Machine learning insights on intensive care unit-acquired weakness

Abstract

Intensive care unit-acquired weakness (ICU-AW) significantly hampers patient recovery and increases morbidity. With the absence of established preventive strategies, this study utilizes advanced machine learning methodologies to unearth key predictors of ICU-AW. Employing a sophisticated multilayer perceptron neural network, the research methodically assesses the predictive power for ICU-AW, pinpointing the length of ICU stay and duration of mechanical ventilation as pivotal risk factors. The findings advocate for minimizing these elements as a preventive approach, offering a novel perspective on combating ICU-AW. This research illuminates critical risk factors and lays the groundwork for future explorations into effective prevention and intervention strategies.

Key Words: Length of intensive care unit stay, Intensive care unit-acquired weakness, Machine learning, Likelihood factors, Precautionary measures

Core Tip: The study categorized patients into two groups: Intensive care unit-acquired weakness (ICU-AW) and non-ICU-AW, based on their condition on the 14^th day post-ICU admission. The researchers collected data from the initial 14 d of the ICU stay, which included age, comorbidities, sedative and vasopressor dosages, duration of mechanical ventilation, length of the ICU stay, and rehabilitation therapy. They then examined the relationships between these variables and ICU-AW.

INTRODUCTION

Artificial Intelligence (AI) showed some improvement in prediction accuracy, which shows a promising potential to transform the management of complicated neurological cases in the intensive care unit (ICU)[1]. ICU-acquired weakness (ICU-AW) is a common complication that may happen in seriously sick patients[2]. It is distinguished by symmetrical muscle weakness and can lead to secondary neurological and muscular impairments, impacting a patient's quality of life and survival. Predicting and assessing the risk of ICU-AW is crucial to implementing interventions that can reduce its incidence and improve patient outcomes[3]. The emergence of ICU-AW is influenced by complex factors, and the use of artificial intelligence and machine learning, specifically neural network models, has exhibited significant potential in the prognosis and detection of medical ailments.

To prevent ICU-AW, strategies focus on identifying high-risk factors and implementing corresponding measures[4]. In a recent study, a neural network assessed the risk of ICU-AW among patients, revealing a model with commendable recognition performance[5]. The study findings reveal that ICU-Acquired Weakness (ICU-AW) is most significantly linked to period of mechanical ventilation and the time of stay in the ICU. Additionally, the presence of sepsis and the total dosage of sedatives and vasopressor drugs are also associated with ICU-AW.

Wang et al[6] present a study conducted by analyzing data from 1063 cases, which unequivocally demonstrated that ICU-AW is a prevailing issue affecting a staggering 34.81% of patients. The study's significance level was P < 0.05, which firmly establishes the credibility of the findings. The measured data were such as length of stay, course of treatment, age, and comorbidities. The team evaluated the multilayer perceptron neural network model's predictive ability for ICU-AW using ROC curve analysis, which was an authoritative approach for determining the predictive efficacy of the model[6].

The study unequivocally demonstrated that the period of stay in the ICU (100.0%) and the time length of mechanical ventilation (54.9%) were the crucial factors affecting the incident of ICU-AW. The dosage of sedatives (33.4%) and vasopressor medications (19.5%) were also contributing factors. The ICU-AW group demonstrated a longer stay in the ICU, prolonged mechanical ventilation, and higher Midazolam and Norepinephrine dosages, which were statistically significant (P < 0.05; Z = 278.696, 29.905, 127.872, 81.127). Moreover, it was observed that the group of patients with ICU-Acquired Weakness showed increased rates of particular comorbidities as distinguished from the group without ICU-AW which was deemed a significant and compelling finding.

The study presents a robust and relevant approach to understanding and predicting ICU-AW, leveraging advanced machine-learning techniques to analyze and identify key risk factors. The use of a multilayer perceptron neural network is noteworthy for its capacity to handle complex, nonlinear relationships between variables, which is often the case in medical data. The high predictive performance of the model is impressive and suggests that the approach is valid and could be useful in clinical settings. However, there are areas where the research could be expanded or improved.

More details are needed on how the data used for training the model, such as sample size, diversity, and representativeness, which are crucial for evaluating the generalizability of the findings. Further, details on the neural network architecture, training process, and validation methods would provide more insight into the robustness of the predictive model.

In addition, While the study identifies the length of ICU stay and mechanical ventilation duration as significant factors, it does not discuss other variables considered or their relative importance. A more comprehensive analysis of variables could uncover additional insights and preventive factors. Also, The conclusions suggest minimizing ICU stay and mechanical ventilation duration without discussing the feasibility or implications of such strategies in clinical practice. It would be beneficial to explore how these recommendations could be implemented or if there are potential negative consequences.

The study acknowledges the need to clarify the mechanism of ICU-AW occurrence but does not offer preliminary insights or hypotheses based on the data analyzed. Integrating current literature on pathophysiology with the study's findings could enrich the discussion and offer more nuanced perspectives on prevention. While the study calls for further research, specific areas for exploration, such as interventions that could mitigate risk factors or longitudinal studies to track the evolution of ICU-AW, could provide clearer guidance for subsequent investigations.

ICU-AW can lead to limited mobility, heightened use of sedatives and muscle relaxants, as well as sepsis, hypoxia, and malnutrition, which can result in neuro-muscular damage. However, the study examining this relationship has limitations as it was derived from a single center and did not account for disease severity or treatment efficacy. Future research should incorporate multiple centers and more biomarkers for a more comprehensive understanding. To prevent ICU-AW, clinicians should aim to minimize the duration of stay and mechanical ventilation under clinical conditions while also acknowledging other contributing factors. These findings are valuable for informed decision-making in the prevention and treatment of ICU-AW.

CONCLUSION

ICU-AW can impact a patient's recovery process and lead to adverse outcomes. A study was conducted to identify significant risk factors for ICU-AW and provide recommendations for its prevention and treatment. The research analysis highlighted that the foremost determinants that led to ICU-AW were the time length of mechanical ventilation and the period of the stay in the ICU. Healthcare providers can minimize the duration of mechanical ventilation and length of ICU stay through early mobilization, physical therapy, and other forms of rehabilitation.