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
Enterprises are continuously aiming at improving the execution of processes to achieve a competitive edge.One of the established ways of improving process performance is to assign the most appropriate resources to eac...Enterprises are continuously aiming at improving the execution of processes to achieve a competitive edge.One of the established ways of improving process performance is to assign the most appropriate resources to each task of the process.However,evaluations of business process improvement approaches have established that a method that can guide decision-makers to identify the most appropriate resources for a task of process improvement in a structured way,is missing.It is because the relationship between resources and tasks is less understood and advancement in business process intelligence is also ignored.To address this problem an integrated resource classification framework is presenting that identifies competence,suitability,and preference as the relationship of task with resources.But,only the competence relationship of human resources with a task is presented in this research as a resource competence model.Furthermore,the competency calculation method is presented as a user guider layer for business process intelligencebased resource competence evaluation.The computed capabilities serve as a basic input for choosing the most appropriate resources for each task of the process.Applicability of method is illustrated through a heathcare case study.展开更多
目的构建本土化、科学可靠的麻醉恢复室(post anesthesia care unit,PACU)护理质量敏感指标体系,为护理质量评价提供标准化工具。方法以结构-过程-结果质量评价模型为理论依据,基于循证分析和半结构式访谈制订函询问卷,通过德尔菲法对1...目的构建本土化、科学可靠的麻醉恢复室(post anesthesia care unit,PACU)护理质量敏感指标体系,为护理质量评价提供标准化工具。方法以结构-过程-结果质量评价模型为理论依据,基于循证分析和半结构式访谈制订函询问卷,通过德尔菲法对14名专家进行3轮函询,采用层次分析法确定指标权重。结果3轮专家咨询的问卷有效回收率均为100.0%、专家意见的肯德尔和谐系数分别为0.198~0.235、0.267~0.316、0.364~0.386(均P<0.05)。最终形成了包括2项结构指标、3项过程指标、6项结果指标的麻醉恢复室护理质量敏感指标体系,所有指标各项重要性赋值均数>3.50、变异系数<0.25。结论PACU护理质量敏感指标体系具有较高的协调性和一致性,可为PACU护理质量的管理提供科学性、可靠性、可操行性较强的依据。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Enterprises are continuously aiming at improving the execution of processes to achieve a competitive edge.One of the established ways of improving process performance is to assign the most appropriate resources to each task of the process.However,evaluations of business process improvement approaches have established that a method that can guide decision-makers to identify the most appropriate resources for a task of process improvement in a structured way,is missing.It is because the relationship between resources and tasks is less understood and advancement in business process intelligence is also ignored.To address this problem an integrated resource classification framework is presenting that identifies competence,suitability,and preference as the relationship of task with resources.But,only the competence relationship of human resources with a task is presented in this research as a resource competence model.Furthermore,the competency calculation method is presented as a user guider layer for business process intelligencebased resource competence evaluation.The computed capabilities serve as a basic input for choosing the most appropriate resources for each task of the process.Applicability of method is illustrated through a heathcare case study.
文摘目的构建本土化、科学可靠的麻醉恢复室(post anesthesia care unit,PACU)护理质量敏感指标体系,为护理质量评价提供标准化工具。方法以结构-过程-结果质量评价模型为理论依据,基于循证分析和半结构式访谈制订函询问卷,通过德尔菲法对14名专家进行3轮函询,采用层次分析法确定指标权重。结果3轮专家咨询的问卷有效回收率均为100.0%、专家意见的肯德尔和谐系数分别为0.198~0.235、0.267~0.316、0.364~0.386(均P<0.05)。最终形成了包括2项结构指标、3项过程指标、6项结果指标的麻醉恢复室护理质量敏感指标体系,所有指标各项重要性赋值均数>3.50、变异系数<0.25。结论PACU护理质量敏感指标体系具有较高的协调性和一致性,可为PACU护理质量的管理提供科学性、可靠性、可操行性较强的依据。
