This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patie...This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patient recovery.Highlightingthe paradox of modern medical advances,it emphasizes the urgent needfor early identification and intervention to mitigate ICU-AW's impact.Innovatively,the study by Wang et al is showcased for employing a multilayer perceptronneural network model,achieving high accuracy in predicting ICU-AWrisk.This advancement underscores the potential of neural network models inenhancing patient care but also calls for continued research to address limitationsand improve model applicability.The editorial advocates for the developmentand validation of sophisticated predictive tools,aiming for personalized carestrategies to reduce ICU-AW incidence and severity,ultimately improving patientoutcomes in critical care settings.展开更多
In this editorial,comments are made on an interesting article in the recent issue of the World Journal of Clinical Cases by Wang and Long.The authors describe the use of neural network model to identify risk factors f...In this editorial,comments are made on an interesting article in the recent issue of the World Journal of Clinical Cases by Wang and Long.The authors describe the use of neural network model to identify risk factors for the development of intensive care unit(ICU)-acquired weakness.This condition has now become common with an increasing number of patients treated in ICUs and continues to be a source of morbidity and mortality.Despite identification of certain risk factors and corrective measures thereof,lacunae still exist in our understanding of this clinical entity.Numerous possible pathogenetic mechanisms at a molecular level have been described and these continue to be increasing.The amount of retrievable data for analysis from the ICU patients for study can be huge and enormous.Machine learning techniques to identify patterns in vast amounts of data are well known and may well provide pointers to bridge the knowledge gap in this condition.This editorial discusses the current knowledge of the condition including pathogenesis,diagnosis,risk factors,preventive measures,and therapy.Furthermore,it looks specifically at ICU acquired weakness in recipients of lung transplantation,because–unlike other solid organ transplants-muscular strength plays a vital role in the preservation and survival of the transplanted lung.Lungs differ from other solid organ transplants in that the proper function of the allograft is dependent on muscle function.Muscular weakness especially diaphragmatic weakness may lead to prolonged ventilation which has deleterious effects on the transplanted lung–ranging from ventilator associated pneumonia to bronchial anastomotic complications due to prolonged positive pressure on the anastomosis.展开更多
文摘This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patient recovery.Highlightingthe paradox of modern medical advances,it emphasizes the urgent needfor early identification and intervention to mitigate ICU-AW's impact.Innovatively,the study by Wang et al is showcased for employing a multilayer perceptronneural network model,achieving high accuracy in predicting ICU-AWrisk.This advancement underscores the potential of neural network models inenhancing patient care but also calls for continued research to address limitationsand improve model applicability.The editorial advocates for the developmentand validation of sophisticated predictive tools,aiming for personalized carestrategies to reduce ICU-AW incidence and severity,ultimately improving patientoutcomes in critical care settings.
文摘In this editorial,comments are made on an interesting article in the recent issue of the World Journal of Clinical Cases by Wang and Long.The authors describe the use of neural network model to identify risk factors for the development of intensive care unit(ICU)-acquired weakness.This condition has now become common with an increasing number of patients treated in ICUs and continues to be a source of morbidity and mortality.Despite identification of certain risk factors and corrective measures thereof,lacunae still exist in our understanding of this clinical entity.Numerous possible pathogenetic mechanisms at a molecular level have been described and these continue to be increasing.The amount of retrievable data for analysis from the ICU patients for study can be huge and enormous.Machine learning techniques to identify patterns in vast amounts of data are well known and may well provide pointers to bridge the knowledge gap in this condition.This editorial discusses the current knowledge of the condition including pathogenesis,diagnosis,risk factors,preventive measures,and therapy.Furthermore,it looks specifically at ICU acquired weakness in recipients of lung transplantation,because–unlike other solid organ transplants-muscular strength plays a vital role in the preservation and survival of the transplanted lung.Lungs differ from other solid organ transplants in that the proper function of the allograft is dependent on muscle function.Muscular weakness especially diaphragmatic weakness may lead to prolonged ventilation which has deleterious effects on the transplanted lung–ranging from ventilator associated pneumonia to bronchial anastomotic complications due to prolonged positive pressure on the anastomosis.