In a breakthrough that could help experts better treat COVID-19 patients, a group of scientists have realised the existence of six distinct types of coronavirus, all with their own symptoms.

A new study done by researchers from King’s College London, collated via a COVID Symptom Study app has revealed that different forms of the virus are directly affecting the severity of symptoms among patients.

These findings have huge implications for the management and treatment of COVID-19, as it could help doctors predict who is most at risk and likely to need professional care.

Although there are a number of symptoms including coughing, fever and loss of smell highlighted as the main symptoms of COVID-19, data gathered via the app show that people in reality experience a large range of different symptoms. Some have reported fatigue, confusion, loss of appetite, muscle pain, headaches and shortness of breathe among others. The range of these symptoms also varies widely, with some experiencing mild flu-like symptoms, while others have severe or even fatal experiences.

A machine algorithm was used to analyse data taken from the app to pair symptoms that most commonly appear together. Roughly 1600 users from the US and UK regularly logged their symptoms via the app.

Six specific groupings of symptoms were revealed by the analysis, representing six distinct types of the virus that take place at different time points in the progression of the virus.

The algorithm was tested again on a second independent data set of 1000 users from the US, Sweden and the UK, who all logged their symptoms in May.

“All people reporting symptoms experienced headache and loss of smell, with varying combinations of additional symptoms at various times. Some of these, such as confusion, abdominal pain and shortness of breath, are not widely known as COVID-19 symptoms, yet are hallmarks of the most severe forms of the disease,” said King’s College London in a statement.

The team managed to break the six forms down as follows:

  1. (‘flu-like’ with no fever): Headache, loss of smell, muscle pains, cough, sore throat, chest pain, no fever.
  2. (‘flu-like’ with fever): Headache, loss of smell, cough, sore throat, hoarseness, fever, loss of appetite.
  3. (gastrointestinal): Headache, loss of smell, loss of appetite, diarrhea, sore throat, chest pain, no cough.
  4. (severe level one, fatigue): Headache, loss of smell, cough, fever, hoarseness, chest pain, fatigue.
  5. (severe level two, confusion): Headache, loss of smell, loss of appetite, cough, fever, hoarseness, sore throat, chest pain, fatigue, confusion, muscle pain.
  6. (severe level three, abdominal and respiratory): Headache, loss of smell, loss of appetite, cough, fever, hoarseness, sore throat, chest pain, fatigue, confusion, muscle pain, shortness of breath, diarrhea, abdominal pain.

The team also investigated which cluster of symptoms was more likely to lead to patients requiring breathing support.

“It was discovered that only 1.5% of people with cluster 1, 4.4% of people with cluster 2 and 3.3% of people with cluster 3 COVID-19 required breathing support. These figures were 8.6%, 9.9% and 19.8% for clusters 4,5 and 6 respectively. Furthermore, nearly half of the patients in cluster 6 ended up in hospital, compared with just 16% of those in cluster 1,” says King’s College London researchers.

People with cluster 4,5 or 6 symptoms tend to be frailer and older and are more likely to be overweight or be struggling an existing condition, unlike those with type 1, 2 or 3.

A model was then developed using all information gathered about age, BMI, sex and any preexisting conditions, along with symptoms experienced after five days of infection.

Using this information scientists are able to predict which cluster a patient falls into and what their specific risks are.

“These findings have important implications for care and monitoring of people who are most vulnerable to severe COVID-19,” said Dr Claire Steves from King’s College London. “If you can predict who these people are at day five, you have time to give them support and early interventions such as monitoring blood oxygen and sugar levels, and ensuring they are properly hydrated – simple care that could be given at home, preventing hospitalisations and saving lives.”

”Our study illustrates the importance of monitoring symptoms over time to make our predictions about individual risk and outcomes more sophisticated and accurate. This approach is helping us to understand the unfolding story of this disease in each patient so they can get the best care,”says Lead researcher Dr Carole Sudre from King’s College London.

“Being able to gather big datasets through the app and apply machine learning to them is having a profound impact on our understanding of the extent and impact of COVID-19, and human health more widely,” said Sebastien Ourselin, professor of healthcare engineering at King’s College London and senior author of the study.

”Data is our most powerful tool in the fight against COVID-19. We urge everyone to get in the habit of using the app daily to log their health over the coming months, helping us to stay ahead of any local hotspots or a second wave of infections,” adds Professor Tim Spector.

The pre-print, non-peer reviewed paper is available online: Carole H Sudre et al. Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app (2020) medRxiv doi.org/10.1101/2020.06.12.20129056

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