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。

Guided Care护理模式对老年骨质疏松症患者自我管理能力和生活质量的影响

目的探讨分析Guided Care护理模式对老年骨质疏松症患者自我管理能力和生活质量的影响。方法简单随机选取2022年1月—2023年9月山东省泰安荣军医院收治的60例老年骨质疏松症患者为研究对象,按随机数表法将其分为常规组(实施常规护理干预),模式组(在常规组的基础上给予Guided Care护理模式干预),每组30例,两组均持续干预2个月,比较两组患者干预前后的健康认知水平、跌倒风险、自我管理能力、生活质量、护理满意度。结果干预2个月后,模式组的健康认知水平评分高于常规组,差异有统计学意义(P<0.05)。干预2个月后,模式组的跌倒风险评分低于常规组,差异有统计学意义(P<0.05)。干预2个月后,模式组的自我管理能力评分高于常规组,生活质量评分低于常规组,差异有统计学意义(P均<0.05)。干预2个月后,模式组的护理满意度为93.33%,高于常规组的护理满意度73.33%,差异有统计学意义(χ^(2)=4.320,P<0.05)。结论在老年骨质疏松症患者中,应用Guided Care护理模式,可以有效地提升患者的健康认知水平及自我管理能力,提高患者的生活质量,降低患者跌倒的风险。

Electrochemical biosensors for point-of-care testing

Point-of-care testing(POCT)is the practice of diagnosing and monitoring diseases where the patient is located,as opposed to traditional treatment conducted solely in a medical laboratory or other clinical setting.POCT has been less common in the recent past due to a lack of portable medical devices capable of facilitating effective medical testing.However,recent growth has occurred in this field due to advances in diagnostic technologies,device miniaturization,and progress in wearable electronics.Among these developments,electrochemical sensors have attracted interest in the POCT field due to their high sensitivity,compact size,and affordability.They are used in various applications,from disease diagnosis to health status monitoring.In this paper we explore recent advancements in electrochemical sensors,the methods of fabricating them,and the various types of sensing mechanisms that can be used.Furthermore,we delve into methods for immobilizing specific biorecognition elements,including enzymes,antibodies,and aptamers,onto electrode surfaces and how these sensors are used in real-world POCT settings.

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.

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.

Effect of cetuximab plus FOLFOX4 regimen on clinical outcomes in advanced gastric carcinoma patients receiving evidence-based care

BACKGROUND Although chemotherapy is effective for treating advanced gastric carcinoma(aGC),it may lead to an adverse prognosis.Establishing a highly effective and low-toxicity chemotherapy regimen is necessary for improving efficacy and outcomes in aGC patients.AIM To determine the efficacy and safety of cetuximab(CET)combined with the FOLFOX4 regimen(infusional fluorouracil,folinic acid,and oxaliplatin)as firstline therapy for patients with aGC,who received evidence-based care(EBC).METHODS A total of 117 aGC patients who received EBC from March 2019 to March 2022 were enrolled.Of these,60 in the research group(RG)received CET+FOLFOX4 as first-line therapy,whereas 57 in the control group(CG)received FOLFOX4.The efficacy[clinical response rate(RR)and disease control rate(DCR)],safety(liver and kidney dysfunction,leukopenia,thrombocytopenia,rash,and diarrhea),serum tumor marker expression[STMs;carbohydrate antigen(CA)19-9,CA72-4,and carcinoembryonic antigen(CEA)],inflammatory indicators[interleukin(IL)-2 and IL-10],and quality of life(QOL)of the two groups were compared.RESULTS A markedly higher RR and DCR were observed in the RG compared with the CG,with an equivalent safety profile between the two groups.RG exhibited notably reduced CA19-9,CA72-4,CEA,and IL-2 levels following treatment,which were lower than the pre-treatment levels and those in the CG.Post-treatment IL-10 was statistically increased in RG,higher than the pre-treatment level and the CG.Moreover,a significantly improved QOL was evident in the RG.CONCLUSION The CET+FOLFOX4 regimen is highly effective as first-line treatment for aGC patients receiving EBC.It facilitates the suppression of STMs,ameliorates the serum inflammatory microenvironment,and enhances QOL,without increased adverse drug effects.

Water, Sanitation and Hygiene in Lower-Level Health Care Facilities of Dar es Salaam Region in Tanzania: Status towards Achieving the Sustainable Development Goals and Way Forward

Background: Improving Water, Sanitation and Hygiene in health care settings is a critical prerequisite for achieving national health goals and Sustainable Development Goals (SDGs). The World Health Organization (WHO) has set a target for each United Nations member state to reach by 2030. Each member state is required to reach by 2022, 2025 and 2030 at least 60%, 80% and 100%, respectively of basic level of service of the five elements which are water, sanitation, hygiene, waste management and environmental cleaning. Methods: This study aimed to evaluate and document the current state of basic water, sanitation, and hygiene services in all lower-level health care facilities in the Dar es Salaam region of Tanzania as of July 2022. A cross-sectional study was conducted in 99 public dispensaries in the Dar es Salaam region’s five councils: Ubungo, Kigamboni, Kinondoni and Temeke Municipalities, and Ilala City. The interviewee form and observational checklists were both digitalized using the Kobo tool software. The respondents were health care facility in-charges or nurse in-charges. Data were downloaded, validated, and imported to Stata version 15 for analysis. Results: The basic WASH level per JMP is far below the target in 2022. Each member state by 2022 is required to reach at least 60% of the basic level of service of each element. We found a low coverage of basic WASH in the 99 dispensaries included in this study. The basic WASH coverage was met in only 10 (10.1%) of the dispensaries, while the remaining 89 (89.9%) dispensaries fall on limited WASH services. Conclusion: This study revealed lower coverage of basic WASH services in dispensaries. An urgent need is required to improve the status of WASH in all the dispensaries and facilitate the provision of quality health care services, patient safety and reduce health care associated infections.

Development and validation of a nomogram for predicting in-hospital mortality of intensive care unit patients with liver cirrhosis

BACKGROUND Liver cirrhosis patients admitted to intensive care unit(ICU)have a high mortality rate.AIM To establish and validate a nomogram for predicting in-hospital mortality of ICU patients with liver cirrhosis.METHODS We extracted demographic,etiological,vital sign,laboratory test,comorbidity,complication,treatment,and severity score data of liver cirrhosis patients from the Medical Information Mart for Intensive Care IV(MIMIC-IV)and electronic ICU(eICU)collaborative research database(eICU-CRD).Predictor selection and model building were based on the MIMIC-IV dataset.The variables selected through least absolute shrinkage and selection operator analysis were further screened through multivariate regression analysis to obtain final predictors.The final predictors were included in the multivariate logistic regression model,which was used to construct a nomogram.Finally,we conducted external validation using the eICU-CRD.The area under the receiver operating characteristic curve(AUC),decision curve,and calibration curve were used to assess the efficacy of the models.RESULTS Risk factors,including the mean respiratory rate,mean systolic blood pressure,mean heart rate,white blood cells,international normalized ratio,total bilirubin,age,invasive ventilation,vasopressor use,maximum stage of acute kidney injury,and sequential organ failure assessment score,were included in the multivariate logistic regression.The model achieved AUCs of 0.864 and 0.808 in the MIMIC-IV and eICU-CRD databases,respectively.The calibration curve also confirmed the predictive ability of the model,while the decision curve confirmed its clinical value.CONCLUSION The nomogram has high accuracy in predicting in-hospital mortality.Improving the included predictors may help improve the prognosis of patients.

Personalized Perioperative Care for a Patient with a 10-Year-Old Postpartum Old Thrombus after Neoadjuvant Therapy for Breast Cancer

Breast cancer is one of the most common malignant tumors in women, and has become the main cause threatening women’s health. A case of breast cancer with neoadjuvant chemotherapy was discharged after active treatment and nursing.

ICARE理念对县级敬老院养老护理员失能照护能力及职业精神与态度的影响

目的探讨ICARE理念对县级敬老院养老护理员失能照护能力及职业精神与态度的影响。方法将2023年1—6月绵阳市4家县级敬老院的养老护理员作为研究对象,采用便利抽样法抽取其中两家的40名养老护理员作为观察组,另外两家的40名养老护理员作为对照组。对照组采用常规培训模式,观察组则在对照组基础上以ICARE理念为指引实施养老护理员培训。干预前后,对两组失能照护能力、职业精神与态度进行评价。结果干预前,两组失能照护能力、职业精神与态度比较差异无统计学意义。干预后,两组失能照护能力、职业精神与态度比较差异有统计学意义,观察组优于对照组。结论融入ICARE理念的养老护理员培训能提高养老护理员失能照护能力,转变其职业态度,有利于养老护理队伍整体水平提升。

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.

Improvement of the nutritional support management system for patients in intensive care units

BACKGROUND Nutritional support for patients hospitalized in the intensive care unit(ICU)is an important part of clinical treatment and care,but there are significant implementation difficulties.AIM To introduce a modified nutritional support management system for ICU patients based on closed-loop information management and psychological counseling.METHODS The division of functions,personnel training,system construction,development of an intelligent decision-making software system,quality control,and improvement of the whole process were carried out to systematically manage nutritional support for ICU patients.RESULTS Following the implementation of the whole process management system,the scores of ICU medical staff’s knowledge,attitudes/beliefs,and practices regarding nutritional support were comprehensively enhanced.The proportion of hospital bed-days of total enteral nutrition(EN)in ICU patients increased from 5.58%to 11.46%,and the proportion of EN plus parenteral nutrition increased from 42.71%to 47.07%.The rate of EN initiation within 48 h of ICU admission increased from 37.50%to 48.28%,and the EN compliance rate within 72 h elevated from 20.59%to 31.72%.After the implementation of the project,the Self-rating Anxiety Scale score decreased from 61.07±9.91 points to 52.03±9.02 points,the Self-rating Depression Scale score reduced from 62.47±10.50 points to 56.34±9.83 points,and the ICU stay decreased from 5.76±2.77 d to 5.10±2.12 d.CONCLUSION The nutritional support management system based on closed-loop information management and psychological counseling achieved remarkable results in clinical applications in ICU patients.

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.

Knowledge and practice skills on home-based urinary catheter care among parents of under-five children with urinary catheter

Objectives:The objectives of this study were to assess the knowledge and practice skills on home-based urinary catheter care among parents of under-five children with urinary catheter.Materials and Methods:This cross-sectional study was conducted from June 1,2021,to September 11,2021,in a tertiary hospital in north India.Purposive sampling was used to select 50 participants.Three instruments were employed for data collection after fulfilling sample criteria;for baseline information demographic tool,knowledge questionnaires,and a practice checklist.Data were analyzed using descriptive and inferential statistics.Results:On assessment of 50 participants,the majority of parents aged above 30 years(74%).Most of the participants were male(82%),graduated(38%),and working in the private sector(58%).Similarly,two-thirds of participants were residing in a nuclear family(64%)with a single child 32(64%)and family income<5000 rupees per month(60%).The mean score of knowledge was 1.94±0.81 and that of practice skills was 1.98±0.85 on home-based care.Regression analysis showed that knowledge of parents was significantly associated with qualification(β:1.821,P=0.002).Similarly,association of practice skills of parents with gender(β:1.235,P=0.050)and qualification(β:1.889,P=0.00)was significant.Conclusion:The general findings of our study showed that parents’education and occupation played a significant role in a child’s care.Parental education and catheter care skills positively affect the child and reduce readmission rates.