摘要
目的基于机器学习算法构建重症脑出血患者肠内营养喂养不耐受(FI)的风险预测模型并验证。方法回顾性分析2020年1月—2022年12月扬州大学附属苏北人民医院神经重症监护室485例脑出血患者的临床资料,以7∶3比例将患者随机分为训练集(n=339)和验证集(n=146),采用5种机器学习算法构建FI风险预测模型。绘制受试者工作特征(ROC)曲线,通过曲线下面积(AUC)筛选出预测性能最优的模型,基于最优模型构建列线图模型。通过校准曲线和决策曲线分析(DCA)评估列线图模型的校准度和临床净获益情况。结果重症脑出血患者肠内营养FI发生率为38.4%(186/485)。5种机器学习算法模型中,Logistic回归模型的预测效能最优(AUC=0.88)。Logistic回归模型分析结果显示,使用利尿剂、使用机械通气、格拉斯哥昏迷量表评分≤5分、使用血管活性药物、白蛋白<35 g/L是重症脑出血患者发生肠内营养FI的危险因素,基于5项危险因素进一步构建列线图模型。校准曲线分析结果显示,校准曲线与理想曲线贴合度较高,说明该列线图模型的校准度高;DCA结果显示,当阈值概率在5%~73%时,应用该列线图模型筛查能使患者临床获益。结论基于机器学习算法构建重症脑出血患者肠内营养FI风险预测列线图模型,有助于早期筛查肠内营养FI高危患者并及时制订预防措施,从而降低重症脑出血患者肠内营养FI发生率。
Objective To construct and validate a risk prediction model for enteral nutrition feeding intolerance(FI)in patients with severe cerebral hemorrhage based on machine learning algorithms.Methods The clinical data of 485 patients with cerebral hemorrhage admitted to the neurological intensive care unit of Northern Jiangsu People′s Hospital Affiliated to Yangzhou University from January 2020 to December 2022 were retrospectively analyzed.The patients were randomly divided into training set(n=339)and validation set(n=146)in a 7 to 3 ratio.Five machine learning algorithms were used to construct FI risk prediction models.The receiver operating characteristic(ROC)curve was plotted,and the model with the best predictive performance was selected based on the area under the curve(AUC).A nomogram model was constructed based on the optimal model.The calibration curve and decision curve analysis(DCA)were used to evaluate the calibration and clinical net benefit of the nomogram model.Results The incidence of enteral nutrition FI in patients with severe cerebral hemorrhage was 38.4%(186/485).Among the five machine learning algorithm models,the Logistic regression model had the best predictive performance(AUC=0.88).The analysis results of the Logistic regression model showed that the use of diuretics,mechanical ventilation,Glasgow Coma Scale score≤5,vasoactive drugs,and albumin level<35 g/L were risk factors for enteral nutrition FI in patients with severe cerebral hemorrhage.A nomogram model was further constructed based on these five risk factors.The calibration curve analysis showed that the calibration curve fitted well with the ideal curve,indicating a high calibration degree of the nomogram model.The DCA results showed that when the threshold probability was 5%to 73%,the application of the nomogram model for screening could clinically benefit patients.Conclusion The construction of a nomogram model for predicting the risk of enteral nutrition FI in patients with severe cerebral hemorrhage based on machine learning methods can help to early screen high-risk patients for enteral nutrition FI and timely formulate preventive measures,thereby reducing the incidence of enteral nutrition FI in patients with severe cerebral hemorrhage.
作者
丁佳莉
刘晓光
史甜
马强
祁雅婕
李育平
于海龙
卢光玉
DING Jiali;LIU Xiaoguang;SHI Tian;MA Qiang;QI Yajie;LI Yuping;YU Hailong;LU Guangyu(School of Nursing of Yangzhou University,Yangzhou,Jiangsu,225003;Department of Neurosurgery,Northern Jiangsu People′s Hospital Affiliated to Yangzhou University,Yangzhou,Jiangsu,225001;School of Public Health of Yangzhou University,Yangzhou,Jiangsu,225003)
出处
《实用临床医药杂志》
CAS
2024年第12期1-6,共6页
Journal of Clinical Medicine in Practice
基金
国家自然科学基金项目(72374178,82172603)
江苏省扬州市-扬州大学市校合作共建科技创新平台大数据分析与知识服务重点实验室开放课题(YBK202202)。
关键词
喂养不耐受
重症脑出血
肠内营养
机器学习算法
预测模型
列线图
feeding intolerance
severe cerebral hemorrhage
enteral nutrition
machine learning algorithm
prediction model
nomogram
