Critical care : the official journal of the Critical Care Forum
-
Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication that results in increased morbidity and mortality after cardiac surgery. Most established prediction models are limited to the analysis of nonlinear relationships and fail to fully consider intraoperative variables, which represent the acute response to surgery. Therefore, this study utilized an artificial intelligence-based machine learning approach thorough perioperative data-driven learning to predict CSA-AKI. ⋯ In this study, machine learning methods were successfully established to predict CSA-AKI, which determines risks following cardiac surgery, enabling the optimization of postoperative treatment strategies to minimize the postoperative complications following cardiac surgeries.
-
Patients in the intensive care unit (ICU) are known to be at increased risk of developing delirium, but the risk of subsequent neuropsychiatric disorders is unclear. We therefore sought to examine the association between the presence of delirium in the ICU and incident neuropsychiatric disorders (including depressive, anxiety, trauma-and-stressor-related, and neurocognitive disorders) post-ICU stay among adult medical-surgical ICU patients. ⋯ The diagnosis of new onset of neurocognitive disorders is associated with ICU-acquired delirium. In this study, significant associations were not observed for depressive, anxiety, and trauma-and-stressor-related disorders.
-
Randomized Controlled Trial
Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care.
Pre-hospital circumstances, cardiac arrest characteristics, comorbidities and clinical status on admission are strongly associated with outcome after out-of-hospital cardiac arrest (OHCA). Early prediction of outcome may inform prognosis, tailor therapy and help in interpreting the intervention effect in heterogenous clinical trials. This study aimed to create a model for early prediction of outcome by artificial neural networks (ANN) and use this model to investigate intervention effects on classes of illness severity in cardiac arrest patients treated with targeted temperature management (TTM). ⋯ A supervised machine learning model using ANN predicted neurological recovery, including survival excellently, and outperformed a conventional model based on logistic regression. Among the data available at the time of hospitalisation, factors related to the pre-hospital setting carried most information. ANN may be used to stratify a heterogenous trial population in risk classes and help determine intervention effects across subgroups.