Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients
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ABSTRACT:
SUBMITTER: Richards D
PROVIDER: S-EPMC8576003 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Non-invasive continuous alcohol (ethanol) monitoring has potential applications in both population research and in clinical management of acute alcohol intoxication or chronic alcoholism. Current wearable monitors based on transdermal alcohol content (TAC) sensing have limited accessibility and blood alcohol content (BAC) quantification accuracy. Here we describe the development of a self-contained discreet wearable transdermal alcohol (TAC) sensor in the form of a wristband or armband. This sensor can detect vapor-phase alcohol in perspiration from 0.09 ppm (equivalent to 0.09 mg/dL sweat alcohol concentration at 25 °C under Henry's Law equilibrium) to over 500 ppm at one-minute time resolution. Additionally, a digital sensor was employed to monitor the temperature and humidity levels inside the sensing chamber. Two male human subjects were recruited to conduct studies with alcohol consumption using calibrated prototype TAC sensors to validate the performance. Our preliminary data demonstrated that, under well-controlled conditions, this sensor can acquire TAC curves at low doses (1-2 standard drinks). Moreover, TAC data for different doses can be easily distinguished. However, substantial interpersonal and intrapersonal variabilities in measurement data were also observed in experiments with less controlled conditions. Our observations suggest that perspiration rate might be an important contributing factor to these variabilities. Further studies with sufficient sample sizes are required to validate and characterize the impact of different perspiration rates on TAC sensors, which may inform more reproducible and accurate sensor designs in the future.
| S-EPMC7978232 | biostudies-literature
Project description:BackgroundDuring the COVID-19 pandemic, novel digital health technologies have the potential to improve our understanding of SARS-CoV-2 and COVID-19, improve care delivery, and produce better health outcomes. The National Institutes of Health called on digital health leaders to contribute to a high-quality data repository that will support researchers to make discoveries that are otherwise not possible with small, limited data sets.ObjectiveTo this end, we seek to develop a COVID-19 digital biomarker for early detection of physiological exacerbation or decompensation. We propose the development and validation of a COVID-19 decompensation Index (CDI) in a 2-phase study that builds on existing wearable biosensor-derived analytics generated by physIQ's end-to-end cloud platform for continuous physiological monitoring with wearable biosensors. This effort serves to achieve two primary objectives: (1) to collect adequate data to help develop the CDI and (2) to collect rich deidentified clinical data correlating with outcomes and symptoms related to COVID-19 progression. Our secondary objectives include evaluation of the feasibility and usability of pinpointIQ, a digital platform through which data are gathered, analyzed, and displayed.MethodsThis is a prospective, nonrandomized, open-label, 2-phase study. Phase I will involve data collection for the digital data hub of the National Institutes of Health as well as data to support the preliminary development of the CDI. Phase II will involve data collection for the hub and contribute to continued refinement and validation of the CDI. While this study will focus on the development of a CDI, the digital platform will also be evaluated for feasibility and usability while clinicians deliver care to continuously monitored patients enrolled in the study.ResultsOur target CDI will be a binary classifier trained to distinguish participants with and those without decompensation. The primary performance metric for CDI will be the area under the receiver operating characteristic curve with a minimum performance criterion of ≥0.75 (α=.05; power [1-β]=0.80). Furthermore, we will determine the sex or gender and race or ethnicity of the participants, which would account for differences in the CDI performance, as well as the lead time-time to predict decompensation-and its relationship with the ultimate disease severity based on the World Health Organization COVID-19 ordinal scale.ConclusionsUsing machine learning techniques on a large data set of patients with COVID-19 could provide valuable insights into the pathophysiology of COVID-19 and a digital biomarker for COVID-19 decompensation. Through this study, we intend to develop a tool that can uniquely reflect physiological data of a diverse population and contribute to high-quality data that will help researchers better understand COVID-19.Trial registrationClinicalTrials.gov NCT04575532; https://www.clinicaltrials.gov/ct2/show/NCT04575532.International registered report identifier (irrid)DERR1-10.2196/27271.
| S-EPMC8158529 | biostudies-literature
Project description:BackgroundWearable continuous monitoring biosensor technologies have the potential to transform postoperative care with early detection of impending clinical deterioration.ObjectiveOur aim was to validate the accuracy of Cloud DX Vitaliti continuous vital signs monitor (CVSM) continuous noninvasive blood pressure (cNIBP) measurements in postsurgical patients. A secondary aim was to examine user acceptance of the Vitaliti CVSM with respect to comfort, ease of application, sustainability of positioning, and aesthetics.MethodsIncluded participants were ≥18 years old and recovering from surgery in a cardiac intensive care unit (ICU). We targeted a maximum recruitment of 80 participants for verification and acceptance testing. We also oversampled to minimize the effect of unforeseen interruptions and other challenges to the study. Validation procedures were according to the International Standards Organization (ISO) 81060-2:2018 standards for wearable, cuffless blood pressure (BP) measuring devices. Baseline BP was determined from the gold-standard ICU arterial catheter. The Vitaliti CVSM was calibrated against the reference arterial catheter. In static (seated in bed) and supine positions, 3 cNIBP measurements, each 30 seconds, were taken for each patient with the Vitaliti CVSM and an invasive arterial catheter. At the conclusion of each test session, captured cNIBP measurements were extracted using MediCollector BEDSIDE data extraction software, and Vitaliti CVSM measurements were extracted to a secure laptop through a cable connection. The errors of these determinations were calculated. Participants were interviewed about device acceptability.ResultsThe validation analysis included data for 20 patients. The average times from calibration to first measurement in the static position and to first measurement in the supine position were 133.85 seconds (2 minutes 14 seconds) and 535.15 seconds (8 minutes 55 seconds), respectively. The overall mean errors of determination for the static position were -0.621 (SD 4.640) mm Hg for systolic blood pressure (SBP) and 0.457 (SD 1.675) mm Hg for diastolic blood pressure (DBP). Errors of determination were slightly higher for the supine position, at 2.722 (SD 5.207) mm Hg for SBP and 2.650 (SD 3.221) mm Hg for DBP. The majority rated the Vitaliti CVSM as comfortable. This study was limited to evaluation of the device during a very short validation period after calibration (ie, that commenced within 2 minutes after calibration and lasted for a short duration of time).ConclusionsWe found that the Cloud DX's Vitaliti CVSM demonstrated cNIBP measurement in compliance with ISO 81060-2:2018 standards in the context of evaluation that commenced within 2 minutes of device calibration; this device was also well-received by patients in a postsurgical ICU setting. Future studies will examine the accuracy of the Vitaliti CVSM in ambulatory contexts, with attention to assessment over a longer duration and the impact of excessive patient motion on data artifacts and signal quality.Trial registrationClinicalTrials.gov NCT03493867; https://clinicaltrials.gov/ct2/show/NCT03493867.
| S-EPMC8922156 | biostudies-literature
Project description: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.
| S-EPMC8505507 | biostudies-literature
Project description:Background: Uterine activity (UA) monitoring is an essential element of pregnancy management. The gold-standard intrauterine pressure catheter (IUPC) is invasive and requires ruptured membranes, while the standard-of-care, external tocodynamometry (TOCO)'s accuracy is hampered by obesity, maternal movements, and belt positioning. There is an urgent need to develop telehealth tools enabling patients to remotely access care. Here, we describe and demonstrate a novel algorithm enabling remote, non-invasive detection and monitoring of UA by analyzing the modulation of the maternal electrocardiographic and phonocardiographic signals. The algorithm was designed and implemented as part of a wireless, FDA-cleared device designed for remote pregnancy monitoring. Two separate prospective, comparative, open-label, multi-center studies were conducted to test this algorithm. Methods: In the intrapartum study, 41 laboring women were simultaneously monitored with IUPC and the remote pregnancy monitoring device. Ten patients were also monitored with TOCO. In the antepartum study, 147 pregnant women were simultaneously monitored with TOCO and the remote pregnancy monitoring device. Results: In the intrapartum study, the remote pregnancy monitoring device and TOCO had sensitivities of 89.8 and 38.5%, respectively, and false discovery rates (FDRs) of 8.6 and 1.9%, respectively. In the antepartum study, a direct comparison of the remote pregnancy monitoring device to TOCO yielded a sensitivity of 94% and FDR of 31.1%. This high FDR is likely related to the low sensitivity of TOCO. Conclusion: UA monitoring via the new algorithm embedded in the remote pregnancy monitoring device is accurate and reliable and more precise than TOCO standard of care. Together with the previously reported remote fetal heart rate monitoring capabilities, this novel method for UA detection expands the remote pregnancy monitoring device's capabilities to include surveillance, such as non-stress tests, greatly benefiting women and providers seeking telehealth solutions for pregnancy care.
| S-EPMC9343786 | biostudies-literature
Project description:Anticipation of clinical decompensation is essential for effective emergency and critical care. In this study, we develop a multimodal machine learning approach to predict the onset of new vital sign abnormalities (tachycardia, hypotension, hypoxia) in ED patients with normal initial vital signs. Our method combines standard triage data (vital signs, demographics, chief complaint) with features derived from a brief period of continuous physiologic monitoring, extracted via both conventional signal processing and transformer-based deep learning on ECG and PPG waveforms. We study 19,847 adult ED visits, divided into training (75%), validation (12.5%), and a chronologically sequential held-out test set (12.5%). The best-performing models use a combination of engineered and transformer-derived features, predicting in a 90-minute window new tachycardia with AUROC of 0.836 (95% CI, 0.800-0.870), new hypotension with AUROC 0.802 (95% CI, 0.747-0.856), and new hypoxia with AUROC 0.713 (95% CI, 0.680-0.745), in all cases significantly outperforming models using only standard triage data. Salient features include vital sign trends, PPG perfusion index, and ECG waveforms. This approach could improve the triage of apparently stable patients and be applied continuously for the prediction of near-term clinical deterioration.
| S-EPMC10073111 | biostudies-literature
Project description:This work presents a wrinkled Platinum (wPt) strain sensor with tunable strain sensitivity for applications in wearable health monitoring. These stretchable sensors show a dynamic range of up to 185% strain and gauge factor (GF) of 42. This is believed to be the highest reported GF of any metal thin film strain sensor over a physiologically relevant dynamic range to date. Importantly, sensitivity and dynamic range are tunable to the application by adjusting wPt film thickness. Performance is reliable over 1000 cycles with low hysteresis after sensor conditioning. The possibility of using such a sensor for real-time respiratory monitoring by measuring chest wall displacement and correlating with lung volume is demonstrated.
| S-EPMC9061271 | biostudies-literature
Project description:Elevated core temperature constitutes an important biomarker for COVID-19 infection; however, no standards currently exist to monitor fever using wearable peripheral temperature sensors. Evidence that sensors could be used to develop fever monitoring capabilities would enable large-scale health-monitoring research and provide high-temporal resolution data on fever responses across heterogeneous populations. We launched the TemPredict study in March of 2020 to capture continuous physiological data, including peripheral temperature, from a commercially available wearable device during the novel coronavirus pandemic. We coupled these data with symptom reports and COVID-19 diagnosis data. Here we report findings from the first 50 subjects who reported COVID-19 infections. These cases provide the first evidence that illness-associated elevations in peripheral temperature are observable using wearable devices and correlate with self-reported fever. Our analyses support the hypothesis that wearable sensors can detect illnesses in the absence of symptom recognition. Finally, these data support the hypothesis that prediction of illness onset is possible using continuously generated physiological data collected by wearable sensors. Our findings should encourage further research into the role of wearable sensors in public health efforts aimed at illness detection, and underscore the importance of integrating temperature sensors into commercially available wearables.
| S-EPMC7736301 | biostudies-literature
Project description:Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild COVID-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 32 years; male: 52.9%) with mild COVID-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 (NEWS2) were 0.59 ± 0.7. 1231 manual measurement of physiology parameters were performed during hospital stay (median 15 days). Physiology parameters obtained from wearable biosensors correlated well with manual measurement including pulse rate (r = 0.96, p < 0.0001) and oxygen saturation (r = 0.87, p < 0.0001). A machine learning-derived index reflecting overall health status, Biovitals Index (BI), was generated by autonomous analysis of physiology parameters, symptoms, and other medical data. Daily BI was linearly associated with respiratory tract viral load (p < 0.0001) and NEWS2 (r = 0.75, p < 0.001). BI was superior to NEWS2 in predicting clinical worsening events (sensitivity 94.1% and specificity 88.9%) and prolonged hospitalization (sensitivity 66.7% and specificity 72.7%). Wearable biosensors coupled with machine learning-derived health index allowed automated detection of clinical deterioration.
| S-EPMC7902655 | biostudies-literature
Project description:BackgroundContinuous monitoring using wireless wearable sensors is a promising solution for use in clinical practice and in the home setting. It is important to involve nurses to ensure successful implementation. This paper aims to provide an overview of 1) factors affecting implementation of continuous monitoring using wireless wearable sensors by evaluating nurses' experiences with its use on the nursing ward, and 2) nurses' expectations for use in the home setting.MethodsSemi-structured interviews were conducted with 16 nurses from three teaching hospitals in the Netherlands, covering constructs from the Consolidated Framework for Implementation Research (CFIR). A deductive approach of directed content analysis was applied. One additional factor was added using the Unified Theory for Acceptance of Technology (UTAUT). The quotes and domains were rated on valence (positive, neutral, negative) and strength (strong: - 2, + 2, neutral 0, and weak: - 1, + 1).ResultsData was collected on 27 CFIR constructs and 1 UTAUT construct. In the experience of at least 8 nurses, five constructs had a strong positive influence on implementation on the nursing ward, including relative advantage (e.g., early detection of deterioration), patient needs and resources (e.g. feeling safe), networks and communications (e.g. execute tasks together), personal attributes (e.g. experience with intervention), and implementation leaders (e.g., project leader). Five constructs had a strong negative influence: evidence strength and quality (e.g. lack of evidence from practical experience), complexity (e.g. number of process steps), design quality and packaging (e.g., bad sensor quality), compatibility (e.g, change in work) and facilitating conditions (e.g, Wi-Fi connection). Nurses expected continuous monitoring in the home setting to be hindered by compatibility with work processes and to be facilitated by staff's access to information. Technical facilitating conditions (e.g. interoperability) were suggested to be beneficial for further development.ConclusionsThis paper provides an overview, of factors influencing implementation of continuous monitoring including relative importance, based on nurses' experiences with use on nursing wards, and their perspectives for use in the home setting. Implementation of continuous monitoring is affected by a wide range of factors. This overview may be used as a guideline for future implementations.
| S-EPMC8899789 | biostudies-literature