COVID 19 and changes in the heart
- Conditions
- Other viral pneumonia,
- Registration Number
- CTRI/2020/07/026698
- Lead Sponsor
- Self
- Brief Summary
Currently, the world is suffering from a pandemic caused by a coronavirus infection resulting in COVID-19 disease. Animal data from rabbits as well as human clinical reports indicate that coronavirus frequently enters myocardium causes a myocarditis picture that includes elevated troponins as well as electrocardiographic and heart rhythm changes.1 The known nonspecific electrocardiographic changes appear to arise early in a COVID infection. With the use of machine learning these may permit screening for infection and/or prediction of its severity.
It has been previously demonstrated that a neural network can be trained to identify subtle or nonspecific patterns in an electrocardiogram to identify the presence of occult cardiovascular disease and disorders including left ventricular dysfunction, intermittent atrial fibrillation, hypertrophic cardiomyopathy, as well as other conditions.2-4 In this context, we hypothesize that a neural network can be trained to identify the presence of the coronavirus infection. Given the shortage of reagents with current coronavirus genetic screening tests, and in many geographies delays in obtaining results, a rapid, non-invasive, potentially self-administered and massively scalable via mobile phone test utilizing the electrocardiogram may identify individuals who should preferentially undergo the currently available standard genetic screening test. Moreover, in addition to screening for disease, this test may potentially serve to predict who will suffer from severe disease, to warrant closer observation or admission.
**Methods:**
In this study, we propose to acquire clinical, ECG & echocardiographic data from patients are known to be COVID-19 suspects (both positive and negative). Informed consent shall be taken from every patient at the time of enrolment into the study.
The detailed ECG data will be acquired in a digital format, including the date and time of collection of the individual ECG’s. The details of the COVID-19 test will be recorded (including the date of collection & reporting, result of the test). Single 12 lead ECG recording shall be performed on the suspected patient at all times that a throat swab is collected.
For patients who are being screened for COVID, this shall be at the time of throat swab at the screening center. Personal and clinical data as per the proforma detailed in **Appendix II** shall be collected.
For patients being admitted for in-patient care with severe or critical COVID infection, the ECG shall be additionally collected at the times that there is an echocardiogram performed as per the schedule detailed below.
This 12 lead ECG along with the results of the COVID swab report and the clinical information at the time of the ECG collection shall then be shared in a digital format (.xml) with our collaborators at the Cardiovascular Division of Mayo Clinic, Rochester, USA for further use in the creation of a training dataset for an algorithm to screen for COVID on ECG based on techniques of Machine learning and Artificial Intelligence.
We shall also perform a 12 lead ECG recording in all in-patients who are treated with drugs that have a propensity to increase the QT interval (Hydroxychloroquine; Azithromycin; Sotalol; Fluoroquinolone group of antibiotics) In this group of patients the schedule for recording the ECG shall be as follows
1. Baseline ECG prior to initiation of medication
2. ECG between 48-72 h after initiation of medicine
3. If QT interval prolongs more than 25% compared to the baseline ECG or the previously taken ECG, continuous monitoring of the QT interval with an alarm for the programmable alarm for arrhythmia with a wireless monitoring system shall also be performed in this subgroup of patients.
The echocardiogram shall be performed when there is a change in the clinical condition of the patient admitted for in-patient care as per the following schedule (12 lead ECG shall also be performed now for inpatients)
1. At admission[screening echo for left ventricular(LV) ejection fraction, LV dimensions, regional wall motion abnormality ,pulmonary hypertension presence of valvular lesions , and presence of pericardial effusion)
2. If there is a need for non-invasive ventilation as decided by the treating physician including but not limited to
a. Use of non re breathable masks
b. Use of Bi-level Positive airway pressure ventilation
3. If there is a need for invasive ventilation as decided by the treating physician
4. Presence of clinical worsening of the patient as determined by the treating physician when treated with invasive ventilation
5. Presence of unexplained hypotension
6. Presence of new onset ECG changes
7. At the discretion of the treating physician.
In patients with severe and critical disease who are admitted for management, we propose to perform continuous wireless ambulatory and non-ambulatory monitoring for the screening of arrhythmias using a wireless ECG monitoring system with programmable alarms and alerts to screen for arrhythmias including sinus bradycardia, sinus tachycardia, atrial tachyarrhythmia, ventricular tachyarrhythmia and episodes of varying degrees of AV block. The data shall be stored on a central server provided by the device company which shall be available for analysis and algorithm creation later.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Not Yet Recruiting
- Sex
- All
- Target Recruitment
- 3000
All patients screened for COVID infection.
Absence of consent Patients <18 y of age.
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method 1.Range and extent of conduction tissue abnormalities due to COVID-19 infection at admission , during admission and at discharge . The data shall be collected through out the course of admission of admission of the patients and the algorithm shall be created once we have sufficient patient number to create an algorithm using machine learning and deep learning techniques | Anticipated time period | 1. Day of admission Day 0 | 2. Day of discharge day 14 | 3. Creation of algorithm : 6months after the enrolment of the last patient 2. Attempts for continuous monitoring shall be made since a point evaluation for the patient The data shall be collected through out the course of admission of admission of the patients and the algorithm shall be created once we have sufficient patient number to create an algorithm using machine learning and deep learning techniques | Anticipated time period | 1. Day of admission Day 0 | 2. Day of discharge day 14 | 3. Creation of algorithm : 6months after the enrolment of the last patient may not be useful given the varying incidence and transient nature of the cardiovascular manifestations The data shall be collected through out the course of admission of admission of the patients and the algorithm shall be created once we have sufficient patient number to create an algorithm using machine learning and deep learning techniques | Anticipated time period | 1. Day of admission Day 0 | 2. Day of discharge day 14 | 3. Creation of algorithm : 6months after the enrolment of the last patient
- Secondary Outcome Measures
Name Time Method Echocardiographic Outcomes in Patients with COVID-19 Creation and assessment of artificial intelligence-based ECG Screening tool for the diagnosis and prognosis of the disease
Trial Locations
- Locations (1)
Jayadeva Institute of Cardiovascular Sciences
🇮🇳Bangalore, KARNATAKA, India
Jayadeva Institute of Cardiovascular Sciences🇮🇳Bangalore, KARNATAKA, IndiaDr Jayprakash ShentharPrincipal investigator9845028386epsjic@gmail.com