Weani ng from mechanical ventilation in the in tensive care unit (ICU) is a complex clinical problem and relevant for future organ engineering. Prolonged mechanical ventilation (MV) leads to a range of medical complic...Weani ng from mechanical ventilation in the in tensive care unit (ICU) is a complex clinical problem and relevant for future organ engineering. Prolonged mechanical ventilation (MV) leads to a range of medical complications that increases length of stay and costs as well as contributes to morbidity and even mortality and long-term quality of life. The need to reduce MV is both clinical and economical. Artificial intelligence or machine learning (ML) methods are promising opportunities to positively influence patient outcomes. ML methods have been proposed to enhance clinical decisions processes by using the large amount of digital information generated in the ICU setting. There is a particular interest in empirical methods (such as ML) to improve management of "difficult-to-wean" patients, due to the associated costs and adverse events associated with this population. A systematic literature search was performed using the OVID, IEEEXplore, PubMed, and Web of Science databases. All publications that included (1) the application of ML to weaning from MV in the ICU and (2) a clinical outcome measurement were reviewed. A checklist to assess the study quality of medical ML publications was modified to suit the critical assessment of ML in MV weaning literature. The systematic search identified nine studies that used ML for weaning management from MV in critical care. The weaning management application areas included (1) prediction of successful spontaneous breathing trials (SBTs),(2) prediction of successful extubation,(3) prediction of arterial blood gases, and (4) ventilator setting and oxygenation-adjustment advisory systems. Seven of the nine studies scored seven out of eight on the quality index. The remaining two of the nine studies scored one out of eight on the quality index. This scoring may, in part, be explained by the publications' focus on technical novelty, and therefore focusing on issues most important to a technical audience, instead of issues most important for a systematic medical review. This review showed that only a limited number of studies have started to assess the efficacy and effectiveness of ML for MV in the ICU. However, ML has the potential to be applied to the prediction of SBT failure, extubation failure, and blood gases, and also the adjustment of ventilator and oxygenation settings. The available databases for the development of ML in this clinical area may still be inadequate. None of the reviewed studies reported on the procedure, treatment, or sedation strategy undergone by patients. Such information is unlikely to be required in a technical publication but is potentially vital to the development ML techniques that are sufficiently robust to meet the needs of the"difficult-to-wean"patient population.展开更多
文摘Weani ng from mechanical ventilation in the in tensive care unit (ICU) is a complex clinical problem and relevant for future organ engineering. Prolonged mechanical ventilation (MV) leads to a range of medical complications that increases length of stay and costs as well as contributes to morbidity and even mortality and long-term quality of life. The need to reduce MV is both clinical and economical. Artificial intelligence or machine learning (ML) methods are promising opportunities to positively influence patient outcomes. ML methods have been proposed to enhance clinical decisions processes by using the large amount of digital information generated in the ICU setting. There is a particular interest in empirical methods (such as ML) to improve management of "difficult-to-wean" patients, due to the associated costs and adverse events associated with this population. A systematic literature search was performed using the OVID, IEEEXplore, PubMed, and Web of Science databases. All publications that included (1) the application of ML to weaning from MV in the ICU and (2) a clinical outcome measurement were reviewed. A checklist to assess the study quality of medical ML publications was modified to suit the critical assessment of ML in MV weaning literature. The systematic search identified nine studies that used ML for weaning management from MV in critical care. The weaning management application areas included (1) prediction of successful spontaneous breathing trials (SBTs),(2) prediction of successful extubation,(3) prediction of arterial blood gases, and (4) ventilator setting and oxygenation-adjustment advisory systems. Seven of the nine studies scored seven out of eight on the quality index. The remaining two of the nine studies scored one out of eight on the quality index. This scoring may, in part, be explained by the publications' focus on technical novelty, and therefore focusing on issues most important to a technical audience, instead of issues most important for a systematic medical review. This review showed that only a limited number of studies have started to assess the efficacy and effectiveness of ML for MV in the ICU. However, ML has the potential to be applied to the prediction of SBT failure, extubation failure, and blood gases, and also the adjustment of ventilator and oxygenation settings. The available databases for the development of ML in this clinical area may still be inadequate. None of the reviewed studies reported on the procedure, treatment, or sedation strategy undergone by patients. Such information is unlikely to be required in a technical publication but is potentially vital to the development ML techniques that are sufficiently robust to meet the needs of the"difficult-to-wean"patient population.
