Scientists in Switzerland have announced that for the first time they have found a way to predict whether a patient is at risk of developing the so-called long Covid-19, (having symptoms of the disease for months after recovery). It is essentially a "signature" found in the immune system, namely low levels of certain antibodies (which are more common in those with long Covid-19), combined with a history of asthma, age and some initial symptoms of infection.
The acute phase of coronavirus infection can affect many organs and, even after its remission, the symptoms can persist for a long time. About one in three coronavirus patients report that their symptoms last for more than four weeks. The most common persistent symptoms are fatigue, shortness of breath and cognitive impairment. The reasons for the persistence of symptoms in some people and not in others are not well understood so far.
This "signature" can be used to predict the long-term risk of Covid-19, which was confirmed by monitoring another 395 patients with coronavirus.
The researchers, led by Dr. Onur Boyman of the University and University Hospital of Zurich, who published the study in the journal Nature Communications, studied 215 people, of whom 175 were positive for coronavirus and 40 were healthy (control group). Patients were followed for up to one year after the initial infection. 89 cases of mild disease and 45 cases of severe Covid-19 were recorded. 54% of people who became mildly ill and 82% of those who became seriously ill developed one or more persistent symptoms for more than a month.
Comparative analysis of antibody levels and other clinical parameters revealed a "signature" - a set of markers - based on a combination of lower levels of total immunoglobulin M (IgM) and immunoglobulin G3 (IgC3), older age, history of asthma and five symptoms (fever, fatigue, cough, shortness of breath, gastrointestinal disorders) during the initial acute infection. This "signature" can be used to predict the long-term risk of Covid-19, which was confirmed by monitoring another 395 patients with coronavirus.
However, the researchers said that further research was needed to confirm that their method could indeed be used clinically.