Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app

Sudre, Carole H.; Lee, Karla A.; Lochlainn, Mary Ní; Varsavsky, Thomas; Murray, Benjamin; Graham, Mark S.; Menni, Cristina; Modat, Marc; Bowyer, Ruth C E; Nguyen, Long H.; Drew, David A.; Joshi, Amit D.; Ma, Wenjie; Guo, Chuan‐Guo; Lo, Chun‐Han; Ganesh, Sajaysurya; Buwe, Abubakar; Pujol, Joan Capdevila; Cadet, Julien Lavigne du; Visconti, Alessia; Freidin, Maxim B.; Moustafa, Julia S. El-Sayed; Falchi, Mario; Davies, Richard; Gomez, Maria F.; Fall, Tove; Cardoso, M. Jorge; Wolf, Jonathan; Franks, Paul W.; Chan, Andrew T.; Spector, Tim D.; Steves, Claire J.; Ourselin, Sébastien

doi:10.1126/sciadv.abd4177

Cited by 128 publications

(126 citation statements)

References 15 publications

(20 reference statements)

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“…Recently, a similar approach was proposed for monitoring COVID-19 patients for the need of respiratory support 29 .…”

Section: With a Similar Classification Technique Based On Non-ct Clinical Features Of Acute Covid And Earlymentioning

confidence: 99%

Investigating phenotypes of pulmonary COVID-19 recovery – a longitudinal observational prospective multicenter trial

Sonnweber

Tymoszuk

Sachanic

et al. 2021

Preprint

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BackgroundCOVID-19 is associated with long-term pulmonary symptoms and may result in chronic pulmonary impairment. The optimal procedures to prevent, identify, monitor, and treat these pulmonary sequelae are elusive.Research questionTo characterize the kinetics of pulmonary recovery, risk factors and constellations of clinical features linked to persisting radiological lung findings after COVID-19.Study design and methodsA longitudinal, prospective, multicenter, observational cohort study including COVID-19 patients (n = 108). Longitudinal pulmonary imaging and functional readouts, symptom prevalence, clinical and laboratory parameters were collected during acute COVID-19 and at 60-, 100- and 180-days follow-up visits. Recovery kinetics and risk factors were investigated by logistic regression. Classification of clinical features and study participants was accomplished by k-means clustering, the k-nearest neighbors (kNN), and naive Bayes algorithms.ResultsAt the six-month follow-up, 51.9% of participants reported persistent symptoms with physical performance impairment (27.8%) and dyspnea (24.1%) being the most frequent. Structural lung abnormalities were still present in 45.4% of the collective, ranging from 12% in the outpatients to 78% in the subjects treated at the ICU during acute infection. The strongest risk factors of persisting lung findings were elevated interleukin-6 (IL6) and C-reactive protein (CRP) during recovery and hospitalization during acute COVID-19. Clustering analysis revealed association of the lung lesions with increased anti-S1/S2 antibody, IL6, CRP, and D-dimer levels at the early follow-up suggesting non-resolving inflammation as a mechanism of the perturbed recovery.Finally, we demonstrate the robustness of risk class assignment and prediction of individual risk of delayed lung recovery employing clustering and machine learning algorithms.InterpretationSeverity of acute infection, and systemic inflammation is strongly linked to persistent post-COVID-19 lung abnormality. Automated screening of multi-parameter health record data may assist the identification of patients at risk of delayed pulmonary recovery and optimize COVID-19 follow-up management.Clinical Trial RegistrationClinicalTrials.gov: NCT04416100

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“…Recently, a similar approach was proposed for monitoring COVID-19 patients for the need of respiratory support 29 .…”

Section: With a Similar Classification Technique Based On Non-ct Clinical Features Of Acute Covid And Earlymentioning

confidence: 99%

Investigating phenotypes of pulmonary COVID-19 recovery – a longitudinal observational prospective multicenter trial

Sonnweber

Tymoszuk

Sachanic

et al. 2021

Preprint

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“…Symptom Study app 6 to create diagnostic models and leverage the added value of big data to study new diseases such as COVID-19. 7,10 Despite some promising results in identifying the main symptoms of COVID-19 and their correlation to outcomes, most proposed models only use the information available at the time of maximum symptom intensity (defined as the peak of symptoms), and, hence, are not as conducive for early diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Early detection of COVID-19 in the UK using self-reported symptoms: a large-scale, prospective, epidemiological surveillance study

Canas¹,

Sudre²,

Pujol³

et al. 2021

The Lancet Digital Health

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Background Self-reported symptoms during the COVID-19 pandemic have been used to train artificial intelligence models to identify possible infection foci. To date, these models have only considered the culmination or peak of symptoms, which is not suitable for the early detection of infection. We aimed to estimate the probability of an individual being infected with SARS-CoV-2 on the basis of early self-reported symptoms to enable timely self-isolation and urgent testing. MethodsIn this large-scale, prospective, epidemiological surveillance study, we used prospective, observational, longitudinal, self-reported data from participants in the UK on 19 symptoms over 3 days after symptoms onset and COVID-19 PCR test results extracted from the COVID-19 Symptom Study mobile phone app. We divided the study population into a training set (those who reported symptoms between April 29, 2020, and Oct 15, 2020) and a test set (those who reported symptoms between Oct 16, 2020, and Nov 30, 2020), and used three models to analyse the selfreported symptoms: the UK's National Health Service (NHS) algorithm, logistic regression, and the hierarchical Gaussian process model we designed to account for several important variables (eg, specific COVID-19 symptoms, comorbidities, and clinical information). Model performance to predict COVID-19 positivity was compared in terms of sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) in the test set. For the hierarchical Gaussian process model, we also evaluated the relevance of symptoms in the early detection of COVID-19 in population subgroups stratified according to occupation, sex, age, and body-mass index. Findings The training set comprised 182 991 participants and the test set comprised 15 049 participants. When trained on 3 days of self-reported symptoms, the hierarchical Gaussian process model had a higher prediction AUC (0•80 [95% CI 0•80-0•81]) than did the logistic regression model (0•74 [0•74-0•75]) and the NHS algorithm (0•67 [0•67-0•67]). AUCs for all models increased with the number of days of self-reported symptoms, but were still high for the hierarchical Gaussian process model at day 1 (0•73 [95% CI 0•73-0•74]) and day 2 (0•79 [0•78-0•79]). At day 3, the hierarchical Gaussian process model also had a significantly higher sensitivity, but a non-statistically lower specificity, than did the two other models. The hierarchical Gaussian process model also identified different sets of relevant features to detect COVID-19 between younger and older subgroups, and between health-care workers and non-health-care workers. When used during different pandemic periods, the model was robust to changes in populations.Interpretation Early detection of SARS-CoV-2 infection is feasible with our model. Such early detection is crucial to contain the spread of COVID-19 and efficiently allocate medical resources.

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“…The odd ratios for being hospitalised or having >5 symptoms in the first week were 0.36 [0.28-0.46] and 0.51 [0.42-0.61] respectively. We have previously shown that experiencing more than 5 symptoms in the acute period was associated both with severity of disease (19) and duration of symptoms (20). In the 60+ group, we found lower risk of symptoms lasting for more than 28 days (OR=0.72, 95%CI [0.51-1.00]).…”

Section: Illness Profile In Vaccinated and Unvaccinated Individuals With Sars-cov-2 Infectionmentioning

confidence: 64%

“…Individuals with COVID-19 have varying symptoms and differing clinical needs (19). Elucidating symptom profiles in individuals with COVID-19 post-vaccination has clinical utility: facilitating identification of risk groups for intervention, predicting medical resource requirements and informing appropriate testing guidelines.…”

Section: Introductionmentioning

confidence: 99%

Post-vaccination SARS-CoV-2 infection: risk factors and illness profile in a prospective, observational community-based case-control study

Antonelli

Merino

Sudre

et al. 2021

Preprint

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Background: Both BNT162b2 and ChAdOx1 vaccines show good efficacy in clinical trials and real-world data. However, some still contract SARS-CoV-2 post-vaccination. This study identifies risk factors associated with SARS-CoV-2 infection at least 14 days after first vaccination and describes characteristics of post-vaccination illness. Methods: Cases were UK adults reporting post-vaccination SARS-CoV-2 infection between 8th December 2020 and 1st May 2021, reporting on the COVID Symptom Study app. We assessed the associations of age, frailty, comorbidity, area-level deprivation and lifestyle factors with infection (vaccinated cases vs. negative-vaccinated controls); and vaccination with illness profile (vaccinated cases vs positive-unvaccinated controls). Findings: Post-vaccination infection risk was substantially higher in older adults with frailty (OR= 2.78, 95% CI [1.98-3.89], p-value<0.0001) and in individuals living in most deprived areas (OR vs. intermediate group=1.22, 95%CI [1.04-1.43], p-value=0.01). Risk was lower in individuals with a healthier diet (OR=0.73, 95%CI [0.62-0.86], p-value<0.0001) and without obesity (OR=0.6, 95% CI [0.44-0.82], p-value=0.001). Vaccination was associated with reduced odds of hospitalisation (OR=0.36, 95%CI [0.28-0.46], p-value<0.0001), and high acute-symptom burden (OR=0.51, 95%CI [0.42-0.61], p-value<0.0001). In the 60+ age group, risk of >28 days illness was lower following vaccination (OR=0.72 , 95%CI [0.51-1.00], p-value=0.05). Most symptoms were reported less in positive-vaccinated vs. positive-unvaccinated individuals, except sneezing, which was more common post-vaccination (OR=1.24, 95%CI [1.05-1.46], p-value=0.01). Interpretation: Our findings highlight reduced symptom burden and duration in those infected post-vaccination. Whilst reassuring, our data should prompt efforts to boost vaccine effectiveness in at-risk populations; moreover, targeted infection control measures will still be appropriate to minimise SARS-CoV-2 infection.

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Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app

Cited by 128 publications

References 15 publications

Investigating phenotypes of pulmonary COVID-19 recovery – a longitudinal observational prospective multicenter trial

Investigating phenotypes of pulmonary COVID-19 recovery – a longitudinal observational prospective multicenter trial

Early detection of COVID-19 in the UK using self-reported symptoms: a large-scale, prospective, epidemiological surveillance study

Post-vaccination SARS-CoV-2 infection: risk factors and illness profile in a prospective, observational community-based case-control study

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