Published online Jul 6, 2024. doi: 10.12998/wjcc.v12.i19.3665
Revised: April 24, 2024
Accepted: May 11, 2024
Published online: July 6, 2024
Processing time: 106 Days and 15.1 Hours
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 diaph
Core Tip: Increasing number of patients are being treated in the intensive care units (ICUs) with good outcomes. However, the incidence of ICU acquired weakness (ICU-AW) is also on the rise and is a cause of significant mortality and morbidity. While certain risk factors and pathogenetic mechanisms for the development of this condition have been identified, there still exists significant lacunae in our understanding of the same. The use of artificial intelligence with artificial neural networks and machine learning techniques appears promising to provide vital information which can be used to prevent and treat ICU-AW. The impact of this condition on lung transplantation, in the setting of globally prevalent donor organ scarcity, is discussed.