BACKGROUND Enteral nutrition(EN)is essential for critically ill patients.However,some patients will have enteral feeding intolerance(EFI)in the process of EN.AIM To develop a clinical prediction model to predict the r...BACKGROUND Enteral nutrition(EN)is essential for critically ill patients.However,some patients will have enteral feeding intolerance(EFI)in the process of EN.AIM To develop a clinical prediction model to predict the risk of EFI in patients receiving EN in the intensive care unit.METHODS A prospective cohort study was performed.The enrolled patients’basic information,medical status,nutritional support,and gastrointestinal(GI)symptoms were recorded.The baseline data and influencing factors were compared.Logistic regression analysis was used to establish the model,and the bootstrap resampling method was used to conduct internal validation.RESULTS The sample cohort included 203 patients,and 37.93%of the patients were diagnosed with EFI.After the final regression analysis,age,GI disease,early feeding,mechanical ventilation before EN started,and abnormal serum sodium were identified.In the internal validation,500 bootstrap resample samples were performed,and the area under the curve was 0.70(95%CI:0.63-0.77).CONCLUSION This clinical prediction model can be applied to predict the risk of EFI.展开更多
为预测危重症患者在重症监护病房的住院时间(length of stay in intensive care unit, ICU LOS),并探索实验室指标对ICU LOS的影响,本研究基于危重症患者的25个临床指标构建XGBoost模型,对患者是否发生超过3 d的ICU LOS进行预测,并基于S...为预测危重症患者在重症监护病房的住院时间(length of stay in intensive care unit, ICU LOS),并探索实验室指标对ICU LOS的影响,本研究基于危重症患者的25个临床指标构建XGBoost模型,对患者是否发生超过3 d的ICU LOS进行预测,并基于SHAP模型对最佳性能模型进行解释性评估。结果显示,XGBoost模型准确率为87.9%。相比于其他预测模型,XGBoost模型在准确率、敏感度和区分度上均有明显优势。同时,SHAP模型增加了集成模型的可解释性和可靠性。研究表明,XGBoost模型可有效识别ICU LOS较长的患者,辅助医生优化临床治疗方案,改善患者预后状况。展开更多
文摘BACKGROUND Enteral nutrition(EN)is essential for critically ill patients.However,some patients will have enteral feeding intolerance(EFI)in the process of EN.AIM To develop a clinical prediction model to predict the risk of EFI in patients receiving EN in the intensive care unit.METHODS A prospective cohort study was performed.The enrolled patients’basic information,medical status,nutritional support,and gastrointestinal(GI)symptoms were recorded.The baseline data and influencing factors were compared.Logistic regression analysis was used to establish the model,and the bootstrap resampling method was used to conduct internal validation.RESULTS The sample cohort included 203 patients,and 37.93%of the patients were diagnosed with EFI.After the final regression analysis,age,GI disease,early feeding,mechanical ventilation before EN started,and abnormal serum sodium were identified.In the internal validation,500 bootstrap resample samples were performed,and the area under the curve was 0.70(95%CI:0.63-0.77).CONCLUSION This clinical prediction model can be applied to predict the risk of EFI.
文摘为预测危重症患者在重症监护病房的住院时间(length of stay in intensive care unit, ICU LOS),并探索实验室指标对ICU LOS的影响,本研究基于危重症患者的25个临床指标构建XGBoost模型,对患者是否发生超过3 d的ICU LOS进行预测,并基于SHAP模型对最佳性能模型进行解释性评估。结果显示,XGBoost模型准确率为87.9%。相比于其他预测模型,XGBoost模型在准确率、敏感度和区分度上均有明显优势。同时,SHAP模型增加了集成模型的可解释性和可靠性。研究表明,XGBoost模型可有效识别ICU LOS较长的患者,辅助医生优化临床治疗方案,改善患者预后状况。
