Researchers in the UK, US and Sweden have come up with new evidence that can help identify people at risk of “long-term COVID” – a protracted course of symptoms of Coronavirus Disease 2019 (COVID-19) that is longer lasts than 28 days.
Study: Attributes and Predictors of Long COVID: Analysis of COVID cases and their symptoms recorded with the Covid Symptoms Study app. Photo credit: Tomas Ragina / Shutterstock
The results come from an analysis of the data available for more than 4,000 people with COVID-19 who reported their symptoms prospectively using the COVID Symptom Studies app.
Claire Steves (King’s College London) and the team found that long-term COVID was characterized by fatigue, headache, difficulty breathing (dyspnoea) and a loss of sense of smell (anosmia).
They also found that COVID was more likely to develop with age, body mass index (BMI), and female gender.
In addition, a predictive model developed by the researchers showed that the occurrence of more than five symptoms in the first week of illness was a significant predictor of long-term COVID.
“This model could be used to identify individuals for clinical trials to reduce long-term symptoms and provide targeted education and rehabilitation services,” write Steves and colleagues.
A pre-print version of the paper is available on the medRxiv * server while the article is being peer reviewed.
Symptoms by duration. For each symptom (ordered from top to bottom according to increasing frequency of occurrence) the mean report duration is represented by the entire (hollowed out) bar height with the associated interquartile range, which is represented by the black line for the short duration LC28 and LC56. The filled bars indicate the number of times a report was generated. This underscores the differences in symptoms in terms of their intermittency over the course of the disease. (Abbreviations DE – delirium, AP – abdominal pain, HV – hoarse voice, DI – diarrhea, CP – chest pain, SM – skipped meals, UMP – unusual muscle pain, FV – fever, ST – sore throat, PC – persistent cough, LOS – loss of smell, SOB – shortness of breath, HA – headache, FA – tiredness)
Long-term COVID reports are increasing
An increasing number of long COVID-19 cases are being reported. Little is known, however, about the prevalence or risk factors of this protracted illness, or whether it is possible to predict its development from early symptoms.
In addition, “few studies prospectively capture symptoms in the general population to pinpoint the duration of illness and the prevalence of long-lasting symptoms,” said Steves and his team. “At the population level, it is important to quantify exposure to long-term COVID in order to better assess the impact on the health system and to be able to distribute resources appropriately.”
What did the researchers do?
The team analyzed the available data for a subset of 4,182 cases of COVID-19 incidents who reported their symptoms prospectively using the COVID Symptom Studies app.
Specifically, the subgroup consisted of people who had tested PCR swabs positive for COVID-19 and reported feeling “physically normal” prior to the onset of the disease (up to 14 days before the test), which helped the team Determine the beginning.
Of the 4,182 app users, 558 (13.3%) fulfilled the definition of long-term COVID (symptoms that lasted longer than 28 days), of which 189 (4.5%) had symptoms of more than eight weeks and 95 (2, 3%) reported symptoms longer than 12 weeks.
In contrast, 1,591 (38.0%) met the definition of brief COVID (symptoms that lasted less than ten days).
What did the study find out?
The most commonly reported symptoms in patients with prolonged COVID were fatigue (97%) and headache (91.2%), followed by anosmia and lower respiratory symptoms.
The team identified a significant association between age and the prevalence of long-term COVID, increasing from 9.9% in those aged 18 to 49 to 21.9% in those over 70.
Long COVID was significantly more common in women than in men, with a prevalence of 14.9% versus 9.5%, although this gender-specific effect was not significant in the elderly.
Predicting a long COVID based on early disease characteristics
Next, the researchers assessed whether early disease characteristics could predict the risk of long-term COVID.
Steves and colleagues found that people who reported more than five symptoms in the first week of illness were more than three times more likely to have long-term COVID.
The team then created random forest prediction models using a combination of symptoms reported in the first week, personal characteristics, and comorbidities.
When all characteristics were taken into account, the model successfully differentiated between long COVID and short COVID with the strongest predictor being age followed by a range of symptoms in the first week, BMI, hoarse voice, shortness of breath, and gender.
When the researchers simplified this predictive model to include only the number of symptoms in the first week and age and gender, they found that it was still possible to accurately differentiate between people with long COVID and those with short COVID.
The results were validated in an independent data set
It is important that the predictive findings have been validated in an independent data set of 2,472 people who stated that they were positive for COVID-19 antibodies two weeks after the onset of symptoms.
Again, the number of symptoms in the first week of illness was the strongest predictor of a long COVID.
“This important information could be found in much-needed, targeted educational material for both patients and healthcare providers,” said Steves and colleagues.
“In addition, the method could help identify at-risk groups and could be used to facilitate early intervention studies on treatment (e.g. with dexamethasone and remdesivir) and clinical service developments to support rehabilitation in primary and specialist care for the relief of Long -COVID and carry out timely recovery facilitation, “concludes the team.
* Important NOTE
medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be considered conclusive, guide clinical practice / health-related behavior, or be treated as established information.