Abstract
Objective: To establish a predictive model for acute respiratory distress syndrome (ARDS) in critical burn patients with the screened independent risk factors, and to validate its predictive value. Methods: Totally 131 critical burn patients (101 males and 30 females, aged 18-84 years) who met the inclusion criteria were admitted to the Department of Burns of the Second Affiliated Hospital of Zhejiang University School of Medicine from January 2018 to December 2019. A retrospective case-control study was conducted. The patients were divided into ARDS group (54 cases) and non-ARDS group (77 cases) according to whether ARDS occurred or not. The statistics of patients in the two groups were recorded including the gender, age, burn index, combination of inhalation injury, smoking history, delayed resuscitation, indwelling nasogastric tube, and complication of sepsis, and the data were statistically analyzed with independent sample t test, chi-square test, and Fisher's exact probability test. The multivariate logistic regression analysis was performed on the indicators with statistically significant differences between the two groups to screen the independent risk factors for developing ARDS in critical burn patients, and the corresponding nomograph prediction model for the risk of ARDS in critical burn patients was established. The risk scores for patients developing ARDS were therefore obtained based on the above-mentioned nomograph, and the corresponding receiver operating characteristic (ROC) curve was drawn to calculate the area under the curve. The internal validation of the above-mentioned ARDS prediction model was performed using the Bootstrap method, and the area under the ROC curve was calculated for modeling group (79 cases) and validation group (52 cases), respectively. A calibration curve was drawn to assess the predictive conformity of the above-mentioned ARDS prediction model for the occurrence of ARDS in critical burn patients. Results: The burn index, proportion of combination of inhalation injury, and proportion of complication of sepsis of patients were significantly higher in ARDS group than in non-ARDS group (t=0.36, χ(2)=33.78, 49.92, P<0.01). The gender, age, smoking history, delayed resuscitation, and indwelling nasogastric tube of patients in ARDS group were close to those in non-ARDS group (P>0.05). The multivariate logistic regression analysis showed that the burn index, combination of inhalation injury, and complication of sepsis were the independent risk factors for developing ARDS in critical burn patients (odds ratio=1.05, 15.33, 5.02, 95% confidence interval=1.01-1.10, 2.65-88.42, 1.28-19.71, P<0.05 or P<0.01). The overall area under the ROC curve of the above-mentioned ARDS prediction model was 0.92 (95% confidence interval=0.88-0.97), and the area under the ROC curve was 0.95 and 0.91 (95% confidence interval=0.90-1.00, 0.86-0.97) for validation group and modeling group, respectively. When applying the above-mentioned ARDS prediction model for ARDS incidence prediction, there might be some risk of overestimating ARDS incidence when the prediction probability was <35.0% or >85.0%, and some risk of underestimating ARDS incidence when the prediction probability was 35.0%-85.0%. Conclusions: The burn index, inhalation injury, and sepsis are the independent risk factors for the occurrence of ARDS in critical burn patients. The risk prediction model for ARDS based on these three indicators has good predictive ability for ARDS in critical burn patients.