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1.
Sci Rep ; 12(1): 3797, 2022 03 08.
Article in English | MEDLINE | ID: covidwho-1735276

ABSTRACT

Infectious threats, like the COVID-19 pandemic, hinder maintenance of a productive and healthy workforce. If subtle physiological changes precede overt illness, then proactive isolation and testing can reduce labor force impacts. This study hypothesized that an early infection warning service based on wearable physiological monitoring and predictive models created with machine learning could be developed and deployed. We developed a prototype tool, first deployed June 23, 2020, that delivered continuously updated scores of infection risk for SARS-CoV-2 through April 8, 2021. Data were acquired from 9381 United States Department of Defense (US DoD) personnel wearing Garmin and Oura devices, totaling 599,174 user-days of service and 201 million hours of data. There were 491 COVID-19 positive cases. A predictive algorithm identified infection before diagnostic testing with an AUC of 0.82. Barriers to implementation included adequate data capture (at least 48% data was needed) and delays in data transmission. We observe increased risk scores as early as 6 days prior to diagnostic testing (2.3 days average). This study showed feasibility of a real-time risk prediction score to minimize workforce impacts of infection.


Subject(s)
Algorithms , COVID-19/diagnosis , Monitoring, Physiologic/methods , Area Under Curve , COVID-19/virology , Humans , Military Personnel , Monitoring, Physiologic/instrumentation , ROC Curve , SARS-CoV-2/isolation & purification , User-Computer Interface , Wearable Electronic Devices
2.
Biosensors (Basel) ; 11(12)2021 Dec 17.
Article in English | MEDLINE | ID: covidwho-1581025

ABSTRACT

In light of the recent Coronavirus disease (COVID-19) pandemic, peripheral oxygen saturation (SpO2) has shown to be amongst the vital signs most indicative of deterioration in persons with COVID-19. To allow for the continuous monitoring of SpO2, we attempted to demonstrate accurate SpO2 estimation using our custom chest-based wearable patch biosensor, capable of measuring electrocardiogram (ECG) and photoplethysmogram (PPG) signals with high fidelity. Through a breath-hold protocol, we collected physiological data with a wide dynamic range of SpO2 from 20 subjects. The ratio of ratios (R) used in pulse oximetry to estimate SpO2 was robustly extracted from the red and infrared PPG signals during the breath-hold segments using novel feature extraction and PPGgreen-based outlier rejection algorithms. Through subject independent training, we achieved a low root-mean-square error (RMSE) of 2.64 ± 1.14% and a Pearson correlation coefficient (PCC) of 0.89. With subject-specific calibration, we further reduced the RMSE to 2.27 ± 0.76% and increased the PCC to 0.91. In addition, we showed that calibration is more efficiently accomplished by standardizing and focusing on the duration of breath-hold rather than the resulting range in SpO2. The accurate SpO2 estimation provided by our custom biosensor and the algorithms provide research opportunities for a wide range of disease and wellness monitoring applications.


Subject(s)
COVID-19 , Monitoring, Physiologic/instrumentation , Wearable Electronic Devices , Biosensing Techniques , COVID-19/diagnosis , Electrocardiography , Humans , Oximetry , Oxygen , Photoplethysmography , Sternum
3.
Comput Math Methods Med ; 2021: 8591036, 2021.
Article in English | MEDLINE | ID: covidwho-1523094

ABSTRACT

During the ongoing COVID-19 pandemic, Internet of Things- (IoT-) based health monitoring systems are potentially immensely beneficial for COVID-19 patients. This study presents an IoT-based system that is a real-time health monitoring system utilizing the measured values of body temperature, pulse rate, and oxygen saturation of the patients, which are the most important measurements required for critical care. This system has a liquid crystal display (LCD) that shows the measured temperature, pulse rate, and oxygen saturation level and can be easily synchronized with a mobile application for instant access. The proposed IoT-based method uses an Arduino Uno-based system, and it was tested and verified for five human test subjects. The results obtained from the system were promising: the data acquired from the system are stored very quickly. The results obtained from the system were found to be accurate when compared to other commercially available devices. IoT-based tools may potentially be valuable during the COVID-19 pandemic for saving people's lives.


Subject(s)
COVID-19/physiopathology , Computer Systems , Internet of Things , Monitoring, Physiologic/instrumentation , Adult , Body Temperature , COVID-19/diagnosis , COVID-19/epidemiology , Computational Biology , Computer Systems/statistics & numerical data , Equipment Design , Female , Heart Rate , Humans , Male , Middle Aged , Mobile Applications , Monitoring, Physiologic/statistics & numerical data , Pandemics , SARS-CoV-2 , User-Computer Interface , Young Adult
5.
Sci Rep ; 11(1): 20144, 2021 10 11.
Article in English | MEDLINE | ID: covidwho-1462037

ABSTRACT

Pulmonary function testing (PFT) allows for quantitative analysis of lung function. However, as a result of the coronavirus disease 2019 (COVID-19) pandemic, a majority of international medical societies have postponed PFTs in an effort to mitigate disease transmission, complicating the continuity of care in high-risk patients diagnosed with COVID-19 or preexisting lung pathologies. Here, we describe the development of a non-contact wearable pulmonary sensor for pulmonary waveform analysis, pulmonary volume quantification, and crude thoracic imaging using the eddy current (EC) phenomenon. Statistical regression analysis is performed to confirm the predictive validity of the sensor, and all data are continuously and digitally stored with a sampling rate of 6,660 samples/second. Wearable pulmonary function sensors may facilitate rapid point-of-care monitoring for high-risk individuals, especially during the COVID-19 pandemic, and easily interface with patient hospital records or telehealth services.


Subject(s)
COVID-19/diagnosis , Monitoring, Physiologic/instrumentation , Point-of-Care Systems , Respiratory Function Tests/instrumentation , Wearable Electronic Devices , COVID-19/epidemiology , COVID-19/transmission , COVID-19/virology , Feasibility Studies , Healthy Volunteers , Humans , Infection Control , Infectious Disease Transmission, Patient-to-Professional/prevention & control , Monitoring, Physiologic/methods , Pandemics/prevention & control , Respiratory Function Tests/methods , Respiratory Physiological Phenomena
8.
Nat Commun ; 12(1): 4876, 2021 08 12.
Article in English | MEDLINE | ID: covidwho-1356557

ABSTRACT

While the printed circuit board (PCB) has been widely considered as the building block of integrated electronics, the world is switching to pursue new ways of merging integrated electronic circuits with textiles to create flexible and wearable devices. Herein, as an alternative for PCB, we described a non-printed integrated-circuit textile (NIT) for biomedical and theranostic application via a weaving method. All the devices are built as fibers or interlaced nodes and woven into a deformable textile integrated circuit. Built on an electrochemical gating principle, the fiber-woven-type transistors exhibit superior bending or stretching robustness, and were woven as a textile logical computing module to distinguish different emergencies. A fiber-type sweat sensor was woven with strain and light sensors fibers for simultaneously monitoring body health and the environment. With a photo-rechargeable energy textile based on a detailed power consumption analysis, the woven circuit textile is completely self-powered and capable of both wireless biomedical monitoring and early warning. The NIT could be used as a 24/7 private AI "nurse" for routine healthcare, diabetes monitoring, or emergencies such as hypoglycemia, metabolic alkalosis, and even COVID-19 patient care, a potential future on-body AI hardware and possibly a forerunner to fabric-like computers.


Subject(s)
Biosensing Techniques/instrumentation , Precision Medicine/instrumentation , Textiles , Wearable Electronic Devices , Wireless Technology/instrumentation , Biosensing Techniques/methods , COVID-19/diagnosis , COVID-19/prevention & control , COVID-19/virology , Equipment Design , Humans , Monitoring, Physiologic/instrumentation , Monitoring, Physiologic/methods , Precision Medicine/methods , SARS-CoV-2/physiology , Sweat/physiology
9.
Adv Med Sci ; 66(2): 388-395, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1347463

ABSTRACT

Electrical impedance tomography (EIT) is a non-invasive, radiation-free method of diagnostics imaging, allowing for a bedside, real-time dynamic assessment of lung function. It stands as an alternative for other imagining methods, such as computed tomography (CT) or ultrasound. Even though the technique is rather novel, it has a wide variety of possible applications. In the era of modern mechanical ventilation, a dynamic assessment of patient's respiratory condition appears to fulfil the idea of personalized treatment. Additionally, an increasing frequency of respiratory failure among intensive care populations raises demand for improved monitoring tools. This review aims to raise awareness and presents possible implications for the use of EIT in the intensive care setting.


Subject(s)
Electric Impedance , Monitoring, Physiologic , Respiration, Artificial/methods , Tomography/methods , COVID-19/therapy , Humans , Monitoring, Physiologic/instrumentation , Monitoring, Physiologic/methods , Respiratory Distress Syndrome/diagnosis , Respiratory Distress Syndrome/therapy , SARS-CoV-2
10.
Philos Trans R Soc Lond B Biol Sci ; 376(1831): 20200228, 2021 08 16.
Article in English | MEDLINE | ID: covidwho-1284967

ABSTRACT

The goal of achieving enhanced diagnosis and continuous monitoring of human health has led to a vibrant, dynamic and well-funded field of research in medical sensing and biosensor technologies. The field has many sub-disciplines which focus on different aspects of sensor science; engaging engineers, chemists, biochemists and clinicians, often in interdisciplinary teams. The trends which dominate include the efforts to develop effective point of care tests and implantable/wearable technologies for early diagnosis and continuous monitoring. This review will outline the current state of the art in a number of relevant fields, including device engineering, chemistry, nanoscience and biomolecular detection, and suggest how these advances might be employed to develop effective systems for measuring physiology, detecting infection and monitoring biomarker status in wild animals. Special consideration is also given to the emerging threat of antimicrobial resistance and in the light of the current SARS-CoV-2 outbreak, zoonotic infections. Both of these areas involve significant crossover between animal and human health and are therefore well placed to seed technological developments with applicability to both human and animal health and, more generally, the reviewed technologies have significant potential to find use in the measurement of physiology in wild animals. This article is part of the theme issue 'Measuring physiology in free-living animals (Part II)'.


Subject(s)
Biosensing Techniques/instrumentation , COVID-19/diagnosis , Synthetic Biology/methods , Wearable Electronic Devices , Zika Virus Infection/veterinary , Zoonoses/diagnosis , Animals , Animals, Wild/microbiology , Animals, Wild/parasitology , Animals, Wild/virology , Biomarkers/analysis , Cell Engineering/methods , Humans , Monitoring, Physiologic/instrumentation , Monitoring, Physiologic/methods , Nanotechnology/instrumentation , Nanotechnology/methods , Point-of-Care Testing , Zika Virus Infection/diagnosis
11.
Open Heart ; 8(1)2021 06.
Article in English | MEDLINE | ID: covidwho-1259016

ABSTRACT

AIMS: In response to the COVID-19 pandemic, the UK was placed under strict lockdown measures on 23 March 2020. The aim of this study was to quantify the effects on physical activity (PA) levels using data from the prospective Triage-HF Plus Evaluation study. METHODS: This study represents a cohort of adult patients with implanted cardiac devices capable of measuring activity by embedded accelerometery via a remote monitoring platform. Activity data were available for the 4 weeks pre-implementation and post implementation of 'stay at home' lockdown measures in the form of 'minutes active per day' (min/day). RESULTS: Data were analysed for 311 patients (77.2% men, mean age 68.8, frailty 55.9%. 92.2% established heart failure (HF) diagnosis, of these 51.2% New York Heart Association II), with comorbidities representative of a real-world cohort.Post-lockdown, a significant reduction in median PA equating to 20.8 active min/day was seen. The reduction was uniform with a slightly more pronounced drop in PA for women, but no statistically significant difference with respect to age, body mass index, frailty or device type. Activity dropped in the immediate 2-week period post-lockdown, but steadily returned thereafter. Median activity week 4 weeks post-lockdown remained significantly lower than 4 weeks pre-lockdown (p≤0.001). CONCLUSIONS: In a population of predominantly HF patients with cardiac devices, activity reduced by approximately 20 min active per day in the immediate aftermath of strict COVID-19 lockdown measures. TRIAL REGISTRATION NUMBER: NCT04177199.


Subject(s)
Accelerometry , COVID-19 , Communicable Disease Control , Heart Failure , Monitoring, Physiologic , Physical Distancing , Telemedicine , Accelerometry/instrumentation , Accelerometry/methods , Accelerometry/statistics & numerical data , Activities of Daily Living , Aged , COVID-19/epidemiology , COVID-19/prevention & control , Communicable Disease Control/methods , Communicable Disease Control/statistics & numerical data , Exercise , Female , Heart Failure/diagnosis , Heart Failure/epidemiology , Humans , Male , Monitoring, Physiologic/instrumentation , Monitoring, Physiologic/methods , Monitoring, Physiologic/statistics & numerical data , SARS-CoV-2 , Telemedicine/instrumentation , Telemedicine/methods , Telemedicine/statistics & numerical data , United Kingdom/epidemiology , Wearable Electronic Devices
12.
Sci Adv ; 7(20)2021 05.
Article in English | MEDLINE | ID: covidwho-1226704

ABSTRACT

Soft, skin-integrated electronic sensors can provide continuous measurements of diverse physiological parameters, with broad relevance to the future of human health care. Motion artifacts can, however, corrupt the recorded signals, particularly those associated with mechanical signatures of cardiopulmonary processes. Design strategies introduced here address this limitation through differential operation of a matched, time-synchronized pair of high-bandwidth accelerometers located on parts of the anatomy that exhibit strong spatial gradients in motion characteristics. When mounted at a location that spans the suprasternal notch and the sternal manubrium, these dual-sensing devices allow measurements of heart rate and sounds, respiratory activities, body temperature, body orientation, and activity level, along with swallowing, coughing, talking, and related processes, without sensitivity to ambient conditions during routine daily activities, vigorous exercises, intense manual labor, and even swimming. Deployments on patients with COVID-19 allow clinical-grade ambulatory monitoring of the key symptoms of the disease even during rehabilitation protocols.


Subject(s)
Accelerometry/instrumentation , Accelerometry/methods , Electrocardiography, Ambulatory/instrumentation , Electrocardiography, Ambulatory/methods , Wearable Electronic Devices , Body Temperature , COVID-19 , Exercise/physiology , Heart Rate , Humans , Monitoring, Physiologic/instrumentation , Monitoring, Physiologic/methods , SARS-CoV-2
13.
Yearb Med Inform ; 30(1): 272-279, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1196877

ABSTRACT

INTRODUCTION: Mobile phone-based interventions in cardiovascular disease are growing in popularity. A randomised control trial (RCT) for a novel smartphone app-based model of care, named TeleClinical Care - Cardiac (TCC-Cardiac), commenced in February 2019, targeted at patients being discharged after care for an acute coronary syndrome or episode of decompensated heart failure. The app was paired to a digital sphygmomanometer, weighing scale and a wearable fitness band, all loaned to the patient, and allowed clinicians to respond to abnormal readings. The onset of the COVID-19 pandemic necessitated several modifications to the trial in order to protect participants from potential exposure to infection. The use of TCC-Cardiac during the pandemic inspired the development of a similar model of care (TCC-COVID), targeted at patients being managed at home with a diagnosis of COVID-19. METHODS: Recruitment for the TCC-Cardiac trial was terminated shortly after the World Health Organization announced COVID-19 as a global pandemic. Telephone follow-up was commenced, in order to protect patients from unnecessary exposure to hospital staff and patients. Equipment was returned or collected by a 'no-contact' method. The TCC-COVID app and model of care had similar functionality to the original TCC-Cardiac app. Participants were enrolled exclusively by remote methods. Oxygen saturation and pulse rate were measured by a pulse oximeter, and symptomatology measured by questionnaire. Measurement results were manually entered into the app and transmitted to an online server for medical staff to review. RESULTS: A total of 164 patients were involved in the TCC-Cardiac trial, with 102 patients involved after the onset of the pandemic. There were no hospitalisations due to COVID-19 in this cohort. The study was successfully completed, with only three participants lost to follow-up. During the pandemic, 5 of 49 (10%) of patients in the intervention arm were readmitted compared to 12 of 53 (23%) in the control arm. Also, in this period, 28 of 29 (97%) of all clinically significant alerts received by the monitoring team were managed successfully in the outpatient setting, avoiding hospitalisation. Patients found the user experience largely positive, with the average rating for the app being 4.56 out of 5. 26 patients have currently been enrolled for TCC-COVID. Recruitment is ongoing. All patients have been safely and effectively monitored, with no major adverse clinical events or technical malfunctions. Patient satisfaction has been high. CONCLUSION: The TCC-Cardiac RCT was successfully completed despite the challenges posed by COVID-19. Use of the app had an added benefit during the pandemic as participants could be monitored safely from home. The model of care inspired the development of an app with similar functionality designed for use with patients diagnosed with COVID-19.


Subject(s)
Acute Coronary Syndrome/therapy , COVID-19 , Fitness Trackers , Heart Failure/therapy , Mobile Applications , Monitoring, Physiologic/instrumentation , Telemedicine , Aged , Humans , Male , Monitoring, Physiologic/methods , Pilot Projects , Smartphone
14.
Sci Prog ; 104(2): 368504211009670, 2021.
Article in English | MEDLINE | ID: covidwho-1195898

ABSTRACT

As the coronavirus disease 2019 (COVID-19) spreads globally, hospital departments will need take steps to manage their treatment procedures and wards. The preparations of high-risk departments (infection, respiratory, emergency, and intensive care unit) were relatively well within this pandemic, while low-risk departments may be unprepared. The spine surgery department in The First Affiliated Hospital of Anhui Medical University in Hefei, China, was used as an example in this study. The spine surgery department took measures to manage the patients, medical staff and wards to avoid the cross-infection within hospital. During the outbreak, no patients or healthcare workers were infected, and no treatment was delayed due to these measures. The prevention and control measures effectively reduced the risk of nosocomial transmission between health workers and patients while providing optimum care. It was a feasible management approach that was applicable to most low-risk and even high-risk departments.


Subject(s)
COVID-19/prevention & control , Infection Control/methods , Pandemics , Patient Isolation/organization & administration , Patient Isolators/supply & distribution , SARS-CoV-2/pathogenicity , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19/transmission , China/epidemiology , Cross Infection/prevention & control , Disinfection/methods , Disinfection/organization & administration , Health Personnel/education , Humans , Infection Control/organization & administration , Monitoring, Physiologic/instrumentation , Monitoring, Physiologic/methods , Orthopedic Procedures/instrumentation , Orthopedic Procedures/methods , Patient Isolation/methods , Patients' Rooms/organization & administration , Personal Protective Equipment/supply & distribution , Spine/surgery
15.
IEEE J Transl Eng Health Med ; 9: 4900311, 2021.
Article in English | MEDLINE | ID: covidwho-1189590

ABSTRACT

OBJECTIVE: Controlling the spread of the COVID-19 pandemic largely depends on scaling up the testing infrastructure for identifying infected individuals. Consumer-grade wearables may present a solution to detect the presence of infections in the population, but the current paradigm requires collecting physiological data continuously and for long periods of time on each individual, which poses limitations in the context of rapid screening. Technology: Here, we propose a novel paradigm based on recording the physiological responses elicited by a short (~2 minutes) sequence of activities (i.e. "snapshot"), to detect symptoms associated with COVID-19. We employed a novel body-conforming soft wearable sensor placed on the suprasternal notch to capture data on physical activity, cardio-respiratory function, and cough sounds. RESULTS: We performed a pilot study in a cohort of individuals (n=14) who tested positive for COVID-19 and detected altered heart rate, respiration rate and heart rate variability, relative to a group of healthy individuals (n=14) with no known exposure. Logistic regression classifiers were trained on individual and combined sets of physiological features (heartbeat and respiration dynamics, walking cadence, and cough frequency spectrum) at discriminating COVID-positive participants from the healthy group. Combining features yielded an AUC of 0.94 (95% CI=[0.92, 0.96]) using a leave-one-subject-out cross validation scheme. Conclusions and Clinical Impact: These results, although preliminary, suggest that a sensor-based snapshot paradigm may be a promising approach for non-invasive and repeatable testing to alert individuals that need further screening.


Subject(s)
COVID-19/physiopathology , Monitoring, Physiologic/instrumentation , Monitoring, Physiologic/methods , Adult , Aged , Area Under Curve , COVID-19/diagnosis , Case-Control Studies , Cough/diagnosis , Exercise , Female , Heart Rate , Humans , Male , Middle Aged , Pilot Projects , Quarantine , Walking , Wearable Electronic Devices
17.
Math Biosci Eng ; 18(2): 1513-1528, 2021 01 28.
Article in English | MEDLINE | ID: covidwho-1150821

ABSTRACT

The internet of things (IoT) and deep learning are emerging technologies in diverse research fields, including the provision of IT services in medical domains. In the COVID-19 era, intelligent medication behavior monitoring systems for stable patient monitoring are further required, because many patients cannot easily visit hospitals. Several previous studies made use of wearable devices to detect medication behaviors of patients. However, the wearable devices cause inconvenience while equipping the devices. In addition, they suffer from inconsistency problems due to errors of measured values. We devise a medication behavior monitoring system that uses the IoT and deep learning to avoid sensing errors and improve user experiences by effectively detecting various activities of patients. Based on the real-time operation of our proposed IoT device, the proposed solution processes captured images of patents via OpenPose to check medication situations. The proposed system identifies medication status on time by using a human activity recognition scheme and provides various notifications to patients' mobile devices. To support reliable communication between our system and doctors, we employ MQTT protocol with periodic data transmissions. Thus, the measured information of patient's medication status is transmitted to the doctors so that they can periodically perform remote treatments. Experimental results show that all medication behaviors are accurately detected and notified to the doctor efficiently, improving the accuracy of monitoring the patient's medication behavior.


Subject(s)
COVID-19/drug therapy , Deep Learning , Medication Adherence , Monitoring, Physiologic/methods , SARS-CoV-2 , Biomedical Engineering , Computer Systems , Directly Observed Therapy , Equipment Design , Humans , Internet of Things , Medication Adherence/psychology , Medication Adherence/statistics & numerical data , Monitoring, Physiologic/instrumentation , Monitoring, Physiologic/statistics & numerical data , Neural Networks, Computer , Pandemics , Software , Wearable Electronic Devices
18.
Br J Nurs ; 30(5): 288-295, 2021 Mar 11.
Article in English | MEDLINE | ID: covidwho-1140806

ABSTRACT

This article explores body temperature and the physiological process of thermoregulation. Normal body temperature and body temperature changes are discussed, including comorbidities associated with body temperature and signs of hyperthermia and hypothermia, and the factors that affect intraoperative temperature regulation. The evidence base behind thermometry is discussed and is applied to contemporary clinical conditions and symptoms, including: sepsis and suspected COVID-19. After discussing clinical considerations and regulations that encompass thermometry, three case studies present the use of the Genius 3 Tympanic Thermometer in clinical practice, with user feedback supporting its benefits, which include speed, accuracy and ease of use.


Subject(s)
Body Temperature/physiology , Thermometers , Tympanic Membrane/physiology , COVID-19/diagnosis , Complementary Therapies , Critical Care , Humans , Monitoring, Physiologic/instrumentation , Reproducibility of Results , Sepsis/diagnosis , Time Factors
19.
Sci Rep ; 11(1): 5895, 2021 03 15.
Article in English | MEDLINE | ID: covidwho-1135701

ABSTRACT

Between March and April 2020, Cyprus and Greece health authorities enforced three escalated levels of public health interventions to control the COVID-19 pandemic. We quantified compliance of 108 asthmatic schoolchildren (53 from Cyprus, 55 from Greece, mean age 9.7 years) from both countries to intervention levels, using wearable sensors to continuously track personal location and physical activity. Changes in 'fraction time spent at home' and 'total steps/day' were assessed with a mixed-effects model adjusting for confounders. We observed significant mean increases in 'fraction time spent at home' in Cyprus and Greece, during each intervention level by 41.4% and 14.3% (level 1), 48.7% and 23.1% (level 2) and 45.2% and 32.0% (level 3), respectively. Physical activity in Cyprus and Greece demonstrated significant mean decreases by - 2,531 and - 1,191 (level 1), - 3,638 and - 2,337 (level 2) and - 3,644 and - 1,961 (level 3) total steps/day, respectively. Significant independent effects of weekends and age were found on 'fraction time spent at home'. Similarly, weekends, age, humidity and gender had an independent effect on physical activity. We suggest that wearable technology provides objective, continuous, real-time location and activity data making possible to inform in a timely manner public health officials on compliance to various tiers of public health interventions during a pandemic.


Subject(s)
Asthma/epidemiology , COVID-19/epidemiology , Monitoring, Physiologic/methods , SARS-CoV-2 , Wearable Electronic Devices , Adolescent , Asthma/diagnosis , Child , Child, Preschool , Cyprus , Female , Greece , Humans , Male , Monitoring, Physiologic/instrumentation , Public Health Surveillance , Severity of Illness Index , Social Mobility
20.
ASAIO J ; 67(3): 254-262, 2021 03 01.
Article in English | MEDLINE | ID: covidwho-1101913

ABSTRACT

Viscoelastic coagulation monitor (VCM) is a portable device developed to evaluate the viscoelastic properties of whole blood activated by contact with glass. In this study, VCM was employed to analyze the viscoelastic profiles of 36 COVID-19 intensive care patients. Full anticoagulant dose heparin (unfractionated [UFH]; low molecular weight [LMWH]) was administrated to all patients. The association between VCM and laboratory parameters was retrospectively analyzed. The administration of UFH-influenced VCM parameters prolonging clotting time (CT) and clot formation time (CFT) and reducing angle (alpha) and amplitudes of the VCM tracings (A10, A20, and maximum clot firmness [MCF]) compared with LMWH therapy. A tendency toward hypercoagulation was observed by short CT and CFT in patients receiving LMWH. Clotting time was correlated with UFH dose (Spearman's rho = 0.48, p ≤ 0.001), and no correlation was found between CT and LMWH. All VCM tracings failed to show lysis at 30 and 45 minutes, indicating the absence of fibrinolysis. A10, A20, and MCF exhibited very-good to good diagnostic accuracy for detecting platelet count and fibrinogen above the upper reference limit of the laboratory. In conclusion, VCM provided reliable results in COVID-19 patients and was easy to perform with minimal training at the bedside.


Subject(s)
COVID-19/blood , Monitoring, Physiologic/instrumentation , Point-of-Care Systems , Thrombelastography/instrumentation , Adult , Blood Coagulation , COVID-19/complications , Female , Humans , Male , Middle Aged , Monitoring, Physiologic/methods , Retrospective Studies , SARS-CoV-2 , Thrombelastography/methods , Thrombosis/diagnosis , Thrombosis/virology
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