Establishment and validation of a risk prediction model for the occurrence of enteral nutrition intolerance in adult patients with severe burns
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摘要:
目的 构建成年严重烧伤患者发生肠内营养不耐受（ENI）风险预测模型并对其进行验证。 方法 该研究为回顾性队列研究。将2020年11月—2024年11月于江南大学附属医院住院的155例符合入选标准的成年严重烧伤患者纳入建模组,其中男127例、女28例,年龄19~85岁；将2022年11月—2024年11月于南通大学附属医院住院的40例符合入选标准的成年严重烧伤患者纳入验证组,其中男30例、女10例,年龄25~79岁。比较2组患者性别、年龄、体重指数、基础疾病数、烧伤总面积,入院24 h内改良早期预警评分、机械通气情况、白细胞计数、C反应蛋白水平、血清白蛋白水平、空腹血糖水平,治疗期间抗生素使用种类数、肠道益生菌使用情况、镇静镇痛剂使用情况。根据ENI发生情况,将建模组患者分为发生ENI（96例）与未发生ENI（59例）2类,比较2类患者前述资料,筛选建模组患者发生ENI的独立预测因素,据此构建建模组患者发生ENI风险预测模型并绘制静态及网页版动态列线图。采用受试者操作特征（ROC）曲线评估预测模型的性能,对建模组数据采用Bootstrap法进行重复抽样1 000次后进行预测模型内部验证,在验证组中进行预测模型外部验证,采用校准曲线和临床决策曲线分析法分别对预测模型的校准度及临床实用性进行验证。 结果 建模组与验证组患者烧伤总面积、治疗期间抗生素使用种类数、入院24 h内机械通气情况与血清白蛋白水平比较的差异均有统计学意义（Z值分别为-2.35、-2.68,χ2值分别为4.58、4.63,P<0.05）,其余资料比较的差异均无统计学意义（P>0.05）。发生ENI与未发生ENI患者基础疾病数、入院24 h内空腹血糖水平、治疗期间抗生素使用种类数、入院24 h内白细胞计数、治疗期间肠道益生菌使用情况及镇静镇痛剂使用情况比较,差异均有统计学意义（Z值分别为2.04、4.24、3.36,χ2值分别为26.02、24.13、4.49,P<0.05）。多因素logistic回归分析显示,基础疾病数、烧伤总面积、入院24 h内白细胞计数与空腹血糖水平、治疗期间肠道益生菌使用情况均是建模组患者发生ENI的独立预测因素（比值比分别为2.33、1.03、0.11、1.22、0.08,95%置信区间分别为1.25~4.32、1.00~1.06、0.04~0.30、1.04~1.42、0.03~0.24,P<0.05）。根据前述独立预测因素成功构建建模组患者发生ENI风险预测模型并绘制静态及网页版动态列线图,该预测模型的ROC曲线下面积为0.90（95%置信区间为0.84~0.95）,敏感度为86.50%,特异度为84.70%,最大约登指数为0.712,最佳阈值为61.4%。在内部验证和外部验证中,预测模型的ROC曲线下面积分别为0.88和0.91（95%置信区间为0.82~0.94和0.81~0.99）,预测模型的校准曲线均在参考线附近,临床决策曲线显示的预测模型阈值概率范围分别为2%~96%和18%~98%且其净收益率均>0。 结论 成年严重烧伤患者发生ENI的独立预测因素包括基础疾病数、烧伤总面积、入院24 h内白细胞计数与空腹血糖水平、治疗期间肠道益生菌使用情况,据此构建的列线图预测模型对成年严重烧伤患者发生ENI风险具有较好的预测效能。 Abstract:Objective To establish and validate a risk prediction model for the occurrence of enteral nutrition intolerance (ENI) in adult patients with severe burns. Methods This study was a retrospective cohort study. A total of 155 adult patients with severe burns who met the inclusion criteria and hospitalized at the Affiliated Hospital of Jiangnan University between November 2020 and November 2024 were enrolled in modeling group, including 127 males and 28 females, aged 19 to 85 years. An additional 40 adult patients with severe burns who met the inclusion criteria and hospitalized at the Affiliated Hospital of Nantong University between November 2022 and November 2024 were enrolled in validation group, including 30 males and 10 females, aged 25 to 79 years. The gender, age, body mass index, number of underlying diseases, total burn area, modified early warning score, mechanical ventilation, white blood cell count, C-reactive protein level, serum albumin level, and fasting blood glucose level within 24 hours of admission, number of antibiotic types, intestinal probiotics, and sedatives and analgesics used during treatment were compared between the two groups of patients. Based on the occurrence of ENI, patients in modeling group were divided into two categories: those who developed ENI (96 cases) and those who did not (59 cases). The aforementioned data of these two categories of patients were compared to identify independent predictive factors for ENI of patients in modeling group. Accordingly, a risk prediction model for ENI of patients in modeling group was constructed, and both static and web-based dynamic nomograms were developed. The performance of the prediction model was evaluated using receiver operating characteristic (ROC) curves. The modeling group data was repeatedly sampled 1 000 times using Bootstrap method for internal validation, and external validation was conducted in validation group. Calibration curves and clinical decision curve analysis were used to analyze the calibration accuracy and clinical practicality of the prediction model, respectively. Results There were statistically significant differences between modeling group and validation group in terms of total burn area, number of antibiotic types used during treatment, mechanical ventilation and serum albumin levels within 24 h of admission of patients (with Z values of -2.35 and -2.68, χ2 values of 4.58 and 4.63, respectively, P<0.05). No statistically significant differences were observed in other variables between the two groups (P>0.05). There were statistically significant differences between patients developed ENI and those who did not in the number of underlying diseases, fasting blood glucose level within 24 h of admission, the number of antibiotic types used during treatment, white blood cell count within 24 h of admission, and the use of intestinal probiotics and that of sedative and analgesic agents (with Z values of 2.04, 4.24, and 3.36, χ2 values of 26.02, 24.13, and 4.49, respectively, P<0.05). Multivariate logistic regression analysis showed that the number of underlying diseases, total burn area, white blood cell count and fasting blood glucose level within 24 h of admission, and the use of intestinal probiotics during treatment were independent predictive factors for ENI of patients in modeling group (with odds ratios of 2.33, 1.03, 0.11, 1.22, and 0.08, 95% confidence intervals of 1.25-4.32, 1.00-1.06, 0.04-0.30, 1.04-1.42, and 0.03-0.24, respectively, P<0.05). Based on the aforementioned independent predictive factors, a risk prediction model for ENI in patient of modeling group was successfully established, and both a static and a web-based dynamic nomogram were developed. The area under the ROC curve of the prediction model was 0.90 (with 95% confidence interval of 0.84-0.95), with a sensitivity of 86.50%, a specificity of 84.70%, a maximum Youden's index of 0.712, and an optimal threshold of 61.4%. In internal and external validation, the areas under the ROC curves of the prediction model were 0.88 and 0.91, respectively (with 95% confidence intervals of 0.82-0.94 and 0.81-0.99, respectively), the calibration curves of the prediction model were all near the reference line, and the threshold probability ranges displayed by the clinical decision curves were 2%-96% and 18%-98%, respectively, with net returns>0. Conclusions The independent predictive factors for the occurrence of ENI in adult patients with severe burns include the number of underlying diseases, total burn area, white blood cell count and fasting blood glucose level within 24 h of admission, and the use of intestinal probiotics during treatment. The nomogram prediction model constructed on this basis has a good predictive efficiency for the risk of the occurrence of ENI in adult patients with severe burns. -
Key words:
- Burns /
- Enteral nutrition /
- Risk factors /
- Nomograms /
- Feeding intolerance /
- Predictive model
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参考文献
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图 1 静态列线图预测模型预测的建模组中1例严重烧伤患者ENI发生概率
注：该例患者入院24 h内空腹血糖水平5.00 mmol/L、入院24 h内白细胞计数<12.1×109/L、未使用肠道益生菌、烧伤总面积为65.0%体表总面积（TBSA）、基础疾病数为0；第1行分数线上红色圆点为下方各分项得分对应点,箭头指示对应发生肠内营养不耐受（ENI）的概率
图 3 成年严重烧伤患者发生肠内营养不耐受风险预测模型内部验证及外部验证的受试者操作特征曲线。3A.内部验证；3B.外部验证
注：内部验证为对建模组（155例患者）数据重复抽样1 000次后进行,外部验证在验证组（40例患者）中进行
图 4 成年严重烧伤患者发生肠内营养不耐受风险预测模型内部验证及外部验证的校准曲线
注：内部验证为对建模组（155例患者）数据重复抽样1 000次后进行,外部验证在验证组（40例患者）中进行
图 5 成年严重烧伤患者发生肠内营养不耐受风险预测模型内部验证及外部验证的临床决策曲线。5A.内部验证；5B.外部验证
注：内部验证为对建模组（155例患者）数据重复抽样1 000次后进行,外部验证在验证组（40例患者）中进行
Table 1. 2组成年严重烧伤患者临床特征比较
组别 例数 性别（例） 年龄（岁, 体重指数[kg/m2,M（Q1,Q3）] 基础疾病数[个,M（Q1,Q3）] 烧伤总面积[%TBSA,M（Q1,Q3）] 男 女 建模组 155 127 28 49±14 24.15（21.80,25.81） 0.00（0.00,1.00） 60.0（46.0,75.0） 验证组 40 30 10 53±15 23.77（20.32,26.12） 0.00（0.00,1.75） 49.5（30.3,73.3） 统计量值 χ2=0.98 t=-1.46 Z=-0.45 Z=1.38 Z=-2.35 P值 0.323 0.146 0.650 0.169 0.019 注：TBSA为体表总面积；改良早期预警评分、机械通气情况、白细胞计数、C反应蛋白水平、血清白蛋白水平、空腹血糖水平为入院24 h内统计数据,抗生素使用种类数、肠道益生菌使用情况、镇静镇痛剂使用情况均为治疗期间统计数据 
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Table 2. 建模组中发生与未发生ENI成年严重烧伤患者临床特征比较
患者分类 例数 性别（例） 年龄（岁, 体重指数[kg/m2,M（Q1,Q3）] 基础疾病数[个,M（Q1,Q3）] 烧伤总面积[%TBSA,M（Q1,Q3）] 男 女 发生ENI 96 79 17 48±14 24.15（21.30,26.20） 0.00（0.00,1.00） 60.0（47.3,78.8） 未发生ENI 59 48 11 50±16 24.15（22.10,25.40） 0.00（0.00,0.00） 60.0（40.0,70.0） 统计量值 χ2=0.02 t=0.76 Z=-0.28 Z=2.04 Z=-1.95 P值 0.883 0.447 0.779 0.041 0.052 注：ENI为肠内营养不耐受,TBSA为体表总面积；改良早期预警评分、机械通气情况、白细胞计数、C反应蛋白水平、血清白蛋白水平、空腹血糖水平为入院24 h内统计数据,抗生素使用种类数、肠道益生菌使用情况、镇静镇痛剂使用情况均为治疗期间统计数据
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Table 3. 建模组155例成年严重烧伤患者发生肠内营养不耐受的多因素logistic回归分析阳性结果
项目 比值比 95%置信区间 P值 常量 0.03 — 0.021 基础疾病数（个） 2.33 1.25~4.32 0.007 烧伤总面积（%TBSA） 1.03 1.00~1.06 0.025 入院24 h内白细胞计数（×109/L） 0.11 0.04~0.30 <0.001 入院24 h内空腹血糖水平（mmol/L） 1.22 1.04~1.42 0.016 治疗期间肠道益生菌使用情况 0.08 0.03~0.24 <0.001 注：TBSA为体表总面积；“—”表示无此项
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