[Establishment of nomogram predicting model for the death risk of extremely severe burn patients and the predictive value]

Abstract

Objective: To explore the death risk factors of extremely severe burn patients, establish a death risk nomogram predicting model, and investigate the predictive value for death risk of extremely severe burn patients. Methods: The medical records of 231 extremely severe burn patients (190 males and 41 females, aged 18-60 years) who were admitted to the Institute of Burn Research of the First Affiliated Hospital of Army Medical University from January 2010 to October 2018 and met the inclusion criteria were analyzed retrospectively. According to the final outcome, the patients were divided into survival group of 173 patients and death group of 58 patients. The sex, age, severity of inhalation injury, total burn area, full-thickness burn area, burn index, rehydration coefficient and urine volume coefficient of the first and second 24 h after injury, the first base excess, shock index, and hematocrit (HCT) after admission, whether to have pre-hospital fluid infusion, use of ventilator, and use of continuous renal replacement therapy (CRRT), and abbreviated burn severity index (ABSI ) and Baux score on admission of patients in the two groups were recorded or calculated. According to the use of ventilator, the patients were divided into with ventilator group of 131 patients and without ventilator group of 100 patients, and the death, total burn surface area, burn index, incidence and severity of inhalation injury were recorded. According to the use of CRRT, the patients were divided into with CRRT group of 59 patients and without CRRT group of 172 patients, and the death, total burn surface area, and burn index were recorded. Data were statistically analyzed with t test, chi-square test, and Mann-Whitney U test to screen the death related factors of patients. The indexes with statistically significant differences between survival group and death group were included in the multivariate logistic regression analysis to screen the independent death risk factors of patients, and the death risk nomogram predicting model was constructed based on the results.The Bootstrap method was used to validate the death risk nomogram predicting model internally. The predictive value of the nomogram model for predicting death risk of patients was detected by drawing calibration graph and calculating concordance index, and the death risk scores of 231 patients were acquired according to the death risk nomogram model. The receiver's operating characteristic (ROC) curve was drawn, and the optimal threshold and the sensitivity and specificity of optimal threshold in the ROC curve and the area under the curve were calculated. Results: (1) There were statistically significant differences in burn index, ABSI on admission, severity of inhalation injury, total burn area, full-thickness burn area, rehydration coefficient at the first 24 h after injury, use of ventilator, use of CRRT, and Baux score on admission of patients between the two groups (Z=-7.696, -7.031, χ(2)=18.304, 63.065, 23.300, 13.073, 34.240, 59.586, t=-7.536, P<0.01). (2) There were statistically significant differences in death, incidence and severity of inhalation injury, total burn area, and burn index of patients between with ventilator group and without ventilator group (χ(2)=34.240, 17.394, 25.479, Z=-6.557, -7.049, P<0.01). (3) There were statistically significant differences in death, total burn area, and burn index of patients between with CRRT group and without CRRT group (χ(2)=62.982, Z= -47.421, -6.678, P<0.01). (4) The use of ventilator, use of CRRT, and burn index were independent risk factors for the death of extremely severe burn patients (odds ratio=3.277, 5.587, 1.067, 95% confidence interval=1.073-10.008, 2.384-13.093, 1.038-1.096, P<0.05 or P<0.01). (5) The initial concordance index of nomogram predicting model was 0.90 and the corrected concordance index was 0.89. The concordance indexes before and after correction were higher and similar, which showed that the nomogram had good concordance and predictive effect. The optimum threshold of ROC curve was 0.23, the sensitivity and specificity of optimum threshold were 86.0% and 80.0%, respectively, and the area under ROC curve was 0.90 (95% confidence interval=0.86-0.94, P<0.01). Conclusions: Severe burns and damage and/or failure of organ are the main death causes of extremely severe burn patients. The death risk nomogram predicting model established on the basis of use of ventilator, use of CRRT, and burn index have good predictive ability for death of extremely severe burn patients.

Keywords: Burns; Death risk; Forecasting; Nomogram model.