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1.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-320071

ABSTRACT

Background: We investigated machine learning based identification of the pre-symptomatic coronavirus disease 2019 (COVID-19) and detection of infection-related changes in physiology using a wearable device (the Ava bracelet).Methods: Participants from an ongoing cohort study (GAPP) of the general population in Liechtenstein were included in the current sub-study (COVI-GAPP). Nightly they wore the fertility bracelet that measured every ten seconds skin temperature, heart rate, respiratory rate, skin perfusion, and heart rate variability. Participants reported daily symptoms in a complementary app. Laboratory reverse transcription polymerase chain reaction (RT-PCR) and/or COVID-19 serology samples were collected from all participants. Long short-term memory (LSTM) based recurrent neural networks (RNN) were chosen for the binary classification of an individual as healthy or infected on a given day in a derivation and validation procedure.Findings: A total of 1ž5 million hours of physiological data were recorded from 1163 participants (mean age 44 +/- 5ž5 years). COVID-19 was confirmed in 127 participants. Of these, 66 (52%) had worn their device from baseline to symptom onset and were included in the analysis and RNN. Multi-level modelling revealed significantly different values in pre- versus post-symptomatic respiratory rate, temperature, heart rate, heart rate variability ratio, and skin perfusion. The developed RNN algorithm had a recall of 0ž73 in the training set and 0ž68 in the testing set (overall recall of 0ž71) when detecting COVID-19 up to two days prior to symptom onset.Interpretation: Our proposed RNN algorithm identified 71% of COVID-19 positive participants two days prior to symptom onset. Wearable sensor technology can therefore enable COVID-19’ detection during the pre-symptomatic period.Funding: IMI grant agreement number 101005177, the Princely House of the Principality of Liechtenstein, the government of the Principality of Liechtenstein, and the Hanela Foundation in Switzerland.Declaration of Interest: Lorenz Risch, and Martin Risch are key shareholders of the Dr Risch Medical Laboratory. David Conen has received consulting fees from Roche Diagnostics, outside of the current work. The other authors have no financial or personal conflicts of interest to declare.Ethical Approval: The local ethics committee approved the study protocol, andwritten informed consent was obtained from each participant (BASEC 2020-00786).

2.
BMJ ; 374: n2209, 2021 09 30.
Article in English | MEDLINE | ID: covidwho-1448003

ABSTRACT

OBJECTIVE: To determine if virtual care with remote automated monitoring (RAM) technology versus standard care increases days alive at home among adults discharged after non-elective surgery during the covid-19 pandemic. DESIGN: Multicentre randomised controlled trial. SETTING: 8 acute care hospitals in Canada. PARTICIPANTS: 905 adults (≥40 years) who resided in areas with mobile phone coverage and were to be discharged from hospital after non-elective surgery were randomised either to virtual care and RAM (n=451) or to standard care (n=454). 903 participants (99.8%) completed the 31 day follow-up. INTERVENTION: Participants in the experimental group received a tablet computer and RAM technology that measured blood pressure, heart rate, respiratory rate, oxygen saturation, temperature, and body weight. For 30 days the participants took daily biophysical measurements and photographs of their wound and interacted with nurses virtually. Participants in the standard care group received post-hospital discharge management according to the centre's usual care. Patients, healthcare providers, and data collectors were aware of patients' group allocations. Outcome adjudicators were blinded to group allocation. MAIN OUTCOME MEASURES: The primary outcome was days alive at home during 31 days of follow-up. The 12 secondary outcomes included acute hospital care, detection and correction of drug errors, and pain at 7, 15, and 30 days after randomisation. RESULTS: All 905 participants (mean age 63.1 years) were analysed in the groups to which they were randomised. Days alive at home during 31 days of follow-up were 29.7 in the virtual care group and 29.5 in the standard care group: relative risk 1.01 (95% confidence interval 0.99 to 1.02); absolute difference 0.2% (95% confidence interval -0.5% to 0.9%). 99 participants (22.0%) in the virtual care group and 124 (27.3%) in the standard care group required acute hospital care: relative risk 0.80 (0.64 to 1.01); absolute difference 5.3% (-0.3% to 10.9%). More participants in the virtual care group than standard care group had a drug error detected (134 (29.7%) v 25 (5.5%); absolute difference 24.2%, 19.5% to 28.9%) and a drug error corrected (absolute difference 24.4%, 19.9% to 28.9%). Fewer participants in the virtual care group than standard care group reported pain at 7, 15, and 30 days after randomisation: absolute differences 13.9% (7.4% to 20.4%), 11.9% (5.1% to 18.7%), and 9.6% (2.9% to 16.3%), respectively. Beneficial effects proved substantially larger in centres with a higher rate of care escalation. CONCLUSION: Virtual care with RAM shows promise in improving outcomes important to patients and to optimal health system function. TRIAL REGISTRATION: ClinicalTrials.gov NCT04344665.


Subject(s)
Aftercare/methods , Monitoring, Ambulatory/methods , Surgical Procedures, Operative/nursing , Telemedicine/methods , Aged , COVID-19/epidemiology , Canada/epidemiology , Female , Humans , Male , Medication Errors/statistics & numerical data , Middle Aged , Pain, Postoperative/epidemiology , Pandemics , Patient Discharge , Postoperative Period , Surgical Procedures, Operative/mortality
3.
CMAJ Open ; 9(1): E142-E148, 2021.
Article in English | MEDLINE | ID: covidwho-1115548

ABSTRACT

BACKGROUND: After nonelective (i.e., semiurgent, urgent and emergent) surgeries, patients discharged from hospitals are at risk of readmissions, emergency department visits or death. During the coronavirus disease 2019 (COVID-19) pandemic, we are undertaking the Post Discharge after Surgery Virtual Care with Remote Automated Monitoring Technology (PVC-RAM) trial to determine if virtual care with remote automated monitoring (RAM) compared with standard care will increase the number of days adult patients remain alive at home after being discharged following nonelective surgery. METHODS: We are conducting a randomized controlled trial in which 900 adults who are being discharged after nonelective surgery from 8 Canadian hospitals are randomly assigned to receive virtual care with RAM or standard care. Outcome adjudicators are masked to group allocations. Patients in the experimental group learn how to use the study's tablet computer and RAM technology, which will measure their vital signs. For 30 days, patients take daily biophysical measurements and complete a recovery survey. Patients interact with nurses via the cellular modem-enabled tablet, who escalate care to preassigned and available physicians if RAM measurements exceed predetermined thresholds, patients report symptoms, a medication error is identified or the nurses have concerns they cannot resolve. The primary outcome is number of days alive at home during the 30 days after randomization. INTERPRETATION: This trial will inform management of patients after discharge following surgery in the COVID-19 pandemic and offer insights for management of patients who undergo nonelective surgery in a nonpandemic setting. Knowledge dissemination will be supported through an online multimedia resource centre, policy briefs, presentations, peer-reviewed journal publications and media engagement. TRIAL REGISTRATION: ClinicalTrials.gov, no. NCT04344665.


Subject(s)
Aftercare/trends , Monitoring, Ambulatory/methods , Patient Discharge/standards , Remote Consultation/instrumentation , Adult , COVID-19/diagnosis , COVID-19/epidemiology , Canada/epidemiology , Computers, Handheld/supply & distribution , Humans , Middle Aged , Postoperative Period , SARS-CoV-2/genetics , User-Computer Interface
4.
J Clin Med ; 9(12)2020 Dec 09.
Article in English | MEDLINE | ID: covidwho-969190

ABSTRACT

Pan-immunoglobulin assays can simultaneously detect IgG, IgM and IgA directed against the receptor binding domain (RBD) of the S1 subunit of the spike protein (S) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 S1-RBD Ig). In this work, we aim to evaluate a quantitative SARS-CoV-2 S1-RBD Ig electrochemiluminescence immunoassay (ECLIA) regarding analytical, diagnostic, operational and clinical characteristics. Our work takes the form of a population-based study in the principality of Liechtenstein, including 125 cases with clinically well-described and laboratory confirmed SARS-CoV-2 infection and 1159 individuals without evidence of coronavirus disease 2019 (COVID-19). SARS-CoV-2 cases were tested for antibodies in sera taken with a median of 48 days (interquartile range, IQR, 43-52) and 139 days (IQR, 129-144) after symptom onset. Sera were also tested with other assays targeting antibodies against non-RBD-S1 and -S1/S2 epitopes. Sensitivity was 97.6% (95% confidence interval, CI, 93.2-99.1), whereas specificity was 99.8% (95% CI, 99.4-99.9). Antibody levels linearly decreased from hospitalized patients to symptomatic outpatients and SARS-CoV-2 infection without symptoms (p < 0.001). Among cases with SARS-CoV-2 infection, smokers had lower antibody levels than non-smokers (p = 0.04), and patients with fever had higher antibody levels than patients without fever (p = 0.001). Pan-SARS-CoV-2 S1-RBD Ig in SARS-CoV-2 infection cases significantly increased from first to second follow-up (p < 0.001). A substantial proportion of individuals without evidence of past SARS-CoV-2 infection displayed non-S1-RBD antibody reactivities (248/1159, i.e., 21.4%, 95% CI, 19.1-23.4). In conclusion, a quantitative SARS-CoV-2 S1-RBD Ig assay offers favorable and sustained assay characteristics allowing the determination of quantitative associations between clinical characteristics (e.g., disease severity, smoking or fever) and antibody levels. The assay could also help to identify individuals with antibodies of non-S1-RBD specificity with potential clinical cross-reactivity to SARS-CoV-2.

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