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.

Risk factors for intensive-care-unit-acquired weakness

Wang et al reported 1063 cases from the initial 14 d of intensive care unit(ICU)stay,and analyzed relevant data such as age,comorbidities,recent dosages,vapor pressure dosages,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,which are closely related to ICU-acquired weakness(ICUAW).It is suggested that the length of ICU stay and the duration of mechanical ventilation are the main factors.ICU-AW is the most common neuromuscular injury in the ICU,which affects clinical progression and outcomes of patients.This manuscript helps to improve the early recognition of ICU-AW,thereby reducing mortality and improving prognosis.

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.

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.

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.

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.

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.

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.

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.

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.

Clinical Effect of Modified Shengyang Yiwei Decoction in the Treatment of Diarrhea-Predominant Irritable Bowel Syndrome due to Spleen and Stomach Weakness

Objective:To explore the therapeutic effect of Shengyang Yiwei Decoction in patients with diarrhea-predominant irritable bowel syndrome(IBS)due to spleen and stomach weakness.Methods:40 patients with diarrhea-predominant IBS who were treated from April 2018 to April 2020 were taken as samples.TCM(traditional Chinese medicine)syndrome differentiation found that they were all due to spleen and stomach weakness.They were randomly divided into two groups.Group A was treated with modified prescriptions of Shengyang Yiwei Decoction,while Group B was treated with Western medicine.The therapeutic effects in the two groups were compared.Results:The treatment efficacy in Group A was higher than that in Group B(P<0.05);the symptom scores of Group A such as loose stools,chills,physical weakness,poor appetite,and abdominal distension after meals were all lower than those in Group B(P<0.05);the SF-36(36-Item Short Form Health Survey)scores of patients with diarrhea-predominant IBS in Group A were higher than those in Group B(P<0.05);the treatment satisfaction of Group A was higher than that of Group B(P<0.05).Conclusion:Treatment of diarrhea-predominant IBS patients with spleen and stomach weakness by Shengyang Yiwei Decoction can promote the disappearance of gastrointestinal discomfort symptoms,improve the quality of life,and enhance treatment efficacy.Hence,it is an efficient and feasible treatment for diarrhea-predominant IBS due to spleen and stomach weakness.

Histological Effects of Guchangcuzhang Powder on Intestine and Spleen in Experimental Piglets with Spleen Weakness

[Objective] This study aimed to explore the histological effects of Guchangcuzhang powder on intestine and spleen in experiment piglets with spleen weakness.[Method] The 12 piglets with diarrhea were divided into two groups,namely the Guchangcuzhang powder group and the control group,six in each.On two and eight days after the drug discontinuance respectively,three piglets were selected from each of the group and killed by bloodletting from the jugular vein.Afterwards,paraffin sections of intestine and spleen were made and stained by HE staining solution,followed by observation of the histological changes.[Result] The structures of intestine and spleen in piglets drenched with Guchangcuzhang powder restored or approachedto normal.[Conlusion] Guchangcuzhang powder has the function of healing piglet diarrhea.

Strength and Weakness of Eco-agricultural Sightseeing Development in Baise City and Corresponding Strategies

The development of eco-agricultural sightseeing in Baise City shows a sound foundation and outstanding advantages as well as a rapid growth trend in recent years, however, there are also problems to be settled, such as shortage of talents, insufficient planning, low level of development, limited products and lack of competitive products. In view of such a condition, development concepts should be reformed first, competitive and special products made, marketing and propaganda enhanced to promote the healthy, coordinated and ordered development of eco-agricultural sightseeing in Baise City, Guangxi Zhuang Nationality Autonomous Region.

WOD模式优劣势分析及改进策略研究

为顺利推广应用水安全保障导向的开发(WOD)模式,有必要深入分析WOD模式的运行机制、优势和劣势,并加以完善与改进。采用对比分析法对WOD模式的优劣势进行了系统分析,优势包括不增加政府负债、支持项目总体进行打包开发、一体化实施、支持资金来源多元化等,劣势包括行业跨度大、见效周期长、土地开发风险大、不易实现自平衡等。针对存在的短板提出了改进措施,包括完善投融资体制机制、建立可持续发展模式、探索多元化融资模式、政府承担部分项目资本金、充分利用水土资源优势、推动公益类项目和可开发经营性用地打包开发试点、鼓励金融机构尽早介入等。采用WOD模式丰富了现有水安全保障项目投融资方法,具有推广应用价值,但应试点先行、依法合规,稳妥有序推行。

Weaknesses of Chomsky's Theory of Language

As seemingly persuasive as Chomsky's theory is, it is significantly flawed in four aspects: 1. ignoring a much fundamental creative force in early infancy; 2. equation of first-language learning with second-language learning; 3. methodology; 4. employment of terms.

突破“双重劣势”:数字企业国际化创新机制研究——基于最优区分视角

数字企业国际化创新面临来源国劣势和新进劣势共同作用所带来的“双重劣势”挑战,为突破“双重劣势”,数字企业必须拟定有效的国际化创新战略,在“差异化”和“合法性”之间取得平衡。本文以TikTok进军美国市场的成功经验为例,运用最优区分理论提炼出了数字企业国际化创新战略演化模型。研究发现:数字企业通过反应式协同平衡数字平台的差异化与合法性进而突破“新进劣势”,借助前瞻式协同平衡数字平台的差异化与合法性进而打破“来源国劣势”,最终占领国际市场。研究结论从最优区分视角为数字企业国际化创新提供了系统的分析框架和管理启示。

克服中国企业跨国并购双重劣势的新策略:中企境外身份作用机制的实证分析

近20年来,中资企业跨国并购活动既呈现高速增长特征,又表现出低于发达经济体甚至一些新兴经济体的并购交易完成率。中资企业开展跨国并购活动面临外来者劣势的影响,受到新兴经济体身份的来源国劣势危害。本文基于2003~2019年中资企业在126个国家的跨国并购数据,建立反事实研究框架,使用多种匹配方法,实证证明了中资企业在实施跨国并购时使用境外子公司身份可以作为企业层面一项减少外来者劣势和来源国劣势危害、提高并购交易成功概率的主动性策略。异质性分析的结论显示,该策略在中资企业跨国并购的主要流向国家中均适用,对非发达经济体和新兴经济体采用该策略可以获得高约两倍的成功概率增幅。

三代间健康劣势传递以及教育的阻断作用——中国的经验证据

利用CHARLS数据,本文在祖辈—父母—孩子(G1-G2-G3)框架下研究三代间健康劣势传递。结果表明,我国家庭内存在显著的三代间健康劣势代际传递和跨代传递现象。代际传递表现为,祖辈健康劣势会显著增加父母健康劣势概率,父母健康劣势会显著增加孩子健康劣势概率;跨代传递表现为,祖辈健康劣势会显著增加孩子健康劣势概率。这些传递关系在多种检验中表现出稳健性。本文还发现:父母完成义务教育对健康劣势传递具有统计显著的阻断作用,但其作用程度较为有限。这提示我们,要阻断健康劣势传递,仅仅依赖改善教育是不够的,还需要寻求教育之外的手段和途径。机制分析表明,教育有助于个体改善健康行为,获得更好职业、更高收入以及更多社会关系支持,从而降低个体及其后代的健康劣势概率。

土耳其樱桃出口到中国的优劣势及合作潜力分析

土耳其樱桃种质资源丰富,是“一带一路”沿线的国家。从土耳其樱桃生产概况、进出口贸易、出口到我国的优劣势、合作潜等维度进行分析,旨在为我国进一步制定和调整与土耳其樱桃产业相关的合作模式和政策提供理论依据。

Azimuthal elastic impedance-based Fourier coefficient variation with angle inversion for fracture weakness

Quantitative inversion of fracture weakness plays an important role in fracture prediction.Considering reservoirs with a set of vertical fractures as horizontal transversely isotropic media,the logarithmic normalized azimuthal elastic impedance(EI)is rewritten in terms of Fourier coefficients(FCs),the 90°ambiguity in the azimuth estimation of the symmetry axis is resolved by judging the sign of the second FC,and we choose the FCs with the highest sensitivity to fracture weakness and present a feasible inversion workflow for fracture weakness,which involves:(1)the inversion for azimuthal EI datasets from observed azimuthal angle gathers;(2)the prediction for the second FCs and azimuth of the symmetry axis from the estimated azimuthal EI datasets;and(3)the estimation of fracture weakness combining the extracted second FCs and azimuth of the symmetry axis iteratively,which is constrained utilizing the Cauchy sparse regularization and the low-frequency regularization in a Bayesian framework.Tests on synthetic and field data demonstrate that the 90°ambiguity in the azimuth estimation of the symmetry axis has been removed,and reliable fracture weakness can be obtained when the estimated azimuth of the symmetry axis deviates less than 30°,which can guide the prediction of fractured reservoirs.