WAVE. Wearable-based COVID-19 Markers for Prediction of Clinical Trajectories

NCT04357834 | Completed

• 1 other identifier: WAVE
• Study Type: observational
• Target: 46
• Locations: 1 country
• Sites: 1

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.

Conditions

COVID; COVID 19; SARS-CoV 2

Keywords

Smartwatch; Intensive care; Intensive care unit; Prediction; Artificial intelligence; Clinical trajectories

Outcome Measures

Primary Outcomes (1)

• 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).

  Accuracy of the WAVE-model will be assessed using physiological data recorded by the smartwatch (Garmin vivoactive 4) during hospitalization complemented by demographic and health-related patient-information and will be analysed using applied machine learning technology for ICU prediction.

  until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)

Secondary Outcomes (20)

• 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).

  until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)

• Diagnostic accuracy of predicting hospital discharge without ICU admission in COVID-19 contracted in-patients quantified as area under the receiver operator characteristics curve

  until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)

• Change of heart rate from baseline (hospitalization) to ICU admission

  until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)

• Change of heart rate variability from baseline (hospitalization) to ICU admission

  until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)

• Change of skin temperature from baseline (hospitalization) to ICU admission

  until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)

Study Arms (1)

Smartwatch group

Other: Equipment with smartwatch throughout hospital stay on the general ward

Interventions

Equipment with smartwatch throughout hospital stay on the general ward OTHER

Participants with confirmed SARS-CoV-2 infection or suspicion of COVID-19 will be equipped with a smartwatch and wear the device throughout the hospital stay on the general ward.

Smartwatch group

Eligibility Criteria

• Age: 18 Years - 120 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Insel Gruppe AG, University Hospital Bern: lead
• ETH Zurich: collaborator

Study Sites (1)

Emergency Department, University Hospital Bern, Inselspital

Bern, 3010, Switzerland

Location

Related Publications (1)

• 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.

  PMID: 35613417 DERIVED

MeSH Terms

Conditions

COVID-19

Interventions

Equipment and Supplies

Condition Hierarchy (Ancestors)

Pneumonia, Viral; Pneumonia; Respiratory Tract Infections; Infections; Virus Diseases; Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Lung Diseases; Respiratory Tract Diseases

Study Officials

• Aristomenis Extradaktylos, Prof. MD

  University Hospital Bern - Department of Emergency Medicine

  STUDY CHAIR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 17, 2020
• First Posted: April 22, 2020
• Study Start: October 22, 2020
• Primary Completion: June 30, 2021
• Study Completion: June 30, 2021
• Last Updated: August 26, 2021

Record last verified: 2021-08

Locations

CH (1)