Sensor Based Vital Signs Monitoring of Covid 19 Patients During Home Isolation (HSC19)

• ClinicalTrials.gov Identifier: NCT04335097
• Recruitment Status: Recruiting
• First Posted: April 6, 2020
• Last Update Posted: May 17, 2021
• Sponsor: Lars Wik
• Collaborators: University of Stavanger; Oslo University Hospital; Norwegian Telemedicine; University of Basque Country (UPV/EHU)
• Information provided by (Responsible Party): Lars Wik, Ullevaal University Hospital

Study Description

Brief Summary:

Severe acute respiratory syndrome (SARS) SARS-Cov-2 disease (COVID-19) is an infectious disease caused by a coronavirus. The pandemic first described in Wuhan, China, has since spread across the whole world and caused dramatic strain on health care in many countries. Patients infected with the virus mostly report mild to moderate respiratory symptoms like shortness of breath and coughing, and febrile symptoms. It is of paramount importance to preserve health service capacity by identifying those with serious illness without transferring all infected patients to emergency rooms or Hospitals. In addition, it is important to identify seriously ill patients early enough and before they reach a point of deterioration where they can be extremely challenging to handle in both prehospital and hospital environment.

The present study is designed to sample biosensor data from patients treated and observed at home due to mild and moderate SARS-Cov-2 disease. Such a system would be useful, both for the treatment of individual patients as well as for assessing the efficacy and safety of care given to these patients. Investigators intend to improve quality and safety of home care by continuous monitoring and a set of rules for follow-up.

Investigators hypothesized that patients and local health system may benefit from the feedback of a simple monitoring system, which detects changes in respiration, temperature and circulation variables in combination with the patient's subjective experiences of care. Patients may be referred to hospitalization earlier. In the present study we will use live continuous and non-continuous biosensor data to monitor the development of vital parameters for Covid 19 patients compared with patients who are not monitored electronically (standard of care).

Condition or disease	Intervention/treatment	Phase
COVID 19	Device: Biosensors	Not Applicable

Detailed Description:

Severe acute respiratory syndrome (SARS) SARS-Cov-2 disease (COVID-19) is an infectious disease caused by a coronavirus. The pandemic first described in Wuhan, China, has since spread across the whole world and caused dramatic strain on health care in many countries. The virus spreads primarily through droplets of saliva or discharge from the nose when an infected person coughs or sneezes.1 Patients infected with the virus mostly report mild to moderate respiratory symptoms like shortness of breath and coughing, and febrile symptoms. Most recover without requiring special treatment. However, older people, and those with underlying medical problems (cardiovascular disease, diabetes, chronic respiratory disease, and cancer) are more likely to develop serious illness.1 Younger patients have been reported with serious illness as well. In the present situation, it is of paramount importance to preserve health service capacity by identifying those with serious illness without transferring all infected patients to emergency rooms or Hospitals. In addition, it is important to identify seriously ill patients early enough and before they reach a point of deterioration where they can be extremely challenging to handle in both prehospital and hospital environment.

The number of subjects with positive test of the virus is increasing and so does the number of patients hospitalized.2 In parallel, most patients with positive test result or typical clinical symptoms are at home with information what to do if their clinical symptom status deteriorates.2 The Norwegian Interaction Reform was implemented in 2012.3 Key elements of the reform are guidance of the health care in the future and identify new directions. Prevention and early efforts are important and this will be achieved by creating co-working arenas for different parts of our health system. More health services must be moved closer to where the inhabitants live and simultaneously strengthening the community health system. New tools for monitoring the well-being of the patients must be developed in order to act early enough to avoid severe deterioration of health status and avoid new hospitalization. This goal has become even more important during the Covid 19 pandemic because the healthcare system is not prepared or built to take care of all these patients in hospitals.

In the local community's wearable and wireless biosensors collecting continuous physiological data (CPD) in real time in order to generate information reflecting the patients' current state is established. This is recognized as welfare technology, and it is a generic term for a heterogeneous group of technologies.4 There are few studies documenting their efficacy, effectiveness and efficiency. One key driver for the development of wearable biosensors is the potential to use CPD to generate real-time, clinically actionable insights from predictive analytics that include early warnings of clinical deterioration and prompts for behavioral changes. The advent of machine learning methods that can detect subtle patterns from large sets of CPD may make this achievable.

Using CPD to guide clinical decisions may be a major advance for patients with chronic diseases and at present time when our health system is put on an extreme stretch. This may drive the evolution from episodic to continuous patient care.

The present study is designed to sample biosensor data from patients treated and observed at home due to mild and moderate SARS-Cov-2 disease. Such a system would be useful, both for the treatment of individual patients as well as for assessing the efficacy and safety of care given to these patients. Investigators intend to improve quality and safety of home care by continuous monitoring and a set of rules for follow-up.

Investigators hypothesized that patients and local health system may benefit from the feedback of a simple monitoring system, which detects changes in respiration, temperature and circulation variables in combination with the patient's subjective experiences of care. Patients may be referred to hospitalization earlier. In the present study investigators will use live continuous and non-continuous biosensor data to monitor the development of vital parameters for Covid 19 patients compared with patients who are not monitored electronically (standard of care).

Study Design

• Study Type: Interventional (Clinical Trial)
• Estimated Enrollment: 214 participants
• Allocation: Randomized
• Intervention Model: Parallel Assignment
• Intervention Model Description: Two groups, control and intervention.
• Masking: None (Open Label)
• Masking Description: Sensor monitoring can not be masked since it is the prerequisite for the measures.
• Primary Purpose: Supportive Care
• Official Title: Sensor Based Vital Signs Monitoring of Patients With Clinical Manifestation of Covid 19 Disease During Home Isolation, a Randomized Feasibility Study
• Actual Study Start Date: April 22, 2020
• Estimated Primary Completion Date: April 8, 2022
• Estimated Study Completion Date: December 20, 2025

Arms and Interventions

Arm	Intervention/treatment
Active Comparator: Control; Follow general recommendations fram doctor and health authorities what to do and pay attention to before new contact with health service.	Device: Biosensors; Sensor that detect vital signs; Other Name: Self reporting status
Active Comparator: Intervention; Follow general recommendations fram doctor and health authorities what to do and pay attention to before new contact with health service. I addition active reporting of clinical status and continuous vital sign monitoring based on electronic sensors (Welfare technology).	Device: Biosensors; Sensor that detect vital signs; Other Name: Self reporting status

Outcome Measures

Primary Outcome Measures :

1. Stop home isolation [ Time Frame: 1 to 21 days ]
   Day during home isolation it was stopped due to hospitalization
2. NEWS score [ Time Frame: 1 to 21 days ]
   5 or >3 for one organ system

Secondary Outcome Measures :

1. Clinic at hospitalization [ Time Frame: At admittance hospital ]
   Relevant vital clinical findings
2. Symptoms developed [ Time Frame: Duration of home isolation ]
   Symptoms developed during home isolation
3. Relative/peers evaluation of the patient [ Time Frame: Duration of home isolation ]
   Their description of vital sign development
4. Serious of symptoms at admittance hospital [ Time Frame: Hospital stay ]
   Referred to ICU, intubated, length of stay

Eligibility Criteria

• Ages Eligible for Study: 18 Years and older (Adult, Older Adult)
• Sexes Eligible for Study: All
• Gender Based Eligibility: Yes
• Gender Eligibility Description: Ti legally give consent
• Accepts Healthy Volunteers: Yes

Criteria

Inclusion Criteria:

• Valid informed consent.
• All Covid 19 positive patients age ≥18 years who are under care at home for Covid 19 infection.
• Patients with typical Covid 19 clinical symptoms where a test has not been taken may also be included if a test later is positive.
• Able to log into internet.

Exclusion Criteria:

• Age <18 years.
• Covid 19 negative.
• Internals in prison.
• Individuals living in special homes due to need of care.
• Refusal of participation.
• Comorbidity that hinder the patient to run the system.

Contacts and Locations

Contacts

Contact: Lars Wik, MD; 91728966; lars.wik@medisin.uio.no

Locations

Norway

Lillestrom legevakt; Recruiting

Lillestrom, Norway

Contact: Tonje Lorem

Sponsors and Collaborators

Lars Wik

University of Stavanger

Oslo University Hospital

Norwegian Telemedicine

University of Basque Country (UPV/EHU)

Investigators

• Principal Investigator: Lars Wik, MD; Oslo University Hospital

More Information

Additional Information:

World Health Organization (WHO) recommendation 

Folke Helse Instituttet (FHI) recommendations 

WHO 

Omsyn Welfare Technology 

E Health 

Publications of Results:

Samsudin MI, Liu N, Prabhakar SM, Chong SL, Kit Lye W, Koh ZX, Guo D, Rajesh R, Ho AFW, Ong MEH. A novel heart rate variability based risk prediction model for septic patients presenting to the emergency department. Medicine (Baltimore). 2018 Jun;97(23):e10866. doi: 10.1097/MD.0000000000010866.

Melillo P, Izzo R, Orrico A, Scala P, Attanasio M, Mirra M, De Luca N, Pecchia L. Automatic prediction of cardiovascular and cerebrovascular events using heart rate variability analysis. PLoS One. 2015 Mar 20;10(3):e0118504. doi: 10.1371/journal.pone.0118504. eCollection 2015.

Other Publications:

Bodapati RK, Kizer JR, Kop WJ, Kamel H, Stein PK. Addition of 24-Hour Heart Rate Variability Parameters to the Cardiovascular Health Study Stroke Risk Score and Prediction of Incident Stroke: The Cardiovascular Health Study. J Am Heart Assoc. 2017 Jul 21;6(7). pii: e004305. doi: 10.1161/JAHA.116.004305.

Seamless Healthcare Monitoring Advancements in Wearable, Attachable, and Invisible Devices. Chapter 5 Ballistocardiography.

The Royal College of Physicians. National Early Warning Score (NEWS) 2: Standardising the assessment of acute-illness severity in the NHS. London: RCP; 2017. p. 1-77.

Williams B, Alberti G, Ball C, et al; Royal College for Physicians: National Early Warning Score (NEWS): Standardising the Assessment of Acute-Illness Severity in the NHS. 2012London, ENG, Royal College of Physicians.

Meld. St. 16 (2010-2011) Report to the Storting (white paper) Summary - National Health and Care Services Plan. https://www.regjeringen.no/en/dokumenter/meld.-st.-16-2010-2011/id639794/

• Responsible Party: Lars Wik, Consultant in Anesthesiology, senior researcher, Ullevaal University Hospital
• ClinicalTrials.gov Identifier: NCT04335097
• Other Study ID Numbers: 127157
• First Posted: April 6, 2020
• Last Update Posted: May 17, 2021
• Last Verified: May 2021

Individual Participant Data (IPD) Sharing Statement:

• Plan to Share IPD: No

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No

Keywords provided by Lars Wik, Ullevaal University Hospital:

symptoms; biosensors; home isolation; NEWS; Outcome

Additional relevant MeSH terms:

COVID-19; Respiratory Tract Infections; Infections; Pneumonia, Viral; Pneumonia; Virus Diseases; Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Lung Diseases; Respiratory Tract Diseases