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目的:筛选危重烧伤患者发生ARDS的独立危险因素，以此构建危重烧伤患者发生ARDS预测模型并分析其预测价值。方法:2018年1月—2019年12月，浙江大学医学院附属第二医院烧伤科收治131例符合入选标准的危重烧伤患者（男101例、女30例，年龄18~84岁），对其进行回顾性病例对照研究。根据是否发生ARDS，将患者分为ARDS组（54例）和非ARDS组（77例）。统计2组患者性别、年龄、烧伤指数、合并吸入性损伤情况、吸烟史、延迟复苏情况、留置鼻胃管情况和并发脓毒症情况，对数据进行独立样本n t检验、n χ2检验、Fisher确切概率法检验。对2组比较差异有统计学意义的指标进行多因素logistic回归分析，筛选危重烧伤患者发生ARDS的独立危险因素，并据此构建危重烧伤患者发生ARDS风险的列线图预测模型。根据前述列线图得到患者发生ARDS的风险评分，绘制受试者操作特征（ROC）曲线，计算曲线下面积；采用Bootstrap法对前述ARDS预测模型进行内部验证，分别计算建模组（79例）和验证组（52例）的ROC曲线下面积；绘制校准曲线评估前述ARDS预测模型对危重烧伤患者发生ARDS的预测符合度。n 结果:ARDS组患者烧伤指数、合并吸入性损伤比例和并发脓毒症比例均显著高于非ARDS组（n t=0.36，n χ2=33.78、49.92，n P0.05）。多因素logistic回归分析显示，烧伤指数、吸入性损伤、脓毒症是危重烧伤患者发生ARDS的独立危险因素（比值比=1.05、15.33、5.02，95%置信区间=1.01~1.10、2.65~88.42、1.28~19.71，n P<0.05或n P<0.01）。前述ARDS预测模型的总体ROC曲线下面积为0.92，建模组和验证组的ROC曲线下面积分别为0.95和0.91。应用前述ARDS预测模型进行ARDS发生率预测中，当预测概率85%时，可能存在一定的高估ARDS发生率的风险；当预测概率为35%~85%时，可能存在一定的低估ARDS发生率的风险。n 结论:烧伤指数、吸入性损伤和脓毒症是危重烧伤患者发生ARDS的独立危险因素，基于这3个指标建立的ARDS风险预测模型对危重烧伤患者发生ARDS具有较好的预测能力。“,”Objective:To establish a prediction 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 n t test, n χ2 test, and Fisher's exact probability method test. The multivariate logistic regression analysis was performed on the indicators with statistically significant differences between the two groups to screen 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.n 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 (n t=0.36, n χ2=33.78, 49.92, n P0.05). The multivariate logistic regression analysis showed that the burn index, inhalation injury, and sepsis were 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,n P<0.05 orn P<0.01). The overall area under the ROC curve of the above-mentioned ARDS prediction model was 0.92, and the area under the ROC curve was 0.95 and 0.91 for modeling group and validation 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 85%, and some risk of underestimating ARDS incidence when the prediction probability was 35%-85%.n Conclusions:The burn index, inhalation injury, and sepsis are independent risk factors for 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.