Having downloaded this app a few months ago, and religiously reported our health status each day, it is nice to see that the study has produced these findings to develop a predictive model to improve the management of covid-19.
Analysis of data from the COVID Symptom Study app, led by researchers from King’s College London and the health technology company ZOE, reveals that there are six distinct ‘types’ of COVID-19, each distinguished by a particular cluster of symptoms. Moreover, the team found that these types differed in the severity of the disease and the need for respiratory support during hospitalisation.
The findings have major implications for clinical management of COVID-19, and could help doctors predict who is most at risk and likely to need hospital care in a second wave of coronavirus infections. Launched in March in the UK and extended to the US and Sweden, the COVID Symptom Study app asks participants to log their health and any new potential symptoms of COVID-19 on a daily basis. With more than 4 million users, this represents the largest study of its kind in the world.
The team also investigated whether people experiencing particular symptom clusters were more likely to require breathing support in the form of ventilation or additional oxygen.
The six clusters are as follows:
1 (‘flu-like’ with no fever): Headache, loss of smell, muscle pains, cough, sore throat, chest pain, no fever. (1.5% required breathing support)
2 (‘flu-like’ with fever): Headache, loss of smell, cough, sore throat, hoarseness, fever, loss of appetite. (4.4% required breathing support)
3 (gastrointestinal): Headache, loss of smell, loss of appetite, diarrhea, sore throat, chest pain, no cough. (3.3% required breathing support)
4 (severe level one, fatigue): Headache, loss of smell, cough, fever, hoarseness, chest pain, fatigue. (8.6% required breathing support)
5 (severe level two, confusion): Headache, loss of smell, loss of appetite, cough, fever, hoarseness, sore throat, chest pain, fatigue, confusion, muscle pain. (9.9% required breathing support).
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. (19.8% required breathing support).
Broadly, people with cluster 4,5 or 6 COVID-19 symptoms tended to be older and frailer, and were more likely to be overweight and have pre-existing conditions such as diabetes or lung disease than those with type 1,2 or 3.
Predictive treatment model
The researchers then developed a model combining information about age, sex, BMI and pre-existing conditions together with symptoms gathered over just five days from the onset of the illness. This was able to predict which cluster a patient falls into and their risk of requiring hospitalisation and breathing support with a higher likelihood of being correct than an existing risk model based purely on age, sex, BMI and pre-existing conditions alone.
Given that most people who require breathing support come to hospital around 13 days after their first symptoms, this extra eight days represents a significant ‘early warning’ as to who is most likely to need more intensive care.
* The COVID Symptom Study has now identified skin rash as a key symptom of COVID-19 in up to one in ten cases. However, it was not recognised as a symptom during the time when the data was gathered for this analysis so it is currently unknown how skin rashes map on to these six clusters.
“These findings have important implications for care and monitoring of people who are most vulnerable to severe COVID-19,” explains consultant geriatrician Dr Claire Steves, one of the team working on the study. “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.”
Lead researcher Dr Carole Sudre said:
”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.”