Some studies have recently suggested that up to 50 percent of all people who have overcome infection experience symptoms of long COVID, which include chest pains, tiredness, difficulty breathing, loss of taste or smell, and sore joints.
As indicated in a The Daily Beast report, even though vaccines remarkably lessen the chances of severe COVID-19 infection, "long COVID" remains a risk most people would want to avoid.
It has been a challenge to find out who could be more vulnerable to long COVID, also called "post-acute coronavirus syndrome" or PACS, as detailed in a report published in StatPearls.
Fortunately, though, to some immunological understandings from a study carried out by Swiss researchers, both patients and doctors alike, might soon have a new tool that will predict the possibility of an individual to develop the condition.
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Antibody Levels Varying Between Long COVID and Non-Long COVID Sufferers
According to immunologist Onur Boyman, from the University of Zurich and the new study's senior author, it took some time until there comes the realization among people of what COVID-19 is, and an even prolonged time to realize that a subset of COVID-19 patients did not recover from the severe disease.
Boyman also explained, the symptoms patients had experienced with PACS can be quite different and they impact several organs.
He added, they are looking at a condition with different causes, which makes it harder to find relevant patterns to understand the virus.
The findings, published in Nature Communications, are the first time researchers have discovered how antibody levels differ between people suffering long COVID and those who are not.
PACS Patients Showing Lower Antibody Levels
Through the combination of new data and a few risk factors, Boyman, together with his colleagues, developed a model that can compute long COVID risk for any patient in the infection's early stages.
As part of the research, the study investigators examined a group of 175 COVID-19 patients and 40 healthy people for one year.
Among the COVID-19 patients, more than 50 percent of mild infections and over 82 percent of severe infections resulted in long COVID symptoms.
In examining the COVID-19 patients' blood, the researchers discovered that PACS patients typically showed lower antibody levels. These include the IgM, which is essential in early immune reactions to infections; and IgG3, playing a big role in the fight against viruses.
Such levels were stable over one year. Meaning, a blood sample was taken at any point in time, no matter what the infection status is, always returned the same results of IgM and IgG3.
Development of a Predictive Model
Boyman's team used the antibody data, along with risk factors including asthma and old age for the development of a predictive model that would score the risk for long COVID of a person, on a scale of one to 100.
They tested the model out on another set of more than 390 patients of COVID-19. Even though accuracy differed between settings and groups, the researchers discovered the model was clinically helpful in guiding decisions for treatment for any individual whose PACS score was above 55.
The model is not ready yet for the computation of risk before COVID-19 infection occurs since it's using the severity of the symptoms as part of the computation. Nonetheless, though, it needs to be possible to use antibody measurements as well, obtained already before the infection.
The Swiss scientists are hoping to improve the prediction model through additional testing, including on patient groups outside of Central Europe. More so, eventually, they look forward to finding a way to predict long COVID based only on blood work.
The Swiss researchers hope to improve the prediction model through further testing, including on patient cohorts outside of Central Europe. And eventually, they hope to find a way to predict long COVID based solely on blood work.
Related information about tools that help calculate risk factors of developing COVID-19 is shown on WDIV's YouTube video below:
Check out more news and information on COVID-19 on Science Times.
