Construction and validation of a decision tree based on biomarkers for predicting severe acute kidney injury in critically ill patients
10.3760/cma.j.cn121430-20200509-00371
- VernacularTitle:基于生物标志物预测重症患者急性肾损伤决策树模型的构建和验证研究
- Author:
Ruibin CHI
1
;
Meihua LIANG
;
Qiming ZOU
;
Chaofeng LI
;
Huifen ZHOU
;
Zhigang JIAN
Author Information
1. 南方医科大学附属小榄医院重症医学科，广东中山 528415
- From:
Chinese Critical Care Medicine
2020;32(6):721-725
- CountryChina
- Language:Chinese
-
Abstract:
Objective: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.
