Establishment of nomogram predicting model for the death risk of extremely severe burn patients and the predictive value
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摘要: 目的 探讨特重度烧伤患者的死亡风险因素,以此建立死亡风险列线图预测模型,并分析其对特重度烧伤患者死亡风险的预测价值。 方法 回顾性分析陆军军医大学第一附属医院全军烧伤研究所2010年1月—2018年10月收治的231例(男190例、女41例,年龄18~60岁)符合入选标准的特重度烧伤患者的病历资料,根据患者最终预后分为存活组173例和死亡组58例。统计2组患者性别、年龄、吸入性损伤程度、烧伤总面积、Ⅲ度烧伤面积、烧伤指数,伤后第1、2个24 h补液系数和尿量系数,入院后首次碱剩余、休克指数、血细胞比容(HCT),有无院前补液、是否使用呼吸机、是否进行连续性肾脏替代治疗(CRRT),计算入院时简化烧伤严重指数(ABSI)、Baux评分。根据呼吸机使用情况将患者分为使用呼吸机组131例和未使用呼吸机组100例,统计2组患者的死亡情况、烧伤总面积、烧伤指数、吸入性损伤发生情况和程度;根据CRRT应用情况将患者分为进行CRRT组59例和未进行CRRT组172例,统计2组患者的死亡情况、烧伤总面积和烧伤指数。对数据行
t 检验、χ 2检验、Mann-WhitneyU 检验,筛选患者死亡的相关因素。对存活组和死亡组组间比较差异有统计学意义的指标进行多因素logistic回归分析,筛选患者死亡的独立风险因素,并据此建立死亡风险列线图预测模型。采用Bootstrap法对死亡风险列线图预测模型进行内部验证,通过绘制校准曲线和计算一致性系数来评估死亡风险列线图预测模型对患者死亡风险的预测价值。根据死亡风险的列线图得到231例患者的死亡风险评分,并绘制受试者工作特征(ROC)曲线,计算其最佳阈值与最佳阈值下的敏感度、特异度及曲线下面积。 结果 (1)存活组和死亡组患者烧伤指数、入院时ABSI、吸入性损伤程度、烧伤总面积、Ⅲ度烧伤面积、伤后第1个24 h补液系数、使用呼吸机、进行CRRT及入院时Baux评分比较,差异有统计学意义(Z =-7.696、-7.301,χ 2=18.304、63.065、23.300、13.073、34.240、59.586,t =-7.536,P <0.01)。(2)使用呼吸机组和未使用呼吸机组患者死亡情况、吸入性损伤伤发生情况及程度、烧伤总面积、烧伤指数比较,差异有统计学意义(χ 2=34.240、17.394、25.479,Z =-6.557、-7.049,P <0.01)。(3)进行CRRT组和未进行CRRT组患者的死亡情况、烧伤总面积和烧伤指数比较,差异有统计学意义(χ 2=62.982,Z =-47.421、-6.678,P <0.01)。(4)使用呼吸机、进行CRRT和烧伤指数是特重度烧伤患者死亡的独立危险因素(比值比=3.277、5.587、1.067,95%置信区间=1.073~10.008、2.384~13.093、1.038~1.096,P <0.05或P <0.01)。(5)列线图预测模型初始一致性系数为0.90,校正后的一致性系数为0.89,校正前后的一致性系数相近且均较高,列线图预测模型的一致性和预测效果较好。ROC曲线最佳阈值为0.23,最佳阈值下的敏感度是86.0%、特异度是80.0%。ROC曲线下面积为0.90(95%置信区间=0.86~0.94,P <0.01)。 结论 烧伤程度重及器官的损伤和/或衰竭是特重度烧伤患者死亡的根本原因。基于使用呼吸机、进行CRRT和烧伤指数3个指标建立的死亡风险列线图预测模型对特重度烧伤患者死亡有较好的预测能力。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 witht test, chi-square test, and Mann-WhitneyU 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 orP <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.-
Key words:
- Burns /
- Forecasting /
- Death risk /
- Nomogram model
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