Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning

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.

Unveiling significant risk factors for intensive care unit-acquired weakness:Advancing preventive care

In this editorial,we discuss an article titled,“Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning,”published in a recent issue of the World Journal of Clinical Cases.Intensive care unit-acquired weakness(ICU-AW)is a debilitating condition that affects critically ill patients,with significant implications for patient outcomes and their quality of life.This study explored the use of artificial intelligence and machine learning techniques to predict ICU-AW occurrence and identify key risk factors.Data from a cohort of 1063 adult intensive care unit(ICU)patients were analyzed,with a particular emphasis on variables such as duration of ICU stay,duration of mechanical ventilation,doses of sedatives and vasopressors,and underlying comorbidities.A multilayer perceptron neural network model was developed,which exhibited a remarkable impressive prediction accuracy of 86.2%on the training set and 85.5%on the test set.The study highlights the importance of early prediction and intervention in mitigating ICU-AW risk and improving patient outcomes.

Intensive care unit-acquired weakness–preventive,and therapeutic aspects;future directions and special focus on lung transplantation

In this editorial,comments are made on an interesting article in the recent issue of the World Journal of Clinical Cases by Wang and Long.The authors describe the use of neural network model to identify risk factors for the development of intensive care unit(ICU)-acquired weakness.This condition has now become common with an increasing number of patients treated in ICUs and continues to be a source of morbidity and mortality.Despite identification of certain risk factors and corrective measures thereof,lacunae still exist in our understanding of this clinical entity.Numerous possible pathogenetic mechanisms at a molecular level have been described and these continue to be increasing.The amount of retrievable data for analysis from the ICU patients for study can be huge and enormous.Machine learning techniques to identify patterns in vast amounts of data are well known and may well provide pointers to bridge the knowledge gap in this condition.This editorial discusses the current knowledge of the condition including pathogenesis,diagnosis,risk factors,preventive measures,and therapy.Furthermore,it looks specifically at ICU acquired weakness in recipients of lung transplantation,because–unlike other solid organ transplants-muscular strength plays a vital role in the preservation and survival of the transplanted lung.Lungs differ from other solid organ transplants in that the proper function of the allograft is dependent on muscle function.Muscular weakness especially diaphragmatic weakness may lead to prolonged ventilation which has deleterious effects on the transplanted lung–ranging from ventilator associated pneumonia to bronchial anastomotic complications due to prolonged positive pressure on the anastomosis.

Advancing critical care recovery:The pivotal role of machine learning in early detection of intensive care unit-acquired weakness

This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patient recovery.Highlightingthe paradox of modern medical advances,it emphasizes the urgent needfor early identification and intervention to mitigate ICU-AW's impact.Innovatively,the study by Wang et al is showcased for employing a multilayer perceptronneural network model,achieving high accuracy in predicting ICU-AWrisk.This advancement underscores the potential of neural network models inenhancing patient care but also calls for continued research to address limitationsand improve model applicability.The editorial advocates for the developmentand validation of sophisticated predictive tools,aiming for personalized carestrategies to reduce ICU-AW incidence and severity,ultimately improving patientoutcomes in critical care settings.

Evaluating neuromuscular electrical stimulation for preventing and managing intensive care unit-acquired weakness:Current evidence and future directions

Intensive care unit-acquired weakness(ICU-AW)is a prevalent issue in critical care,leading to significant muscle atrophy and functional impairment.Aiming to address this,Neuromuscular Electrical Stimulation(NMES)has been explored as a therapy.This systematic review assesses NMES's safety and effectiveness in enhancing functional capacity and mobility in pre-and post-cardiac surgery patients.NMES was generally safe and feasible,with intervention sessions varying in frequency and duration.Improvements in muscle strength and 6-minute walking test distances were observed,particularly in preoperative settings,but postoperative benefits were inconsistent.NMES showed promise in preventing muscle loss and improving strength,although its impact on overall functional capacity remained uncertain.Challenges such as short ICU stays and body composition affecting NMES efficacy were noted.NMES also holds potential for other conditions like cerebral palsy and stroke.Further research is needed to optimize NMES protocols and better understand its full benefits in preventing ICU-AW and improving patient outcomes.

Predicting intensive care unit-acquired weakness:A multilayer perceptron neural network approach

In this editorial,we comment on the article by Wang and Long,published in a recent issue of the World Journal of Clinical Cases.The article addresses the challenge of predicting intensive care unit-acquired weakness(ICUAW),a neuromuscular disorder affecting critically ill patients,by employing a novel processing strategy based on repeated machine learning.The editorial presents a dataset comprising clinical,demographic,and laboratory variables from intensive care unit(ICU)patients and employs a multilayer perceptron neural network model to predict ICUAW.The authors also performed a feature importance analysis to identify the most relevant risk factors for ICUAW.This editorial contributes to the growing body of literature on predictive modeling in critical care,offering insights into the potential of machine learning approaches to improve patient outcomes and guide clinical decision-making in the ICU setting.

Machine learning insights on intensive care unit-acquired weakness

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.

Intensive care unit-acquired weakness: Recent insights

Intensive care unit-acquired weakness(ICU-AW)is a common complication in critically ill patients and is associated with a variety of adverse outcomes.These include the need for prolonged mechanical ventilation and ICU stay;higher ICU,in-hospital,and 1-year mortality;and increased in-hospital costs.ICU-AW is associated with multiple risk factors including age,underlying disease,severity of illness,organ failure,sepsis,immobilization,receipt of mechanical ventilation,and other factors related to critical care.The pathological mechanism of ICUAW remains unclear and may be considerably varied.This review aimed to evaluate recent insights into ICU-AW from several aspects including risk factors,pathophysiology,diagnosis,and treatment strategies;this provides new perspectives for future research.

Intensive care unit-acquired weakness:Unveiling significant risk factors and preemptive strategies through machine learning

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-ness(ICU-AW).ICU-AW is a serious neuromuscular complication seen in criti-cally ill patients,characterized by muscle dysfunction,weakness,and sensory impairments.Post-discharge,patients may encounter various obstacles impacting their quality of life.The pathogenesis involves intricate changes in muscle and nerve function,potentially leading to significant disabilities.Given its global significance,ICU-AW has become a key research area.The study identified critical risk factors using a multilayer perceptron neural network model,highlighting the impact of intensive care unit stay duration and mechanical ventilation duration on ICU-AW.Recommendations were provided for preventing ICU-AW,empha-sizing comprehensive interventions and risk factor mitigation.This editorial stresses the importance of external validation,cross-validation,and model tran-sparency to enhance model reliability.Moreover,the application of machine learning in clinical medicine has demonstrated clear benefits in improving disease understanding and treatment decisions.While machine learning presents oppor-tunities,challenges such as model reliability and data management necessitate thorough validation and ethical considerations.In conclusion,integrating ma-chine learning into healthcare offers significant potential and challenges.Enhan-cing data management,validating models,and upholding ethical standards are crucial for maximizing the benefits of machine learning in clinical practice.

Pioneering role of machine learning in unveiling intensive care unitacquired weakness

In the research published in the World Journal of Clinical Cases,Wang and Long conducted a quantitative analysis to delineate the risk factors for intensive care unit-acquired weakness(ICU-AW)utilizing advanced machine learning methodologies.The study employed a multilayer perceptron neural network to accurately predict the incidence of ICU-AW,focusing on critical variables such as ICU stay duration and mechanical ventilation.This research marks a significant advancement in applying machine learning to clinical diagnostics,offering a new paradigm for predictive medicine in critical care.It underscores the importance of integrating artificial intelligence technologies in clinical practice to enhance patient management strategies and calls for interdisciplinary collaboration to drive innovation in healthcare.

Eff ects of early standardized enteral nutrition on preventing acute muscle loss in the acute exacerbation of chronic obstructive pulmonary disease patients with mechanical ventilation

BACKGROUND:To investigate the eff ects of early standardized enteral nutrition(EN)on the crosssectional area of erector spine muscle(ESMcsa),plasma growth diff erentiation factor-15(GDF-15),and 28-day mortality of acute exacerbation of chronic obstructive pulmonary disease(AECOPD)patients with invasive mechanical ventilation(MV).METHODS:A total of 97 AECOPD patients with invasive MV were screened in the ICUs of the First People's Hospital of Lianyungang.The conventional EN group(stage Ⅰ)and early standardized EN group(stage Ⅱ)included 46 and 51 patients,respectively.ESMcsa loss and GDF-15 levels on days 1 and 7 of ICU admission and 28-day survival rates were analyzed.RESULTS:On day 7,the ESMcsa of the early standardized EN group was significantly higher than that of the conventional EN group,while the plasma GDF-15 levels were significantly lower than those in the conventional EN group(ESMcsa:28.426±6.130 cm^(2) vs.25.205±6.127 cm^(2);GDF-15:1661.608±558.820 pg/mL vs.2541.000±634.845 pg/mL;all P<0.001).The 28-day survival rates of the patients in the early standardized EN group and conventional EN group were 80.40%and 73.90%,respectively(P=0.406).CONCLUSION:ESMcsa loss in AECOPD patients with MV was correlated with GDF-15 levels,both of which indicated acute muscular atrophy and skeletal muscle dysfunction.Early standardized EN may prevent acute muscle loss and intensive care unit-acquired weakness(ICU-AW)in AECOPD patients.

Sonographic muscle mass assessment in patients after cardiac surgery

BACKGROUND Patients undergoing cardiac surgery particularly those with comorbidities and frailty,experience frequently higher rates of post-operative morbidity,mortality and prolonged hospital length of stay.Muscle mass wasting seems to play important role in prolonged mechanical ventilation(MV)and consequently in intensive care unit(ICU)and hospital stay.AIM To investigate the clinical value of skeletal muscle mass assessed by ultrasound early after cardiac surgery in terms of duration of MV and ICU length of stay.METHODS In this observational study,we enrolled consecutively all patients,following their admission in the Cardiac Surgery ICU within 24 h of cardiac surgery.Bedside ultrasound scans,for the assessment of quadriceps muscle thickness,were performed at baseline and every 48 h for seven days or until ICU discharge.Muscle strength was also evaluated in parallel,using the Medical Research Council(MRC)scale.RESULTS Of the total 221 patients enrolled,ultrasound scans and muscle strength assessment were finally performed in 165 patients(patients excluded if ICU stay<24 h).The muscle thickness of rectus femoris(RF),was slightly decreased by 2.2%[(95%confidence interval(CI):-0.21 to 0.15),n=9;P=0.729]and the combined muscle thickness of the vastus intermedius(VI)and RF decreased by 3.5%[(95%CI:-0.4 to 0.22),n=9;P=0.530].Patients whose combined VI and RF muscle thickness was below the recorded median values(2.5 cm)on day 1(n=80),stayed longer in the ICU(47±74 h vs 28±45 h,P=0.02)and remained mechanically ventilated more(17±9 h vs 14±9 h,P=0.05).Moreover,patients with MRC score≤48 on day 3(n=7),required prolonged MV support compared to patients with MRC score≥49(n=33),(44±14 h vs 19±9 h,P=0.006)and had a longer duration of extracorporeal circulation was(159±91 min vs 112±71 min,P=0.025).CONCLUSION Skeletal quadriceps muscle thickness assessed by ultrasound shows a trend to a decrease in patients after cardiac surgery post-ICU admission and is associated with prolonged duration of MV and ICU length of stay.