重症颅脑损伤患者营养支持不达标的影响因素及其列线图模型的构建和验证

Influencing factors of substandard nutritional support in patients with severe craniocerebral injury and construction and validation of nomogram model

目的 分析影响重症颅脑损伤患者营养支持不达标的因素,并构建其列线图模型,为临床早期识别高危人群、及时实施针对性防控措施提供依据。方法 选取2022年1月至2023年6月郑州大学第一附属医院收治的280例重症颅脑损伤患者作为研究对象,根据肠内营养是否达标分为达标组(n=132)和未达标组(n=148),比较两组患者的ICU住院时间、总住院时间、急性生理与慢性健康(APACHEⅡ)评分,并对影响重症颅脑损伤患者营养支持不达标的影响因素进行单因素及多因素Logistic回归分析,构建列线图预测模型,利用受试者工作特征(ROC)曲线及曲线下面积(AUC)进行验证。结果 达标组患者ICU住院时间、总住院时间分别为(9.68±2.57) d、(17.36±2.96) d,明显短于未达标组的(13.24±3.15) d、(23.24±4.65) d,APACHEⅡ评分为(17.69±2.68)分,明显低于未达标组的(19.74±2.35)分,差异均有统计学意义(P<0.05);单因素分析结果显示,两组患者在使用儿茶酚胺类药物、肠内营养不耐受、营养支持中断、应用营养支持管理系统、胃肠功能分级、PCT、前白蛋白、白蛋白、空腹血糖水平方面比较差异均有统计学意义(P<0.05);多因素Logistic回归分析结果显示,使用儿茶酚胺类药物、肠内营养不耐受、营养支持中断、胃肠功能分级、PCT、空腹血糖均是重症颅脑损伤患者营养支持不达标的危险因素(P<0.05),应用营养支持管理系统、前白蛋白、白蛋白均是其保护因素(P<0.05);列线图预测模型的校准度为0.862,AUC为0.891 (95%CI:0.852~0.930),外部验证显示该模型行未达标预测的AUC为0.874 (95%CI:0.821~0.926)。结论 使用儿茶酚胺类药物、肠内营养不耐受、营养支持中断、胃肠功能分级、PCT、空腹血糖、前白蛋白、白蛋白及应用营养支持管理系统是重症颅脑损伤患者营养支持不达标的影响因素,列线图预测模型对营养支持不达标具有一定预测价值,可早期识别高危人群,及时制定针对性护理措施,以提升危重症患者营养支持效果。

Objective To analyze the factors that affect the substandard nutritional support in patients with se-vere craniocerebral injury,and to construct a nomogram model to provide a basis for early clinical identification of high-risk groups and timely implementation of targeted prevention and control measures.Methods A total of 280 pa-tients with severe craniocerebral injury admitted to the First Affiliated Hospital of Zhengzhou University from January 2022 to June 2023 were selected as the research subjects.They were divided into the standard group(n=132)and the sub-standard group(n=148)according to whether they met the standards for enteral nutrition.The length of ICU stay,total length of hospital stay,and Acute Physiology and Chronic Health EvaluationⅡ(APACHEⅡ)score were compared between the two groups.Univariate and multivariate logistic regression analysis was conducted to analyze the factors affecting substandard nutritional support in patients with severe craniocerebral injury.A nomogram prediction model was constructed and validated using receiver operating characteristic(ROC)curves and area under the curve(AUC).Results The length of ICU stay and total length of hospital stay of the standard group were(9.68±2.57)d and(17.36±2.96)d,respectively,which were significantly shorter than(13.24±3.15)d and(23.24±4.65)d of the substandard group(P<0.05).The APACHEⅡscore of the standard group was(17.69±2.68)points,which was significantly lower than(19.74±2.35)points of the substandard group(P<0.05).Univariate analysis showed that there were significant differenc-es in the use of catecholamines,intolerance to enteral nutrition,interruption of nutritional support,application of nutri-tional support management system,gastrointestinal function classification,PCT,prealbumin,albumin,and fasting blood glucose levels between the two groups(P<0.05).Multivariate logistic regression analysis showed that the use of cate-cholamines,intolerance to enteral nutrition,interruption of nutritional support,gastrointestinal function classification,PCT,and fasting blood glucose were risk factors for substandard nutritional support in patients with severe craniocere-bral injury(P<0.05),while the application of nutritional support management system,prealbumin,and albumin were pro-tective factors(P<0.05).The calibration of the nomogram prediction model was 0.862,and the AUC was 0.891(95%CI:0.852-0.930).External validation showed that the AUC of the model for predicting substandard nutritional support was 0.874(95%CI:0.821-0.926).Conclusion The use of catecholamine drugs,enteral nutrition intolerance,interruption of nutritional support,gastrointestinal function classification,PCT,fasting blood glucose,prealbumin,albumin,and the ap-plication of nutritional support management system are the influencing factors for substandard nutritional support in pa-tients with severe head injury.The nomogram prediction model has certain predictive value for substandard nutritional support,which can help identify high-risk populations early and develop targeted nursing measures in a timely manner to improve the nutritional support effect in critically ill patients.