摘要目的:构建和评价基于生物标志物预测重症患者发生急性肾损伤（AKI）的决策树模型。方法:前瞻性选择2017年1月至2018年6月入住南方医科大学附属小榄医院重症医学科的重症患者。记录患者临床资料，入重症监护病房（ICU）即刻测定生物标志物〔血清胱抑素C（sCys C）、尿N-乙酰-β-D-氨基葡萄糖苷酶（uNAG）〕，并记录终点指标。以2017年1月至12月收治的患者作为测试队列，采用决策树分类回归树（CART）算法，以生物标志物最佳截断值为决策节点，构建预测重症AKI的生物标志物决策树模型，采用整体精准度和受试者工作特征曲线（ROC）评估该决策树模型的预测价值。以2018年1月至6月收治的患者作为验证队列，进一步验证该决策树模型的整体精准度和预测能力。结果:在测试队列研究中，共263例患者入选，其中57例（21.7%）发生重症AKI〔定义为改善全球肾脏病预后组织（KDIGO）AKI 2期及3期〕。与非重症AKI患者相比，重症AKI患者年龄更大〔岁：64（49，74）比52（41，66）〕，急性生理学与慢性健康状况评分Ⅱ（APACHEⅡ）更高〔分：23（19，28）比15（11，20）〕，罹患高血压、糖尿病等基础疾病及合并脓毒症的比例更高（64.9%比40.3%、28.1%比10.7%、63.2%比29.6%），sCys C和uNAG水平更高 〔sCys C（mg/L）：1.38（1.12，2.02）比0.79（0.67，0.98），uNAG（U/mmol肌酐）：5.91（2.43，10.68）比2.72（1.60，3.90）〕，住院病死率和90 d病死率更高（21.1%比4.4%，52.6%比13.1%），ICU住院时间更长〔d：6.0（4.0，9.5）比3.0（1.0，6.0）〕，肾脏替代治疗需求更多（22.8%比1.9%），差异均有统计学意义（均 P<0.05）。ROC曲线分析显示，sCys C、uNAG预测重症AKI的ROC曲线下面积（AUC）分别为0.857〔95%可信区间（95% CI）为0.809～0.897〕和0.735（95% CI为0.678～0.788），最佳截断值分别为1.05 mg/L和5.39 U/mmol肌酐。以生物标志物最佳截断值构建的决策树模型结构直观，预测重症AKI的整体精准度为86.0%，AUC为0.905（95% CI为0.863～0.937），敏感性为0.912，特异性为0.796。在130例患者的验证队列中，该决策树模型预测重症AKI的整体精准度为81.0%，AUC为0.909（95% CI为0.846～0.952），敏感性为0.906，特异性为0.816。 结论:基于生物标志物预测重症患者AKI的决策树模型具有较高的准确性，直观明了，可执行性强，有助于临床医师进行判断和采取决策。

abstractsObjective:To construct and evaluate a decision tree based on biomarkers for predicting severe acute kidney injury (AKI) in critical patients.Methods:A prospectively study was conducted. Critical patients who had been admitted to the department of critical care medicine of Xiaolan Hospital of Southern Medical University from January 2017 to June 2018 were enrolled. The clinical data of the patients were recorded, and the biomarkers, including serum cystatin C (sCys C) and urinary N-acetyl-β-D-glucosaminidase (uNAG) were established immediately after admission to intensive care unit (ICU), and the end points were recorded. The test cohort was established with patient data from January to December 2017. The decision tree classification and regression tree (CART) algorithm was used, and the best cut-off values of biomarkers were used as the decision node to construct a biomarker decision tree model for predicting severe AKI. The accuracy of the decision tree model was evaluated by the overall accuracy and the receiver operating characteristic (ROC) curve. The validation cohort, established on patient data from January to June 2018, was used to further validate the accuracy and predictive ability of the decision tree.Results:In test cohort, 263 patients were enrolled, of whom 57 developed severe AKI [defined as phase 2 and 3 of Kidney Disease: Improving Global Outcomes (KDIGO) criterion]. Compared with patients without severe AKI, severe AKI patients were older [years old: 64 (49, 74) vs. 52 (41, 66)], acute physiology and chronic health evaluation Ⅱ (APACHEⅡ) score were higher [23 (19, 27) vs. 15 (11, 20)], the incidence of hypertension, diabetes and other basic diseases and sepsis were higher (64.9% vs. 40.3%, 28.1% vs. 10.7%, 63.2% vs. 29.6%), the levels of sCys C and uNAG were higher [sCys C (mg/L): 1.38 (1.12, 2.02) vs. 0.79 (0.67, 0.98), uNAG (U/mmol Cr): 5.91 (2.43, 10.68) vs. 2.72 (1.60, 3.90)], hospital mortality and 90-day mortality were higher (21.1% vs. 4.4%, 52.6% vs. 13.1%), the length of ICU stay was longer [days: 6.0 (4.0, 9.5) vs. 3.0 (1.0, 6.0)], and renal replacement therapy requirement was higher (22.8% vs. 1.9%), with statistically significant differences (all P < 0.05). ROC curve analysis showed that the areas under ROC curve (AUC) of sCys C and uNAG in predicting severe AKI were 0.857 [95% confidence interval (95% CI) was 0.809-0.897) ] and 0.735 (95% CI was 0.678-0.788), and the best cut-off values were 1.05 mg/L and 5.39 U/mmol Cr, respectively. The structure of the biomarker decision tree model constructed by biomarkers were intuitive. The overall accuracy in predicting severe AKI was 86.0%, and AUC was 0.905 (95% CI was 0.863-0.937), the sensitivity was 0.912, and the specificity was 0.796. In validation cohort of 130 patients, this decision tree yielded an excellent AUC of 0.909 (95% CI was 0.846-0.952), the sensitivity was 0.906, and the specificity was 0.816, with an overall accuracy of 81.0%. Conclusion:The decision tree model based on biomarkers for predicting severe AKI in critical patients is highly accurate, intuitive and executable, which is helpful for clinical judgment and decision.