2023 年11 期 第31 卷
脑卒中专题研究急性脑梗死患者并发急性肾损伤的影响因素及其风险预测列线图模型构建
Influencing Factors of Acute Kidney Injury in Patients with Acute Cerebral Infarction and Construction of NomogramModel for Predicting Its Risk
作者:刘张波,葛中林,刘珊,韩红,刘红
- 单位:
- 1.222000江苏省连云港市第二人民医院肾内科 2.222000江苏省连云港市第二人民医院神经内科 3.222000江苏省连云港市第四人民医院肾内科
- Units:
- 1.Department of Nephrology, the Second People's Hospital of Lianyungang, Lianyungang 222000, China2.Department of Neurology, the Second People's Hospital of Lianyungang, Lianyungang 222000, China3.Department of Nephrology, the Fourth People's Hospital of Lianyungang, Lianyungang 222000, China
- 关键词:
- 脑梗死;急性肾损伤;影响因素分析;列线图
- Keywords:
- Brain infarction; Acute kidney injury; Root cause analysis; Nomograms
- CLC:
- R 743.33
- DOI:
- 10.12114/j.issn.1008-5971.2023.00.297
- Funds:
- 江苏省自然科学基金面上项目(BK20220243)
摘要:
目的 探讨急性脑梗死患者并发急性肾损伤(AKI)的影响因素,构建其风险预测列线图模型并进行验证。方法 回顾性选取2019年1月至2023年1月连云港市第二人民医院收治的急性脑梗死患者100例为研究对象,收集患者的临床资料,记录患者发病3个月内并发AKI情况,并将其分为无AKI组和并发AKI组。采用多因素Logistic回归分析探讨急性脑梗死患者并发AKI的影响因素;采用R 3.6.3软件构建急性脑梗死患者并发AKI的风险预测列线图模型;采用Bootstrap法(重复抽样1 000次)予以内部验证,计算一致性指数,采用Hosmer-Lemeshoe拟合优度检验和校准曲线评价该列线图模型的拟合程度;采用ROC曲线分析该列线图模型对急性脑梗死患者并发AKI的预测价值。结果100例急性脑梗死患者中,31例并发AKI,AKI发生率为31.0%。并发AKI组患者年龄≥60岁、合并糖尿病、合并高血压、合并高脂血症、超敏C反应蛋白(hs-CRP)≥15.8 mg/L者占比高于无AKI组(P<0.05)。多因素Logistic回归分析结果显示,年龄、合并高血压、hs-CRP是急性脑梗死患者并发AKI的独立影响因素(P<0.05)。基于多因素Logistic回归分析结果,构建急性脑梗死患者并发AKI的风险预测列线图模型,其中年龄≥60岁为75分,合并高血压为82分,hs-CRP≥15.8 mg/L为100分。该列线图模型的一致性指数为0.772〔95%CI(0.685,0.846)〕。Hosmer-Lemeshoe拟合优度检验结果显示,该列线图模型拟合较好(χ2=0.254,P=0.508)。ROC曲线分析结果显示,该列线图模型预测急性脑梗死患者并发AKI的AUC为0.885〔95%CI(0.800,0.969)〕。结论 急性脑梗死患者AKI的发生率较高,且年龄≥60岁、合并高血压、hs-CRP≥15.8 mg/L是急性脑梗死患者并发AKI的独立危险因素,基于上述危险因素构建的列线图模型对急性脑梗死患者并发AKI具有一定预测价值。
Abstract:
Objective To explore the influencing factors of acute kidney injury (AKI) in patients with acute cerebralinfarction, and to construct and validate the nomogram model for predicting its risk. Methods A total of 100 patients with acutecerebral infarction admitted to the Second People's Hospital of Lianyungang from January 2019 to January 2023 were selectedas the research subjects. The clinical data of the patients were collected, the occurrence of AKI within 3 months after onset wererecorded, and the patients were divided into without AKI group and AKI group. Multivariate Logistic regression analysis was usedto analyze the influencing factors of AKI in patients with acute cerebral infarction. The nomogram model for predicting the risk ofAKI in patients with acute cerebral infarction was constructed by using the R 3.6.3 software. The internal validation was performedby the Bootstrap method (1 000 repetitive samples) , and the consistency index was calculated. Hosmer-Lemeshoe goodness of fittest and calibration curve were used to evaluate the fitting degree of the nomogram model, and the ROC curve was used to analyzethe predictive value of the nomogram model for AKI in patients with acute cerebral infarction. Results The incidence of AKI in100 patients with acute cerebral infarction was 31.0% (31/100) . The proportion of patients with age ≥ 60 years old, proportion ofpatients with diabetes, proportion of patients with hypertension, proportion of patients with hyperlipidemia, proportion of patients with hypersensitive C-reactive protein (hs-CRP) ≥ 15.8 mg/L in the AKI group were higher than those in the without AKI group(P < 0.05) . Multivariate Logistic regression analysis showed that age, concomitant hypertension, and hs-CRP were the influencingfactors of AKI in patients with acute cerebral infarction (P < 0.05) . The nomogram model for predicting the risk of AKI in patientswith acute cerebral infarction was constructed based on the multivariate Logistic regression analysis results, among which age ≥ 60years old was 75 points, concomitant hypertension was 82 points, and hs-CRP ≥ 15.8 mg/L was 100 points. The consistency indexof the nomogram model was 0.772 [95%CI (0.685, 0.846) ] . The results of Hosmer-Lemeshoe goodness of fit test showed that thenomogram model fit well (χ2=0.254, P=0.508) . The results of ROC curve analysis showed that the AUC of the nomogram modelfor predicting AKI in patients with acute cerebral infraction was 0.885 [95%CI (0.800, 0.969) ] . Conclusion The incidence ofAKI in patients with acute cerebral infarction is relatively high, and age ≥ 60 years old, concomitant hypertension, and hs-CRP ≥15.8 mg/L are independent risk factors of AKI in patients with acute cerebral infarction. The nomogram model constructed basedon the above factors has a certain predictive value for AKI in patients with acute cerebral infarction.
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