COVID-19 patients may not need to wait long after being infected to know if they will develop post-acute COVID-19 syndrome (PACS), also known as long COVID, according to a paper published this week in Nature Communications.
“We want to be able to recognize and identify, as early as possible, who is at risk of developing long COVID,” said Dr. Onur Boyman, a co-author of the study and researcher in the department of immunology at University Hospital Zurich.
“The most frequent symptoms of long COVID are reported to be fatigue, dyspnea [shortness of breath], and cognitive impairment (also termed ‘brain fog,’ which includes loss of concentration and memory), as well as pain and aches at different sites (including headache), cough, change in smell or taste, and diarrhea,” the study said.
The syndrome has also been referenced as “long-haul COVID” or “post-COVID syndrome” for patients who have been infected with COVID-19 but continue to experience long-term side effects and symptoms, according to the Mayo Clinic.
“Estimates show that 10%–30% of people who become infected with COVID-19 will end up coming down with long-haul COVID,” says Dr. Greg Vanichkachorn, medical director of Mayo Clinic’s COVID Activity Rehabilitation Program.
The World Health Organization defines the syndrome as symptoms that persist for usually at least three months after a patient gets COVID-19 without finding any alternative diagnosis, but the Centers for Disease Control and Prevention defines it more generally under “post-COVID conditions” that patients experience four or more weeks after being infected with COVID-19.
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The Swiss researchers defined long COVID for their study as the persistence of one or more COVID-19 related symptoms for more than four weeks after the start of their first COVID-19 related symptom.
The team evaluated the medical histories of 175 patients who were diagnosed with COVID-19 and compared them to 40 healthy patients without COVID-19 for the duration of the one-year study, finding 82.2% with severe infections had long COVID versus 53.9% of patients with mild infections, using the World Health Organization classification for a mild or severe COVID-19 infection.
The authors found patients who developed long COVID had lower levels of IgM and IgG3 antibodies, which help fight infections in the bloodstream, throughout the disease compared to those with milder infections.
The authors termed this antibody response an “immunoglobulin signature,” because unlike inflammatory markers that only transiently increase early in the course of the disease, the detected antibodies are stable over time, which makes them attractive biomarkers.
When they combined this “signature” with the participant’s age, past medical history of asthma and five particular symptoms during primary infection, the researchers were 75% effective in being able to predict the risk of long COVID, regardless of when the patient’s blood was sampled.
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NBC News Digital reported, however, because the study was conducted between April 2020 and August 2021, a time before omicron was not known to be circulating, it is unclear if the findings apply to patients now, as over 99% all COVID-19 cases in the United States are secondary to omicron, according to the Centers for Disease Control and Prevention.
Dr. Claire Steves, a senior clinical lecturer at King’s College London, noted an additional limitation of the study was it did not take into account the participants’ vaccination status.
“It would be important to look to see whether these markers are still predictive in vaccinated people as more of the world is vaccinated or has prior infection.”
She noted another key limitation was the study’s definition of long COVID-19, which defined the syndrome as lingering symptoms for greater than four weeks, which contrasts with the international consensus to focus more on persistent symptoms lasting more than 12 weeks.
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“With cases high still, more people are at risk of developing long-term symptoms. We urgently need to scale up research on how to prevent this happening. Tools such as these predictive models could be used to identify people at higher risk for enrollment into research trials for therapeutics,” Steves added.