Establishment of a prognostic nomogram model for predicting acute renal injury in patients with moderate and severe burns.
10.3760/cma.j.cn121430-20220616-00577
- Author:
Xin YANG
1
;
Xinli TIAN
2
,
3
;
Jiang LIU
1
;
Ying LI
1
;
Wenli GUO
1
;
Santao OU
1
;
Weihua WU
1
Author Information
1. Department of Nephrology, the Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China.
2. Department of Plastic and Burn Surgery, the Affiliated Hospital of Southwest Medical University, National Key Clinical Construction Specialty, Luzhou 646000, Sichuan, China. Yang Xin is working at the Department of Nephrology, Zigong First People's Hospital, Zigong 643000, Sichuan, China; Guo Wenli is working at the Department of Nephrology and Rheumatology, Medical Center Hospital of Qionglai City, Chengdu 610000, Sichuan, China. Corresponding author: Ou Santao, Email: ousantao@
3. com.
- Publication Type:Journal Article
- MeSH:
Humans;
Prognosis;
Nomograms;
Retrospective Studies;
Burns/complications*;
Acute Kidney Injury/etiology*;
ROC Curve
- From:
Chinese Critical Care Medicine
2023;35(7):736-740
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To establish a prediction model of acute kidney injury (AKI) in moderate and severe burn patients, so as to provide basic research evidence for early identification of burn-related AKI.
METHODS:Patients who were admitted to the department of plastic burn surgery of the Affiliated Hospital of Southwest Medical University from November 2018 to January 2021 were selected, and their clinical characteristics, laboratory examinations and other indicators were recorded. Multivariate Logistic regression analysis was used to screen out the risk factors of AKI related to moderate and severe burns, and R software was used to establish the nomogram of moderate and severe burn patients complicated with AKI. The Bootstrap method model was used for internal verification by repeating sample for 1 000 times. Consistency index and calibration curve were used to evaluate the accuracy of the model, and the receiver operator characteristic curve (ROC curve) and the area under the curve (AUC) were used to evaluate the prediction efficiency, decision curve analysis (DCA) was used to evaluate the clinical utility of the model.
RESULTS:A total of 186 patients with moderate and severe burn were included, among which 54 patients suffered from AKI, and the incidence rate was 29.03%. Multivariate Logistic regression analysis showed that the total burn surface area [TBSA; odds ratio (OR) = 1.072, 95% confidence interval (95%CI) was 1.031-1.115, P = 0.001], estimated glomerular filtration rate (eGFR; OR = 0.960, 95%CI was 0.931-0.990, P = 0.010), neutrophil (NEU; OR = 1.190, 95%CI was 1.021-1.386, P = 0.026), neutrophil/lymphocyte ratio (NLR; OR = 0.867, 95%CI was 0.770-0.977, P = 0.019), D-dimer (OR = 4.603, 95%CI was 1.792-11.822, P = 0.002) were the risk factors for patients with moderate and severe burn complicated with AKI. Taking the above indexes as predictive factors, a nomogram prediction model was established, the ROC curve was plotted with AUC of 0.998 (95%CI was 0.988-1.000). Optimum threshold of ROC curve was -0.862, the sensitivity was 98.0% and the specificity was 98.2%, and the consistency index was 0.998 (95%CI was 0.988-1.000). The calibration curve showed that the prognostic nomogram model was accurate, DCA showed that most patients can benefit from this model.
CONCLUSIONS:The burned patients with higher TBSA, NEU, NLR, D-dimer and lower eGFR tend to suffer from AKI. The nomogram based on the above five risk factors has high accuracy and clinical value, which can be used as a predictive tool to evaluate the risk of AKI in moderate and severe burn patients.
