Published online Mar 6, 2024. doi: 10.12998/wjcc.v12.i7.1235
Peer-review started: November 6, 2023
First decision: January 9, 2024
Revised: January 20, 2024
Accepted: February 18, 2024
Article in press: February 18, 2024
Published online: March 6, 2024
Processing time: 115 Days and 13.1 Hours
Intensive care unit-acquired weakness (ICU-AW) is a common complication that significantly impacts the patient's recovery process, even leading to adverse outcomes. Currently, there is a lack of effective preventive measures.
Provide meaningful insights for the prevention of ICU-AW.
Identify the main risk factors for ICU-AW.
Utilizing iterative machine learning techniques, a multilayer perceptron neural network model was developed, and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve, and analyzed the importance of independent variables in models.
The most influential factors contributing to ICU-AW were identified as the length of ICU stay (100.0%) and the duration of mechanical ventilation (54.9%). The neural network model predicted ICU-AW with an area under the curve of 0.941, sensitivity of 92.2%, and specificity of 82.7%.
The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.
A primary preventive strategy, when feasible, involves minimizing both ICU stay and mechanical ventilation duration. Future research needs to clarify the mechanism of ICU-AW occurrence and refine prevention strategies.