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术中数据可增强基线预测模型对高危急性肾损伤患者的检测能力。

Intraoperative Data Enhance the Detection of High-Risk Acute Kidney Injury Patients When Added to a Baseline Prediction Model.

机构信息

From the Department of Anesthesiology, Columbia University Medical Center, New York, New York.

Department of Epidemiology.

出版信息

Anesth Analg. 2021 Feb 1;132(2):430-441. doi: 10.1213/ANE.0000000000005057.

Abstract

BACKGROUND

Aspects of intraoperative management (eg, hypotension) are associated with acute kidney injury (AKI) in noncardiac surgery patients. However, it is unclear if and how the addition of intraoperative data affects a baseline risk prediction model for postoperative AKI.

METHODS

With institutional review board (IRB) approval, an institutional cohort (2005-2015) of inpatient intra-abdominal surgery patients without preoperative AKI was identified. Data from the American College of Surgeons National Surgical Quality Improvement Program (preoperative and procedure data), Anesthesia Information Management System (intraoperative data), and electronic health record (postoperative laboratory data) were linked. The sample was split into derivation/validation (70%/30%) cohorts. AKI was defined as an increase in serum creatinine ≥0.3 mg/dL within 48 hours or >50% within 7 days of surgery. Forward logistic regression fit a baseline model incorporating preoperative variables and surgical procedure. Forward logistic regression fit a second model incorporating the previously selected baseline variables, as well as additional intraoperative variables. Intraoperative variables reflected the following aspects of intraoperative management: anesthetics, beta-blockers, blood pressure, diuretics, fluids, operative time, opioids, and vasopressors. The baseline and intraoperative models were evaluated based on statistical significance and discriminative ability (c-statistic). The risk threshold equalizing sensitivity and specificity in the intraoperative model was identified.

RESULTS

Of 2691 patients in the derivation cohort, 234 (8.7%) developed AKI. The baseline model had c-statistic 0.77 (95% confidence interval [CI], 0.74-0.80). The additional variables added to the intraoperative model were significantly associated with AKI (P < .0001) and the intraoperative model had c-statistic 0.81 (95% CI, 0.78-0.83). Sensitivity and specificity were equalized at a risk threshold of 9.0% in the intraoperative model. At this threshold, the baseline model had sensitivity and specificity of 71% (95% CI, 65-76) and 69% (95% CI, 67-70), respectively, and the intraoperative model had sensitivity and specificity of 74% (95% CI, 69-80) and 74% (95% CI, 73-76), respectively. The high-risk group had an AKI risk of 18% (95% CI, 15-20) in the baseline model and 22% (95% CI, 19-25) in the intraoperative model.

CONCLUSIONS

Intraoperative data, when added to a baseline risk prediction model for postoperative AKI in intra-abdominal surgery patients, improves the performance of the model.

摘要

背景

术中管理的各个方面(如低血压)与非心脏手术患者的急性肾损伤(AKI)有关。然而,目前尚不清楚术中数据的增加是否以及如何影响术后 AKI 的基线风险预测模型。

方法

在获得机构审查委员会(IRB)批准的情况下,确定了一个机构队列(2005-2015 年),其中包括无术前 AKI 的住院腹部手术患者。美国外科医师学会国家手术质量改进计划(术前和手术数据)、麻醉信息管理系统(术中数据)和电子健康记录(术后实验室数据)的数据被链接在一起。该样本被分为推导/验证(70%/30%)队列。AKI 的定义为术后 48 小时内血清肌酐升高≥0.3mg/dL 或术后 7 天内升高≥50%。向前逻辑回归拟合了一个包含术前变量和手术程序的基线模型。向前逻辑回归拟合了第二个模型,该模型包含先前选择的基线变量以及其他术中变量。术中变量反映了术中管理的以下方面:麻醉剂、β受体阻滞剂、血压、利尿剂、液体、手术时间、阿片类药物和血管加压药。基于统计学意义和判别能力(c 统计量)评估了基线和术中模型。确定了术中模型中灵敏度和特异性相等的风险阈值。

结果

在推导队列的 2691 名患者中,有 234 名(8.7%)发生 AKI。基线模型的 C 统计量为 0.77(95%置信区间[CI],0.74-0.80)。术中模型中添加的其他变量与 AKI 显著相关(P<0.0001),且术中模型的 C 统计量为 0.81(95%CI,0.78-0.83)。在术中模型中,风险阈值为 9.0%时,灵敏度和特异性相等。在该阈值下,基线模型的灵敏度和特异性分别为 71%(95%CI,65-76)和 69%(95%CI,67-70),而术中模型的灵敏度和特异性分别为 74%(95%CI,69-80)和 74%(95%CI,73-76)。高危组在基线模型中的 AKI 风险为 18%(95%CI,15-20),在术中模型中的 AKI 风险为 22%(95%CI,19-25)。

结论

在腹部手术患者术后 AKI 的基线风险预测模型中加入术中数据可提高模型的性能。

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