21st century critical care medicine:An overview

Critical care medicine in the 21st century has witnessed remarkable advancements that have significantly improved patient outcomes in intensive care units(ICUs).This abstract provides a concise summary of the latest developments in critical care,highlighting key areas of innovation.Recent advancements in critical care include Precision Medicine:Tailoring treatments based on individual patient characteristics,genomics,and biomarkers to enhance the effectiveness of therapies.The objective is to describe the recent advancements in Critical Care Medicine.Telemedicine:The integration of telehealth technologies for remote patient monitoring and consultation,facilitating timely interventions.Artificial intelligence(AI):AI-driven tools for early disease detection,predictive analytics,and treatment optimization,enhancing clinical decision-making.Organ Support:Advanced life support systems,such as Extracorporeal Membrane Oxygenation and Continuous Renal Replacement Therapy provide better organ support.Infection Control:Innovative infection control measures to combat emerging pathogens and reduce healthcare-associated infections.Ventilation Strategies:Precision ventilation modes and lung-protective strategies to minimize ventilatorinduced lung injury.Sepsis Management:Early recognition and aggressive management of sepsis with tailored interventions.Patient-Centered Care:A shift towards patient-centered care focusing on psychological and emotional wellbeing in addition to medical needs.We conducted a thorough literature search on PubMed,EMBASE,and Scopus using our tailored strategy,incorporating keywords such as critical care,telemedicine,and sepsis management.A total of 125 articles meeting our criteria were included for qualitative synthesis.To ensure reliability,we focused only on articles published in the English language within the last two decades,excluding animal studies,in vitro/molecular studies,and non-original data like editorials,letters,protocols,and conference abstracts.These advancements reflect a dynamic landscape in critical care medicine,where technology,research,and patient-centered approaches converge to improve the quality of care and save lives in ICUs.The future of critical care promises even more innovative solutions to meet the evolving challenges of modern medicine.

Palliative care with adequate pain relief challenges the need for euthanasia legislation

Background:In today’s society the ongoing discussion about euthanasia triggers emotionally charged debates surrounding the delicate balance between valuing life and respecting an individual’s autonomy.With the persistence of this debate,there has been the emergence of the concept of the so-called alternative:palliative care.Positioned as a substitute for euthanasia,palliative care aims to alleviate suffering in terminally ill patients without engaging in the ethical dilemmas associated with euthanasia.Methods:This paper explores the facets of palliative care highlighting its core objectives such as providing adequate pain relief as a compassionate alternative to euthanasia.Results:By examining palliative care as a comprehensive approach to end of life support,this study challenges the perceived necessity of euthanasia and advocates,for compassionate and dignified end of life experiences.Conclusion:In conclusion,palliative care emerges as a viable and ethically sound alternative to euthanasia,emphasizing the importance of compassionate end-of-life care and pain management.

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.

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.

Personalized and continuous care intervention affects rehabilitation,living quality,and negative emotions of patients with breast cancer

BACKGROUND Breast cancer is among the most common malignancies worldwide.With progress in treatment methods and levels,the overall survival period has been prolonged,and the demand for quality care has increased.AIM To investigate the effect of individualized and continuous care intervention in patients with breast cancer.METHODS Two hundred patients with breast cancer who received systemic therapy at The First Affiliated Hospital of Hebei North University(January 2021 to July 2023)were retrospectively selected as research participants.Among them,134 received routine care intervention(routing group)and 66 received personalized and continuous care(intervention group).Self-rating anxiety scale(SAS),self-rating depression scale(SDS),and Functional Assessment of Cancer Therapy-Breast(FACT-B)scores,including limb shoulder joint activity,complication rate,and care satisfaction,were compared between both groups after care.RESULTS SAS and SDS scores were lower in the intervention group than in the routing group at one and three months after care.The total FACT-B scores and five dimensions in the intervention group were higher than those in the routing group at three months of care.The range of motion of shoulder anteflexion,posterior extension,abduction,internal rotation,and external rotation in the intervention group was higher than that in the routing group one month after care.The incidence of postoperative complications was 18.18%lower in the intervention group than in the routing group(34.33%;P<0.05).Satisfaction with care was 90.91% higher in the intervention group than in the routing group(78.36%;P<0.05).CONCLUSION Personalized and continuous care can alleviate negative emotions in patients with breast cancer,quicken rehabilitation of limb function,decrease the incidence of complications,and improve living quality and care satisfaction.

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.

Mitigating the Prevalence of Diabetic Retinopathy in the United States: Utilization of the Chronic Care Model as a Public Health Framework

As the prevalence of diabetic retinopathy continues to be on the rise, the Chronic Care Model (CCM) offers a transformative, patient-focused approach for efficient diabetic retinopathy care, emphasizing the need for urgent and innovative strategies in the United States. The model integrates community resources, healthcare organizations, self-management support, delivery system design, decision support, and clinical information systems. Addressing challenges and solutions, the model emphasizes proactive and preventive measures, collaborative multidisciplinary care, technological integration, and overcoming resistance to change. This paper proposes the utilization of the Chronic Care Model (CCM) as a possible public health framework for comprehensive management of diabetic retinopathy in the United States. Implementing the CCM offers a comprehensive approach to diabetic retinopathy care, addressing both individual and systemic factors, essential for improving public health outcomes.

Decision-Making and Management of Self-Care in Persons with Traumatic Spinal Cord Injuries: A Preliminary Study

Patients and physicians understand the importance of self-care following spinal cord injury (SCI), yet many individuals with SCI do not adhere to recommended self-care activities despite logistical supports. Neurobehavioral determinants of SCI self-care behavior, such as impulsivity, are not widely studied, yet understanding them could inform efforts to improve SCI self-care. We explored associations between impulsivity and self-care in an observational study of 35 US adults age 18 - 50 who had traumatic SCI with paraplegia at least six months before assessment. The primary outcome measure was self-reported self-care. In LASSO regression models that included all neurobehavioral measures and demographics as predictors of self-care, dispositional measures of greater impulsivity (negative urgency, lack of premeditation, lack of perseverance), and reduced mindfulness were associated with reduced self-care. Outcome (magnitude) sensitivity, a latent decision-making parameter derived from computationally modeling successive choices in a gambling task, was also associated with self-care behavior. These results are preliminary;more research is needed to demonstrate the utility of these findings in clinical settings. Information about associations between impulsivity and poor self-care in people with SCI could guide the development of interventions to improve SCI self-care and help patients with elevated risks related to self-care and secondary health conditions.

Impact of the economic crisis and drug shortage on Lebanese cancer patients’care

BACKGROUND As a consequence of the economic crisis,the sociopolitical instability and the advent of the coronavirus disease-19 pandemic,nested challenges faced the Lebanese healthcare system.These have resulted in critical shortages of essential resources,including medications vital for oncologic patients.AIM To assess the ramifications of the ongoing economic crisis on oncology patient care focusing on our outpatient oncology department.METHODS A questionnaire was distributed during the month of February 2022 to oncology patients in Hôtel Dieu de France University Hospital in Beirut during their outpatient therapy.The primary objective was to assess the far-reaching impact of the economic crisis on patient care and the resulting psychological implications.RESULTS Among 182 interviewed patients,31.87%experienced treatment interruption mainly due to acute drug shortages.Despite 87.91%of the patients benefiting from third-party coverage,69.60%had to self-pay for their medications leading to 69.78%of patients perceiving that healthcare was more difficult to access after 2020.Psychologically,one-third of the patients exhibited symptoms of anxiety and/or depression,with 7 patients reporting suicidal ideations.Notably,37.93%of patients who interrupted cancer treatment reported a history of comorbidities,and 89.66%who altered their treatment cited financial difficulties.CONCLUSION Lebanese cancer patients face complex challenges spanning economic,healthcare,and psychological realms.Income inequalities exacerbated by the economic crisis hindered healthcare access.

Future of neurocritical care:Integrating neurophysics,multimodal monitoring,and machine learning

Multimodal monitoring(MMM)in the intensive care unit(ICU)has become increasingly sophisticated with the integration of neurophysical principles.However,the challenge remains to select and interpret the most appropriate combination of neuromonitoring modalities to optimize patient outcomes.This manuscript reviewed current neuromonitoring tools,focusing on intracranial pressure,cerebral electrical activity,metabolism,and invasive and noninvasive autoregulation moni-toring.In addition,the integration of advanced machine learning and data science tools within the ICU were discussed.Invasive monitoring includes analysis of intracranial pressure waveforms,jugular venous oximetry,monitoring of brain tissue oxygenation,thermal diffusion flowmetry,electrocorticography,depth electroencephalography,and cerebral microdialysis.Noninvasive measures include transcranial Doppler,tympanic membrane displacement,near-infrared spectroscopy,optic nerve sheath diameter,positron emission tomography,and systemic hemodynamic monitoring including heart rate variability analysis.The neurophysical basis and clinical relevance of each method within the ICU setting were examined.Machine learning algorithms have shown promise by helping to analyze and interpret data in real time from continuous MMM tools,helping clinicians make more accurate and timely decisions.These algorithms can integrate diverse data streams to generate predictive models for patient outcomes and optimize treatment strategies.MMM,grounded in neurophysics,offers a more nuanced understanding of cerebral physiology and disease in the ICU.Although each modality has its strengths and limitations,its integrated use,especially in combination with machine learning algorithms,can offer invaluable information for individualized patient care.

Influences of Horticultural Therapy and Functional Recovery Training at a Day-Care Center for the Elderly and Text Mining of Care Records by AI

Background: In Japan, due to the COVID-19 pandemic, various behavioral restrictions were implemented from March 2020, resulting in an increased number of elderly people with reduced participation in care and rehabilitation. As a result, the decline in motor and cognitive functions became more pronounced. Purpose: The study examined how activities combining passive horticultural therapy, which is always introduced, with active functional recovery training, which is used in the functional recovery training of patients with hemiplegia, influence the behavior of elder people using the facility. Methods: 36 elder people with care need 1 or 2 who regularly used the day-care facilities after the pandemic ended and who agreed to participate in the study was included. The assessment data were every three months from December 2021 to December 2023 after the COVID-19 pandemic, the MMSE and Moca-J, TMIG-Index, and GDS15. AI-based text mining was used to compare changes in visit reports before and after the introduction of the Posture Chart and Red Code, and the relationship between the language used. Results: The results of MMSE and Moca-J were both significantly higher in Group B and C than in Group A. TIMG assessment, was significantly higher in Group B and C than in Group A, and Group C was significantly higher than Group B. The GDS, an assessment of depression in old age, tended to be significantly higher in Group A than in Groups B and C. In the “Co-Occurrence Network Analysis” shown after the introduction of the latest functional recovery training equipment such as the Red Cord, its results showed a high association between the words Bathing and CARE and words such as POSTURE, Red-Code, and AI, App, which are used in functional training and rehabilitation, and their analysis. Conclusion: At day-care facilities specializing in functional recovery training and rehabilitation, a combination of horticultural therapy and the latest functional training equipment found to help maintain the physical and mental health of elderly people certified as requiring nursing care. Since this is a short-term study following behavioral restrictions, long-term research should continue to study the effects of adding functional training, handled by rehabilitation professionals, to horticultural therapy.

Revolutionizing Healthcare—The Integration of Virtual Worlds, AR, and Metaverse Technology

This paper explores the transformative impact of virtual worlds, augmented reality (AR), and the metaverse in the healthcare sector. It delves into the ways these technologies are reshaping patient care, medical education, and research, while also addressing the challenges and opportunities they present. The paper highlights the potential benefits of these technologies and emphasizes the need for comprehensive regulatory frameworks and ethical guidelines to ensure responsible integration. Finally it outlines their transformative impact and discusses the challenges and opportunities they present for the future of healthcare provision.

Artificial intelligence:Applications in critical care gastroenterology

Gastrointestinal(GI)complications frequently necessitate intensive care unit(ICU)admission.Additionally,critically ill patients also develop GI complications requiring further diagnostic and therapeutic interventions.However,these patients form a vulnerable group,who are at risk for developing side effects and complications.Every effort must be made to reduce invasiveness and ensure safety of interventions in ICU patients.Artificial intelligence(AI)is a rapidly evolving technology with several potential applications in healthcare settings.ICUs produce a large amount of data,which may be employed for creation of AI algorithms,and provide a lucrative opportunity for application of AI.However,the current role of AI in these patients remains limited due to lack of large-scale trials comparing the efficacy of AI with the accepted standards of care.

A Health Service Transition to Adult Patient Care for Sickle Cell Disease

This paper explores the challenges related to long-term conditions,focusing on sickle cell disease(SCD)as a case study.Long-term conditions,characterized by the need for ongoing management,present a substantial burden on healthcare systems globally.A careful transition from pediatric to adult healthcare is needed for SCD.The discussion extends to the broader health service transition to adult patient care in SCD,emphasizing the World Health Organization’s definition of care transitions and the necessity for an integrated healthcare service.The emphasis is on a multidisciplinary approach to medical,mental health,and educational problems.A person-centered model of care should be used more consistently to resolve these long-term condition-related challenges.To evaluate the effectiveness of new interventions in improving the transition of care from pediatrics to adult patients with SCD,continuous quality improvement strategies should be implemented and prospectively measured in younger patients.In conclusion,this study highlights the critical importance of an effective transition from pediatric to adult healthcare.The continued research of effective transition practices is essential for the future and there is still a requirement to develop pragmatic approaches to enhance research on the transition to improve the quality of healthcare for patients with long-term conditions.

Exploration of the Talent Cultivation Path of New Higher Vocational Compound Childcare Integration

With the continuous development of society,people’s concept of early childhood education is also gradually upgrading.In the report of the 19th National Congress of the Communist Party of China(CPC),“childcare”is listed as one of the seven national key livelihood tasks,and the model of“integration of childcare and early childhood”is gradually being widely used in infant and toddler education.The application of this model not only promotes the development of infant and toddler education but also brings new challenges to the training of talents specialized in childcare in higher vocational colleges.Higher vocational colleges and universities should adjust the talent training mode according to the requirements of the childcare integration mode,so as to build a high-quality skilled personnel team and promote the further development of infant and toddler education.

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.

CICARE沟通模式联合细节护理及呼吸训练在心脏磁共振检查患者中的应用

目的探讨CICARE沟通模式联合细节护理及呼吸训练在心脏磁共振检查患者中的应用效果。方法选取2022年1月—2022年12月在医院行心脏磁共振检查患者180例,按照组间基线资料可比的原则分为对照组和观察组,各90例,对照组给予常规护理,观察组在对照组基础上给予CICARE沟通模式联合细节护理及呼吸训练,比较两组患者护理前后的Zung焦虑自评量表(SAS)和汉密尔顿焦虑自评量表(HAMA)评分、检查相关情况、图像质量、患者满意度。结果护理干预后,观察组患者SAS、HAMA评分低于对照组,差异有统计学意义(P<0.05)。观察组患者检查时间短于对照组,差异有统计学意义(P<0.05),两组扫描成功率比较,差异无统计学意义(P>0.05)。观察组患者图像质量优于对照组,图像伪影发生率为18.89%,低于对照组的50.00%,差异有统计学意义(P<0.05)。观察组患者物理环境、护理、护理技术、安全、指导沟通满意度评分高于对照组,差异有统计学意义(P<0.05)。结论CICARE沟通模式联合细节护理及呼吸训练应用于心脏磁共振检查患者中,可有效缓解焦虑情况,提高图像质量、扫描成功率和患者满意度,提高检查效率。

Impact of continuous care on cardiac function in patients with lung cancer complicated by coronary heart disease

BACKGROUND Despite sharing similar pathogenic factors,cancer and coronary heart disease(CHD)occur in comparable populations at similar ages and possess similar susceptibility factors.Consequently,it is increasingly commonplace for patients to experience the simultaneous occurrence of cancer and CHD,a trend that is steadily rising.AIM To determine the impacts of continuing care on lung cancer patients with CHD following percutaneous coronary intervention(PCI).METHODS There were 94 lung cancer patients with CHD following PCI who were randomly assigned to the intervention group(n=38)and the control group(n=41).In the intervention group,continuing care was provided,while in the control group,routine care was provided.An evaluation of cardiac and pulmonary function,medication compliance,a 6-min walk test,and patient quality of life was performed.RESULTS Differences between the two groups were significant in left ventricular ejection fraction,6-min walk test,oxygen uptake,quality of life and medication compliance(P<0.05).In comparison with the control group,the enhancement in the intervention group was more significant.The intervention group had more patients with high medication compliance than the control group,with a statistically significant difference(P<0.05).CONCLUSION After undergoing PCI,lung patients with CHD could benefit from continued care in terms of cardiac and pulmonary function,medications compliance,and quality of life.

Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

Assessment of Breast Cancer Prevention Practices among Women Attending Primary Health Care in Abha City, Aseer Region, Saudi Arabia

Cancer is a leading cause of death worldwide, with breast cancer being the most common (2.26 million new cases and 685,000 deaths). In Saudi Arabia, breast cancer ranked the first among females in 2014, accounting for 15.9% of all cancers reported among Saudi nationals and 28.7% of all cancers reported among females of all ages. Early detection of breast cancer could decrease the risks, have a better prognosis, and have better outcomes/more successful treatments. Prevalence of breast cancer reached more than 25% of all diagnosed cancer in the kingdom among women. Aim: This study aims to assess the knowledge and performance of women attending primary care centers about breast self-examination and mammogram screening for prevention and early detection of breast cancer in Abha city primary healthcare centers, Kingdom of Saudi Arabia. Research Method: cross sectional design was conducted by using questionnaire, which was distributed to primary care center nurses. The collected data was statistically analyzed using the Statistical Package for Social Sciences, version 25. Results: The study found that participants had poor awareness and knowledge about breast self-examination, risk factors for breast cancer, and trends and practices in early diagnosis of breast cancer. Conclusion and Recommendations: It recommends increasing awareness campaigns and providing educational programs to improve knowledge and practices.