑卒中后发生AKI的独立影响因素（P<0.05）。构建脑卒中后发生AKI风险预测模型：y=1/（1+e^-a），其中a=-4.047+1.222×男性+1.386×高血压史+1.716×NIHSS评分+1.098×袢利尿剂用药史+0.830×机械取栓史+1.739×β₂-MG+1.202×尿素氮+2.160×sCysC。Hosmer-Lemeshow检验预测模型拟合效果，χ²=6.523，P=0.367。脑卒中后发生AKI风险预测模型在建模组中预测脑卒中患者发生AKI的ROC曲线下面积（AUC）为0.916〔95%CI（0.891，0.940）〕，最佳截断值为12.8%，灵敏度为0.857，特异度为0.832，约登指数为0.689。脑卒中后发生AKI风险预测模型在验证组中预测脑卒中患者发生AKI的AUC为0.906〔95%CI（0.853，0.960）〕。结合多因素Logistic回归分析结果得出的变量的系数四舍五入到最近整数，赋分后编制量表，最终建立了总分值为11分、截断值为4分、AUC为0.900〔95%CI（0.843，0.957），P<0.001〕的脑卒中后发生AKI简易风险评分量表，实际应用的正确率为88.39%。

本研究构建的脑卒中后发生AKI风险预测模型可以有效地预测脑卒中后AKI发生风险，灵敏度、特异度较高，且据此编制的脑卒中后发生AKI简易风险评分量表为临床提供了一种简便可行的客观、量化高危患者的评估工具，为医护人员早期及时对不同AKI发生风险的脑卒中患者采取预防性治疗提供借鉴。