Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Purpose: Nutritional support for adult critically ill patients is essential due to the high risk of malnutrition, which can lead to severe complications. This paper aims to develop evidence-based guidelines to optimize nutritional support in intensive care units (ICUs). Methods: The Grading Recommendations, Assessment, Development and Evaluation process was used to develop and summarize the evidence on which the recommendations were based. Clinical outcomes were assessed for seven key questions. Results: We recommend the following: (1) initiate enteral nutrition (EN) within 48 hours after treatment as it is associated with improved outcomes, including reduced infection rates and shorter ICU stays; (2) early EN is preferred over early parenteral nutrition due to better clinical outcomes; (3) the use of supplementary parenteral nutrition to meet energy targets during the first week of ICU admission in patients receiving early EN is conditionally recommended based on patient-specific needs; (4) limited caloric support should be supplied to prevent overfeeding and related complications, particularly in the early phase of critical illness; (5) higher protein intake is suggested to improve clinical outcomes, such as muscle preservation and overall recovery; (6) additional enteral or parenteral glutamine is conditionally recommended against due to the lack of significant benefit and potential harm; and (7) fish oil-containing lipid emulsions is conditionally recommended due to their potential to enhance clinical outcomes, including reduced infection rates and shorter ICU stays. Conclusion These evidence-based recommendations can improve clinical outcomes and support healthcare providers in making informed decisions about nutritional interventions in the ICU.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Purpose: Nutritional support for adult critically ill patients is essential due to the high risk of malnutrition, which can lead to severe complications. This paper aims to develop evidence-based guidelines to optimize nutritional support in intensive care units (ICUs). Methods: The Grading Recommendations, Assessment, Development and Evaluation process was used to develop and summarize the evidence on which the recommendations were based. Clinical outcomes were assessed for seven key questions. Results: We recommend the following: (1) initiate enteral nutrition (EN) within 48 hours after treatment as it is associated with improved outcomes, including reduced infection rates and shorter ICU stays; (2) early EN is preferred over early parenteral nutrition due to better clinical outcomes; (3) the use of supplementary parenteral nutrition to meet energy targets during the first week of ICU admission in patients receiving early EN is conditionally recommended based on patient-specific needs; (4) limited caloric support should be supplied to prevent overfeeding and related complications, particularly in the early phase of critical illness; (5) higher protein intake is suggested to improve clinical outcomes, such as muscle preservation and overall recovery; (6) additional enteral or parenteral glutamine is conditionally recommended against due to the lack of significant benefit and potential harm; and (7) fish oil-containing lipid emulsions is conditionally recommended due to their potential to enhance clinical outcomes, including reduced infection rates and shorter ICU stays. Conclusion These evidence-based recommendations can improve clinical outcomes and support healthcare providers in making informed decisions about nutritional interventions in the ICU.

Purpose: Nutritional support for adult critically ill patients is essential due to the high risk of malnutrition, which can lead to severe complications. This paper aims to develop evidence-based guidelines to optimize nutritional support in intensive care units (ICUs). Methods: The Grading Recommendations, Assessment, Development and Evaluation process was used to develop and summarize the evidence on which the recommendations were based. Clinical outcomes were assessed for seven key questions. Results: We recommend the following: (1) initiate enteral nutrition (EN) within 48 hours after treatment as it is associated with improved outcomes, including reduced infection rates and shorter ICU stays; (2) early EN is preferred over early parenteral nutrition due to better clinical outcomes; (3) the use of supplementary parenteral nutrition to meet energy targets during the first week of ICU admission in patients receiving early EN is conditionally recommended based on patient-specific needs; (4) limited caloric support should be supplied to prevent overfeeding and related complications, particularly in the early phase of critical illness; (5) higher protein intake is suggested to improve clinical outcomes, such as muscle preservation and overall recovery; (6) additional enteral or parenteral glutamine is conditionally recommended against due to the lack of significant benefit and potential harm; and (7) fish oil-containing lipid emulsions is conditionally recommended due to their potential to enhance clinical outcomes, including reduced infection rates and shorter ICU stays. Conclusion These evidence-based recommendations can improve clinical outcomes and support healthcare providers in making informed decisions about nutritional interventions in the ICU.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Purpose: Nutritional support for adult critically ill patients is essential due to the high risk of malnutrition, which can lead to severe complications. This paper aims to develop evidence-based guidelines to optimize nutritional support in intensive care units (ICUs). Methods: The Grading Recommendations, Assessment, Development and Evaluation process was used to develop and summarize the evidence on which the recommendations were based. Clinical outcomes were assessed for seven key questions. Results: We recommend the following: (1) initiate enteral nutrition (EN) within 48 hours after treatment as it is associated with improved outcomes, including reduced infection rates and shorter ICU stays; (2) early EN is preferred over early parenteral nutrition due to better clinical outcomes; (3) the use of supplementary parenteral nutrition to meet energy targets during the first week of ICU admission in patients receiving early EN is conditionally recommended based on patient-specific needs; (4) limited caloric support should be supplied to prevent overfeeding and related complications, particularly in the early phase of critical illness; (5) higher protein intake is suggested to improve clinical outcomes, such as muscle preservation and overall recovery; (6) additional enteral or parenteral glutamine is conditionally recommended against due to the lack of significant benefit and potential harm; and (7) fish oil-containing lipid emulsions is conditionally recommended due to their potential to enhance clinical outcomes, including reduced infection rates and shorter ICU stays. Conclusion These evidence-based recommendations can improve clinical outcomes and support healthcare providers in making informed decisions about nutritional interventions in the ICU.

Purpose: Nutritional support for adult critically ill patients is essential due to the high risk of malnutrition, which can lead to severe complications. This paper aims to develop evidence-based guidelines to optimize nutritional support in intensive care units (ICUs). Methods: The Grading Recommendations, Assessment, Development and Evaluation process was used to develop and summarize the evidence on which the recommendations were based. Clinical outcomes were assessed for seven key questions. Results: We recommend the following: (1) initiate enteral nutrition (EN) within 48 hours after treatment as it is associated with improved outcomes, including reduced infection rates and shorter ICU stays; (2) early EN is preferred over early parenteral nutrition due to better clinical outcomes; (3) the use of supplementary parenteral nutrition to meet energy targets during the first week of ICU admission in patients receiving early EN is conditionally recommended based on patient-specific needs; (4) limited caloric support should be supplied to prevent overfeeding and related complications, particularly in the early phase of critical illness; (5) higher protein intake is suggested to improve clinical outcomes, such as muscle preservation and overall recovery; (6) additional enteral or parenteral glutamine is conditionally recommended against due to the lack of significant benefit and potential harm; and (7) fish oil-containing lipid emulsions is conditionally recommended due to their potential to enhance clinical outcomes, including reduced infection rates and shorter ICU stays. Conclusion These evidence-based recommendations can improve clinical outcomes and support healthcare providers in making informed decisions about nutritional interventions in the ICU.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.