Clinical trial NCT04357834: WAVE. Wearable-based COVID-19 Markers for Prediction of Clinical Trajectories
Official title: Wearable-based COVID-19 Markers for Prediction of Clinical Trajectories. The WAVE Study.
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: COVID
COVID 19
SARS-CoV 2
Keywords: Smartwatch
Intensive care
Intensive care unit
Prediction
Artificial intelligence
Clinical trajectories
Lead sponsor: Insel Gruppe AG, University Hospital Bern
Sponsor class: OTHER
Collaborators: name: ETH Zurich
First posted: 2020-04-22
Enrollment: 46
Sex: ALL
Minimum age (years): 18
Maximum age (years): 120
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with suspicion of COVID-19 or tested positive for SARS-CoV-2 that are or will be hospitalized on the general ward

Brief summary: The aim is to develop a wearable-based ICU (intensive care unit) prediction algorithm for inpatients contracted with SARS-CoV-2. Inpatients with suspicion of COVID-19 or with confirmed SARS-CoV-2 infection will be included. The participants will be equipped with a smartwatch, which gathers physiological data throughout hospitalisation.

Detailed description: The SARS-CoV-2 pandemic puts an unprecedented burden on the healthcare system, specifically its healthcare providers and the resource demands for intensive care units (ICUs). To support effective care despite large case numbers, hospital operations urgently need improved decision support in early identification of patients at risk of an acute COVID-19 deterioration that requires ICU.

The investigators aim at developing a wearable-based ICU algorithm for inpatients contracted with SARS-CoV-2. Inpatients on the general ward with suspicion of COVID-19 or with confirmed SARS-CoV-2 infection will be included. The participant will be equipped with a smartwatch and wear the device throughout the hospital stay until the patient (1) is discharged home, (2) is transferred to the ICU, or (3) palliative care is initiated. The smartwatch collects several physiological parameters (e.g. heart rate, heart rate variability, respiration rate, oxygen saturation). The collected data will be used to develop an ICU prediction algorithm to detect patients at risk of an acute COVID-19 deterioration that requires ICU.

Eligibility criteria: Inclusion Criteria:

* Informed consent as documented by signature
* Age \>= 18 years
* Suspicion of COVID-19 or patient tested positive for SARS-CoV-2
* Hospitalisation on the general ward

Exclusion Criteria:

* Smartwatch cannot be attached around the wrist of the patient
* Direct transfer from the emergency department or external institution to ICU (i.e. no hospitalization on the general ward)
* Known allergies to components of the smartwatch
* Rejection of ICU transfer in the patient decree

Primary outcomes:
measure: Diagnostic accuracy of smartwatch data in predicting ICU requirement in COVID-19 contracted inpatients quantified as the area under the receiver operator characteristics curve (AUC ROC > 0.85).
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Secondary outcomes:
measure: Diagnostic accuracy of routine physiological data in predicting ICU requirement in COVID-19 contracted in-patients quantified as the area under the receiver operator characteristics curve (AUC ROC > 0.85).
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Diagnostic accuracy of predicting hospital discharge without ICU admission in COVID-19 contracted in-patients quantified as area under the receiver operator characteristics curve
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Change of heart rate from baseline (hospitalization) to ICU admission
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Change of heart rate variability from baseline (hospitalization) to ICU admission
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Change of skin temperature from baseline (hospitalization) to ICU admission
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Change of blood oxygen saturation from baseline (hospitalization) to ICU admission
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Change of respiration rate from baseline (hospitalization) to ICU admission
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Change of physical activity from baseline (hospitalization) to ICU admission
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Change of stress level from baseline (hospitalization) to ICU admission
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Change of sleep pattern from baseline (hospitalization) to ICU admission
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Change of steps per day from baseline (hospitalization) to ICU admission
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Change of systolic blood pressure from baseline (hospitalization) to ICU admission
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Change of diastolic blood pressure from baseline (hospitalization) to ICU admission
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Change of body temperature from baseline (hospitalization) to ICU admission
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Change of oxygen partial pressure (pO2) from baseline (hospitalization) to ICU admission
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Change of CO2 partial pressure (pCO2) from baseline (hospitalization) to ICU admission
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Change of blood pH from baseline (hospitalization) to ICU admission
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Change of bicarbonate from baseline (hospitalization) to ICU admission
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Change of base excess from baseline (hospitalization) to ICU admission
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
measure: Change of oxygen flow rate from baseline (hospitalization) to ICU admission
timeFrame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Other outcomes:
Locations:
city: Bern
country: Switzerland
facility: Emergency Department, University Hospital Bern, Inselspital
Study officials:
affiliation: University Hospital Bern - Department of Emergency Medicine
name: Aristomenis Extradaktylos, Prof. MD
role: STUDY_CHAIR
References:
citation: Foll S, Lison A, Maritsch M, Klingberg K, Lehmann V, Zuger T, Srivastava D, Jegerlehner S, Feuerriegel S, Fleisch E, Exadaktylos A, Wortmann F. A Scalable Risk-Scoring System Based on Consumer-Grade Wearables for Inpatients With COVID-19: Statistical Analysis and Model Development. JMIR Form Res. 2022 Jun 21;6(6):e35717. doi: 10.2196/35717.