Case Control Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Mar 6, 2024; 12(7): 1235-1242
Published online Mar 6, 2024. doi: 10.12998/wjcc.v12.i7.1235
Significant risk factors for intensive care unit-acquired weakness: A processing strategy based on repeated machine learning
Ling Wang, Deng-Yan Long
Ling Wang, Deng-Yan Long, Intensive Care Unit, People's Hospital of Qiandongnan Miao and Dong Autonomous Prefecture, Kaili 556000, Guizhou Province, China
Author contributions: Wang L contributed to the research design, research implementation, data management, statistical analysis, manuscript writing-review and editing; Long DY contributed to the research conduct, data organization, research execution, review.
Supported by Science and Technology Support Program of Qiandongnan Prefecture, No. Qiandongnan Sci-Tech Support [2021]12; and Guizhou Province High-Level Innovative Talent Training Program, No. Qiannan Thousand Talents [2022]201701.
Institutional review board statement: The study was approved by the Medical Ethics Committee of People's Hospital of Qiandongnan Miao and Dong Autonomous Prefecture (No. 2021012).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: No benefits in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article.
Data sharing statement: Dataset available from the corresponding author at 463082910@qq.com.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ling Wang, FRCS (Hon), Additional Professor, Chief Physician, Intensive Care Unit, People's Hospital of Qiandongnan Miao and Dong Autonomous Prefecture, No. 31 Shaoshan South Road, Kaili 556000, Guizhou Province, China. 463082910@qq.com
Received: November 6, 2023
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
Abstract
BACKGROUND

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.

AIM

To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.

METHODS

Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission. Relevant data from the initial 14 d of ICU stay, such as age, comorbidities, sedative dosage, vasopressor dosage, duration of mechanical ventilation, length of ICU stay, and rehabilitation therapy, were gathered. The relationships between these variables and ICU-AW were examined. 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.

RESULTS

Within the ICU-AW group, age, duration of mechanical ventilation, lorazepam dosage, adrenaline dosage, and length of ICU stay were significantly higher than in the non-ICU-AW group. Additionally, sepsis, multiple organ dysfunction syndrome, hypoalbuminemia, acute heart failure, respiratory failure, acute kidney injury, anemia, stress-related gastrointestinal bleeding, shock, hypertension, coronary artery disease, malignant tumors, and rehabilitation therapy ratios were significantly higher in the ICU-AW group, demonstrating statistical significance. 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%.

CONCLUSION

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.

Keywords: Intensive care unit-acquired weakness, Risk factors, Machine learning, Prevention, Strategies

Core Tip: The study, utilizing machine learning, identified key risk factors for intensive care unit-acquired weakness (ICU-AW). Findings emphasized the significant impact of length of ICU stay and the duration of mechanical ventilation. Other factors, including age, medication dosage, and specific disease states, were also implicated. The study employed a multilayer perceptron neural network model with an impressive area under receiver operating characteristic curve of 0.941, sensitivity of 92.2%, and specificity of 82.7%. The results underscore the importance of decreasing length of ICU stay and the duration of mechanical ventilation as a primary strategy in preventing ICU-AW, when feasible.