Parsimonious Model for Blood Glucose Level Monitoring in Type 2 Diabetes patients

To establish the parsimonious model for blood glucose monitoring in patients with type 2 diabetes receiving oral hypoglycemic agent treatment. One hundred and fifty-nine adult Chinese type 2 diabetes patients were randomized to receive rapid-acting or sustained-release gliclazide therapy for 12 weeks.

Patient Centered Real-Time Mobile Health Monitoring System

In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.

Health Behaviors and Outcomes of Mobile Health Apps and Patient Engagement in the USA

Mobile health applications, or mHealth apps, have gained popularity due to their practical functions and strengthening the connection between patients and healthcare professionals. These apps are designed for managing health and well-being on portable devices, allowing individuals to self-manage their health or healthcare practitioners to enhance patient care. Key features include personalized recommendations, data synchronization with other health devices, and connectivity with healthcare professionals. The research describes how mobile health applications support healthy behaviors, facilitate communication between patients and physicians, and empower individuals in the United States to take charge of their health. This study also examines how adults in the US use mobile health applications, or mHealth apps, on their tablets or smartphones for health-seeking purposes. The information was taken from Cycle 4 of the Health Information National Trends Survey (HINTS 4). The challenges regarding these mobile health apps have also been evaluated with possible remedies. Around 100 university students participated in a cross-sectional study by answering questions on their eating habits, physical activity, lifestyle choices related to health, and use of mobile health apps. The data was then analyzed and concluded as a result. Mobile health applications have brought about a significant shift in the way patients connect with their healthcare providers by providing them with convenient access to health services and information. By keeping track of health markers like diet, exercise, and medication compliance, patients may use these tools to help better manage their chronic conditions. Mobile health applications can improve patient outcomes and save healthcare costs by empowering patients to take charge of their health. Through the facilitation of communication between patients and healthcare professionals, mobile health apps also offer virtual consultations and remote monitoring.

Automated Patient Discomfort Detection Using Deep Learning

The Internet of Things(IoT)has been transformed almost all fields of life,but its impact on the healthcare sector has been notable.Various IoTbased sensors are used in the healthcare sector and offer quality and safe care to patients.This work presents a deep learning-based automated patient discomfort detection system in which patients’discomfort is non-invasively detected.To do this,the overhead view patients’data set has been recorded.For testing and evaluation purposes,we investigate the power of deep learning by choosing a Convolution Neural Network(CNN)based model.The model uses confidence maps and detects 18 different key points at various locations of the body of the patient.Applying association rules and part affinity fields,the detected key points are later converted into six main body organs.Furthermore,the distance of subsequent key points is measured using coordinates information.Finally,distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions.The accuracy of the proposed system is assessed on various test sequences.The experimental outcomes reveal the worth of the proposed system’by obtaining a True Positive Rate of 98%with a 2%False Positive Rate.

IoT Based Nurse Activities Monitoring and Controlling System

IoT technology has emerged as a valuable tool in modern healthcare, providing real-time monitoring of patients, effective management of healthcare, and proper administration of patient information. The proposed system aims to develop a system that can prevent backward blood flow from stopping saline fluid, as well as monitor the temperature, heart rate, and oxygen level of patients by using multiple sensors like weight, temperature and heart rate, etc. Additionally, the proposed system can monitor the room temperature and humidity for contributing to the patient’s overall comfort. In emergency situations, it includes an emergency push button for quick alert medical staff and initiates timely interventions. It is designed to support nurses and doctors in monitoring patients and providing timely interventions to prevent complications.

无线体温传感器iThermonitor■在手术病人核心体温监测中的应用

[目的]探讨无线体温传感器iThermonitor■在手术病人核心体温监测中的应用效果。[方法]选取2018年6月—12月胸外科、神经外科手术病人226例,术前在病人腋窝处粘贴无线体温传感器iThermonitor■监测病人的核心体温,记录手术病人低体温发生率、围术期体温变化及相关因素、术后住院时间及医疗费用。[结果]胸外科手术病人低体温发生率为70.83%,体温跌幅范围为0.008~2.883℃;神经外科手术病人低体温发生率为73.59%,体温跌幅范围为0.007~2.789℃。回归分析显示,手术时长、体质指数(BMI)、年龄是胸外科手术病人体温的影响因素;手术时长、BMI是神经外科手术病人体温的影响因素。胸外科手术病人发生低体温,术后住院时间与费用增高;神经外科手术病人发生低体温,术后住院时间与费用减少。[结论]手术室护理人员应根据手术病人情况,采用智能化监测方法进行全程链式体温管理,保证手术病人的安全。

Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System

Cardiovascular diseases are the most common cause of death worldwide over the last few decades in the developed as well as underdeveloped and developing countries. Early detection of cardiac diseases and continuous supervision of clinicians can reduce the mortality rate. However, accurate detection of heart diseases in all cases and consultation of a patient for 24 hours by a doctor is not available since it requires more sapience, time and expertise. In this?study, a tentative design of a cloud-based heart disease prediction system had been proposed to detect impending heart disease using Machine learning techniques. For the accurate detection of the heart disease, an efficient machine learning technique should be used which had been derived from a distinctive analysis among several machine learning algorithms in a Java Based Open Access Data Mining Platform, WEKA. The proposed algorithm was validated using two widely used open-access database, where 10-fold cross-validation is applied in order to analyze the performance of heart disease detection. An accuracy level of 97.53% accuracy was found from the SVM algorithm along with sensitivity and specificity of 97.50% and 94.94%respectively. Moreover, to monitor the heart disease patient round-the-clock by his/her caretaker/doctor, a real-time patient monitoring system was developed and presented using Arduino, capable of sensing some real-time parameters such as body temperature, blood pressure, humidity, heartbeat. The developed system can transmit the recorded data to a central server which are updated every 10 seconds. As a result, the doctors can visualize the patient’s real-time sensor data by using the application and start live video streaming if instant medication is required. Another important feature of the proposed system was that as soon as any real-time parameter of the patient exceeds the threshold, the prescribed doctor is notified at once through GSM technology.

A New Social and Technological Paradigm to Assess Chronic Patient Management Process： Preliminary Results

The present research intends to address in a comprehensive, transversal, and interdisciplinary manner the chronic patient management process in the research project named ＂PRO DOMO SUD＂ in order to identify operational inefficiencies, thus demonstrating that these are largely attributable to incurred costs and, thus, evaluate possible solutions for providing effective and appropriate responses by healthcare and social services. Can patients/older people be treated, monitored, and managed successfully with mobile and wearable technologies？ The project involved three different groups of patients/participants： Patients with heart failure shock in ＂Home Monitoring Scenario＂; Patients with different pathologies in ＂Virtual Ward Scenario＂; Patients with limited mobility due to Neurological and Orthopaedic disease in ＂Rehabilitation Scenario＂. Due to the complexity of the issue, the methodological approach adopted must be multidimensional and interdisciplinary, addressing the complexity of the chronic patient from all viewpoints, not reducing it, yet analysing, understanding, rearranging, and managing it in an organic manner. The three different scenarios were allowed to identify several impacts on organizational and clinic management of chronic diseases, the tests showed significant improvements in quality of life of patients enrolled in the project. The data deriving from the three scenario demonstrate that wearable divide and ICT, in general, can empower both patients and physician personnel allowing them to be active part in the chronic disease management process. The PRO DOMO SUD experience derived from the Living Lab, this is a new paradigm for industrial research and development activities which allows the final users to actively collaborate with the designers and technicians in the development and test of new products and services aimed to them. The Living Labs stimulate social innovation by transferring research results from the closed industrial laboratory towards real life contexts where citizens and users become co-developers.

Multi-outcome predictive modelling of anesthesia patients

Conjunctive use of anesthetic agents results in drug interactions which can alter or influence multiple patient outcomes such as anesthesia depth,and cardiorespiratory parameters which can also be altered by patient conditions and surgical procedures.Using artificial intelligence technology to continuously gather data of drug infusion and patient outcomes,we can generate reliable computer models individualized for a patient during specific stages of particular surgical procedures.This data can then be used to extend the current anesthesia monitoring functions to include future impact prediction,drug administration planning,and anesthesia decisions.

重症患者新型冠状病毒感染应用奈玛特韦/利托那韦的药学监护实践

目的总结临床药师对治疗新型冠状病毒感染(COVID-19)的口服新药——奈玛特韦片/利托那韦片组合包装(Paxlovid)的药学监护要点。方法收集2022年12月~2023年1月奥密克戎毒株暴发期间,山东大学附属威海市立医院住院重症患者应用Paxlovid的病例10例,临床药师从适应证、启用时机、用法用量、不良反应、药物间相互作用等方面评价药物应用的合理性,并参与临床实施药学监护。结果10例患者中,男性4例,女性6例,平均年龄(73.8±12.3)岁。临床药师会诊确定这10例患者的COVID-19的分型为中型,符合Paxlovid的适应证,除1例患者启用时间较晚,其余患者均在核酸阳性5 d内应用Paxlovid,服药后核酸转阴时间平均为(7.9±4.7)d。临床药师通过计算患者的肾小球滤过率来调整剂量,并查阅文献资料对其中3例无法口服的重症患者采取鼻饲给药方式。通过不良反应评估量表,判断2例患者出现的消化道不良反应与Paxlovid的使用很可能相关。对患者进行药物重整,发现3例患者应用的药物与Paxlovid有潜在相互作用风险,临床药师与主治医师进行沟通,对有相互作用风险的药物进行停药、更换品种或严密监测指标等处理。结论Paxlovid作为口服治疗COVID-19的新药,在临床中表现出较好的优势,但因适用人群、药物相互作用等问题,存在应用上的局限性。临床药师通过总结Paxlovid应用时的注意事项,可在查房时运用思维导图一对一地对患者进行个体化药学监护,以保障药物治疗的安全性和合理性。

基于“-TT”结构经监护仪腹内压监测法降低重症患者喂养不耐受发生率的研究

探究基于“-TT”结构经监护仪腹内压监测法降低重症患者喂养不耐受发生率的效果。选取2022年8月—2023年8月四川省自贡市第四人民医院抢救监护室(emergency intensive care unit,EICU)60例重症需行肠内营养(enteral nutrition,EN)支持的患者作为研究对象,采用随机数字表法将患者分为参照组和试验组,每组各30例。参照组实施常规EN管理,试验组在参照组基础上实施基于“-TT”结构经监护仪腹内压监测法,对比两组患者的喂养不耐受发生率。结果显示,与参照组相比,试验组喂养不耐受发生率较低(P<0.05);试验组达到目标喂养量时间较短(P<0.05);试验组EICU停留时间较短(P<0.05)。研究发现,于EICU重症需行EN支持患者的管理中,基于“-TT”结构经监护仪腹内压监测法具有一定的临床应用价值,通过对患者腹压变化的实时监测,可以及时调整喂养方案,降低其喂养不耐受发生率,缩短患者达到目标喂养量的时间,改善患者预后,值得借鉴。

增强型体外反搏对老年高血压患者血压及内皮功能障碍的改善作用

目的原发性高血压与内皮功能障碍密切相关,本研究旨在评估增强型体外反搏(EECP)对老年高血压患者血压及内皮功能障碍的改善作用。方法入选2021年11月至2022年7月于河北省第八人民医院(河北省老年病医院)住院及门诊就诊的360例老年原发性高血压患者为研究对象,随机分为三组,每组各120例。对照组予以生活方式指导和服药依从性管理,药物组在生活方式指导基础上给予比索洛尔口服,药物+EECP组在生活方式加比索洛尔治疗基础上给与EECP,每周5次,1h/次,共治疗3个月。比较三组患者干预前、后血清内皮素1(ET-1)和一氧化氮(NO)水平、颈动脉超声及临床血压和24 h动态血压情况。记录干预期间副作用。结果与对照组和药物组患者相比,药物+EECP组的内皮功能显著改善:血清ET-1水平、临床血压和24 h动态血压较干预前和其他两组降低,NO水平及颈动脉超声参数收缩峰值速度(PSV)、舒张末期速度(EDV)和流体剪切应力(FSS)升高(P<0.05)。相关分析显示,FSS与ET-1(r=-0.121,P=0.022)、NO-1(r=0.151,P=0.004)和临床收缩压(r=-0.154,P=0.003)变化值(干预后-干预前)之间呈相关关系。即使校正了混杂因素,这种相关性仍存在(P<0.001)。结论比索洛尔联合EECP可改善老年原发性高血压患者血管内皮功能。

实体器官移植受者接受霉酚酸类药物TDM的意愿调查

目的调查实体器官移植受者接受霉酚酸类(MPA)药物治疗药物监测(TDM)的意愿,并探究相关影响因素,为相关指南推荐意见形成及临床决策优化提供参考。方法采用横断面研究设计法,设计MPA TDM患者意愿调查问卷,选取2022年4月14日至2022年6月27日在北京市两家三级甲等综合医院接受MPA类药物治疗的实体器官移植受者进行调研。采用Likert 5级评分法对患者接受MPA TDM的意愿进行评分,采用Pearson相关分析和二元Logistic回归分析患者接受MPA TDM的意愿影响因素及其相关性,采用非参数检验和χ2检验对影响患者意愿决策的因素进行排序及一致性分析。结果共收集到有效问卷140份,有效回收率为77.35%。140例患者接受MPA TDM的平均意愿得分为(4.01±0.65)分,表明总体意愿值较高;有116例(82.86%)患者表示愿意或非常愿意接受MPA TDM;既往接受过TDM与未接受过TDM患者的意愿得分差异有统计学意义[(4.30±0.53)分vs.(3.80±0.65)分,P<0.001]。患者对MPA TDM的了解程度、重视程度与接受意愿呈显著正相关(P<0.001)。患者决策影响因素排序具有统计学上的一致性(P<0.001),影响程度由大到小分别为治疗有效性、安全性、舒适程度、经济性及时间成本。结论实体器官移植受者对MPA TDM的接受意愿较高,MPA TDM既往就诊经历和对MPA TDM的了解、重视程度为影响患者意愿的主要因素。

监护仪数据临床采集系统的开发与应用

目的缩短护士监护仪数据采集时长,提高工作效率,确保监护仪数据采集准确性,规范监护仪的使用。方法开发监护仪数据临床采集系统并应用于患者监护仪数据的采集。比较系统应用前后监护仪数据采集时长,监护仪显示时间与北京时间的时差,监护仪数据采集的准确性,以及护士对该系统的满意度。结果监护仪数据临床采集系统应用后,数据采集时长、监护仪显示时间与北京时间的时差显著短于应用前(均P<0.05);监护仪数据临床采集系统准确性为100%;护士对系统的满意度评分为(4.68±0.54)分。结论监护仪数据临床采集系统的应用可缩短监护仪数据采集时长,提高护士工作效率,确保数据采集的准确性,规范监护仪的使用,护士对该系统满意程度较高。

早期渐进性运动在重症监护病房危重患者床旁超声监测中的干预效果

目的 探讨早期渐进性运动在重症监护病房(ICU)危重患者床旁超声监测中的干预效果。方法 对2020年1月至2022年1月在贵港市人民医院急诊ICU、重症医学科住院的120例行机械通气患者的一般资料进行回顾分析,2组均予以基础治疗和常规护理,根据随机数字表法将其分为对照组(60例,常规康复训练)和观察组(60例,常规康复训练+早期渐进性运动)2组。对比2组在住院期间的呼吸机相关性肺炎(VAP)发生率、呼吸肌功能、肌肉参数、肌力和握力水平。结果 观察组的机械通气、ICU及总住院时间均短于对照组,VAP发生率低于对照组(P均<0.05)。入住ICU 48 h后,观察组的咳嗽峰流速、最大吸气压力水平均高于对照组(P均<0.05)。入住ICU的1周和转出ICU时,观察组的股直肌横截面积、股直肌厚度、股中间肌厚度、胫骨前肌肌肉厚度、肱二头肌厚度、桡侧腕屈肌厚度值均大于同期对照组,英国医学研究委员会量表评分和握力均较同期对照组更优(P均<0.05)。结论 早期渐进性运动在ICU危重患者床旁超声监测中的干预效果显著,可有效减少患者的ICU住院时间,减少VAP的发生,改善其呼吸肌功能,提升其肌肉厚度、肌群肌力和握力水平。

近红外光谱监测在儿童体外膜肺氧合中的应用及研究进展

体外膜肺氧合(extracorporeal membrane oxygenation,ECMO)作为针对循环、呼吸功能衰竭的有效支持技术,近年来在儿童中应用越来越广泛。但使用ECMO支持的患儿脑、肾、胃肠、肢体等组织损伤的发生率仍然较高。ECMO治疗期间,各种传统血流动力学监测方法受到一定限制。近红外光谱(near-infrared spectroscopy,NIRS)技术作为一种无创、实时、连续的监测方法,可以监测局部组织氧饱和度(regional oxygen saturation,rSO_(2))的变化,进而评估组织灌注、代谢及氧供需平衡,成为潜在组织损伤的早期监测指标。本文就NIRS技术在儿童ECMO治疗中的应用现状进行综述,以期为临床通过NIRS技术监测ECMO治疗患儿早期病情变化提供参考。

2023年通辽市主城区花粉浓度对过敏性疾病患者就诊量的影响研究

目的 探讨通辽市主城区花粉浓度对过敏性疾病患者就诊情况的影响,为过敏性疾病的早期预防以及防控策略的制定提供地区性的研究数据。方法 收集2023年6月19日至2023年10月9日通辽市两家三级甲等综合医院的耳鼻喉科、变态反应科和皮肤科就诊并患有过敏性疾病的患者资料。结合花粉监测数据,通过Spearman秩相关法分析花粉浓度与过敏性疾病患者就诊量相关关系,经对数转换后采用多因素线性回归方法分析花粉浓度对过敏性疾病患者就诊量的影响。结果 花粉浓度高峰期为7月25日—9月9日,过敏性疾病患者每日就诊高峰与花粉浓度高峰期高度重合。蒿属、藜科、葎草、禾本科、其他、松科植物花粉浓度与过敏性疾病就诊量具有相关性(r_(S)分别是0.609、0.312、0.673、0.507、0.301、-0.191),蒿属、藜科、葎草、禾本科、其他植物花粉浓度越高,过敏性疾病患者就诊量越多。尤其是蒿属(β=0.001,P<0.01)、葎草(β=0.002,P<0.01)、其他(β=0.001,P<0.05)植物花粉浓度对患者就诊量有显著影响,其中蒿属、葎草为通辽市主要的花粉类型,蒿属花粉浓度最大,与总浓度高峰期重合,对过敏性疾病患者就诊量影响最大。结论 通辽市主城区不同植物花粉浓度与过敏性疾病患者就诊量具有相关性,且蒿属、葎草、以及其他植物花粉浓度是过敏性疾病患者就诊量的影响因素。

老年脊柱结核手术中脑氧饱和度下降与围术期认知功能障碍的关系

目的 探讨老年脊柱结核患者手术中脑氧饱和度(rSO_(2))下降与围术期认知功能障碍(PND)的关系。方法 选取2019年10月至2023年7月在杭州市红十字会医院和浙江省人民医院行脊柱结核手术的患者共136例,应用rSO_(2)监测仪监测rSO_(2)变化。记录患者入手术室面罩以6 L/min的氧流量吸氧2 min(T_(0))、气管插管后5 min(T_(1))、俯卧位30 min(T_(2))、俯卧位1 h(T_(3))、俯卧位2 h(T_(4))、侧卧位病灶清除(T_(5))和术毕时(T_(6))的左右两侧rSO_(2)值及生命体征情况。分别于术前1天、术后第3、7天应用简易精神状态量表(MMSE)评估患者认知功能,根据评估结果分为PND组和非PND组,比较两组患者术中rSO_(2)的差异和术后ICU入住时间。结果 136例脊柱结核手术患者分为PND组34例和非PND组102例,PND发生率25.0%。PND组rSO_(2)下降20.0%的比例(44.1%)高于非PND组(14.7%),差异有统计学意义(P<0.05);术后第3、7天MMSE评分均低于非PND组,差异均有统计学意义(均P<0.05);PND组在T_(5)时点的平均动脉压、rSO_(2)均低于非PND组,差异均有统计学意义(均P<0.05);PND组患者术后ICU入住时间长于非PND组,差异有统计学意义(P<0.05)。结论 术中rSO_(2)值下降超过术前值的20.0%是PND的预测因素;术中rSO_(2)急速下降主要发生在病灶清除过程中;PND的发生与术中低血压有关。

医院感染监测与患者睡眠质量的研究

本研究旨在探讨医院感染监测与患者睡眠质量之间的关系。通过对住院患者的病例进行回顾性分析,同时设计数据表格记录相关数据。研究结果表明,医院感染监测与患者睡眠质量呈负相关,即医院感染的发生率越高,患者的睡眠质量越差。此外,还发现患者年龄、住院时间等因素对睡眠质量也有影响。本研究为改善患者住院期间的睡眠质量提供了有价值的参考。

糖尿病血糖管理技术在老年糖尿病患者中的应用进展

综述糖尿病技术的发展及在老年糖尿病患者血糖管理的应用现状,包括连续血糖监测仪,持续皮下胰岛素输注,混合闭环等方面,并提出糖尿病技术在老年糖尿病患者应用中的挑战及发展建议,以期提高老年糖尿病患者低血糖和高血糖管理能力体系建设。