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

Diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets

Variability in case severity and in the range of symptoms experienced has been apparent from the earliest months of the COVID-19 pandemic. From a clinical perspective, symptom variability might indicate various routes/mechanisms by which infection leads to disease, with different routes requiring potentially different treatment approaches. For public health and control of transmission, symptoms in community cases were the prompt upon which action such as PCR testing and isolation was taken. However, interpreting symptoms presents challenges, for instance, in balancing the sensitivity and specificity of individual symptoms with the need to maximise case finding, whilst managing demand for limited resources such as testing. For both clinical and transmission control reasons, we require an approach that allows for the possibility of distinct symptom phenotypes, rather than assuming variability along a single dimension. Here we address this problem by bringing together four large and diverse datasets deriving from routine testing, a population-representative household survey and participatory smartphone surveillance in the United Kingdom. Through the use of cutting-edge unsupervised classification techniques from statistics and machine learning, we characterise symptom phenotypes among symptomatic SARS-CoV-2 PCR-positive community cases. We first analyse each dataset in isolation and across age bands, before using methods that allow us to compare multiple datasets. While we observe separation due to the total number of symptoms experienced by cases, we also see a separation of symptoms into gastrointestinal, respiratory and other types, and different symptom co-occurrence patterns at the extremes of age. In this way, we are able to demonstrate the deep structure of symptoms of COVID-19 without usual biases due to study design. This is expected to have implications for the identification and management of community SARS-CoV-2 cases and could be further applied to symptom-based management of other diseases and syndromes.

SARS-CoV-2 infection following booster vaccination: Illness and symptom profile in a prospective, observational community-based case-control study

BACKGROUND

Booster COVID-19 vaccines have shown efficacy in clinical trials and effectiveness in real-world data against symptomatic and severe illness. However, some people still become infected with SARS-CoV-2 following a third (booster) vaccination. This study describes the characteristics of SARS-CoV-2 illness following a third vaccination and assesses the risk of progression to symptomatic disease in SARS-CoV-2 infected individuals with time since vaccination.

METHODS

This prospective, community-based, case-control study used data from UK-based, adult (≥18 years) users of the COVID Symptom Study mobile application, self-reporting a first positive COVID-19 test between June 1, 2021 and April 1, 2022. To describe the characteristics of SARS-CoV-2 illness following a third vaccination, we selected cases and controls who had received a third and second dose of monovalent vaccination against COVID-19, respectively, and reported a first positive SARS-CoV-2 test at least 7 days after most recent vaccination. Cases and controls were matched (1:1) based on age, sex, BMI, time between first vaccination and infection, and week of testing. We used logistic regression models (adjusted for age, sex, BMI, level of social deprivation and frailty) to analyse associations of disease severity, overall disease duration, and individual symptoms with booster vaccination status. To assess for potential waning of vaccine effectiveness, we compared disease severity, duration, and symptom profiles of individuals testing positive within 3 months of most recent vaccination (reference group) to profiles of individuals infected between 3 and 4, 4-5, and 5-6 months, for both third and second dose. All analyses were stratified by time period, based on the predominant SARS-CoV-2 variant at time of infection (Delta: June 1, 2021-27 Nov, 2021; Omicron: 20 Dec, 2021-Apr 1, 2022).

FINDINGS

During the study period, 50,162 (Delta period) and 162,041 (Omicron) participants reported a positive SARS-CoV-2 test. During the Delta period, infection following three vaccination doses was associated with lower odds of long COVID (symptoms≥ 4 weeks) (OR=0.83, CI[0.50-1.36], p < 0.0001), hospitalisation (OR=0.55, CI[0.39-0.75], p < 0.0001) and severe symptoms (OR=0.36, CI[0.27-0.49], p < 0.0001), and higher odds of asymptomatic infection (OR=3.45, CI[2.86-4.16], p < 0.0001), compared to infection following only two vaccination doses. During the Omicron period, infection following three vaccination doses was associated with lower odds of severe symptoms (OR=0.48, CI[0.42-0.55], p < 0.0001). During the Delta period, infected individuals were less likely to report almost all individual symptoms after a third vaccination. During the Omicron period, individuals were less likely to report most symptoms after a third vaccination, except for upper respiratory symptoms e.g. sneezing (OR=1.40, CI[1.18-1.35], p < 0.0001), runny nose (OR=1.26, CI[1.18-1.35], p < 0.0001), sore throat (OR=1.17, CI[1.10-1.25], p < 0.0001), and hoarse voice (OR=1.13, CI[1.06-1.21], p < 0.0001), which were more likely to be reported. There was evidence of reduced vaccine effectiveness during both Delta and Omicron periods in those infected more than 3 months after their most recent vaccination, with increased reporting of severe symptoms, long duration illness, and most individual symptoms.

INTERPRETATION

This study suggests that a third dose of monovalent vaccine may reduce symptoms, severity and duration of SARS-CoV-2 infection following vaccination. For Omicron variants, the third vaccination appears to reduce overall symptom burden but may increase upper respiratory symptoms, potentially due to immunological priming. There is evidence of waning vaccine effectiveness against progression to symptomatic and severe disease and long COVID after three months. Our findings support ongoing booster vaccination promotion amongst individuals at high risk from COVID-19, to reduce severe symptoms and duration of illness, and health system burden. Disseminating knowledge on expected symptoms following booster vaccination may encourage vaccine uptake.

Distinct temporal trajectories and risk factors for Post-acute sequelae of SARS-CoV-2 infection

Background

The understanding of Post-acute sequelae of SARS-CoV-2 infection (PASC) can be improved by longitudinal assessment of symptoms encompassing the acute illness period. To gain insight into the various disease trajectories of PASC, we assessed symptom evolution and clinical factors associated with the development of PASC over 3 months, starting with the acute illness period.

Methods

We conducted a prospective cohort study to identify parameters associated with PASC. We performed cluster and case control analyses of clinical data, including symptomatology collected over 3 months following infection.

Results

We identified three phenotypic clusters associated with PASC that could be characterized as remittent, persistent, or incident based on the 3-month change in symptom number compared to study entry: remittent (median; min, max: -4; -17, 3), persistent (-2; -14, 7), or incident (4.5; -5, 17) (p = 0.041 remittent vs. persistent, p < 0.001 remittent vs. incident, p < 0.001 persistent vs. incident). Despite younger age and lower hospitalization rates, the incident phenotype had a greater number of symptoms (15; 8, 24) and a higher proportion of participants with PASC (63.2%) than the persistent (6; 2, 9 and 52.2%) or remittent clusters (1; 0, 6 and 18.7%). Systemic corticosteroid administration during acute infection was also associated with PASC at 3 months [OR (95% CI): 2.23 (1.14, 4.36)].

Conclusion

An incident disease phenotype characterized by symptoms that were absent during acute illness and the observed association with high dose steroids during acute illness have potential critical implications for preventing PASC.

Post-COVID-19 syndrome management: Utilizing the potential of dietary polysaccharides

The COVID-19 pandemic has caused significant global impact, resulting in long-term health effects for many individuals. As more patients recover, there is a growing need to identify effective management strategies for ongoing health concerns, such as post-COVID-19 syndrome, characterized by persistent symptoms or complications beyond several weeks or months from the onset of symptoms. In this review, we explore the potential of dietary polysaccharides as a promising approach to managing post-COVID-19 syndrome. We summarize the immunomodulatory, antioxidant, antiviral, and prebiotic activities of dietary polysaccharides for the management of post-COVID-19 syndrome. Furthermore, the review investigates the role of polysaccharides in enhancing immune response, regulating immune function, improving oxidative stress, inhibiting virus binding to ACE2, balancing gut microbiota, and increasing functional metabolites. These properties of dietary polysaccharides may help alleviate COVID-19 symptoms, providing a promising avenue for effective treatment strategies.

Clusters of SARS-CoV-2 Infection Across Six Schools for Students with Intellectual and Developmental Disabilities

INTRODUCTION

Individuals with intellectual and developmental disabilities are at increased risk for adverse outcomes from coronavirus disease 2019. Clusters of COVID-19 infections can be used to track SARS-CoV-2 transmission. This is particularly important in environments frequently used for individuals with intellectual and developmental disabilities, such as schools. The objective of this study was to compare the number of clusters of student and staff cases identified during three distinct periods (pre-Delta, Delta, and Omicron) of the COVID-19 pandemic.

METHODS

Weekly COVID-19 testing occurred from November 23, 2020 to May 27, 2022 during three phases of the COVID-19 pandemic: pre-Delta, Delta, and Omicron. Structured interviews were conducted with positive cases to determine if they contracted COVID-19 in the school environment, and interviews with school administrators responsible for contact tracing determined school-based clusters.

RESULTS

160 cases of COVID-19 were identified and 55 cluster positives were recorded during the study period. 0 (0%) cluster positives were recorded during the pre-Delta variant wave, 3 (5%) cluster positives were recorded during the Delta variant wave, and 52 (95%) cluster positives were recorded during the Omicron variant wave. Additionally, 23 (85%) of all positives during pre-Delta, 12 (50%) of all positives during Delta, 66 (61%) of all positives during Omicron, and 36 (69%) of cluster positives during Omicron did not receive CDC-recommended dosages of the COVID-19 vaccine.

CONCLUSION

The Omicron variant led to an increase in cluster-based transmission, and staying up to date with vaccination guidelines was crucial in limiting transmission.

CLINICAL TRIAL REGISTRATION

Prior to enrollment, this study was registered at ClinicalTrials.gov on September 25, 2020 (identifier NCT04565509; titled "Supporting the Health and Well-being of Children with Intellectual and Developmental Disability During COVID-19 Pandemic").

Casirivimab + imdevimab accelerates symptom resolution linked to improved COVID-19 outcomes across susceptible antibody and risk profiles

Severe, protracted symptoms are associated with poor outcomes in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In a placebo-controlled study of casirivimab and imdevimab (CAS + IMD) in persons at high risk of severe coronavirus disease 2019 (COVID-19; n = 3816), evolution of individual symptoms was assessed for resolution patterns across risk factors, and baseline SARS-CoV-2-specific antibody responses against S1 and N domains. CAS + IMD versus placebo provided statistically significant resolution for 17/23 symptoms, with greater response linked to absence of endogenous anti-SARS-CoV-2 immunoglobulin (Ig)G, IgA, or specific neutralizing antibodies at baseline, or high baseline viral load. Resolution of five key symptoms (onset days 3-5)-dyspnea, cough, feeling feverish, fatigue, and loss of appetite-independently correlated with reduced hospitalization and death (hazard ratio range: 0.31-0.56; P < 0.001-0.043), and was more rapid in CAS + IMD-treated patients lacking robust early antibody responses. Those who seroconverted late still benefited from treatment. Thus, highly neutralizing COVID-19-specific antibodies provided by CAS + IMD treatment accelerated key symptom resolution associated with hospitalization and death in those at high risk for severe disease as well as in those lacking early, endogenous neutralizing antibody responses.

Profiling post-COVID-19 condition across different variants of SARS-CoV-2: a prospective longitudinal study in unvaccinated wild-type, unvaccinated alpha-variant, and vaccinated delta-variant populations

BACKGROUND

Self-reported symptom studies rapidly increased understanding of SARS-CoV-2 during the COVID-19 pandemic and enabled monitoring of long-term effects of COVID-19 outside hospital settings. Post-COVID-19 condition presents as heterogeneous profiles, which need characterisation to enable personalised patient care. We aimed to describe post-COVID-19 condition profiles by viral variant and vaccination status.

METHODS

In this prospective longitudinal cohort study, we analysed data from UK-based adults (aged 18-100 years) who regularly provided health reports via the Covid Symptom Study smartphone app between March 24, 2020, and Dec 8, 2021. We included participants who reported feeling physically normal for at least 30 days before testing positive for SARS-CoV-2 who subsequently developed long COVID (ie, symptoms lasting longer than 28 days from the date of the initial positive test). We separately defined post-COVID-19 condition as symptoms that persisted for at least 84 days after the initial positive test. We did unsupervised clustering analysis of time-series data to identify distinct symptom profiles for vaccinated and unvaccinated people with post-COVID-19 condition after infection with the wild-type, alpha (B.1.1.7), or delta (B.1.617.2 and AY.x) variants of SARS-CoV-2. Clusters were then characterised on the basis of symptom prevalence, duration, demography, and previous comorbidities. We also used an additional testing sample with additional data from the Covid Symptom Study Biobank (collected between October, 2020, and April, 2021) to investigate the effects of the identified symptom clusters of post-COVID-19 condition on the lives of affected people.

FINDINGS

We included 9804 people from the COVID Symptom Study with long COVID, 1513 (15%) of whom developed post-COVID-19 condition. Sample sizes were sufficient only for analyses of the unvaccinated wild-type, unvaccinated alpha variant, and vaccinated delta variant groups. We identified distinct profiles of symptoms for post-COVID-19 condition within and across variants: four endotypes were identified for infections due to the wild-type variant (in unvaccinated people), seven for the alpha variant (in unvaccinated people), and five for the delta variant (in vaccinated people). Across all variants, we identified a cardiorespiratory cluster of symptoms, a central neurological cluster, and a multi-organ systemic inflammatory cluster. These three main clusers were confirmed in a testing sample. Gastrointestinal symptoms clustered in no more than two specific phenotypes per viral variant.

INTERPRETATION

Our unsupervised analysis identified different profiles of post-COVID-19 condition, characterised by differing symptom combinations, durations, and functional outcomes. Our classification could be useful for understanding the distinct mechanisms of post-COVID-19 condition, as well as for identification of subgroups of individuals who might be at risk of prolonged debilitation.

FUNDING

UK Government Department of Health and Social Care, Chronic Disease Research Foundation, The Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation, UK Alzheimer's Society, and ZOE.

A retrospective cohort analysis leveraging augmented intelligence to characterize long COVID in the electronic health record: A precision medicine framework

Physical and psychological symptoms lasting months following an acute COVID-19 infection are now recognized as post-acute sequelae of COVID-19 (PASC). Accurate tools for identifying such patients could enhance screening capabilities for the recruitment for clinical trials, improve the reliability of disease estimates, and allow for more accurate downstream cohort analysis. In this retrospective cohort study, we analyzed the EHR of hospitalized COVID-19 patients across three healthcare systems to develop a pipeline for better identifying patients with persistent PASC symptoms (dyspnea, fatigue, or joint pain) after their SARS-CoV-2 infection. We implemented distributed representation learning powered by the Machine Learning for modeling Health Outcomes (MLHO) to identify novel EHR features that could suggest PASC symptoms outside of typical diagnosis codes. MLHO applies an entropy-based feature selection and boosting algorithms for representation mining. These improved definitions were then used for estimating PASC among hospitalized patients. 30,422 hospitalized patients were diagnosed with COVID-19 across three healthcare systems between March 13, 2020 and February 28, 2021. The mean age of the population was 62.3 years (SD, 21.0 years) and 15,124 (49.7%) were female. We implemented the distributed representation learning technique to augment PASC definitions. These definitions were found to have positive predictive values of 0.73, 0.74, and 0.91 for dyspnea, fatigue, and joint pain, respectively. We estimated that 25 percent (CI 95%: 6-48), 11 percent (CI 95%: 6-15), and 13 percent (CI 95%: 8-17) of hospitalized COVID-19 patients will have dyspnea, fatigue, and joint pain, respectively, 3 months or longer after a COVID-19 diagnosis. We present a validated framework for screening and identifying patients with PASC in the EHR and then use the tool to estimate its prevalence among hospitalized COVID-19 patients.

Trends and symptoms of SARS-CoV-2 infection: a longitudinal study on an Alpine population representative sample

OBJECTIVES

The continuous monitoring of SARS-CoV-2 infection waves and the emergence of novel pathogens pose a challenge for effective public health surveillance strategies based on diagnostics. Longitudinal population representative studies on incident events and symptoms of SARS-CoV-2 infection are scarce. We aimed at describing the evolution of the COVID-19 pandemic during 2020 and 2021 through regular monitoring of self-reported symptoms in an Alpine community sample.

DESIGN

To this purpose, we designed a longitudinal population representative study, the Cooperative Health Research in South Tyrol COVID-19 study.

PARTICIPANTS AND OUTCOME MEASURES

A sample of 845 participants was retrospectively investigated for active and past infections with swab and blood tests, by August 2020, allowing adjusted cumulative incidence estimation. Of them, 700 participants without previous infection or vaccination were followed up monthly until July 2021 for first-time infection and symptom self-reporting: COVID-19 anamnesis, social contacts, lifestyle and sociodemographic data were assessed remotely through digital questionnaires. Temporal symptom trajectories and infection rates were modelled through longitudinal clustering and dynamic correlation analysis. Negative binomial regression and random forest analysis assessed the relative importance of symptoms.

RESULTS

At baseline, the cumulative incidence of SARS-CoV-2 infection was 1.10% (95% CI 0.51%, 2.10%). Symptom trajectories mimicked both self-reported and confirmed cases of incident infections. Cluster analysis identified two groups of high-frequency and low-frequency symptoms. Symptoms like fever and loss of smell fell in the low-frequency cluster. Symptoms most discriminative of test positivity (loss of smell, fatigue and joint-muscle aches) confirmed prior evidence.

CONCLUSIONS

Regular symptom tracking from population representative samples is an effective screening tool auxiliary to laboratory diagnostics for novel pathogens at critical times, as manifested in this study of COVID-19 patterns. Integrated surveillance systems might benefit from more direct involvement of citizens' active symptom tracking.

The interRAI COVID-19 vulnerability screener: Results of a health surveillance initiative for vulnerable adults in the community during the COVID-19 pandemic

During the pandemic, the interRAI COVID-19 Vulnerability Screener (CVS) was used to identify community-dwelling older adults or adults with disabilities at risk of negative outcomes and facilitate triage for follow-up with health/social services. The interRAI CVS, a standardized self-report instrument administered virtually by a lay-person, includes COVID-19-related items and psychosocial and physical vulnerability. Our objective was to describe those assessed and identify sub-groups at highest risk of adverse outcomes. Seven community-based organizations in Ontario, Canada, implemented the interRAI CVS. We used descriptive statistics to report results and created a priority indicator for monitoring and/or intervention based on possible COVID-19 symptoms and psychosocial/physical vulnerabilities. We used logistic regression to examine the association between priority level and risk of poor outcomes using fair/poor self-rated health as a proxy measure. The sample included 942 adults assessed (April-November 2020; mean age=79). About 10% of individuals reported potential COVID-19 symptoms and <1% had a positive COVID-19 test/diagnosis. Of those with psychosocial/physical vulnerabilities (73.1%), most common were depressed mood (20.9%), loneliness (21.6%), and limited access to food/medications (7.5%). Overall, 45.7% had a recent doctor or nurse practitioner visit. Odds of fair/poor self-reported health were highest among those who reported both possible symptoms of COVID-19 and psychosocial/physical vulnerabilities (OR 10.9, 95% CI 5.96-20.12) compared to those with neither symptoms nor psychosocial/physical vulnerabilities. The sample represents a population largely unaffected by COVID-19 itself but with identified vulnerabilities. The interRAI CVS allows community providers to stay connected and obtain a better understanding of vulnerable individuals' needs during the pandemic.

Daily Oxygenation Support for Patients Hospitalized With SARS-CoV-2 in an Integrated Health System

BACKGROUND

Many COVID-19 studies are constructed to report hospitalization outcomes, with few large multi-center population-based reports on the time course of intra-hospitalization characteristics, including daily oxygenation support requirements. Comprehensive epidemiologic profiles of oxygenation methods used by day and by week during hospitalization across all severities are important to illustrate the clinical and economic burden of COVID-19 hospitalizations.

METHODS

This was a retrospective, multi-center observational cohort study of 15,361 consecutive hospitalizations of patients with COVID-19 at 25 adult acute care hospitals in Texas participating in the Society of Critical Care Medicine Discovery Viral Respiratory Illness Universal Study COVID-19 registry.

RESULTS

At initial hospitalization, the majority required nasal cannula (44.0%), with an increasing proportion of invasive mechanical ventilation in the first week and particularly the weeks to follow. After 4 weeks of acute illness, 69.9% of adults hospitalized with COVID-19 required intermediate (eg, high-flow nasal cannula, noninvasive ventilation) or advanced respiratory support (ie, invasive mechanical ventilation), with similar proportions that extended to hospitalizations that lasted ≥ 6 weeks.

CONCLUSIONS

Data representation of intra-hospital processes of care drawn from hospitals with varied size, teaching and trauma designations is important to presenting a balanced perspective of care delivery mechanisms employed, such as daily oxygen method utilization.

Association between use of psychotropic medications prior to SARS-COV-2 infection and trajectories of COVID-19 recovery: Findings from the prospective Predi-COVID cohort study

Psychological disturbances are frequent following COVID-19. However, there is not much information about whether pre-existing psychological disorders are associated with the severity and evolution of COVID-19. We aimed to explore the associations between regular psychotropic medication use (PM) before infection as a proxy for mood or anxiety disorders with COVID-19 recovery trajectories. We used data from the Predi-COVID study. We followed adults, tested positive for SARS-CoV-2 and collected demographics, clinical characteristics, comorbidities and daily symptoms 14 days after inclusion. We calculated a score based on 16 symptoms and modeled latent class trajectories. We performed polynomial logistic regression with PM as primary exposure and the different trajectories as outcome. We included 791 participants, 51% were men, and 5.3% reported regular PM before infection. We identified four trajectories characterizing recovery dynamics: "Almost asymptomatic," "Quick recovery," "Slow recovery," and "Persisting symptoms". With a fully adjusted model for age, sex, socioeconomic, lifestyle and comorbidity, we observed associations between PM with the risks of being in more severe trajectories than "Almost Asymptomatic": "Quick recovery" (relative risk (95% confidence intervals) 3.1 (2.7, 3.4), "Slow recovery" 5.2 (3.0, 9.2), and "Persisting symptoms"11.7 (6.9, 19.6) trajectories. We observed a gradient of risk between PM before the infection and the risk of slow or no recovery in the first 14 days. These results suggest that a pre-existing psychological condition increases the risk of a poorer evolution of COVID-19 and may increase the risk of Long COVID. Our findings can help to personalize the care of people with COVID-19.

The Role of Hemogram-derived Ratios in COVID-19 Severity Stratification in a Primary Healthcare Facility

Background

Coronavirus disease 2019 (COVID-19) can cause a wide clinical spectrum, ranging from asymptomatic to severe disease with a high mortality rate. In view of the current pandemic and the increasing influx of patients into healthcare facilities, there is a need to identify simple and reliable tools for stratifying patients.

Objective

Study aimed to analyze whether hemogram-derived ratios (HDRs) can be used to identify patients with a risk of developing a severe clinical form and admission to hospital.

Methods

This cross-sectional and observational study included 500 patients with a confirmed diagnosis of COVID-19. Data on clinical features and laboratory parameters were collected from medical records and 13 HDRs were calculated and analyzed. Descriptive and inferential statistics were included in the analysis.

Results

Of the 500 patients, 43.8% had a severe form of the disease. Lymphocytopenia, monocytopenia, higher C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR) were found in severe patients (p < 0.05). Significantly higher neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), neutrophil-to-platelet ratio (NPR), neutrophil-to-lymphocyte-to-platelet ratio (NLPR) and CRP-to-lymphocyte ratio (CRP/Ly) values were found in severe patients (p < 0.001). In addition, they have statistically significant prognostic potential (p < 0.001). The area under the curve (AUC) for CRP/Ly, dNLR, NLPR, NLR, and NPR were 0.693, 0.619, 0.619, 0.616, and 0.603, respectively. The sensitivity and specificity were 65.7% and 65.6% for CRP/Ly, 51.6% and 70.8 for dNLR, 61.6% and 57.3% for NLPR, 40.6% and 80.4% for NLR, and 48.8% and 69.1% for NPR.

Conclusion

The results of the study suggest that NLR, dNLR, CRP/Ly, NPR, and NLPR can be considered as potentially useful markers for stratifying patients with a severe form of the disease. HDRs derived from routine blood tests results should be included in common laboratory practice since they are readily available, easy to calculate, and inexpensive.

Performance of three screening tools to predict COVID-19 positivity in emergency department patients

BACKGROUND

COVID-19 symptoms vary widely. This retrospective study assessed which of three clinical screening tools-a nursing triage screen (NTS), an ED review of systems (ROS) performed by physicians and physician assistants and a standardised ED attending (ie, consultant) physician COVID-19 probability assessment (PA)-best identified patients with COVID-19 on a subsequent reverse transcription PCR (RT-PCR) confirmation.

METHODS

All patients admitted to Boston Medical Center from the ED between 27 April 2020 and 17 May 2020 were included. Sensitivity, specificity and positive predictive value (PPV) and negative predictive value (NPV) were calculated for each method. Logistic regression assessed each tool's performance.

RESULTS

The attending physician PA had higher sensitivity (0.62, 95% CI 0.53 to 0.71) than the NTS (0.46, 95% CI 0.37 to 0.56) and higher specificity (0.76, 95% CI 0.72 to 0.80) than the NTS (0.71, 95% CI 0.66 to 0.75) and ED ROS (0.62, 95% CI 0.58 to 0.67). Categorisation as moderate or high probability on the ED physician PA was associated with the highest odds of having COVID-19 in regression analyses (adjusted OR=4.61, 95% CI 3.01 to 7.06). All methods had a low PPV (ranging from 0.26 for the ED ROS to 0.40 for the attending physician PA) and a similar NPV (0.84 for both the NTS and the ED ROS, and 0.89 for the attending physician PA).

CONCLUSION

The ED attending PA had higher sensitivity and specificity than the other two methods, but none was accurate enough to replace a COVID-19 RT-PCR test in a clinical setting where transmission control is crucial. Therefore, we recommend universal COVID-19 testing prior to all admissions.

A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptoms

BACKGROUND

Accurate COVID-19 prognosis is a critical aspect of acute and long-term clinical management. We identified discrete clusters of early stage-symptoms which may delineate groups with distinct disease severity phenotypes, including risk of developing long-term symptoms and associated inflammatory profiles.

METHODS

1,273 SARS-CoV-2 positive U.S. Military Health System beneficiaries with quantitative symptom scores (FLU-PRO Plus) were included in this analysis. We employed machine-learning approaches to identify symptom clusters and compared risk of hospitalization, long-term symptoms, as well as peak CRP and IL-6 concentrations.

RESULTS

We identified three distinct clusters of participants based on their FLU-PRO Plus symptoms: cluster 1 ("Nasal cluster") is highly correlated with reporting runny/stuffy nose and sneezing, cluster 2 ("Sensory cluster") is highly correlated with loss of smell or taste, and cluster 3 ("Respiratory/Systemic cluster") is highly correlated with the respiratory (cough, trouble breathing, among others) and systemic (body aches, chills, among others) domain symptoms. Participants in the Respiratory/Systemic cluster were twice as likely as those in the Nasal cluster to have been hospitalized, and 1.5 times as likely to report that they had not returned-to-activities, which remained significant after controlling for confounding covariates (P < 0.01). Respiratory/Systemic and Sensory clusters were more likely to have symptoms at six-months post-symptom-onset (P = 0.03). We observed higher peak CRP and IL-6 in the Respiratory/Systemic cluster (P < 0.01).

CONCLUSIONS

We identified early symptom profiles potentially associated with hospitalization, return-to-activities, long-term symptoms, and inflammatory profiles. These findings may assist in patient prognosis, including prediction of long COVID risk.

Symptom Clusters Seen in Adult COVID-19 Recovery Clinic Care Seekers

BACKGROUND

COVID-19 symptom reports describe varying levels of disease severity with differing periods of recovery and symptom trajectories. Thus, there are a multitude of disease and symptom characteristics clinicians must navigate and interpret to guide care.

OBJECTIVE

To find natural groups of patients with similar constellations of post-acute sequelae of COVID-19 (PASC) symptoms.

DESIGN

Cohort SETTING: Outpatient COVID-19 recovery clinic with patient referrals from 160 primary care clinics serving 36 counties in Texas.

PATIENTS

Adult patients seeking COVID-19 recovery clinic care between November 15, 2020, and July 31, 2021, with laboratory-confirmed mild (not hospitalized), moderate (hospitalized), or severe (hospitalized with critical care) COVID-19.

MAIN MEASURES

Demographics, COVID illness onset, and duration of persistent PASC symptoms via semi-structured medical assessments.

KEY RESULTS

Four hundred forty-one patients (mean age 51.5 years; 295 [66.9%] women; 99 [22%] Hispanic, and 170 [38.5%] non-White, racial minority) met inclusion criteria. Using a k-medoids algorithm, we found that PASC symptoms cluster into two distinct groups: neuropsychiatric (N = 186) (e.g., subjective cognitive dysfunction) and pulmonary (N = 255) (e.g., dyspnea, cough). The neuropsychiatric cluster had significantly higher incidences of otolaryngologic (X2 = 14.3, p < 0.001), gastrointestinal (X2 = 6.90, p = 0.009), neurologic (X2 = 441, p < 0.001), and psychiatric sequelae (X2 = 40.6, p < 0.001) with more female (X2 = 5.44, p = 0.020) and younger age (t = 2.39, p = 0.017) patients experiencing longer durations of PASC symptoms before seeking care (t = 2.44, p = 0.015). Patients in the pulmonary cluster were more often hospitalized for COVID-19 (X2 = 3.98, p = 0.046) and had significantly higher comorbidity burden (U = 20800, p = 0.019) and pulmonary sequelae (X2 = 13.2, p < 0.001).

CONCLUSIONS

Health services clinic data from a large integrated health system offers insights into the post-COVID symptoms associated with care seeking for sequelae that are not adequately managed by usual care pathways (self-management and primary care clinic visits). These findings can inform machine learning algorithms, primary care management, and selection of patients for earlier COVID-19 recovery referral.

TRIAL REGISTRATION

N/A.

Characterising patterns of COVID-19 and long COVID symptoms: evidence from nine UK longitudinal studies

Multiple studies across global populations have established the primary symptoms characterising Coronavirus Disease 2019 (COVID-19) and long COVID. However, as symptoms may also occur in the absence of COVID-19, a lack of appropriate controls has often meant that specificity of symptoms to acute COVID-19 or long COVID, and the extent and length of time for which they are elevated after COVID-19, could not be examined. We analysed individual symptom prevalences and characterised patterns of COVID-19 and long COVID symptoms across nine UK longitudinal studies, totalling over 42,000 participants. Conducting latent class analyses separately in three groups ('no COVID-19', 'COVID-19 in last 12 weeks', 'COVID-19 > 12 weeks ago'), the data did not support the presence of more than two distinct symptom patterns, representing high and low symptom burden, in each group. Comparing the high symptom burden classes between the 'COVID-19 in last 12 weeks' and 'no COVID-19' groups we identified symptoms characteristic of acute COVID-19, including loss of taste and smell, fatigue, cough, shortness of breath and muscle pains or aches. Comparing the high symptom burden classes between the 'COVID-19 > 12 weeks ago' and 'no COVID-19' groups we identified symptoms characteristic of long COVID, including fatigue, shortness of breath, muscle pain or aches, difficulty concentrating and chest tightness. The identified symptom patterns among individuals with COVID-19 > 12 weeks ago were strongly associated with self-reported length of time unable to function as normal due to COVID-19 symptoms, suggesting that the symptom pattern identified corresponds to long COVID. Building the evidence base regarding typical long COVID symptoms will improve diagnosis of this condition and the ability to elicit underlying biological mechanisms, leading to better patient access to treatment and services.

Clinical and temporal characterization of COVID-19 subgroups using patient vector embeddings of electronic health records

OBJECTIVE

To identify and characterize clinical subgroups of hospitalized Coronavirus Disease 2019 (COVID-19) patients.

MATERIALS AND METHODS

Electronic health records of hospitalized COVID-19 patients at NewYork-Presbyterian/Columbia University Irving Medical Center were temporally sequenced and transformed into patient vector representations using Paragraph Vector models. K-means clustering was performed to identify subgroups.

RESULTS

A diverse cohort of 11 313 patients with COVID-19 and hospitalizations between March 2, 2020 and December 1, 2021 were identified; median [IQR] age: 61.2 [40.3-74.3]; 51.5% female. Twenty subgroups of hospitalized COVID-19 patients, labeled by increasing severity, were characterized by their demographics, conditions, outcomes, and severity (mild-moderate/severe/critical). Subgroup temporal patterns were characterized by the durations in each subgroup, transitions between subgroups, and the complete paths throughout the course of hospitalization.

DISCUSSION

Several subgroups had mild-moderate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections but were hospitalized for underlying conditions (pregnancy, cardiovascular disease [CVD], etc.). Subgroup 7 included solid organ transplant recipients who mostly developed mild-moderate or severe disease. Subgroup 9 had a history of type-2 diabetes, kidney and CVD, and suffered the highest rates of heart failure (45.2%) and end-stage renal disease (80.6%). Subgroup 13 was the oldest (median: 82.7 years) and had mixed severity but high mortality (33.3%). Subgroup 17 had critical disease and the highest mortality (64.6%), with age (median: 68.1 years) being the only notable risk factor. Subgroups 18-20 had critical disease with high complication rates and long hospitalizations (median: 40+ days). All subgroups are detailed in the full text. A chord diagram depicts the most common transitions, and paths with the highest prevalence, longest hospitalizations, lowest and highest mortalities are presented. Understanding these subgroups and their pathways may aid clinicians in their decisions for better management and earlier intervention for patients.

Non-Typical Clinical Presentation of COVID-19 Patients in Association with Disease Severity and Length of Hospital Stay

BACKGROUND

This study aimed to investigate the incidence of non-typical symptoms in ambulatory patients with mild-to-moderate COVID-19 infection and their potential association with disease progression.

MATERIALS AND METHODS

Data on the symptomatology of COVID-19 patients presenting to the fast-track emergency department were collected between March 2020 and March 2021. Fever, cough, shortness of breath, and fatigue-weakness were defined as "typical" symptoms, whereas all other symptoms such as nasal congestion, rhinorrhea, gastrointestinal symptoms, etc., were defined as "non-typical".

RESULTS

A total of 570 COVID-19 patients with a mean age of 42.25 years were included, the majority of whom were male (61.3%; N = 349), and were divided according to their symptoms into two groups. The mean length of hospital stay was found to be 9.5 days. A higher proportion of patients without non-typical symptoms were admitted to the hospital (p = 0.001) and the ICU (p = 0.048) as well. No significant differences were observed between non-typical symptoms and outcome (p = 0.685). Patients who did not demonstrate at least one non-typical symptom had an extended length of stay (p = 0.041). No statistically significant differences in length of hospital stay were associated with individual symptoms.

CONCLUSION

With the possible exception of gastrointestinal symptoms, non-typical symptoms of COVID-19 at baseline appear to predispose to a milder disease.

Symptom Number and Reduced Preinfection Training Predict Prolonged Return to Training after SARS-CoV-2 in Athletes: AWARE IV

PURPOSE

This study aimed to determine factors predictive of prolonged return to training (RTT) in athletes with recent SARS-CoV-2 infection.

METHODS

This is a cross-sectional descriptive study. Athletes not vaccinated against COVID-19 ( n = 207) with confirmed SARS-CoV-2 infection (predominantly ancestral virus and beta-variant) completed an online survey detailing the following factors: demographics (age and sex), level of sport participation, type of sport, comorbidity history and preinfection training (training hours 7 d preinfection), SARS-CoV-2 symptoms (26 in 3 categories; "nose and throat," "chest and neck," and "whole body"), and days to RTT. Main outcomes were hazard ratios (HR, 95% confidence interval) for athletes with versus without a factor, explored in univariate and multiple models. HR < 1 was predictive of prolonged RTT (reduced % chance of RTT after symptom onset). Significance was P < 0.05.

RESULTS

Age, level of sport participation, type of sport, and history of comorbidities were not predictors of prolonged RTT. Significant predictors of prolonged RTT (univariate model) were as follows (HR, 95% confidence interval): female (0.6, 0.4-0.9; P = 0.01), reduced training in the 7 d preinfection (1.03, 1.01-1.06; P = 0.003), presence of symptoms by anatomical region (any "chest and neck" [0.6, 0.4-0.8; P = 0.004] and any "whole body" [0.6, 0.4-0.9; P = 0.025]), and several specific symptoms. Multiple models show that the greater number of symptoms in each anatomical region (adjusted for training hours in the 7 d preinfection) was associated with prolonged RTT ( P < 0.05).

CONCLUSIONS

Reduced preinfection training hours and the number of acute infection symptoms may predict prolonged RTT in athletes with recent SARS-CoV-2. These data can assist physicians as well as athletes/coaches in planning and guiding RTT. Future studies can explore whether these variables can be used to predict time to return to full performance and classify severity of acute respiratory infection in athletes.

Using knowledge of, attitude toward, and daily preventive practices for COVID-19 to predict the level of post-traumatic stress and vaccine acceptance among adults in Hong Kong

Introduction

COVID-19 has been perceived as an event triggering a new type of post-traumatic stress (PTSD) that can live during and after the pandemic itself. However, it remains unclear whether such PTSD is partly related to people's knowledge of, attitude toward and daily behavioral practices (KAP) for COVID-19.

Methods

Through a telephone survey, we collected responses from 3,011 adult Hong Kong residents. Then using the Catboost machine learning method, we examined whether KAP predicted the participant's PTSD level, vaccine acceptance and participation in voluntary testing.

Results

Results suggested that having good preventative practices for, poor knowledge of, and negative attitude toward COVID-19 were associated with greater susceptibility to PTSD. Having a positive attitude and good compliance with preventative practices significantly predicted willingness to get vaccinated and participate in voluntary testing. Good knowledge of COVID-19 predicted engagement in testing but showed little association with vaccine acceptance.

Discussion

To maintain good mental health and ongoing vaccine acceptance, it is important to foster people's sense of trust and belief in health professionals' and government's ability to control COVID-19, in addition to strengthening people's knowledge of and compliance with preventative measures.

Associations between Depression and Self-Reported COVID-19 Symptoms among Adults: Results from Two Population-Based Seroprevalence Studies in Switzerland

(1) Mental health may modulate the perceived risk of SARS-CoV-2 infection. However, it is unclear how psychological symptoms may distort symptom perception of COVID-19 and SARS-CoV-2 infection. We assessed whether depressive symptoms predicted self-reported COVID-19 symptoms, independently of serologically confirmed SARS-CoV-2 infection. (2) Participants (aged 20-64) in the Geneva (N = 576) and Ticino (N = 581) Swiss regions completed the Patient Health Questionnaire before being tested for anti-SARS-CoV-2 IgG antibodies and recalled COVID-19-compatible symptoms on two occasions: April-July 2020 (baseline), and January-February 2021 (follow-up). We estimated prevalence ratios for COVID-19 symptoms by depression scores in interaction with serological status. (3) At baseline, in Geneva, higher depression predicted higher probability of reporting systemic, upper airways, and gastro-intestinal symptoms, and fever and/or cough; in Ticino, higher depression predicted systemic, upper airways, and gastro-intestinal symptoms, fever and/or cough, dyspnea, and headache. At follow-up, in Geneva, higher depression predicted higher probability of reporting systemic symptoms and dyspnea; in Ticino, higher depression predicted higher probability of reporting systemic and upper airways symptoms, dyspnea and headache (all p values < 0.05). (4) We found positive associations between depressive symptoms and COVID-19-compatible symptoms, independently of seropositivity. Mental wellbeing has relevant public health implications because it modulates self-reported infection symptoms that inform testing, self-medication, and containment measures, including quarantine and isolation.

MRI Assessment of Cerebral Blood Flow in Nonhospitalized Adults Who Self-Isolated Due to COVID-19

BACKGROUND

Neurological symptoms associated with coronavirus disease 2019 (COVID-19), such as fatigue and smell/taste changes, persist beyond infection. However, little is known of brain physiology in the post-COVID-19 timeframe.

PURPOSE

To determine whether adults who experienced flu-like symptoms due to COVID-19 would exhibit cerebral blood flow (CBF) alterations in the weeks/months beyond infection, relative to controls who experienced flu-like symptoms but tested negative for COVID-19.

STUDY TYPE

Prospective observational.

POPULATION

A total of 39 adults who previously self-isolated at home due to COVID-19 (41.9 ± 12.6 years of age, 59% female, 116.5 ± 62.2 days since positive diagnosis) and 11 controls who experienced flu-like symptoms but had a negative COVID-19 diagnosis (41.5 ± 13.4 years of age, 55% female, 112.1 ± 59.5 since negative diagnosis).

FIELD STRENGTH AND SEQUENCES

A 3.0 T; T1-weighted magnetization-prepared rapid gradient and echo-planar turbo gradient-spin echo arterial spin labeling sequences.

ASSESSMENT

Arterial spin labeling was used to estimate CBF. A self-reported questionnaire assessed symptoms, including ongoing fatigue. CBF was compared between COVID-19 and control groups and between those with (n = 11) and without self-reported ongoing fatigue (n = 28) within the COVID-19 group.

STATISTICAL TESTS

Between-group and within-group comparisons of CBF were performed in a voxel-wise manner, controlling for age and sex, at a family-wise error rate of 0.05.

RESULTS

Relative to controls, the COVID-19 group exhibited significantly decreased CBF in subcortical regions including the thalamus, orbitofrontal cortex, and basal ganglia (maximum cluster size = 6012 voxels and maximum t-statistic = 5.21). Within the COVID-19 group, significant CBF differences in occipital and parietal regions were observed between those with and without self-reported on-going fatigue.

DATA CONCLUSION

These cross-sectional data revealed regional CBF decreases in the COVID-19 group, suggesting the relevance of brain physiology in the post-COVID-19 timeframe. This research may help elucidate the heterogeneous symptoms of the post-COVID-19 condition.

EVIDENCE LEVEL

2.

TECHNICAL EFFICACY

Stage 3.

Assessing Associations Between COVID-19 Symptomology and Adverse Outcomes After Piloting Crowdsourced Data Collection: Cross-sectional Survey Study

BACKGROUND

Crowdsourcing is a useful way to rapidly collect information on COVID-19 symptoms. However, there are potential biases and data quality issues given the population that chooses to participate in crowdsourcing activities and the common strategies used to screen participants based on their previous experience.

OBJECTIVE

The study aimed to (1) build a pipeline to enable data quality and population representation checks in a pilot setting prior to deploying a final survey to a crowdsourcing platform, (2) assess COVID-19 symptomology among survey respondents who report a previous positive COVID-19 result, and (3) assess associations of symptomology groups and underlying chronic conditions with adverse outcomes due to COVID-19.

METHODS

We developed a web-based survey and hosted it on the Amazon Mechanical Turk (MTurk) crowdsourcing platform. We conducted a pilot study from August 5, 2020, to August 14, 2020, to refine the filtering criteria according to our needs before finalizing the pipeline. The final survey was posted from late August to December 31, 2020. Hierarchical cluster analyses were performed to identify COVID-19 symptomology groups, and logistic regression analyses were performed for hospitalization and mechanical ventilation outcomes. Finally, we performed a validation of study outcomes by comparing our findings to those reported in previous systematic reviews.

RESULTS

The crowdsourcing pipeline facilitated piloting our survey study and revising the filtering criteria to target specific MTurk experience levels and to include a second attention check. We collected data from 1254 COVID-19-positive survey participants and identified the following 6 symptomology groups: abdominal and bladder pain (Group 1); flu-like symptoms (loss of smell/taste/appetite; Group 2); hoarseness and sputum production (Group 3); joint aches and stomach cramps (Group 4); eye or skin dryness and vomiting (Group 5); and no symptoms (Group 6). The risk factors for adverse COVID-19 outcomes differed for different symptomology groups. The only risk factor that remained significant across 4 symptomology groups was influenza vaccine in the previous year (Group 1: odds ratio [OR] 6.22, 95% CI 2.32-17.92; Group 2: OR 2.35, 95% CI 1.74-3.18; Group 3: OR 3.7, 95% CI 1.32-10.98; Group 4: OR 4.44, 95% CI 1.53-14.49). Our findings regarding the symptoms of abdominal pain, cough, fever, fatigue, shortness of breath, and vomiting as risk factors for COVID-19 adverse outcomes were concordant with the findings of other researchers. Some high-risk symptoms found in our study, including bladder pain, dry eyes or skin, and loss of appetite, were reported less frequently by other researchers and were not considered previously in relation to COVID-19 adverse outcomes.

CONCLUSIONS

We demonstrated that a crowdsourced approach was effective for collecting data to assess symptomology associated with COVID-19. Such a strategy may facilitate efficient assessments in a dynamic intersection between emerging infectious diseases, and societal and environmental changes.

A machine learning study of COVID-19 serology and molecular tests and predictions

Serology and molecular tests are the two most commonly used methods for rapid COVID-19 infection testing. The two types of tests have different mechanisms to detect infection, by measuring the presence of viral SARS-CoV-2 RNA (molecular test) or detecting the presence of antibodies triggered by the SARS-CoV-2 virus (serology test). A handful of studies have shown that symptoms, combined with demographic and/or diagnosis features, can be helpful for the prediction of COVID-19 test outcomes. However, due to nature of the test, serology and molecular tests vary significantly. There is no existing study on the correlation between serology and molecular tests, and what type of symptoms are the key factors indicating the COVID-19 positive tests. In this study, we propose a machine learning based approach to study serology and molecular tests, and use features to predict test outcomes. A total of 2,467 donors, each tested using one or multiple types of COVID-19 tests, are collected as our testbed. By cross checking test types and results, we study correlation between serology and molecular tests. For test outcome prediction, we label 2,467 donors as positive or negative, by using their serology or molecular test results, and create symptom features to represent each donor for learning. Because COVID-19 produces a wide range of symptoms and the data collection process is essentially error prone, we group similar symptoms into bins. This decreases the feature space and sparsity. Using binned symptoms, combined with demographic features, we train five classification algorithms to predict COVID-19 test results. Experiments show that XGBoost achieves the best performance with 76.85% accuracy and 81.4% AUC scores, demonstrating that symptoms are indeed helpful for predicting COVID-19 test outcomes. Our study investigates the relationship between serology and molecular tests, identifies meaningful symptom features associated with COVID-19 infection, and also provides a way for rapid screening and cost effective detection of COVID-19 infection.

Aplicación de la calculadora de probabilidad fenotípica FEN-COVID en pacientes hospitalizados por COVID-19 en una población latinoamericana

Introducción: la variabilidad del comportamiento clínico del COVID-19 puede ser uno de los determinantes que limitan la toma de decisiones terapéuticas. Se busca clasificar a pacientes latinoamericanos hospitalizados mediante la herramienta FEN-COVID para la identificación de fenotipos clínicos y determinar su asociación con mortalidad e ingreso a la unidad de cuidado intensivo (UCI). Métodos: estudio observacional de cohorte retrospectivo, que incluyó adultos hospitalizados en dos centros de tercer nivel de atención con COVID-19 confirmado entre septiembre 2020 y marzo 2021. A cada paciente seleccionado se asignó un fenotipo aplicando la calculadora FEN-COVID. Se llevó a cabo un análisis multivariado para documentar las asociaciones entre el fenotipo, las complicaciones hospitalarias y los desenlaces clínicos. Resultados: se identificaron 126 pacientes hospitalizados por COVID-19, edad promedio de 58 años, 45 mujeres (35.7%), 23% diabéticos, 45% hipertensos y 20% obesos. 108 (85.7%) fueron del fenotipo B y 18 (14.2%) fenotipo C. Aunque en este último los desenlaces fueron peores (requerimiento de UCI 77.8% vs 45.4% y mortalidad 66% vs 22%, OR 1.408, IC95% 3.191-5.243, p <0.007), esta asociación no se mantuvo en el análisis multivariado con OR 1.110 (IC95% 0.780 - 1.581, p de 0.555) Conclusión: los fenotipos identificados a partir de FEN-COVID parecen discriminar un subgrupo de pacientes que ostenta el peor comportamiento clínico, aunque no tuvo representación del fenotipo más leve. El análisis bivariado documentó asociación con la muerte o ingreso a UCI que no se mantuvo en el modelo multivariado.

Post acute CoViD-19 syndrome (PACS) - Long CoViD

Patients sero-positive for the Systemic Acute Respiratory Syndrome Corona virus2 (SARS-CoV2) virus develop the Corona Virus Disease 2019 (CoViD-19). CoViD-19 may be asymptomatic in some individuals, proffer mild symptoms in other patients, and can be a serious and even lethal disease in a sub-group of the population. The variables that determine the severity of CoViD-19 have not been fully characterized. What is clear is that the patients who survive CoViD-19 return to a state of sero-negativity for SARS-CoV2 generally within 3-5 weeks. However, several cases of repeated infection have been reported, and a large proportion of CoViD-19-recovered patients manifest multi-system and multi-organ symptomatic pathologies several weeks-to-months after resuming sero-negativity for SARS-CoV2. This new pathological condition, originally termed Long Covid, is now recognized as the Post Acute CoViD-19 Syndrome (PACS). The original principal clusters of signs and symptoms of PACS: likelihood of relapse and reinfection, physical fatigue and cognitive slowdown, may actually be broadened to include immune deregulation, cardiovascular disease and coagulation abnormalities. The development and evaluation of new and improved clinical interventions for PACS are critical and timely.

A regression discontinuity analysis of the social distancing recommendations for older adults in Sweden during COVID-19

BACKGROUND

This article investigates the impact of a non-mandatory and age-specific social distancing recommendation on isolation behaviours and disease outcomes in Sweden during the first wave of the coronavirus disease 2019 (COVID-19) pandemic (March to July 2020). The policy stated that people aged 70 years or older should avoid crowded places and contact with people outside the household.

METHODS

We used a regression discontinuity design-in combination with self-reported isolation data from COVID Symptom Study Sweden (n = 96 053; age range: 39-79 years) and national register data (age range: 39-100+ years) on severe COVID-19 disease (hospitalization or death, n = 21 804) and confirmed cases (n = 48 984)-to estimate the effects of the policy.

RESULTS

Our primary analyses showed a sharp drop in the weekly number of visits to crowded places (-13%) and severe COVID-19 cases (-16%) at the 70-year threshold. These results imply that the age-specific recommendations prevented approximately 1800-2700 severe COVID-19 cases, depending on model specification.

CONCLUSIONS

It seems that the non-mandatory, age-specific recommendations helped control COVID-19 disease during the first wave of the pandemic in Sweden, as opposed to not implementing a social distancing policy aimed at older adults. Our study provides empirical data on how populations may react to non-mandatory, age-specific social distancing policies in the face of a novel virus.

Clinical Profiles at the Time of Diagnosis of SARS-CoV-2 Infection in Costa Rica During the Pre-vaccination Period Using a Machine Learning Approach

The clinical manifestations of COVID-19, caused by the SARS-CoV-2, define a large spectrum of symptoms that are mainly dependent on the human host conditions. In Costa Rica, more than 169,000 cases and 2185 deaths were reported during the year 2020, the pre-vaccination period. To describe the clinical presentations at the time of diagnosis of SARS-CoV-2 infection in Costa Rica during the pre-vaccination period, we implemented a symptom-based clustering using machine learning to identify clusters or clinical profiles at the population level among 18,974 records of positive cases. Profiles were compared based on symptoms, risk factors, viral load, and genomic features of the SARS-CoV-2 sequence. A total of 18 symptoms at time of diagnosis of SARS-CoV-2 infection were reported with a frequency > 1%, and those were used to identify seven clinical profiles with a specific composition of clinical manifestations. In the comparison between clusters, a lower viral load was found for the asymptomatic group, while the risk factors and the SARS-CoV-2 genomic features were distributed among all the clusters. No other distribution patterns were found for age, sex, vital status, and hospitalization. In conclusion, during the pre-vaccination time in Costa Rica, the symptoms at the time of diagnosis of SARS-CoV-2 infection were described in clinical profiles. The host co-morbidities and the SARS-CoV-2 genotypes are not specific of a particular profile, rather they are present in all the groups, including asymptomatic cases. In addition, this information can be used for decision-making by the local healthcare institutions (first point of contact with health professionals, case definition, or infrastructure). In further analyses, these results will be compared against the profiles of cases during the vaccination period.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s43657-022-00058-x.

Patient Self-Report Superior to Electronic Medical Record Abstraction for Identifying Positive COVID-19 Symptoms at Illness Onset

Introduction

Most initial COVID-19 research focused on hospitalized patients. Presenting symptomatology in the outpatient setting was poorly characterized, making it difficult for primary care physicians to predict which patients would require hospitalization. The purpose of this study was to characterize the presenting symptoms of COVID-19 infection and baseline patient characteristics and evaluate for correlation with disease severity, duration, and chronicity in the outpatient setting.

Methods

A total of 107 adult, English-speaking patients with suspected and confirmed COVID-19 cases at the 3 primary care practices of Stony Brook University Hospital were studied between March and December 2020. Survey data were collected from patient telephone interviews and electronic medical record abstraction. The potential risk factors assessed included participant demographics, medical comorbidities, and the number and type of symptoms at illness onset. Outcome measures included symptom duration, hospitalizations, and persistence of symptoms at 12 weeks from study enrollment.

Results

Patient self-report survey elicited nearly twice as many symptoms described at illness onset as those recorded in the electronic medical record (p<0.0001). A higher number of symptoms at illness onset was positively associated with symptom duration and chronicity. The presence of fever and hypoxia at the onset of illness were each positively associated with eventual hospitalization for COVID-19 disease.

Conclusions

Early in the setting of newly emerging infectious diseases, particularly those such as COVID-19 that involve multiple organ systems, patient self-report of symptoms using a complete review of systems rather than electronic medical record abstraction alone may be key for accurate disease identification and characterization as well as prediction of eventual disease severity, duration, and chronicity.

Associations of symptom combinations with in-hospital mortality of coronavirus disease-2019 patients using South Korean National data

BACKGROUND

There are various risk factors for death in coronavirus disease-2019 (COVID-19) patients. The effects of symptoms on death have been investigated, but symptoms were considered individually, rather than in combination, as predictors. We examined the effects of symptom combinations on in-hospital mortality.

METHODS

Data from the Korea Disease Control and Prevention Agency were analyzed. A cohort of 5,153 patients confirmed with COVID-19 in South Korea was followed from hospitalization to death or discharge. An exploratory factor analysis was performed to identify symptom combinations, and the hazard ratios (HRs) of death were estimated using the Cox proportional hazard model.

RESULTS

Three sets of symptom factors were isolated for symptom combination. Factor 1 symptoms were cold-like symptoms, factor 2 were neurological and gastrointestinal symptoms, and factor 3 were more severe symptoms such as dyspnea and altered state of consciousness. Factor 1 (HR 1.14, 95% confidence interval [95% CI] 1.01-1.30) and factor 3 (HR 1.25, 95% CI 1.19-1.31) were associated with a higher risk for death, and factor 2 with a lower risk (HR 0.71, 95% CI 0.71-0.96).

CONCLUSIONS

The effect on in-hospital mortality differed according to symptom combination. The results are evidence of the effects of symptoms on COVID-19 mortality and may contribute to lowering the COVID-19 mortality rate. Further study is needed to identify the biological mechanisms underlying the effects of symptom combinations on mortality.

Calculating incidence of Influenza-like and COVID-like symptoms from Flutracking participatory survey data

This article describes a new method for estimating weekly incidence (new onset) of symptoms consistent with Influenza and COVID-19, using data from the Flutracking survey. The method mitigates some of the known self-selection and symptom-reporting biases present in existing approaches to this type of participatory longitudinal survey data. The key novel steps in the analysis are: 1) Identifying new onset of symptoms for three different Symptom Groupings: COVID-like illness (CLI1+, CLI2+), and Influenza-like illness (ILI), for responses reported in the Flutracking survey. 2) Adjusting for symptom reporting bias by restricting the analysis to a sub-set of responses from those participants who have consistently responded for a number of weeks prior to the analysis week. 3) Weighting responses by age to adjust for self-selection bias in order to account for the under- and over-representation of different age groups amongst the survey participants. This uses the survey package [22] in R [30]. 4) Constructing 95% point-wise confidence bands for incidence estimates using weighted logistic regression from the survey package [21] in R [28]. In addition to describing these steps, the article demonstrates an application of this method to Flutracking data for the 12 months from 27th April 2020 until 25th April 2021.

Resistance training and clinical status in patients with postdischarge symptoms after COVID-19: protocol for a randomized controlled crossover trial "The EXER-COVID Crossover Study"

BACKGROUND

Physical exercise induces a coordinated response of multiple organ systems, including the immune system. In fact, it has been proposed that physical exercise may modulate the immune system. However, the potential effect of an exercise program on COVID-19 survivors has not been investigated. Thus, the aim of this study is to evaluate the modifications in immunological parameters, physical condition, inflammatory profile, and perceived persistent symptoms after 6 weeks of supervised resistance training (RT), in addition to the standard care on the clinical status of patients with persistent COVID-19 symptoms. The objective of this protocol is to describe the scientific rationale in detail and to provide information about the study procedures.

METHODS/DESIGN

A total of 100 patients with postdischarge symptoms after COVID-19 will be randomly allocated into either a group receiving standard care (control group) or a group performing a multicomponent exercise program two times a week over a period of 6 weeks. The main hypothesis is that a 6-week multicomponent exercise program (EXER-COVID Crossover Study) will improve the immunological and inflammatory profile, physical condition, and persistent perceived symptoms (fatigue/tiredness, musculoskeletal pain, and shortness of breath) in patients with postdischarge symptoms after COVID-19.

DISCUSSION

Our results will provide insights into the effects of a multicomponent exercise program on immunological parameters, physical condition, inflammatory profile, and persistent perceived symptoms in patients with postdischarge symptoms after COVID-19. Information obtained by this study will inform future guidelines on the exercise training rehabilitation of patients with postdischarge symptoms after COVID-19.

TRIAL REGISTRATION

NCT04797871 , Version 2. Registered on March 15, 2021.

Effects of vaccination, new SARS-CoV-2 variants and reinfections on post-COVID-19 complications

Post-COVID-19 complications involve a variety of long-lasting health complications emerging in various body systems. Since the prevalence of post-COVID-19 complications ranges from 8-47% in COVID-19 survivors, it represents a formidable challenge to COVID-19 survivors and the health care system. Post-COVID-19 complications have already been studied in the connection to risk factors linked to their higher probability of occurrence and higher severity, potential mechanisms underlying the pathogenesis of post-COVID-19 complications, and their functional and structural correlates. Vaccination status has been recently revealed to represent efficient prevention from long-term and severe post-COVID-19 complications. However, the exact mechanisms responsible for vaccine-induced protection against severe and long-lasting post-COVID-19 complications remain elusive. Also, to the best of our knowledge, the effects of new SARS-CoV-2 variants and SARS-CoV-2 reinfections on post-COVID-19 complications and their underlying pathogenesis remain to be investigated. This hypothesis article will be dedicated to the potential effects of vaccination status, SARS-CoV-2 reinfections, and new SARS-CoV-2 variants on post-COVID-19 complications and their underlying mechanisms Also, potential prevention strategies against post-COVID complications will be discussed.

The clinical effectiveness and cost-effectiveness of a 'stepping into day treatment' approach versus inpatient treatment as usual for anorexia nervosa in adult specialist eating disorder services (DAISIES trial): a study protocol of a randomised controlled multi-centre open-label parallel group non-inferiority trial

Background

Anorexia nervosa (AN) is a serious and disabling mental disorder with a high disease burden. In a proportion of cases, intensive hospital-based treatments, i.e. inpatient or day patient treatment, are required, with day patient treatment often being used as a ‘step-down’ treatment after a period of inpatient treatment. Demand for such treatment approaches has seen a sharp rise. Despite this, the relative merits of these approaches for patients, their families, and the NHS and wider society are relatively unknown. This paper describes the rationale for, and protocol of, a two-arm multi-centre open-label parallel group non-inferiority randomised controlled trial, evaluating the effectiveness and cost-effectiveness of these two intensive treatments for adults with severe AN: inpatient treatment as usual and a stepped care day patient approach (the combination of day patient treatment with the option of initial inpatient treatment for medical stabilisation). The main aim of this trial is to establish whether, in adults with severe AN, a stepped care day patient approach is non-inferior to inpatient treatment as usual in relation to improving body mass index (BMI) at 12 months post-randomisation.

Methods

386 patients with a Diagnostic and Statistical Manual 5th edition diagnosis of severe AN or related disorder, with a BMI of ≤16 kg/m² and in need of intensive treatment will be randomly allocated to either inpatient treatment as usual or a stepped care day patient approach. Patients in both groups will receive treatment until they reach a healthy weight or get as close to this point as possible. Assessments will be conducted at baseline (prior to randomisation), and at 6 and 12 months post-randomisation, with additional monthly symptom monitoring. The primary outcome will be BMI at the 12-month post-randomisation assessment. Other outcomes will include psychosocial adjustment; treatment motivation, expectations and experiences; cost-effectiveness; and carer burden.

Discussion

The results of this study will provide a rigorous evaluation of two intensive treatment approaches which will inform future national and international treatment guidelines and service provision.

Trial registration

ISRCTN ISRCTN10166784. Registered 28 February 2020. ISRCTN is a primary registry of the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) network and includes all items from the WHO Trial Registration Data Set.

Disease profile and patient outcomes in vaccinated COVID-19 patients at a tertiary care Indian hospital: An observational, real-world study

Background

There is a lack of real-world evidence evaluating the disease outcomes and patient features in vaccinated coronavirus disease (COVID-19) cases. This study aimed to address this scientific need gap and also compare characteristics between the partially vaccinated and fully vaccinated COVID-19 patients in India.

Methods

This observational cross-sectional study included data of adult patients diagnosed with COVID-19 at a tertiary care Indian hospital with a history of at least single-dose COVID-19 vaccination. Overall evaluation of patient features and disease characteristics was done. Patients were segregated into two groups based on vaccination status (partial or fully vaccinated), and characteristics were compared between these two groups along with COVID-19 outcomes.

Results

Data of 403 vaccinated patients treated for breakthrough COVID-19 infection postvaccination was evaluated. The mean age was 47.7 ± 15.3 years (range: 19-87 years), with the majority being males (73.94%); 54.1% of evaluated cases were fully vaccinated; 74.93% of cases were asymptomatic. The majority of the symptomatic cases (60.39%) suffered from only mild-moderate symptoms; 72.7% of cases needed only home isolation, while only 1.99% died. A significantly higher number of partially vaccinated COVID-19 patients had severe COVID-19 pneumonia vs. fully vaccinated ones (14.59% vs. 5.96%, p < 0.05). The relative risk (RR) for the development of severe COVID-19 infection was 0.32 for the fully vaccinated subgroup, which was a significant finding (CI: 0.19-0.55, p < 0.05).

Conclusion

The majority of vaccinated COVID-19 patients are asymptomatic or suffer from mild clinical features, which can be managed with home isolation. Fully vaccinated patients have a lower risk of developing severe COVID-19 infection in comparison to partially vaccinated cases.

Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort

The use of routine laboratory biomarkers plays a key role in decision making in the clinical practice of COVID-19, allowing the development of clinical screening tools for personalized treatments. This study performed a short-term longitudinal cluster from patients with COVID-19 based on biochemical measurements for the first 72 h after hospitalization. Clinical and biochemical variables from 1039 confirmed COVID-19 patients framed on the “COVID Data Save Lives” were grouped in 24-h blocks to perform a longitudinal k-means clustering algorithm to the trajectories. The final solution of the three clusters showed a strong association with different clinical severity outcomes (OR for death: Cluster A reference, Cluster B 12.83 CI: 6.11−30.54, and Cluster C 14.29 CI: 6.66−34.43; OR for ventilation: Cluster-B 2.22 CI: 1.64−3.01, and Cluster-C 1.71 CI: 1.08−2.76), improving the AUC of the models in terms of age, sex, oxygen concentration, and the Charlson Comorbidities Index (0.810 vs. 0.871 with p < 0.001 and 0.749 vs. 0.807 with p < 0.001, respectively). Patient diagnoses and prognoses remarkably diverged between the three clusters obtained, evidencing that data-driven technologies devised for the screening, analysis, prediction, and tracking of patients play a key role in the application of individualized management of the COVID-19 pandemics.

COVID-19 disease in professional football players: symptoms and impact on pulmonary function and metabolic power during matches

This study aimed at: (1) Reporting COVID-19 symptoms and duration in professional football players; (2) comparing players' pulmonary function before and after COVID-19; (3) comparing players' metabolic power (P_met ) before and after COVID-19. Thirteen male players (Age: 23.9 ± 4.0 years, V̇O_2peak : 49.7 ± 4.0 mL/kg/min) underwent a medical screening and performed a running incremental step test and a spirometry test after COVID-19. Spirometric data were compared with the ones collected at the beginning of the same season. Players' mean P_met of the 10 matches played before COVID-19 was compared with mean P_met of the 10 matches played after COVID-19. Players completed a questionnaire on COVID-19 symptoms and duration 6 months following the disease. COVID-19 positivity lasted on average 15 ± 5 days. "General fatigue" and "muscle fatigue" symptoms were reported by all players during COVID-19 and persisted for 77% (general fatigue) and 54% (muscle fatigue) of the players for 37 ± 28 and 38 ± 29 days after the disease, respectively. No significant changes in spirometric measurements were found after COVID-19, even though some impairments at the individual level were observed. Conversely, a linear mixed-effects model analysis showed a significant reduction of P_met (-4.1 ± 3.5%) following COVID-19 (t = -2.686, p < 0.05). "General fatigue" and "muscle fatigue" symptoms may persist for several weeks following COVID-19 in professional football players and should be considered for a safer return to sport. Players' capacity to compete at high intensities might be compromised after COVID-19.

Symptom-based clusters of hospitalized patients with severe acute respiratory illness by SARS-CoV-2 in Brazil

BACKGROUND

COVID-19 has shown a broad clinical spectrum, ranging from asymptomatic to mild, moderate, and severe infections. Many symptoms have already been identified as typical of COVID-19, but few studies show how they can be useful in identifying clusters of patients with different severity of illness. This interpretation may help to recognize the different profiles of symptoms of COVID-19 expressed in a population at certain time. The aim of this study was to identify symptom-based clusters of hospitalized patients with severe acute respiratory illness by SARS-CoV-2 in Brazil. The clusters were evaluated based on sociodemographic characteristics, admission to the Intensive Care Unit (ICU), use of respiratory support, and outcome.

METHODS

The Multiple Correspondence Analysis (MCA)-based cluster analysis was applied to symptoms presented before admission. Pearson's chi-square test was used to compare the proportions of symptoms between the clusters and to examine differences in the calculated rates for the following variables: sex, age group, race, Brazilian region, use of respiratory support, admission to the ICU and outcome.

RESULTS

Three COVID-19 clusters with distinct symptom profiles were identified by MCA-based cluster analysis. Cluster 1 had the mildest severity profile, with the lowest frequencies for most symptoms investigated. Cluster 2 had a severe respiratory profile, with the highest frequencies of patients with dyspnea, respiratory discomfort and O2 saturation< 95%. Cluster 2 was also the most prevalent in all Brazilian regions and had the highest percentages of patients who used invasive respiratory support (27.4%) (p-value<0.001), were admitted to the ICU (42.6%) (p -value<0.001) and died (39.0%) (p-value<0.001). Cluster 3 had a prominent profile of gastrointestinal symptoms.

CONCLUSIONS

The study identified three distinct COVID-19 clusters based on the symptoms presented by patients with severe acute respiratory illness by SARS-CoV-2, but without distinction in their prevalence in the Brazilian regions.

COVID-19 Vaccination Status Among Healthcare Workers and Its Effect on Disease Manifestations: A Study From Northeast India

Background and objective

Since being declared a global pandemic, coronavirus disease 2019 (COVID-19) has led to millions of cases and deaths worldwide. Although severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to wreak havoc on individuals, healthcare systems, and economies, the intensive vaccination strategies adopted by several countries have significantly slowed the progress and the severity of the disease. In this study, we aimed to determine the COVID-19 vaccination status among healthcare workers (HCWs) and examine the effects of vaccination on disease manifestations.

Materials and methods

This cross-sectional study was conducted at a teaching hospital in Northeast India from April 2021 to September 2021, during the second phase of the COVID-19 pandemic. HCWs employed in the hospital who were laboratory-confirmed cases of COVID-19 based on semiquantitative real-time reverse transcriptase-polymerase chain reaction (RT-PCR) or cartridge-based nucleic acid amplification test (CBNAAT) on oropharyngeal samples were included in the study. Data analysis was performed using Microsoft Excel (Microsoft Office Professional Plus 2019, Microsoft Corp., Redmond, WA)

Results

A total of 178 HCWs reported positive for COVID-19 infection during the study period. Of these, 42 (23.59%) were males and 136 were females (76.40%). Among them, 86 (48.32%) HCWs were fully vaccinated, 58 (32.58%) were partially vaccinated, and 34 (19.10%) were not vaccinated. Most of the HCWs experienced mild disease (145, 81.46%), and only four (2.24%) reported moderate to severe disease. Compared with unvaccinated HCWs, individuals who have had either one or two doses of vaccines were less likely to have moderate to severe disease or seek treatment at the hospital. On symptoms analysis, shortness of breath was found to be more common in unvaccinated individuals than in vaccinated patients, and anosmia and loss of taste were more common in vaccinated than in unvaccinated individuals. No deaths were reported among the participants included in this study.

Conclusions

Following the first and second waves of the COVID-19 pandemic, a substantial proportion of HCWs were infected with SARS-CoV-2, likely as a result of the acquisition of the virus in the community during the early phase of local spread. Fully vaccinated individuals with COVID-19 were more likely to be completely asymptomatic or only mildly symptomatic compared to unvaccinated HCWs.

The Multifaceted Manifestations of Multisystem Inflammatory Syndrome during the SARS-CoV-2 Pandemic

The novel coronavirus SARS-CoV-2, which has similarities to the 2002–2003 severe acute respiratory syndrome coronavirus known as SARS-CoV-1, causes the infectious disease designated COVID-19 by the World Health Organization (Coronavirus Disease 2019). Although the first reports indicated that activity of the virus is centered in the lungs, it was soon acknowledged that SARS-CoV-2 causes a multisystem disease. Indeed, this new pathogen causes a variety of syndromes, including asymptomatic disease; mild disease; moderate disease; a severe form that requires hospitalization, intensive care, and mechanical ventilation; multisystem inflammatory disease; and a condition called long COVID or postacute sequelae of SARS-CoV-2 infection. Some of these syndromes resemble previously described disorders, including those with no confirmed etiology, such as Kawasaki disease. After recognition of a distinct multisystem inflammatory syndrome in children, followed by a similar syndrome in adults, various multisystem syndromes occurring during the pandemic associated or related to SARS-CoV-2 began to be identified. A typical pattern of cytokine and chemokine dysregulation occurs in these complex syndromes; however, the disorders have distinct immunological determinants that may help to differentiate them. This review discusses the origins of the different trajectories of the inflammatory syndromes related to SARS-CoV-2 infection.

Distinct clinical symptom patterns in patients hospitalised with COVID-19 in an analysis of 59,011 patients in the ISARIC-4C study

COVID-19 is clinically characterised by fever, cough, and dyspnoea. Symptoms affecting other organ systems have been reported. However, it is the clinical associations of different patterns of symptoms which influence diagnostic and therapeutic decision-making. In this study, we applied clustering techniques to a large prospective cohort of hospitalised patients with COVID-19 to identify clinically meaningful sub-phenotypes. We obtained structured clinical data on 59,011 patients in the UK (the ISARIC Coronavirus Clinical Characterisation Consortium, 4C) and used a principled, unsupervised clustering approach to partition the first 25,477 cases according to symptoms reported at recruitment. We validated our findings in a second group of 33,534 cases recruited to ISARIC-4C, and in 4,445 cases recruited to a separate study of community cases. Unsupervised clustering identified distinct sub-phenotypes. First, a core symptom set of fever, cough, and dyspnoea, which co-occurred with additional symptoms in three further patterns: fatigue and confusion, diarrhoea and vomiting, or productive cough. Presentations with a single reported symptom of dyspnoea or confusion were also identified, alongside a sub-phenotype of patients reporting few or no symptoms. Patients presenting with gastrointestinal symptoms were more commonly female, had a longer duration of symptoms before presentation, and had lower 30-day mortality. Patients presenting with confusion, with or without core symptoms, were older and had a higher unadjusted mortality. Symptom sub-phenotypes were highly consistent in replication analysis within the ISARIC-4C study. Similar patterns were externally verified in patients from a study of self-reported symptoms of mild disease. The large scale of the ISARIC-4C study enabled robust, granular discovery and replication. Clinical interpretation is necessary to determine which of these observations have practical utility. We propose that four sub-phenotypes are usefully distinct from the core symptom group: gastro-intestinal disease, productive cough, confusion, and pauci-symptomatic presentations. Importantly, each is associated with an in-hospital mortality which differs from that of patients with core symptoms.

Therapies for Long COVID in non-hospitalised individuals: from symptoms, patient-reported outcomes and immunology to targeted therapies (The TLC Study)

INTRODUCTION

Individuals with COVID-19 frequently experience symptoms and impaired quality of life beyond 4-12 weeks, commonly referred to as Long COVID. Whether Long COVID is one or several distinct syndromes is unknown. Establishing the evidence base for appropriate therapies is needed. We aim to evaluate the symptom burden and underlying pathophysiology of Long COVID syndromes in non-hospitalised individuals and evaluate potential therapies.

METHODS AND ANALYSIS

A cohort of 4000 non-hospitalised individuals with a past COVID-19 diagnosis and 1000 matched controls will be selected from anonymised primary care records from the Clinical Practice Research Datalink, and invited by their general practitioners to participate on a digital platform (Atom5). Individuals will report symptoms, quality of life, work capability and patient-reported outcome measures. Data will be collected monthly for 1 year.Statistical clustering methods will be used to identify distinct Long COVID-19 symptom clusters. Individuals from the four most prevalent clusters and two control groups will be invited to participate in the BioWear substudy which will further phenotype Long COVID symptom clusters by measurement of immunological parameters and actigraphy.We will review existing evidence on interventions for postviral syndromes and Long COVID to map and prioritise interventions for each newly characterised Long COVID syndrome. Recommendations will be made using the cumulative evidence in an expert consensus workshop. A virtual supportive intervention will be coproduced with patients and health service providers for future evaluation.Individuals with lived experience of Long COVID will be involved throughout this programme through a patient and public involvement group.

ETHICS AND DISSEMINATION

Ethical approval was obtained from the Solihull Research Ethics Committee, West Midlands (21/WM/0203). Research findings will be presented at international conferences, in peer-reviewed journals, to Long COVID patient support groups and to policymakers.

TRIAL REGISTRATION NUMBER

1567490.

Who Is at Risk of Poor Mental Health Following Coronavirus Disease-19 Outpatient Management?

Background

Coronavirus Disease-19 (COVID-19) convalescents are at risk of developing a de novo mental health disorder or worsening of a pre-existing one. COVID-19 outpatients have been less well characterized than their hospitalized counterparts. The objectives of our study were to identify indicators for poor mental health following COVID-19 outpatient management and to identify high-risk individuals.

Methods

We conducted a binational online survey study with adult non-hospitalized COVID-19 convalescents (Austria/AT: n = 1,157, Italy/IT: n = 893). Primary endpoints were positive screening for depression and anxiety (Patient Health Questionnaire; PHQ-4) and self-perceived overall mental health (OMH) and quality of life (QoL) rated with 4 point Likert scales. Psychosocial stress was surveyed with a modified PHQ stress module. Associations of the mental health and QoL with socio-demographic, COVID-19 course, and recovery variables were assessed by multi-parameter Random Forest and Poisson modeling. Mental health risk subsets were defined by self-organizing maps (SOMs) and hierarchical clustering algorithms. The survey analyses are publicly available (https://im2-ibk.shinyapps.io/mental_health_dashboard/).

Results

Depression and/or anxiety before infection was reported by 4.6% (IT)/6% (AT) of participants. At a median of 79 days (AT)/96 days (IT) post-COVID-19 onset, 12.4% (AT)/19.3% (IT) of subjects were screened positive for anxiety and 17.3% (AT)/23.2% (IT) for depression. Over one-fifth of the respondents rated their OMH (AT: 21.8%, IT: 24.1%) or QoL (AT: 20.3%, IT: 25.9%) as fair or poor. Psychosocial stress, physical performance loss, high numbers of acute and sub-acute COVID-19 complaints, and the presence of acute and sub-acute neurocognitive symptoms (impaired concentration, confusion, and forgetfulness) were the strongest correlates of deteriorating mental health and poor QoL. In clustering analysis, these variables defined subsets with a particularly high propensity of post-COVID-19 mental health impairment and decreased QoL. Pre-existing depression or anxiety (DA) was associated with an increased symptom burden during acute COVID-19 and recovery.

Conclusion

Our study revealed a bidirectional relationship between COVID-19 symptoms and mental health. We put forward specific acute symptoms of the disease as "red flags" of mental health deterioration, which should prompt general practitioners to identify non-hospitalized COVID-19 patients who may benefit from early psychological and psychiatric intervention.

Clinical Trial Registration

[ClinicalTrials.gov], identifier [NCT04661462].

Respiratory muscle sequelae in young university students infected by coronavirus disease 2019: an observational study

BACKGROUND

The infection caused by coronavirus disease 2019 can lead to respiratory sequelae in individuals who have experienced severe or mild symptoms.

METHODS

An observational, cross-sectional study was developed, following the STROBE guidelines. Maximal inspiratory and expiratory mouth pressures were assessed in 50 healthy young students (26 women, 24 men; age 22.20±2.41 years). The inclusion criteria were as follows: aged between 18 and 35 years; control group: not diagnosed with coronavirus disease 2019; and coronavirus disease 2019 group: diagnosed with coronavirus disease 2019, at least 6 months ago. The exclusion criteria were as follows: obese/overweight; infected with coronavirus disease 2019 or coronavirus disease 2019 symptoms in the last 6 months; smokers; and asthmatics.

RESULTS

When comparing with groups, the coronavirus disease 2019 group presented statistically significant lower maximal inspiratory pressure values compared with the control group (88.32±16.62 vs. 101.01±17.42 cm H2O; p=0.01). Regarding the maximal expiratory pressure, no significant differences were found. Similar results were found when performing a subgroup analysis by sex and group.

CONCLUSIONS

Young students who suffered from coronavirus disease 2019 asymptomatically or mildly at least 6 months ago presented a significant decrease in the inspiratory muscle strength as a sequel, so we believe that patients affected by this disease should have a brief postinfection assessment of this musculature to detect the indication for cardiorespiratory rehabilitation.

Clinical Characteristics and Outcomes of COVID-19 Hospitalized Patients: A Comparison between Complete mRNA Vaccination Profile and Natural Immunity

Although laboratory data show that antibody responses to COVID-19 immunization give superior neutralization of certain circulating variations to spontaneous infection, few real-world epidemiological studies demonstrate the advantage of vaccination for previously infected individuals. This paper summarizes the outcomes of a case–control study conducted in Romania between March 2020 and October 2021 on patients previously infected with SARS-CoV-2. A case–control study was implemented after identification of 62 breakthrough cases. These cases were matched by age and gender to a 1:1 ratio with a control group of unvaccinated patients with SARS-CoV-2 reinfection status. There were no significant differences in the severity of cases and mortality between the study groups. However, unvaccinated patients had a shorter protection from natural immunity than patients with full vaccination status (58 days versus 89 days). The unvaccinated cases with SARS-CoV-2 reinfection were also statistically more likely to have a longer hospital admission duration (12.4 days versus 9.8 days), and required more non-invasive oxygen supplementation during their stay than breakthrough cases (37.1% versus 19.4%). Individuals with prior SARS-CoV-2 infection who were not vaccinated are not at a higher risk of severe COVID-19 infection or mortality compared to those who were completely vaccinated with the mRNA vaccine Comirnaty® Pfizer/BioNTech BNT162b2 and acquired a breakthrough infection within 2–3 months of the previous infection with a Beta or Delta SARS-CoV-2 variant. Although our findings are consistent with natural immunity offering similar short-term protection to a second dose of mRNA vaccine, all eligible individuals should be provided with immunization to lower their risk of infection, even if they have already been infected with SARS-CoV-2.

Who is engaging with lateral flow testing for COVID-19 in the UK? The COVID-19 Rapid Survey of Adherence to Interventions and Responses (CORSAIR) study

OBJECTIVES

To investigate uptake of lateral flow testing, reporting of test results and psychological, contextual and socio-demographic factors associated with testing.

DESIGN

A series of four fortnightly online cross-sectional surveys.

SETTING

Data collected from 19 April 2021 to 2 June 2021.

PARTICIPANTS

People living in England and Scotland, aged 18 years or over, excluding those who reported their most recent test was a polymerase chain reaction (PCR) test (n=6646, n≈1600 per survey).

MAIN OUTCOME MEASURES

Having completed at least one lateral flow test (LFT) in the last 7 days.

RESULTS

We used binary logistic regressions to investigate factors associated with having taken at least one LFT. Increased uptake of testing was associated with being vaccinated (adjusted ORs (aORs)=1.52-2.45, 95% CI 1.25 to 3.07, analysed separately by vaccine dose), employed (aOR=1.94, 95% CI 1.63 to 2.32), having been out to work in the last week (aOR=2.30, 95% CI 1.94 to 2.73) and working in a sector that adopted LFT early (aOR=2.54, 95% CI 2.14 to 3.02) . Uptake was higher in people who reported cardinal COVID-19 symptoms in the last week (aOR=1.89, 95% CI 1.34 to 2.66). People who had heard more about LFTs (aOR=2.28, 95% CI 2.06 to 2.51) and knew they were eligible to receive regular LFTs (aOR=2.98, 95% CI 2.35 to 3.78) were also more likely to have tested. Factors associated with not taking a test included agreeing that you do not need to test for COVID-19 unless you have come into contact with a case (aOR=0.51, 95% CI 0.47 to 0.55).

CONCLUSIONS

Uptake of lateral flow testing is low. Encouraging testing through workplaces and places of study is likely to increase uptake, although care should be taken not to pressurise employees and students. Increasing knowledge that everyone is eligible for regular asymptomatic testing and addressing common misconceptions may drive uptake.