Published online Dec 16, 2024. doi: 10.12998/wjcc.v12.i35.6760
Revised: August 22, 2024
Accepted: September 4, 2024
Published online: December 16, 2024
Processing time: 219 Days and 21.6 Hours
This editorial discusses an article recently published in the World Journal of Clinical Cases, focusing on risk factors associated with intensive care unit-acquired weak
Core Tip: This editorial emphasizes the importance of recognizing the risk factors linked to intensive care unit-acquired weakness and highlights the vital role of machine learning in identifying and managing these factors to improve patient outcomes and enhance the quality of care in clinical settings.
- Citation: He XY, Zhao YH, Wan QW, Tang FS. Intensive care unit-acquired weakness: Unveiling significant risk factors and preemptive strategies through machine learning. World J Clin Cases 2024; 12(35): 6760-6763
- URL: https://www.wjgnet.com/2307-8960/full/v12/i35/6760.htm
- DOI: https://dx.doi.org/10.12998/wjcc.v12.i35.6760
Intensive care unit-acquired weakness (ICU-AW) is a debilitating neuromuscular complication that occurs in patients undergoing intensive care treatment in the intensive care unit (ICU)[1]. It is characterized by skeletal muscle dysfunction, leading to various clinical manifestations such as limb muscle weakness, atrophy, diminished deep tendon reflexes, and sensory impairments[2]. Importantly, patients with ICU-AW may also experience a range of challenges after being discharged from the hospital, including physical dysfunction, cognitive impairment, depression, and anxiety disorders[3]. The pathogenesis of ICU-AW is complex and involves intricate functional and structural changes in both muscles and nerves. This condition not only has the potential to result in severe outcomes like tetraplegia or paraplegia but can also lead to long-term disability, significantly impacting the quality of life for patients post-discharge. Given its profound impact on ICU patients, ICU-AW has become a prominent research topic of great interest to scholars worldwide, attracting extensive attention both domestically and internationally[4].
An article published by Wang and Long[5] offers a comprehensive exploration of the risk factors associated with ICU-AW. Through the use of a multilayer perceptron neural network model, the study successfully identified critical risk factors for ICU-AW, emphasizing the substantial impact of ICU stay duration and mechanical ventilation duration on its development. These findings provide actionable recommendations for managing these factors to mitigate the risk of ICU-AW within clinical settings. However, it is essential to recognize that the occurrence of ICU-AW is influenced by various other factors, necessitating comprehensive interventions to reduce its incidence. The study's insights serve as valuable reference information for clinicians, aiding in informed decision-making for the prevention and treatment of ICU-AW. It is recommended to further explain the differences between these risk factors identified by machine learning and actual medical practices and to process the data through an iteratively updated machine-learning approach.
While the article demonstrates significant progress in researching ICU-AW risk factors, there are opportunities for improvement in model construction and validation. To enhance the model's robustness, future studies should incorporate external validation[6] to ensure generalizability and employ cross-validation techniques to improve stability and reliability. To avoid the bias that older data may have on machine learning models, it is recommended to group the data by year or time period. This helps to identify the impact of data over different time periods on the model results and to assess the possibility of time bias. Additionally, comparing the multilayer perceptron neural network approach with other common machine learning algorithms would offer valuable insights into its advantages. It is also recommended that researchers share optimal model parameters, make data and model code publicly available, and encourage reproducibility of the study's results.
We propose the following specific recommendations for preventive strategies and interventions regarding ICU-AW. First, the article suggests that reducing the length of ICU stay and duration of mechanical ventilation on ICU-AW effectively lowers the probability of ICU-AW. This finding highlights the significance of early rehabilitation and ven
All in all, while the article provides valuable insights, it is crucial to enhance the model's stability and reliability th
In recent years, the utilization of machine learning in clinical medicine has expanded, showcasing its unique advantages. Machine learning provides healthcare professionals with a deeper understanding of disease causation and influencing factors, offering a scientific basis and decision support for clinical treatment. For instance, in colorectal cancer diagnosis, traditional methods face limitations that can be overcome by integrating machine learning with medical imaging, which enables more effective early screening[9]. Machine learning also aids in monitoring aggressive lymphomas and provides critical evidence for cases requiring early intervention[10]. Furthermore, in predicting macrosomia during pregnancy, machine learning empowers healthcare professionals to make more precise risk assessments, facilitating timely inter
In the realm of clinical pharmacy, machine learning demonstrates significant potential across drug development and clinical implementation. By leveraging drug-target knowledge to train predictive models capable of assessing interactions between new drugs and targets, machine learning offers valuable insights for drug development[12]. Moreover, when constructing predictive models for adverse drug reactions, integrating data from various sources helps reduce the risks associated with such reactions for patients and the healthcare system[13]. Additionally, machine learning-based Clinical Decision Support Systems (CDSSs) play a pivotal role in enhancing patient safety by providing clinicians with clinically valid alerts, improving decision-making processes, and minimizing medication errors[14]. The implementation of CDSS not only ensures patient safety but also enhances the efficiency and reliability of clinical decision-making within heal
Despite the widespread application of machine learning, we must address potential challenges. Ensuring the reliability and stability of constructed and validated machine learning models is crucial. Rigorous data set divisions, cross-valida
In conclusion, while machine learning offers immense potential in clinical medicine and pharmacy, addressing associated challenges is paramount. Through continuous technological advancements, improved data management practices, and ethical reviews, machine learning is poised to play an increasingly vital role in the pharmaceutical sector, contributing significantly to human health. Anticipating further progress in machine learning applications across diverse fields, we look forward to the continued evolution and impact of this technology.
1. | Wang W, Xu C, Ma X, Zhang X, Xie P. Intensive Care Unit-Acquired Weakness: A Review of Recent Progress With a Look Toward the Future. Front Med (Lausanne). 2020;7:559789. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 11] [Cited by in F6Publishing: 40] [Article Influence: 10.0] [Reference Citation Analysis (0)] |
2. | Li Z, Cai Y, Zhang Q, Zhang P, Sun R, Jiang H, Wan J, Wu F, Wang X, Tao X. Intensive care unit acquired weakness: A protocol for an overview of systematic reviews and meta-analysis. Medicine (Baltimore). 2020;99:e21926. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis (0)] |
3. | Zhang W, Wu J, Gu Q, Gu Y, Zhao Y, Ge X, Sun X, Lian J, Zeng Q. Changes in muscle ultrasound for the diagnosis of intensive care unit acquired weakness in critically ill patients. Sci Rep. 2021;11:18280. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 4] [Cited by in F6Publishing: 17] [Article Influence: 5.7] [Reference Citation Analysis (0)] |
4. | Yang Z, Wang X, Wang F, Peng Z, Fan Y. A systematic review and meta-analysis of risk factors for intensive care unit acquired weakness. Medicine (Baltimore). 2022;101:e31405. [PubMed] [DOI] [Cited in This Article: ] [Cited by in F6Publishing: 3] [Reference Citation Analysis (0)] |
5. | Wang L, Long DY. Significant risk factors for intensive care unit-acquired weakness: A processing strategy based on repeated machine learning. World J Clin Cases. 2024;12:1235-1242. [PubMed] [DOI] [Cited in This Article: ] [Cited by in CrossRef: 11] [Reference Citation Analysis (37)] |
6. | Zhang W, Tang Y, Liu H, Yuan LP, Wang CC, Chen SF, Huang J, Xiao XY. Risk prediction models for intensive care unit-acquired weakness in intensive care unit patients: A systematic review. PLoS One. 2021;16:e0257768. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 1] [Cited by in F6Publishing: 8] [Article Influence: 2.7] [Reference Citation Analysis (0)] |
7. | Heyland DK, Stapleton RD, Mourtzakis M, Hough CL, Morris P, Deutz NE, Colantuoni E, Day A, Prado CM, Needham DM. Combining nutrition and exercise to optimize survival and recovery from critical illness: Conceptual and methodological issues. Clin Nutr. 2016;35:1196-1206. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 65] [Cited by in F6Publishing: 71] [Article Influence: 7.9] [Reference Citation Analysis (0)] |
8. | Wang B, He X, Tian S, Feng C, Feng W, Song L. Influence of Early Multidisciplinary Collaboration on Prevention of ICU-Acquired Weakness in Critically Ill Patients. Dis Markers. 2022;2022:3823368. [PubMed] [DOI] [Cited in This Article: ] [Reference Citation Analysis (0)] |
9. | Peng Y, Deng H. Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer. BMC Med Imaging. 2024;24:24. [PubMed] [DOI] [Cited in This Article: ] [Reference Citation Analysis (0)] |
10. | Bai A, Si M, Xue P, Qu Y, Jiang Y. Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis. BMC Med Inform Decis Mak. 2024;24:13. [PubMed] [DOI] [Cited in This Article: ] [Reference Citation Analysis (0)] |
11. | Shamshuzzoha M, Islam MM. Early Prediction Model of Macrosomia Using Machine Learning for Clinical Decision Support. Diagnostics (Basel). 2023;13. [PubMed] [DOI] [Cited in This Article: ] [Reference Citation Analysis (0)] |
12. | Xu L, Ru X, Song R. Application of Machine Learning for Drug-Target Interaction Prediction. Front Genet. 2021;12:680117. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis (0)] |
13. | Nguyen DA, Nguyen CH, Mamitsuka H. A survey on adverse drug reaction studies: data, tasks and machine learning methods. Brief Bioinform. 2021;22:164-177. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 1.8] [Reference Citation Analysis (0)] |
14. | Chen CY, Chen YL, Scholl J, Yang HC, Li YJ. Ability of machine-learning based clinical decision support system to reduce alert fatigue, wrong-drug errors, and alert users about look alike, sound alike medication. Comput Methods Programs Biomed. 2024;243:107869. [PubMed] [DOI] [Cited in This Article: ] [Reference Citation Analysis (0)] |
15. | Aittokallio T. What are the current challenges for machine learning in drug discovery and repurposing? Expert Opin Drug Discov. 2022;17:423-425. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis (0)] |
16. | Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: Addressing ethical challenges. PLoS Med. 2018;15:e1002689. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 229] [Cited by in F6Publishing: 229] [Article Influence: 38.2] [Reference Citation Analysis (0)] |