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The Union health ministry on Friday revised the dosage of anti-viral drug remdesivir to be administered to coronavirus patients in the moderate stage of illness from the earlier six days to five days as it issued an updated 'Clinical Management Protocols for COVID-19'.

The drug, administered in the form of injection, should be given at a dose of 200 mg on day one followed by 100 mg daily for four days (total five days), the new treatment protocols stated.

The Health Ministry on June 13 had allowed the use of remdesivir for restricted emergency use in moderate cases under "investigational therapies".

"Under emergency use authorisation, remdesivir may be considered for patients in moderate stage requiring oxygen support," the document stated.

It is not recommended for those with severe renal impairment and high level of liver enzymes, pregnant and lactating women, and those below 12 years, it said.

The ministry also okayed off-label application of tocilizumab, a drug that modifies the immune system or its functioning, and convalescent plasma for treating COVID-19 patients in the moderate stage of illness as "investigational therapies".

It also recommended hydroxychloroquine for patients during the early course of the disease and not for critically-ill patients.

On June 27, the ministry had included an inexpensive, widely used steroid dexamethasone in treatment protocols for COVID-19 patients in the moderate to severe stages of their illness among other therapeutic measures.

The ministry advised use of dexamethasone, which is already used in a wide range of conditions for its anti-inflammatory and immunosuppressant effects, as an alternative choice to methylprednisolone for managing moderate to severe cases of coronavirus infection.

India's COVID-19 cases soared by over 20,000 in a day for the first time taking the country's total tally to 6,25,544 on Friday while the death toll climbed to 18,213 with 379 new fatalities, according to the Union Health Ministry data updated at 8 am.

New York, Feb 26:  A new wearable sensor that works in conjunction with artificial intelligence (AI) technology could help doctors remotely detect critical changes in heart failure patients days before a health crisis occurs, says a study.

The researchers said the system could eventually help avert up to one in three heart failure readmissions in the weeks following initial discharge from the hospital and help patients sustain a better quality of life.

"This study shows that we can accurately predict the likelihood of hospitalisation for heart failure deterioration well before doctors and patients know that something is wrong," says the study's lead author Josef Stehlik from University of Utah in the US.

"Being able to readily detect changes in the heart sufficiently early will allow physicians to initiate prompt interventions that could prevent rehospitalisation and stave off worsening heart failure," Stehlik added.

According to the researchers, even if patients survive, they have poor functional capacity, poor exercise tolerance and low quality of life after hospitalisations.

"This patch, this new diagnostic tool, could potentially help us prevent hospitalizations and decline in patient status," Stehlik said.

For the findings, published in the journal Circulation: Heart Failure, the researchers followed 100 heart failure patients, average age 68, who were diagnosed and treated at four veterans administration (VA) hospitals in Utah, Texas, California, and Florida.

After discharge, participants wore an adhesive sensor patch on their chests 24 hours a day for up to three months.

The sensor monitored continuous electrocardiogram (ECG) and motion of each subject.

This information was transmitted from the sensor via Bluetooth to a smartphone and then passed on to an analytics platform, developed by PhysIQ, on a secure server, which derived heart rate, heart rhythm, respiratory rate, walking, sleep, body posture and other normal activities.

Using artificial intelligence, the analytics established a normal baseline for each patient. When the data deviated from normal, the platform generated an indication that the patient's heart failure was getting worse.

Overall, the system accurately predicted the impending need for hospitalization more than 80 per cent of the time.

On average, this prediction occurred 10.4 days before a readmission took place (median 6.5 days), the study said.