| 李素华,陈思思,黄 萱,褚雪倩,李欣赛,陆 晨.构建急性主动脉夹层患者发生急性肾损伤的临床预测模型[J].,2024,(14):2613-2618 |
| 构建急性主动脉夹层患者发生急性肾损伤的临床预测模型 |
| Clinical Prediction Models for Preoperative Acute Kidney Injury due to Acute Aortic Dissection with Different Typing |
| 投稿时间：2023-11-26 修订日期：2023-12-23 |
| DOI：10.13241/j.cnki.pmb.2024.14.003 |
| 中文关键词: 急性主动脉夹层 急性肾损伤 预测因子和模型 |
| 英文关键词: Acute aortic dissection Acute renal injury Predictors and prediction models |
| 基金项目:省部共建中亚高发病成因与防治国家重点实验室开放课题面上项目（SKL-HIDCA-2021-8）；国家自然科学基金地区基金项目（82360139） |
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| 中文摘要: |
| 摘要 目的：分析单中心近3年急性主动脉夹层（AAD）患者的临床资料，构建不同分型AAD患者发生急性肾损伤的临床预测模型，以期指导相关临床医生早期诊断及预防AKI。方法：回顾性收集2019年1月至2021年12月在新疆医科大学第一附属医院住院并确诊为急性主动脉夹层患者的临床资料，将其按照CTA影像学结果分为A型和B型患者，按照KDIGO 标准又将其分别分为 AKI 组及非 AKI 组。比较两组间术前临床资料差异，用二元Logistic回归筛出AAD-AKI的独立预测因子，并构建Logistic临床预测模型。通过绘制各独立预测因子及联合诊断的ROC曲线，评估预测模型对夹层患者发生AKI的诊断价值。结果：多因素Logistic回归分析表明：收缩压、白细胞、入院首次肌酐是TAAAD患者发生AKI的独立预测因子（P＜0.05），TAAAD-AKI预测模型Logit（P）=-5.189+0.019*收缩压+0.109*白细胞+0.012*入院首次肌酐。而TBAAD患者独立预测因子为乳酸浓度、入院首次肌酐、肾脏灌注不良，其预测模型为Logit（P）= -2.976 + 0.295*乳酸浓度 + 0.042*入院首次肌酐 + 0.655*肾脏灌注不良。结论：A型和B型患者发生AKI的最佳单因子预测指标均是入院首次肌酐，由于不同类型的夹层导致患者发生AKI机制也不完全相同，因此根据上述这些预测因子我们构建的临床模型并且发现有良好的预测效价，可为临床医生诊断提供依据。 |
| 英文摘要: |
| ABSTRACT Objective: To analyze the clinical data of patients with acute aortic dissection (AAD) in a single center in recent 3 years, and to construct clinical prediction models of acute renal injury in patients with different types of AAD, in order to guide relevant clinicians in early diagnosis and prevention of AKI. Methods: The clinical data of patients hospitalized and diagnosed with acute aortic dissection at the First Affiliated Hospital of Xinjiang Medical University from January 2019 to December 2021 were retrospectively collected, and they were classified into type A and type B patients according to CTA imaging findings, and further divided into AKI and non-AKI groups, respectively, according to KDIGO criteria. The differences in preoperative clinical data between the two groups were compared, and the independent predictors of AAD-AKI were screened by binary logistic regression, and a logistic clinical prediction model was constructed. The ROC curves of each independent predictor and the combined diagnosis were plotted to evaluate the diagnostic value of the prediction model for the preoperative occurrence of AKI. Results: Multivariate Logistic regression analysis showed that systolic blood pressure, white blood cells and first-time creatinine were independent predictors of AKI in patients with TAAAD. TAAAD-AKI prediction model Logit (P) =-5.189-0.019 * systolic blood pressure + 0.109 * leukocytes + 0.012 * creatinine for the first time on admission. The independent predictors of TBAAD patients were lactic acid concentration, creatinine for the first time on admission and poor renal perfusion, and the predictive model was Logit (P) =-2.976-0.295 * lactic acid concentration + 0.042 * creatinine for the first time on admission + 0.655 * renal poor perfusion. Conclusion: The best univariate predictor of AKI in patients with type An and type B is creatinine for the first time on admission, and the mechanism of AKI in patients with different types of dissection is not the same. Therefore, according to the above predictors, we have established clinical models and found that they all have good predictive titers, which can provide a basis for clinicians' diagnosis. |
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