Characterizing Long COVID: Deep Phenotype of a Complex Condition

Postacute Sequelae From SARS-CoV-2 at the University of Illinois Hospital and Clinics: An Examination of the Effects of Long COVID in an Underserved Population Utilizing Manual Extraction of Electronic Health Records

Background

Although there has been a steady decrease in morbidity and mortality from the SARS-CoV-2 virus since the 2020-2021 period, thousands of Americans are still infected with the virus daily. Some proportion of these infected individuals will go on to develop postacute sequelae from SARS-CoV-2 (PASC, or Long COVID), manifesting symptoms 4 weeks or more after recovery from COVID-19. PASC and its underlying pathophysiology are still poorly described and understood. Although hundreds of peer-reviewed, published investigations on Long COVID exist, few have focused on underserved urban patient populations. Most of the published research has involved reviews of diagnostic codes from electronic health records, or responses to questionnaires.

Methods

We sought to review Long COVID in an underserved population in Chicago, and to go beyond electronic health record reviews of diagnostic codes, utilizing in-depth chart reviews, gleaned via manual extraction, focusing on notations of care providers. We investigated which specific preexisting conditions, if any, might be associated with specific Long COVID symptomatology's, and if any preexisting conditions predicted Long COVID. Study participants included 204 Long COVID patients, 98 COVID-19-positive patients, and 104 healthy (no history of COVID-19 infection) patients from an inner-city health system caring for underserved communities, whose records were reviewed via manual data extraction from electronic health records, focusing on provider notes in patient charts.

Results

Our Long COVID symptom frequencies were distinct compared to frequencies from other reviews that did not focus on underserved populations and done with medical records when only diagnostic codes are utilized. Preexisting medical conditions did not predict similar Long COVID symptomologies, save for the significant association between preexisting cough/dyspnea/pulmonary conditions and preexisting migraine/headache and their analogous Long COVID symptoms.

Conclusions

The odds of having Long COVID increased comparatively in subjects hospitalized with COVID-19, subjects with BMI >30, and female subjects.

Long COVID Characteristics and Experience: A Descriptive Study From the Yale LISTEN Research Cohort

BACKGROUND

The experience of people with long COVID needs further amplification, especially with a comprehensive focus on symptomatology, treatments, and the impact on daily life and finances. Our intent is to describe the experience of people with long COVID symptomatology and characterize the psychological, social, and financial challenges they experience.

METHODS

We collected data from individuals aged 18 and older reporting long COVID as participants in the Yale Listen to Immune, Symptom and Treatment Experiences Now study. The sample population included 441 participants surveyed between May 2022 and July 2023. We evaluated their demographic characteristics, socioeconomic and psychological status, index infection period, health status, quality of life, symptoms, treatments, prepandemic comorbidities, and new-onset conditions.

RESULTS

Overall, the median age of the participants with long COVID was 46 years (interquartile range [IQR]: 38-57 years); 74% were women, 86% were non-Hispanic White, and 93% were from the United States. Participants reported a low health status measured by the Euro-QoL visual analog scale, with a median score of 49 (IQR: 32-61). Participants documented a diverse range of symptoms, with all 96 possible symptom choices being reported. Additionally, participants had tried many treatments (median number of treatments: 19, IQR: 12-28). They were also experiencing psychological distress, social isolation, and financial stress.

CONCLUSIONS

Despite having tried numerous treatments, participants with long COVID continued to experience an array of health and financial challenges-findings that underscore the failure of the healthcare system to address the medical needs of people with long COVID. These insights highlight the need for crucial medical, mental health, financial, and community support services, as well as further scientific investigation to address the complex impact of long COVID.

Non-Invasive Brain Stimulation for Post-COVID-19 Conditions: A Systematic Review

BACKGROUND

Alongside the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic, the number of patients with persistent symptoms following acute infection with SARS-CoV-2 is of concern. It is estimated that at least 65 million people worldwide meet criteria for what the World Health Organization (WHO) defines as "post-COVID-19 condition" - a multisystem disease comprising a wide range of symptoms. Effective treatments are lacking. In the present review, we aim to summarize the current evidence for the effectiveness of non-invasive or minimally invasive brain stimulation techniques in reducing symptoms of post-COVID-19.

METHODS

After pre-registration with PROSPERO, the review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA). The four electronic databases PubMed/MEDLINE, PsycINFO, Web of Science and Scopus were systematically searched for all relevant studies through April 2nd, 2024. Two independent investigators selected empirical papers that reported on the application of non- or minimally invasive brain stimulation in patients with post-COVID-19 conditions.

RESULTS

A total of 19 studies were identified, one using transcutaneous vagus nerve stimulation (tVNS), another using transorbital alternating current stimulation (toACS), 6 studies on transcranial magnetic stimulation (TMS) and 11 studies on transcranial direct current stimulation (tDCS) for the treatment of post-COVID-19 symptoms.

CONCLUSIONS

Existing studies report first promising results, illustrating improvement in clinical outcome parameters. Yet, the mechanistic understanding of post-COVID-19 and how brain stimulation techniques may be benefitial are limited. Directions for future research in the field are discussed.

'We Don't Have to Prove to People How We're Feeling': Understanding the Role of Peer Support Groups in Countering Epistemic Injustices in Long COVID at a US Centre

BACKGROUND

Long COVID, an infection-associated chronic condition characterised by new or worsening signs or symptoms for more than 3 months after a SARS-CoV-2 infection, is a chronic debilitating illness which remains poorly understood. Epistemic injustice in healthcare describes the unique harms or wrongs done to a person in their capacity to share and acquire knowledge about their illness. Although the concept of epistemic injustice has been described in other chronic conditions, few studies have explored these concepts in Long COVID.

OBJECTIVES

(1) To understand the lived experience of epistemic injustice in adults with Long COVID who were participating in a peer support group intervention and (2) to describe the potential impact of the support group on these experiences in participants.

METHODS

Qualitative analysis utilising inductive analysis of semi-structured individual interviews of patients with Long COVID who participated in a peer support group intervention at an academic medical centre in Oregon, USA.

RESULTS

We identified three themes that captured the lived experiences of epistemic injustice in Long COVID support group participants: (1) dismissal and disregard; (2) episodic and unpredictable symptoms and impairment, and (3) knowledge and interpretation practices. We also found that the peer support potentially impacted these experiences of epistemic injustice through (1) recognition and validation; (2) solidarity and community, and (3) information exchange and expectation setting.

CONCLUSIONS

Long COVID patients are at risk of experiencing epistemic injustice in seeking healthcare for this complex condition. Peer support programmes may be one approach to help counter these experiences and should be further studied as a complex intervention for improving patient-centred care in Long COVID.

Effectiveness and tolerance of exercise interventions for long COVID: a systematic review of randomised controlled trials

OBJECTIVES

To examine the effectiveness of exercise interventions to improve long COVID symptoms and the tolerance of exercise interventions among people with long COVID.

DESIGN

Systematic review.

DATA SOURCES

Medline via EBSCOhost, Embase via OVID and CENTRAL via the Cochrane Library up to 28 February 2023.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Inclusion criteria were: (1) participants with long COVID, as defined by study authors; (2) random assignment to either an exercise intervention or a comparison group and (3) a quantitative measure of at least 1 of the 12 core long COVID outcomes. Exclusion criteria were: (1) signs or symptoms not reasonably attributable to prior SARS-CoV-2 infection; (2) pre-exposure or postexposure prophylaxis for COVID-19 or the prevention of long COVID symptoms and (3) interventions where the primary exercise component is breathing or respiratory muscle training.

DATA EXTRACTION AND SYNTHESIS

Two reviewers independently extracted data, and studies were narratively synthesised.

RESULTS

Eight studies were included. Follow-up periods ranged from 2 to 28 weeks (mean=8.5 weeks). Sample sizes ranged from 39 to 119 (mean=56). All studies were in adults (mean age=49.9 years) and both sexes (mean female proportion=53.9%). Four studies were at low risk of bias, two were unclear and two were high. The evidence suggests that exercise interventions lead to short-term improvements in dyspnoea, fatigue, physical function and the physical domain of quality of life among people with long COVID. Of the five studies that reported adverse events, rates were low and, when reported, mild. Of the seven studies that reported sufficient relevant information, 1 of 252 participants who received exercise discontinued the intervention due to tolerance-related issues.

CONCLUSION

Available evidence suggests that exercise interventions may be beneficial and tolerable among some people with long COVID. However, the evidence base consists of a limited number of studies with small sample sizes and short follow-up periods.

Opportunities and Challenges in Using Electronic Health Record Systems to Study Postacute Sequelae of SARS-CoV-2 Infection: Insights From the NIH RECOVER Initiative

The benefits and challenges of electronic health records (EHRs) as data sources for clinical and epidemiologic research have been well described. However, several factors are important to consider when using EHR data to study novel, emerging, and multifaceted conditions such as postacute sequelae of SARS-CoV-2 infection or long COVID. In this article, we present opportunities and challenges of using EHR data to improve our understanding of long COVID, based on lessons learned from the National Institutes of Health (NIH)-funded RECOVER (REsearching COVID to Enhance Recovery) Initiative, and suggest steps to maximize the usefulness of EHR data when performing long COVID research.

Machine learning models predict long COVID outcomes based on baseline clinical and immunologic factors

The post-acute sequelae of SARS-CoV-2 (PASC), also known as long COVID, remain a significant health issue that is incompletely understood. Predicting which acutely infected individuals will go on to develop long COVID is challenging due to the lack of established biomarkers, clear disease mechanisms, or well-defined sub-phenotypes. Machine learning (ML) models offer the potential to address this by leveraging clinical data to enhance diagnostic precision. We utilized clinical data, including antibody titers and viral load measurements collected at the time of hospital admission, to predict the likelihood of acute COVID-19 progressing to long COVID. Our machine learning models achieved median AUROC values ranging from 0.64 to 0.66 and AUPRC values between 0.51 and 0.54, demonstrating their predictive capabilities. Feature importance analysis revealed that low antibody titers and high viral loads at hospital admission were the strongest predictors of long COVID outcomes. Comorbidities, including chronic respiratory, cardiac, and neurologic diseases, as well as female sex, were also identified as significant risk factors for long COVID. Our findings suggest that ML models have the potential to identify patients at risk for developing long COVID based on baseline clinical characteristics. These models can help guide early interventions, improving patient outcomes and mitigating the long-term public health impacts of SARS-CoV-2.

Association of Sedentary Lifestyle with Risk of Acute and Post-Acute COVID-19 Sequelae: A Retrospective Cohort Study

BACKGROUND

Evidence suggests that coronavirus disease 2019 (COVID-19) survivors could experience COVID-19 sequelae. Although various risk factors for COVID-19 sequelae have been identified, little is known about whether a sedentary lifestyle is an independent risk factor.

METHODS

In this retrospective cohort study, 4850 participants self-reported their COVID-19 sequelae symptoms between June and August 2022. A sedentary lifestyle included physical inactivity (<150 min/week of moderate-to-vigorous intensity physical activity) and prolonged sedentary behavior (≥10 h/day) before the fifth COVID-19 wave was recorded. Logistic regression analysis was performed to determine the relationships between sedentary lifestyle and risk of acute and post-acute (lasting ≥2 months) COVID-19 sequelae.

RESULTS

A total of 1443 COVID-19 survivors and 2962 non-COVID-19 controls were included. Of the COVID-19 survivors, >80% and >40% self-reported acute and post-acute COVID-19 sequelae, respectively. In the post-acute phase, COVID-19 survivors who were physically inactive had a 37% lower risk of insomnia, whereas those with prolonged sedentary behavior had 25%, 67%, and 117% higher risks of at least one symptom, dizziness, and "pins and needles" sensation, respectively. For the acute phase, prolonged sedentary behavior was associated with a higher risk of fatigue, "brain fog," dyspnea, muscle pain, joint pain, dizziness, and "pins and needles" sensation. Notably, sedentary behavior, rather than physical inactivity, was correlated with a higher risk of severe post-COVID-19 sequelae in both acute and post-acute phases.

CONCLUSIONS

Prolonged sedentary behavior was independently associated with a higher risk of both acute and post-acute COVID-19 sequelae, whereas physical inactivity played contradictory roles in COVID-19 sequelae.

Age-dependent phenotypes of cognitive impairment as sequelae of SARS-CoV-2 infection

Cognitive changes associated with PASC may not be uniform across populations. We conducted individual-level pooled analyses and meta-analyses of cognitive assessments from eight prospective cohorts, comprising 2,105 patients and 1,432 controls from Argentina, Canada, Chile, Greece, India, Italy, Russia, and the UK. The meta-analysis found no differences by country of origin. The profile and severity of cognitive impairment varied by age, with mild attentional impairment observed in young and middle-aged adults, but memory, language, and executive function impairment in older adults. The risk of moderate to severe impairment doubled in older adults. Moderately severe or severe impairment was significantly associated with infection diagnoses (chi-square = 26.57, p ≤ 0.0001) and the severity of anosmia (chi-square = 31.81, p ≤ 0.0001). We found distinct age-related phenotypes of cognitive impairment in patients recovering from COVID-19. We identified the severity of acute illness and the presence of olfactory dysfunction as the primary predictors of dementia-like impairment in older adults.

Care for post-COVID-19 condition in Germany from the perspectives of patients, informal caregivers and general practitioners: Study protocol for a mixed methods study

BACKGROUND

A large number of individuals suffer from post-COVID-19 condition (PCC), characterised by persistent symptoms following a SARS-CoV-2 infection with an impact on daily personal and professional activities. This study aims at examining which (health) care services are used by PCC patients in the German federal state of Lower Saxony, and how these patients manage their condition. The perspectives of patients, informal caregivers and general practitioners (GPs) will be considered.

METHODS

The study will employ a mixed methods design. Patients' perspective will be evaluated through an online survey of: (1) 21,000 adult individuals with a PCC diagnosis (ICD10 U09.9!) in their statutory health insurance claims data in 2022 ("AOK survey") and (2) a self-selected sample of adult individuals with a proven SARS-CoV-2 infection in 2023 and persistent symptoms ("public survey"). Additional data sources will be claims data (n = 27,275) and 25-30 semi-structured interviews. Informal caregivers' perspective will be collected through an online survey and semi-structured interviews. GPs' perspective will be evaluated through four focus groups involving six to eight participants each and an online survey of all registered and practicing GPs in Lower Saxony (approximately 5,000). All survey data will be descriptively analysed. In addition, correlation analyses and multivariable regression analyses will be conducted, for example on factors influencing affected individuals' use of medical services. Interview and focus group data will be subjected to qualitative content analysis. A health economic analysis will be used to determine the costs of PCC to health care payers, patients and society. The project will conclude with an expert workshop to discuss the results and derive recommendations.

DISCUSSION

The results of the study will provide a multidimensional description of the (health) care situation and needs of patients with PCC, and derive recommendations for improving health care.

TRIAL REGISTRATION

The VePoKaP study is registered at the German Clinical Trials Register (DRKS00032846).

Defining long COVID using a population-based SARS-CoV-2 survey in California

BACKGROUND

More than four years after the start of the COVID-19 pandemic, understanding of SARS-CoV-2 burden and post-acute sequela of COVID (PASC), or long COVID, continues to evolve. However, prevalence estimates are disparate and uncertain. Leveraging survey responses from a large serosurveillance study, we assess prevalence estimates using five different long COVID definitions among California residents.

METHODS

The California Department of Public Health (CDPH) conducted a cross-sectional survey that included questions about acute COVID-19 infection and recovery. A random selection of California households was invited to participate in a survey that included demographic information, clinical symptoms, and COVID-19 vaccination history. We assessed prevalence and predictors of long COVID among those previously testing positive for SARS-CoV-2 across different definitions using logistic regression.

FINDINGS

A total of 2883 participants were included in this analysis; the majority identified as female (62.5 %), and the median age was 39 years (interquartile range: 17-55 years). We found a significant difference in long COVID prevalence across definitions with the highest prevalence observed when participants were asked about incomplete recovery (20.9 %, 95 % confidence interval [CI]: 19.4-22.5) and the lowest prevalence was associated with severe long COVID affecting an estimated 4.9 % (95 % CI 4.1-5.7) of the participant population. Individuals that completed the primary vaccination series had significantly lower prevalence of long COVID compared to those that did not receive COVID vaccination.

INTERPRETATION

There were significant differences in the estimated prevalence of long COVID across different definitions. People who experience a severe initial COVID-19 infection should be considered at a higher probability for developing long COVID.

FUNDING

Centers for Disease Control and Prevention - Epidemiology and Laboratory Capacity.

Differences in brain structure and cognitive performance between patients with long-COVID and those with normal recovery

BACKGROUND

The pathophysiology of protracted symptoms after COVID-19 is unclear. This study aimed to determine if long-COVID is associated with differences in baseline characteristics, markers of white matter diffusivity in the brain, and lower scores on objective cognitive testing.

METHODS

Individuals who experienced COVID-19 symptoms for more than 60 days post-infection (long-COVID) (n = 56) were compared to individuals who recovered from COVID-19 within 60 days of infection (normal recovery) (n = 35). Information regarding physical and mental health, and COVID-19 illness was collected. The National Institute of Health Toolbox Cognition Battery was administered. Participants underwent magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI). Tract-based spatial statistics were used to perform a whole-brain voxel-wise analysis on standard DTI metrics (fractional anisotropy, axial diffusivity, mean diffusivity, radial diffusivity), controlling for age and sex. NIH Toolbox Age-Adjusted Fluid Cognition Scores were used to compare long-COVID and normal recovery groups, covarying for Age-Adjusted Crystallized Cognition Scores and years of education. False discovery rate correction was applied for multiple comparisons.

RESULTS

There were no significant differences in age, sex, or history of neurovascular risk factors between the groups. The long-COVID group had significantly (p < 0.05) lower mean diffusivity than the normal recovery group across multiple white matter regions, including the internal capsule, anterior and superior corona radiata, corpus callosum, superior fronto-occiptal fasciculus, and posterior thalamic radiation. However, the effect sizes of these differences were small (all β<|0.3|) and no significant differences were found for the other DTI metrics. Fluid cognition composite scores did not differ significantly between the long-COVID and normal recovery groups (p > 0.05).

CONCLUSIONS

Differences in diffusivity between long-COVID and normal recovery groups were found on only one DTI metric. This could represent subtle areas of pathology such as gliosis or edema, but the small effect sizes and non-specific nature of the diffusion indices make pathological inference difficult. Although long-COVID patients reported many neuropsychiatric symptoms, significant differences in objective cognitive performance were not found.

Physical exercise-related manifestations of long COVID: A systematic review and meta-analysis

Objective

This study aims to systematically assess physical exercise-related symptoms of post-acute sequelae of SARS-CoV-2 infection (PASC or long COVID) in coronavirus disease 2019 (COVID-19) survivors.

Methods

Eight databases were systematically searched on March 03, 2024. Original studies that compared physical exercise-related parameters measured by exercise testing between COVID-19 survivors who recovered from SARS-CoV-2 infection over 3 months and non-COVID-19 controls were included. A random-effects model was utilized to determine the mean differences (MDs) or standardized MDs in the meta-analysis.

Results

A total of 40 studies with 6241 COVID-19 survivors were included. The 6-min walk test, maximal oxygen consumption (VO2max), and anaerobic threshold were impaired in COVID-19 survivors 3 months post-infection compared with non-COVID-19 controls in exercise testing, while VO2 were comparable between the two groups at rest. In contrast, no differences were observed in SpO2, heart rate, blood pressure, fatigue, and dyspnea between COVID-19 survivors and non-COVID-19 controls in exercise testing.

Conclusion

The findings suggest an underestimation of the manifestations of PASC. COVID-19 survivors also harbor physical exercise-related symptoms of PASC that can be determined by the exercise testing and are distinct from those observed at rest. Exercise testing should be included while evaluating the symptoms of PASC in COVID-19 survivors.

Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative

BACKGROUND

While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID.

METHODS

A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.).

FINDINGS

Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events.

INTERPRETATION

As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions.

FUNDING

Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438.

Systemic oxidative stress associates with the development of post-COVID-19 syndrome in non-hospitalized individuals

BACKGROUND

Post-COVID-19 syndrome (PCS) remains a major health issue worldwide, while its pathophysiology is still poorly understood. Systemic oxidative stress (OS) may be involved in PCS, which is reflected by lower circulating free thiols (R-SH, sulfhydryl groups), as they are receptive to rapid oxidation by reactive species. This study aimed to investigate the longitudinal dynamics of serum R-SH after SARS-CoV-2 infection and its association with the development of PCS in individuals with mild COVID-19.

METHODS

Baseline serum R-SH concentrations were measured and compared between 135 non-hospitalized COVID-19 subjects and 82 healthy controls (HC). In COVID-19 subjects, serum R-SH concentrations were longitudinally measured during the acute disease phase (up to 3 weeks) and at 3, 6, and 12 months of follow-up, and their associations with relevant clinical parameters were investigated, including the development of PCS.

RESULTS

Baseline albumin-adjusted serum R-SH were significantly reduced in non-hospitalized COVID-19 subjects as compared to HC (p = 0.041), reflecting systemic OS. In mild COVID-19 subjects, trajectories of albumin-adjusted serum R-SH levels over a course of 12 months were longitudinally associated with the future presence of PCS 18 months after initial infection (b = -9.48, p = 0.023).

CONCLUSION

Non-hospitalized individuals with COVID-19 show evidence of systemic oxidative stress, which is longitudinally associated with the development of PCS. Our results provide a rationale for future studies to further investigate the value of R-SH as a monitoring biomarker and a potential therapeutic target in the development of PCS.

A Case Demonstration of the Open Health Natural Language Processing Toolkit From the National COVID-19 Cohort Collaborative and the Researching COVID to Enhance Recovery Programs for a Natural Language Processing System for COVID-19 or Postacute Sequelae of SARS CoV-2 Infection: Algorithm Development and Validation

BACKGROUND

A wealth of clinically relevant information is only obtainable within unstructured clinical narratives, leading to great interest in clinical natural language processing (NLP). While a multitude of approaches to NLP exist, current algorithm development approaches have limitations that can slow the development process. These limitations are exacerbated when the task is emergent, as is the case currently for NLP extraction of signs and symptoms of COVID-19 and postacute sequelae of SARS-CoV-2 infection (PASC).

OBJECTIVE

This study aims to highlight the current limitations of existing NLP algorithm development approaches that are exacerbated by NLP tasks surrounding emergent clinical concepts and to illustrate our approach to addressing these issues through the use case of developing an NLP system for the signs and symptoms of COVID-19 and PASC.

METHODS

We used 2 preexisting studies on PASC as a baseline to determine a set of concepts that should be extracted by NLP. This concept list was then used in conjunction with the Unified Medical Language System to autonomously generate an expanded lexicon to weakly annotate a training set, which was then reviewed by a human expert to generate a fine-tuned NLP algorithm. The annotations from a fully human-annotated test set were then compared with NLP results from the fine-tuned algorithm. The NLP algorithm was then deployed to 10 additional sites that were also running our NLP infrastructure. Of these 10 sites, 5 were used to conduct a federated evaluation of the NLP algorithm.

RESULTS

An NLP algorithm consisting of 12,234 unique normalized text strings corresponding to 2366 unique concepts was developed to extract COVID-19 or PASC signs and symptoms. An unweighted mean dictionary coverage of 77.8% was found for the 5 sites.

CONCLUSIONS

The evolutionary and time-critical nature of the PASC NLP task significantly complicates existing approaches to NLP algorithm development. In this work, we present a hybrid approach using the Open Health Natural Language Processing Toolkit aimed at addressing these needs with a dictionary-based weak labeling step that minimizes the need for additional expert annotation while still preserving the fine-tuning capabilities of expert involvement.

Post-acute COVID-19 outcomes including participant-reported long COVID: amubarvimab/romlusevimab versus placebo in the ACTIV-2 trial

Background

It is unknown if early COVID-19 monoclonal antibody (mAb) therapy can reduce risk of Long COVID. The mAbs amubarvimab/romlusevimab were previously demonstrated to reduce risk of hospitalization/death by 79%. This study assessed the impact of amubarvimab/romlusevimab on late outcomes, including Long COVID.

Methods

Non-hospitalized high-risk adults within 10 days of COVID-19 symptom onset enrolled in a randomized, double-blind, placebo-controlled phase 2/3 trial of amubarvimab/romlusevimab for COVID-19 treatment. Late symptoms, assessed using a participant-completed symptom diary, were a pre-specified exploratory endpoint. The primary outcome for this analysis was the composite of Long COVID by participant self-report (presence of COVID-19 symptoms as recorded in the diary at week 36) or hospitalization or death by week 36. Inverse probability weighting (IPW) was used to address incomplete outcome ascertainment, giving weighted risk ratios (wRR) comparing amubarvimab/romlusevimab to placebo.

Findings

Participants received amubarvimab/romlusevimab (n = 390) or placebo (n = 390) between January and July 2021. Median age was 49 years, 52% were female, 18% Black/African American, 49% Hispanic/Latino, and 9% COVID-19-vaccinated at entry. At week 36, 103 (13%) had incomplete outcome ascertainment, and 66 (17%) on amubarvimab/romlusevimab and 92 (24%) on placebo met the primary outcome (wRR = 0.70, 95% confidence interval (CI) 0.53-0.93). The difference was driven by fewer hospitalizations/deaths with amubarvimab/romlusevimab (4%) than placebo (13%). Among 652 participants with available diary responses, 53 (16%) on amubarvimab/romlusevimab and 44 (14%) on placebo reported presence of Long COVID.

Interpretation

Amubarvimab/romlusevimab treatment, while highly effective in preventing hospitalizations/deaths, did not reduce risk of Long COVID. Additional interventions are needed to prevent Long COVID.

Funding

National Institute of Allergy and Infectious Diseases of the National Institutes of Health. Amubarvimab and romlusevimab supplied by Brii Biosciences.

Uncovering the Contrasts and Connections in PASC: Viral Load and Cytokine Signatures in Acute COVID-19 versus Post-Acute Sequelae of SARS-CoV-2 (PASC)

The recent global COVID-19 pandemic has had a profound and enduring impact, resulting in substantial loss of life. The scientific community has responded unprecedentedly by investigating various aspects of the crisis, particularly focusing on the acute phase of COVID-19. The roles of the viral load, cytokines, and chemokines during the acute phase and in the context of patients who experienced enduring symptoms upon infection, so called Post-Acute Sequelae of COVID-19 or PASC, have been studied extensively. Here, in this review, we offer a virologist's perspective on PASC, highlighting the dynamics of SARS-CoV-2 viral loads, cytokines, and chemokines in different organs of patients across the full clinical spectrum of acute-phase disease. We underline that the probability of severe or critical disease progression correlates with increased viral load levels detected in the upper respiratory tract (URT), lower respiratory tract (LRT), and plasma. Acute-phase viremia is a clear, although not unambiguous, predictor of PASC development. Moreover, both the quantity and diversity of functions of cytokines and chemokines increase with acute-phase disease severity. Specific cytokines remain or become elevated in the PASC phase, although the driving factor of ongoing inflammation found in patients with PASC remains to be investigated. The key findings highlighted in this review contribute to a further understanding of PASC and their differences and overlap with acute disease.

Knowledge Representation and Management in the Age of Long Covid and Large Language Models: a 2022-2023 Survey

OBJECTIVES

To select, present, and summarize cutting edge work in the field of Knowledge Representation and Management (KRM) published in 2022 and 2023.

METHODS

A comprehensive set of KRM-relevant articles published in 2022 and 2023 was retrieved by querying PubMed. Topic modeling with Latent Dirichlet Allocation was used to further refine this query and suggest areas of focus. Selected articles were chosen based on a review of their title and abstract.

RESULTS

An initial set of 8,706 publications were retrieved from PubMed. From these, fifteen papers were ultimately selected matching one of two main themes: KRM for long COVID, and KRM approaches used in combination with generative large language models.

CONCLUSIONS

This survey shows the ongoing development and versatility of KRM approaches, both to improve our understanding of a global health crisis and to augment and evaluate cutting edge technologies from other areas of artificial intelligence.

Developing a clinical-pathological framework of long COVID-related fatigue applied to public safety workers

In the wake of the COVID-19 pandemic, millions worldwide are still struggling with persistent or recurring symptoms known as long COVID. Fatigue is one of the most prevalent symptoms associated with long COVID, and for many it can be debilitating. Understanding the potential pathological processes that link fatigue to long COVID is critical to better guide treatment. Challenges with diagnosis and treatment are reviewed, recognizing that post-COVID fatigue does not always present with corroborating clinical evidence, a situation that is frustrating for both patients and healthcare providers. Firefighters are a group of public safety workers who are particularly impacted by long COVID-related fatigue. Firefighters must be able to engage in strenuous physical activity and deal with demanding psychological situations, both of which may be difficult for those suffering from fatigue. Disruption in public safety worker health can potentially impact community welfare. This review creates a framework to explain the clinical-pathological features of fatigue resulting from long COVID, addresses diagnosis and treatment challenges, and explores the unique impact fatigue may pose for public safety workers and their organizations.

Unveiling long COVID symptomatology, co-occurrence trends, and symptom distress post SARS-CoV-2 infection

BACKGROUND

Long COVID, an emerging public health issue, is characterized by persistent symptoms following SARS-CoV-2 infection. This study aims to explore the relationship between post-COVID-19 symptomatology and patient distress employing Latent Class Analysis to uncover symptom co-occurrence patterns and their association with distress.

METHODS

A cross-sectional study was conducted using an online survey among 240 participants from a university and affiliated hospital of southern Taiwan. The survey quantified distress due to persistent symptoms and assessed the prevalence of Long COVID, symptom co-occurrence, and latent symptom classes. Latent Class Analysis (LCA) identified distinct symptom patterns, and multiple regression models evaluated associations between symptom patterns, distress, and demographic factors.

RESULTS

The study found that 80 % of participants experienced Long COVID, with symptoms persisting for over three months. Individuals with multiple COVID-19 infections showed a significant increase in general (β = 1.79), cardiovascular (β = 0.61), and neuropsychological symptoms (β = 2.18), and higher total distress scores (β = 6.35). Three distinct symptomatology classes were identified: "Diverse", "Mild", and "Severe" symptomatology. The "Mild Symptomatology" class was associated with lower distress (-10.61), while the "Severe Symptomatology" class showed a significantly higher distress due to symptoms (13.32).

CONCLUSION

The study highlights the significant impact of Long COVID on individuals, with distinct patterns of symptomatology and associated distress. It emphasizes the cumulative effect of multiple COVID-19 infections on symptom severity and the importance of tailored care strategies.

Composite Autonomic Symptom Score-31 for the diagnosis of cardiovascular autonomic dysfunction in long-term coronavirus disease 2019

BACKGROUND

Composite Autonomic Symptom Score-31 (COMPASS-31) is an easy-to-use screening tool for the evaluation of autonomic dysfunction in various diseases affecting neural function but has rarely been used in the assessment of long coronavirus disease 2019 (COVID-19). This study aimed to evaluate the diagnostic accuracy of the COMPASS-31 score in detecting dysfunction of the autonomic nervous system in patients 3 months after COVID-19 infection.

MATERIALS AND METHODS

Fifty-nine subjects were recruited and grouped into 2: (a) controls (n = 31) who had never had positive polymerase chain reaction results for COVID-19 before and (b) the post-COVID-19 patients (n = 28) who had confirmed COVID-19 infection 3-6 months before recruitment. COMPASS-31 questionnaire was utilized to evaluate subjective symptoms or evidence of autonomic dysfunction. Autonomic dysfunction was assessed objectively by cardiovascular autonomic reflex tests (CARTs) and heart rate variability (HRV). For comparison of quantitative variables between two groups, t-test or Mann-Whitney U test, as appropriate, were used. Sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), negative likelihood ratio (LR), and positive LR were used as measures of diagnostic accuracy. Receiver operating characteristic (ROC) curve analysis determined the overall accuracy of COMPASS-31.

RESULTS

The median COMPASS score was found to be significantly higher in post-COVID-19 participants than controls (15.5 vs. 10, P = 0.021). The median total CART score was also significantly higher in post-COVID-19 participants (0 vs. 1, P < 0.001). Out of 6 domains of the COMPASS score, the median value for orthostatic dysfunction was found to be significantly higher in post-COVID-19 participants than controls (12 vs. 0, P = 0.008). There was significantly fair accuracy of the COMPASS score with an area under the receiver operating curve 0.68 (0.54-0.82) following the total CART score ≥2 as the gold standard in the diagnosis of autonomic dysfunction (P = 0.021). The best cutoff point of the total COMPASS score was 12.5, where the optimal values of sensitivity, specificity, and positive and negative predictive values were achieved. Nonsignificant and weak correlations between CARTs, HRV parameters, and COMPASS score were found.

CONCLUSION

COMPASS-31 could be used as a user-friendly screening tool to detect autonomic dysfunction in post-COVID-19 cases with acceptable sensitivity and specificity.

A Narrative Review on Prolonged Neuropsychiatric Consequences of COVID-19: A Serious Concern

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection is characterized by prolonged, postacute sequelae of COVID-19 (PASC). Marked by persistent or new-onset symptoms within 3 months following COVID-19 recovery, PASC significantly affects a diverse spectrum of survivors. Beyond cardiovascular implications, neuropsychiatric PASC demonstrates prolonged symptoms with diverse phenotypic profiles affecting memory, attention, and mood. The pathophysiologic basis points to SARS-CoV-2’s neurotropism, instigating inflammatory responses in the central nervous system. A comprehensive multimodal assessment, integrating psychological evaluations, fluid examinations, neurophysiology, and imaging, emerges as a critical diagnostic approach. Managing neuropsychiatric PASC necessitates personalized interventions to enhance resilience and coping mechanisms, emphasizing the role of physical fitness, creative engagement, and social support in mitigating its impact on identity and well-being. In addition, early initiation of cognitive rehabilitation and cognitive behavioral therapy is proposed to address symptom chronicity, emotional distress, and cognitive dysfunction, enhancing the quality of life. The urgency for targeted interventions, early neuropsychological support, and ongoing research to comprehensively address the multifaceted neuropsychiatric effects of COVID-19 is underscored in this review. Collaborative efforts involving health-care professionals, support networks, and affected individuals are imperative to navigate the evolving landscape of PASC and its persistent neuropsychiatric implications.

Prevalence and risk factors for post-COVID conditions of COVID-19 among persons with HIV in Washington, DC

Post-COVID conditions (long COVID) are defined as COVID symptoms persisting 28 days post-initial infection. The limited research available on the prevalence and experiences of post-COVID conditions among persons with HIV (PWH) indicates potential increased risk for post-COVID conditions. The purpose of this study was to characterize prevalence, symptom clustering, impact, and potential risk factors of post-COVID conditions among PWH. Data come from the COVID-19 survey, conducted as a sub-study of the DC Cohort Longitudinal HIV Study, an ongoing study of over 12,000 PWH living in Washington, DC. Survey data were matched to electronic medical record data. Prevalence estimates and multivariable logistic regression analyses were calculated comparing those with and without post-COVID conditions. The prevalence of post-COVID conditions among PWH was 46% with no significant differences among demographic or HIV measures. Those with history of asthma were more likely to report post-COVID conditions symptoms. Among those with post-COVID conditions, 81% reported three or more initial COVID symptoms. Retired/disabled PWH were more likely to report post-COVID conditions compared to employed (aOR = 2.37, 95% CI = 1.06, 5.33). Post-COVID conditions significantly limited activities of daily living. Programs are needed to address the long-term impact of post-COVID conditions on activities of daily living among PWH.

Association of post-COVID phenotypic manifestations with new-onset psychiatric disease

Acute COVID-19 infection can be followed by diverse clinical manifestations referred to as Post Acute Sequelae of SARS-CoV2 Infection (PASC). Studies have shown an increased risk of being diagnosed with new-onset psychiatric disease following a diagnosis of acute COVID-19. However, it was unclear whether non-psychiatric PASC-associated manifestations (PASC-AMs) are associated with an increased risk of new-onset psychiatric disease following COVID-19. A retrospective electronic health record (EHR) cohort study of 2,391,006 individuals with acute COVID-19 was performed to evaluate whether non-psychiatric PASC-AMs are associated with new-onset psychiatric disease. Data were obtained from the National COVID Cohort Collaborative (N3C), which has EHR data from 76 clinical organizations. EHR codes were mapped to 151 non-psychiatric PASC-AMs recorded 28-120 days following SARS-CoV-2 diagnosis and before diagnosis of new-onset psychiatric disease. Association of newly diagnosed psychiatric disease with age, sex, race, pre-existing comorbidities, and PASC-AMs in seven categories was assessed by logistic regression. There were significant associations between a diagnosis of any psychiatric disease and five categories of PASC-AMs with odds ratios highest for neurological, cardiovascular, and constitutional PASC-AMs with odds ratios of 1.31, 1.29, and 1.23 respectively. Secondary analysis revealed that the proportions of 50 individual clinical features significantly differed between patients diagnosed with different psychiatric diseases. Our study provides evidence for association between non-psychiatric PASC-AMs and the incidence of newly diagnosed psychiatric disease. Significant associations were found for features related to multiple organ systems. This information could prove useful in understanding risk stratification for new-onset psychiatric disease following COVID-19. Prospective studies are needed to corroborate these findings.

SARS-CoV-2-Induced Type I Interferon Signaling Dysregulation in Olfactory Networks Implications for Alzheimer's Disease

Type I interferon signaling (IFN-I) perturbations are major drivers of COVID-19. Dysregulated IFN-I in the brain, however, has been linked to both reduced cognitive resilience and neurodegenerative diseases such as Alzheimer's. Previous works from our group have proposed a model where peripheral induction of IFN-I may be relayed to the CNS, even in the absence of fulminant infection. The aim of our study was to identify significantly enriched IFN-I signatures and genes along the transolfactory route, utilizing published datasets of the nasal mucosa and olfactory bulb amygdala transcriptomes of COVID-19 patients. We furthermore sought to identify these IFN-I signature gene networks associated with Alzheimer's disease pathology and risk. Gene expression data involving the nasal epithelium, olfactory bulb, and amygdala of COVID-19 patients and transcriptomic data from Alzheimer's disease patients were scrutinized for enriched Type I interferon pathways. Gene set enrichment analyses and gene-Venn approaches were used to determine genes in IFN-I enriched signatures. The Agora web resource was used to identify genes in IFN-I signatures associated with Alzheimer's disease risk based on its aggregated multi-omic data. For all analyses, false discovery rates (FDR) <0.05 were considered statistically significant. Pathways associated with type I interferon signaling were found in all samples tested. Each type I interferon signature was enriched by IFITM and OAS family genes. A 14-gene signature was associated with COVID-19 CNS and the response to Alzheimer's disease pathology, whereas nine genes were associated with increased risk for Alzheimer's disease based on Agora. Our study provides further support to a type I interferon signaling dysregulation along the extended olfactory network as reconstructed herein, ranging from the nasal epithelium and extending to the amygdala. We furthermore identify the 14 genes implicated in this dysregulated pathway with Alzheimer's disease pathology, among which HLA-C, HLA-B, HLA-A, PSMB8, IFITM3, HLA-E, IFITM1, OAS2, and MX1 as genes with associated conferring increased risk for the latter. Further research into its druggability by IFNb therapeutics may be warranted.

Emotional disturbances in postcovid syndrome structure

In this article emotional disturbances developing in the postcovid period, their features and risk factors are reviewed, as well as sleep disorders after coronavirus infection (COVID-19). The nervous system (NS) is one of the SARS-CoV-2 main targets, which is confirmed by hypo-/anosmia, which develops in most patients during the acute period of COVID-19, and in some patients it is the first symptom. Currently, the main direct routes of coronavirus impact on the NS are considered to be hematogenous and neuronal. In addition, there is an immune-mediated effect on the NS due to the cytokine storm. After an acute period of coronavirus infection postcovoid syndrome often develops. Neurological manifestations, in particular emotional disorders, occupy a significant place in its structure. Depression, anxiety, fatigue, as well as sleep disorders bother patients most often. In dynamics, the severity of most symptoms in a certain part of patients decreases, however, according to some studies, postcovid manifestations persist or worsen for a long time. Currently, female gender and psychiatric comorbidity are most often considered risk factors for the development of postcovid emotional disorders. Despite the fact that the pandemic is officially considered over, and the acute period of COVID-19 is currently much easier than in 2020–2021, internists and neurologists are still treated by patients with newly emerged emotional disorders in the postcovid period, which underlines the continuing relevance of this problem. It is worth noting that emotional disorders in the postcovid period can develop in patients of all age groups, reducing their quality of life and workability. Public awareness, early diagnosis and initiation of treatment of these disorders will help to avoid global consequences.

Chronic overlapping pain conditions increase the risk of long COVID features, regardless of acute COVID status

ABSTRACT

Chronic overlapping pain conditions (COPCs) refer to conditions that have similar central nervous system pathophysiologic mechanisms driving widespread pain as well as common comorbid symptoms such as fatigue and problems with sleep, memory, and mood. If COPCs predict the onset of long COVID, this could offer a valuable orientation for long COVID-related research and clinical care. This retrospective cohort study aimed to determine whether having a COPC predicts the onset of long COVID features using US electronic health records and 1:1 propensity score matching without replacement. The study cohorts included (1) people with acute COVID (n = 1,038,402), (2) people with acute influenza (n = 262,092), and (3) a noninfected cohort comprising people with a routine healthcare encounter (n = 1,081,593). Having a COPC increased the risk of long COVID features in all 3 study cohorts. Among those with COVID, having a pre-existing COPC increased the risk by 1.47 (95% CI = 1.46, 1.47). In the influenza cohort, COPCs increased the risk by 1.39 (95% CI = 1.38, 1.40). In the noninfected cohort, COPCs increased the risk by 1.57 (95% CI = 1.56, 1.59). These findings reinforce the likelihood that nociplastic mechanisms play a prominent role in long COVID. Recognizing that this ubiquitous nonspecific syndrome occurs frequently in the population can inform precision medicine therapies that avoid the pitfalls of viewing long COVID exclusively in the framework of postinfectious disease.

Type I interferon signaling, cognition and neurodegeneration following COVID-19: update on a mechanistic pathogenetic model with implications for Alzheimer's disease

COVID-19's effects on the human brain reveal a multifactorial impact on cognition and the potential to inflict lasting neuronal damage. Type I interferon signaling, a pathway that represents our defense against pathogens, is primarily affected by COVID-19. Type I interferon signaling, however, is known to mediate cognitive dysfunction upon its dysregulation following synaptopathy, microgliosis and neuronal damage. In previous studies, we proposed a model of outside-in dysregulation of tonic IFN-I signaling in the brain following a COVID-19. This disruption would be mediated by the crosstalk between central and peripheral immunity, and could potentially establish feed-forward IFN-I dysregulation leading to neuroinflammation and potentially, neurodegeneration. We proposed that for the CNS, the second-order mediators would be intrinsic disease-associated molecular patterns (DAMPs) such as proteopathic seeds, without the requirement of neuroinvasion to sustain inflammation. Selective vulnerability of neurogenesis sites to IFN-I dysregulation would then lead to clinical manifestations such as anosmia and cognitive impairment. Since the inception of our model at the beginning of the pandemic, a growing body of studies has provided further evidence for the effects of SARS-CoV-2 infection on the human CNS and cognition. Several preclinical and clinical studies have displayed IFN-I dysregulation and tauopathy in gene expression and neuropathological data in new cases, correspondingly. Furthermore, neurodegeneration identified with a predilection for the extended olfactory network furthermore supports the neuroanatomical concept of our model, and its independence from fulminant neuroinvasion and encephalitis as a cause of CNS damage. In this perspective, we summarize the data on IFN-I as a plausible mechanism of cognitive impairment in this setting, and its potential contribution to Alzheimer's disease and its interplay with COVID-19.

The knowns and unknowns of long COVID-19: from mechanisms to therapeutical approaches

The coronavirus disease 2019 (COVID-19) pandemic caused by SARS-CoV-2 has been defined as the greatest global health and socioeconomic crisis of modern times. While most people recover after being infected with the virus, a significant proportion of them continue to experience health issues weeks, months and even years after acute infection with SARS-CoV-2. This persistence of clinical symptoms in infected individuals for at least three months after the onset of the disease or the emergence of new symptoms lasting more than two months, without any other explanation and alternative diagnosis have been named long COVID, long-haul COVID, post-COVID-19 conditions, chronic COVID, or post-acute sequelae of SARS-CoV-2 (PASC). Long COVID has been characterized as a constellation of symptoms and disorders that vary widely in their manifestations. Further, the mechanisms underlying long COVID are not fully understood, which hamper efficient treatment options. This review describes predictors and the most common symptoms related to long COVID's effects on the central and peripheral nervous system and other organs and tissues. Furthermore, the transcriptional markers, molecular signaling pathways and risk factors for long COVID, such as sex, age, pre-existing condition, hospitalization during acute phase of COVID-19, vaccination, and lifestyle are presented. Finally, recommendations for patient rehabilitation and disease management, as well as alternative therapeutical approaches to long COVID sequelae are discussed. Understanding the complexity of this disease, its symptoms across multiple organ systems and overlapping pathologies and its possible mechanisms are paramount in developing diagnostic tools and treatments.

Possible Role of Fibrinaloid Microclots in Postural Orthostatic Tachycardia Syndrome (POTS): Focus on Long COVID

Postural orthostatic tachycardia syndrome (POTS) is a common accompaniment of a variety of chronic, inflammatory diseases, including long COVID, as are small, insoluble, 'fibrinaloid' microclots. We here develop the argument, with accompanying evidence, that fibrinaloid microclots, through their ability to block the flow of blood through microcapillaries and thus cause tissue hypoxia, are not simply correlated with but in fact, by preceding it, may be a chief intermediary cause of POTS, in which tachycardia is simply the body's exaggerated 'physiological' response to hypoxia. Similar reasoning accounts for the symptoms bundled under the term 'fatigue'. Amyloids are known to be membrane disruptors, and when their targets are nerve membranes, this can explain neurotoxicity and hence the autonomic nervous system dysfunction that contributes to POTS. Taken together as a system view, we indicate that fibrinaloid microclots can serve to link POTS and fatigue in long COVID in a manner that is at once both mechanistic and explanatory. This has clear implications for the treatment of such diseases.

Computable Phenotypes for Post-acute sequelae of SARS-CoV-2: A National COVID Cohort Collaborative Analysis

Post-acute sequelae of SARS-CoV-2 (PASC) is an increasingly recognized yet incompletely understood public health concern. Several studies have examined various ways to phenotype PASC to better characterize this heterogeneous condition. However, many gaps in PASC phenotyping research exist, including a lack of the following: 1) standardized definitions for PASC based on symptomatology; 2) generalizable and reproducible phenotyping heuristics and meta-heuristics; and 3) phenotypes based on both COVID-19 severity and symptom duration. In this study, we defined computable phenotypes (or heuristics) and meta-heuristics for PASC phenotypes based on COVID-19 severity and symptom duration. We also developed a symptom profile for PASC based on a common data standard. We identified four phenotypes based on COVID-19 severity (mild vs. moderate/severe) and duration of PASC symptoms (subacute vs. chronic). The symptoms groups with the highest frequency among phenotypes were cardiovascular and neuropsychiatric with each phenotype characterized by a different set of symptoms.

The Human Phenotype Ontology in 2024: phenotypes around the world

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

Consistency of inconsistency in long-COVID-19 pain symptoms persistency: A systematic review and meta-analysis

INTRODUCTION

Individuals recovering from acute COVID-19 episodes may continue to suffer from various ongoing symptoms, collectively referred to as Long-COVID. Long-term pain symptoms are amongst the most common and clinically significant symptoms to be reported for this post-COVID-19 syndrome.

OBJECTIVES

This systematic review and meta-analysis aimed to evaluate the proportions of persisting pain symptoms experienced by individuals past the acute phase of COVID-19 and to identify their associated functional consequences and inflammatory correlates.

METHODS

Two online databases were systematically searched from their inception until 31 March 2022. We searched primary research articles in English, which evaluated individuals after laboratory-confirmed COVID-19 acute phase resolution and specifically reported on pain symptoms and their inflammatory and/or functional outcomes.

RESULTS

Of the 611 identified articles, 26 were included, used for data extraction, and assessed for their methodological quality and risk of bias by two independent reviewers. Pain symptoms were grouped under one of six major pain domains, serving as our primary co-outcomes. Proportional meta-analyses of pooled logit-transformed values of single proportions were performed using the random-effects-restricted maximum-likelihood model. An estimated 8%, 6%, 18%, 18%, 17%, and 12% of individuals continued to report the persistence of chest, gastrointestinal, musculoskeletal joint, musculoskeletal muscle, general body, and nervous system-related pain symptoms, respectively, for up to one year after acute phase resolution of COVID-19. Considerable levels of heterogeneity were demonstrated across all results. Functional and quality-of-life impairments and some inflammatory biomarker elevations were associated with the persistence of long-COVID pain symptoms.

CONCLUSION

This study's findings suggest that although not well characterized, long-COVID pain symptoms are being experienced by non-negligible proportions of those recovering from acute COVID-19 episodes, thus highlighting the importance of future research efforts to focus on this aspect.

Effect of Physical Exercise-Based Rehabilitation on Long COVID: A Systematic Review and Meta-analysis

PURPOSE

The number of persons living with post-coronavirus disease 2019 (COVID-19) conditions or long COVID continues to rise worldwide; however, the etiology and the treatment of long COVID remain nebulous. Therefore, efficient, feasible, and cost-effective therapeutic strategies for a large population with long COVID remain warranted. Physical exercise-based rehabilitation is a promising strategy for long COVID, although its therapeutic effects remain to be determined. This systematic review and meta-analysis aimed to examine the effects of physical exercise-based rehabilitation on long COVID.

METHODS

The electronic databases Medline, Embase, Global Health (Ovid), CINAHL (EBSCO), Web of Science, WHO Global Research Database on COVID-19, LitCovid, and Google Scholar were searched from their inception to November 2022. The identified articles were independently screened by three reviewers, and a random-effects model was used to determine the mean differences in the meta-analysis.

RESULTS

Twenty-three studies involving 1579 individuals who had COVID-19 (752 women) were included. Physical exercise-based rehabilitation showed beneficial effects on long COVID-related symptoms characterized by dyspnea, fatigue, and depression, as well as on the 6-min walk test, forced expiratory volume in 1 s/forced vital capacity, and quality of life in people who had COVID-19.

CONCLUSIONS

Physical exercise-based rehabilitation is a potential therapeutic strategy against long COVID and can be applied as a routine clinical practice in people who have recovered from COVID-19. However, customized physical exercise-based rehabilitation programs and their effects on specific types of long COVID require future large-scale studies.

COVID-19 vaccination implementation in 52 African countries: trajectory and implications for future pandemic preparedness

INTRODUCTION

To end the COVID-19 pandemic, the WHO set a goal in 2021 to fully vaccinate 70% of the global population by mid-2022. We projected the COVID-19 vaccination trajectory in 52 African countries and compared the projected to the 'actual' or 'observed' coverage as of December 2022. We also estimated the required vaccination speed needed to have attained the WHO 70% coverage target by December 2022.

METHODS

We obtained publicly available, country-reported daily COVID-19 vaccination data, covering the initial 9 months following the deployment of vaccines. We used a deterministic compartmental Susceptible-Exposed-Infectious-Recovered-type model and fit the model to the number of COVID-19 cases and vaccination coverage in each African country using a Markov chain Monte Carlo approach within a Bayesian framework.

FINDINGS

Only nine of the 52 African countries (Tunisia, Cabo Verde, Lesotho, Mozambique, Rwanda, Seychelles, Morocco, Botswana and Mauritius) were on track to achieve full COVID-19 vaccination coverage rates ranging from 72% to 97% by the end of December 2022, based on their progress after 9 months of vaccine deployment. Of the 52 countries, 26 (50%) achieved 'actual' or 'observed' vaccination coverage rates within ±10 percentage points of their projected vaccination coverage. Among the countries projected to achieve <30% by December 2022, nine of them (Chad, Niger, Nigeria, South Sudan, Tanzania, Somalia, Zambia, Sierra Leone and Côte d'Ivoire) achieved a higher observed coverage than the projected coverage, ranging from 12.3 percentage points in South Sudan to 35.7 percentage points above the projected coverage in Tanzania. Among the 52 countries, 83% (43 out of 52) needed to at least double their vaccination trajectory after 9 months of deployment to reach the 70% target by December 2022.

CONCLUSION

Our findings can guide countries in planning strategies for future global health emergencies and learning from each other, especially those that exceeded expectations and made significant progress towards the WHO's 2022 COVID-19 vaccination target despite projected poor coverage rates.

Gut Microbiota and Mitochondria: Health and Pathophysiological Aspects of Long COVID

The current understanding of long COVID (LC) is still limited. This review highlights key findings regarding the role of gut microbiota, mitochondria, and the main pathophysiological aspects of LC revealed by clinical studies, related to the complex interplay between infection, intestinal dysbiosis, dysfunctional mitochondria, and systemic inflammation generated in a vicious circle, reflecting the molecular and cellular processes from the "leaky gut" to the "leaky electron transport chain (ETC)" into a quantum leap. The heterogeneity of LC has hindered progress in deciphering all the pathophysiological mechanisms, and therefore, the approach must be multidisciplinary, with a special focus not only on symptomatic management but also on addressing the underlying health problems of the patients. It is imperative to further assess and validate the effects of COVID-19 and LC on the gut microbiome and their relationship to infections with other viral agents or pathogens. Further studies are needed to better understand LC and expand the interdisciplinary points of view that are required to accurately diagnose and effectively treat this heterogeneous condition. Given the ability of SARS-CoV-2 to induce autoimmunity in susceptible patients, they should be monitored for symptoms of autoimmune disease after contracting the viral infection. One question remains open, namely, whether the various vaccines developed to end the pandemic will also induce autoimmunity. Recent data highlighted in this review have revealed that the persistence of SARS-CoV-2 and dysfunctional mitochondria in organs such as the heart and, to a lesser extent, the kidneys, liver, and lymph nodes, long after the organism has been able to clear the virus from the lungs, could be an explanation for LC.

The impact of early therapies for COVID-19 on death, hospitalization and persisting symptoms: a retrospective study

PURPOSE

Oral antivirals (nirmatrelvir/ritonavir and molnupiravir), intravenous short treatment of remdesivir and anti-SARS-CoV-2 monoclonal antibodies (mAbs) have been used for early COVID-19 treatments in high risk of disease progression patients. The term long COVID has been used to refer to a range of new, returning, or ongoing symptoms after SARS-CoV-2 infection. Little is known about the impact of such therapies on long COVID.

METHODS

This is a retrospective observational study, including all outpatients evaluated from April 2021 to March 2022 in Brescia, Lombardy, northern Italy. Patients were stratified in three groups: (a) treated with mAbs, (b) treated with antivirals drugs and (c) controls (patients eligible for a or b who refused treatment). Data were collected at baseline and at month 1 and 3 (data on self-reported symptoms were collected using a telephone-administered questionnaire). We assessed early COVID-19 therapies effectiveness in preventing hospitalization, death at 1 or 3 months and persisting symptoms at 3 months after the onset of SARS-CoV-2 infection.

RESULTS

A total of 649 patients were included in the study, of which 242 (37.3%) were treated with mAbs, 197 (30.3%) with antiviral drugs and 210 (32.4%) were not treated. Patients most frequently reported cerebro-cardiovascular diseases (36.7%) followed by obesity (22%). Overall, 29 patients (4.5%) died or were hospitalized at 1 or 3-month follow-up. Death or hospitalization was positively associated with older ages, with a significant linear trend (OR 3.05; 95% CI 1.16-8.06, for patients aged 80 or more years compared to those aged less than 65). Data on long COVID at 3 months were available for 323 (49.8%) patients. A positive association emerged for females compared to men, with an OR of 2.14 (95% CI 1.30-3.53) for any symptoms. Conversely, inverse associations were found for treatment groups as compared to the control one, with significant estimates among patients treated with antiviral drugs for any symptoms (OR 0.43, 95% CI 0.21-0.87) and patients treated with mAbs for any neuro-behavioral symptoms (OR 0.48, 95% CI 0.25-0.92).

CONCLUSIONS

We report beneficial effect of early use of anti-SARS-CoV-2 antivirals and mAbs on long COVID.

A statewide population-based approach to examining Long COVID symptom prevalence and predictors in Michigan

OBJECTIVE

The current broad definition of Long COVID, and an overreliance on clinical and convenience samples, is leading to a wide array of Long COVID estimates with limited generalizability. Our objective was to examine Long COVID symptoms using a statewide population-based probability sample.

METHODS

Among 8000 sampled adults with polymerase-chain-reaction-confirmed SARS-CoV-2 between June 2020 and July 2021 in the Michigan Disease Surveillance System, 2533 completed our survey (response rate 32.2%). Using modified Poisson regression, we examined sociodemographic, behavioral, and clinical predictors of eight Long COVID symptom clusters, defined as at least one applicable symptom lasting 90 or more days post COVID-19 onset.

RESULTS

Neuropsychiatric Long COVID symptoms, including brain fog, were most prevalent (23.7%), followed by systemic symptoms (17.1%), including fatigue, musculoskeletal (11.4%), pulmonary (10.4%), dermatologic (6.7%), cardiovascular (6.1%), gastrointestinal (5.4%), and ear, nose, and throat symptoms (5.3%). In adjusted analyses, female sex, a pre-existing psychological condition, and intensive care unit admission were strong predictors of most Long COVID symptom clusters. Older age was not associated with a higher prevalence of all symptoms - cardiovascular and dermatologic symptoms were most prevalent among middle-aged adults and age was not associated with neuropsychiatric or gastrointestinal symptoms. Additionally, there were fewer associations between pre-existing conditions and cardiovascular, neuropsychiatric, and dermatologic symptoms compared to other symptom clusters.

CONCLUSIONS

While many predictors of Long COVID symptom clusters were similar, the relationship with age and pre-existing conditions varied across clusters. Cardiovascular, neuropsychiatric, and dermatologic symptoms require further study as potentially distinct from other Long COVID symptoms.

Association between long COVID and vaccination: A 12-month follow-up study in a low- to middle-income country

OBJECTIVE

There is a lack of estimates regarding the at-risk population associated with long COVID in Pakistan due to the absence of prospective longitudinal studies. This study aimed to determine the prevalence of long COVID and its association with disease severity and vaccination status of the patient.

DESIGN AND DATA SOURCES

This prospective cohort study was conducted at the Aga Khan University Hospital and recruited patients aged > 18 years who were admitted between February 1 and June 7, 2021. During this time, 901 individuals were admitted, after excluding patients with missing data, a total of 481 confirmed cases were enrolled.

RESULTS

The mean age of the study population was 56.9±14.3 years. Among patients with known vaccination status (n = 474), 19%(n = 90) and 19.2%(n = 91) were fully and partially vaccinated, respectively. Severe/critical disease was present in 64%(n = 312). The mortality rate following discharge was 4.58%(n = 22). Around 18.9%(n = 91) of the population required readmission to the hospital, with respiratory failure (31.8%, n = 29) as the leading cause. Long COVID symptoms were present in 29.9%(n = 144), and these symptoms were more prevalent in the severe/critical (35.5%, n = 111) and unvaccinated (37.9%, n = 105) cohort. The most prominent symptoms were fatigue (26.2%, n = 126) and shortness of breath (24.1%, n = 116), followed by cough (15.2%, n = 73). Vaccinated as compared to unvaccinated patients had lower readmissions (13.8% vs. 21.51%) and post-COVID pulmonary complications (15.4% vs. 24.2%). On multivariable analysis, after adjusting for age, gender, co-morbidity, and disease severity, lack of vaccination was found to be an independent predictor of long COVID with an Odds ratio of 2.42(95% CI 1.52-3.84). Fully and partially vaccinated patients had 62% and 56% reduced risk of developing long COVID respectively.

CONCLUSIONS

This study reports that the patients continued to have debilitating symptoms related to long COVID, one year after discharge, and most of its effects were observed in patients with severe/critical disease and unvaccinated patients.

"The burden of post-acute COVID-19 symptoms in a multinational network cohort analysis"

Persistent symptoms following the acute phase of COVID-19 present a major burden to both the affected and the wider community. We conducted a cohort study including over 856,840 first COVID-19 cases, 72,422 re-infections and more than 3.1 million first negative-test controls from primary care electronic health records from Spain and the UK (Sept 2020 to Jan 2022 (UK)/March 2022 (Spain)). We characterised post-acute COVID-19 symptoms and identified key symptoms associated with persistent disease. We estimated incidence rates of persisting symptoms in the general population and among COVID-19 patients over time. Subsequently, we investigated which WHO-listed symptoms were particularly differential by comparing their frequency in COVID-19 cases vs. matched test-negative controls. Lastly, we compared persistent symptoms after first infections vs. reinfections.Our study shows that the proportion of COVID-19 cases affected by persistent post-acute COVID-19 symptoms declined over the study period. Risk for altered smell/taste was consistently higher in patients with COVID-19 vs test-negative controls. Persistent symptoms were more common after reinfection than following a first infection. More research is needed into the definition of long COVID, and the effect of interventions to minimise the risk and impact of persistent symptoms.

Epstein-Barr virus reactivation is not causative for post-COVID-19-syndrome in individuals with asymptomatic or mild SARS-CoV-2 disease course

PURPOSE

Post-COVID-19-Syndrome (PCS) frequently occurs after an infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). However, the understanding of causative mechanisms is still limited. Aim of this study was to determine the PCS rate among SARS-CoV-2 seropositive blood donors as representatives of supposedly healthy adults, who had experienced an asymptomatic or mild COVID-19 disease course, and to examine whether Epstein-Barr virus (EBV) is reactivated in individuals reporting PCS.

METHODS

The PCS rate was determined using questionnaires that included questions about infection and persistent symptoms. Pre-pandemic blood samples and samples collected at regular, pre-defined times after a SARS-CoV-2 infection were analysed for neopterin, a marker for antiviral immune responses, by an enzyme-linked immunosorbent assay (ELISA). Additionally, we determined the rate of SARS-CoV-2 anti-N total antibodies using an electrochemiluminescence immunoassay (ECLIA). Furthermore, quantitative real-time polymerase chain reaction (qPCR) to detect EBV DNA and ECLIA screening for EBV viral capsid-antigen (VCA) IgM, IgG and EBV nuclear antigen 1 (EBNA) IgG were performed.

RESULTS

Our data reveal that 18% of all infections result in PCS, with symptoms lasting for up to one year. In individuals reporting PCS, no elevated levels of neopterin were detected, indicating no persisting pro-inflammatory, antiviral immune response. SARS-CoV-2 antibody levels were declining in all participants in comparable manner over time, pointing to a successful virus clearance. In individuals with PCS, no EBV DNA could be detected. Furthermore, no differences in EBV specific antibody levels could be shown in PCS groups compared to non-PCS groups.

CONCLUSION

Our data suggest that PCS in per se healthy, immunocompetent adults cannot be ascribed to a reactivation of EBV.

Living with pulmonary sequelae of COVID-19 and the implications for clinical nursing practice: A qualitative systematised review

AIM

To synthesise qualitative research on pulmonary sequelae of COVID-19 and identify patient needs and experiences to develop nursing care strategies.

BACKGROUND

Qualitative research on long COVID by subtype has not yet occurred. As pulmonary sequelae constitute a serious long COVID subtype, exploring patient experience and needs can generate knowledge to guide nursing practice.

DESIGN

Systematised review methodology utilised on a purposive sample of published articles and reported using the PRISMA guidelines and checklists. Searched MEDLINE, Cumulative Index to Nursing and Allied Health, and Google Scholar, for English or French articles published from February 2020 to June 2022; qualitative research with adults recovering from COVID-19 with evidence of pulmonary sequelae.

METHODS

Established principles for data extraction followed related to data reduction, data presentation, data comparison, and conclusion formulation and verification. Analysis was informed by Thorne's Interpretive Description and extended with Meleis' transitions theory, Mishel's uncertainty in illness theory and Moore et al.'s holistic theory of unpleasant symptoms. The quality of included studies was assessed Joanna Briggs Institute critical appraisal tool for qualitative research.

RESULTS

Four articles with six pooled participants provided data to yield three main themes: (1) a novel health-illness transition, (2) lung injury and pulmonary fibrosis as antecedent to illness uncertainty, (3) and pulmonary symptoms that are compounded by fatigue and weakness.

CONCLUSION

Pulmonary sequelae of COVID-19 confers a unique health-illness transition, uncertainties and symptoms that can be addressed by theory informed nursing practice.

RELEVANCE TO CLINICAL PRACTICE

Advocacy, optimising the nurse-patient relationship, offering up-to-date information and addressing uncertainty may help patients cope with pulmonary sequelae, a complex subtype of long COVID with important considerations for clinical nursing care. Despite a lack of evidence-informed clinical pathways, nurses can support patients to understand novel treatments, support discharge planning and acknowledge the synergistic nature of pulmonary symptoms and fatigue to support health-illness transitions.

NO PATIENT OR PUBLIC CONTRIBUTION

This article involved analysis of previously published works.

High unrecognized SARS-CoV-2 exposure of newly admitted and hospitalized psychiatric patients

BACKGROUND

Patients with pre-existing mental disorders are at higher risk for SARS-CoV-2 infection and adverse outcomes, and severe mental illness, including mood and psychosis spectrum disorders, is associated with increased mortality risk. Despite their increased risk profile, patients with severe mental illness have been understudied during the pandemic, with limited estimates of exposure in inpatient settings.

OBJECTIVE

The aim of this study was to describe the SARS-CoV-2 seroprevalence and antibody titers, and pro-inflammatory cytokine concentrations of newly admitted or hospitalized psychiatric inpatients without known history of COVID-19 infection, using robust quantitative multi-antigen assessments, and compare patients' exposure to that of hospital staff.

METHODS

This multi-centric, cross-sectional study compared SARS-CoV-2 seroprevalence and titers of 285 patients (University Psychiatric Centre Duffel [UPCD] N = 194; Assistance-Publique-Hopitaux de Paris [AP-HP] N = 91), and 192 hospital caregivers (UPCD N = 130; AP-HP N = 62) at two large psychiatric care facilities between January 1st and the May 30th 2021. Serum levels of SARS-CoV-2 antibodies against Spike proteins (full length), spike subunit 1 (S1), spike subunit 2 (S2), spike subunit 1 receptor binding domain (S1-RBD) and Nucleocapsid proteins were quantitatively determined using an advanced capillary Western Blot technique. To assess the robustness of the between-group seroprevalence differences, we performed sensitivity analyses with stringent cut-offs for seropositivity. We also assessed peripheral concentrations of IL-6, IL-8 and TNF-a using ELLA assays. Secondary analyses included comparisons of SARS-CoV-2 seroprevalence and titers between patient diagnostic subgroups, and between newly admitted (hospitalization ≤ 7 days) and hospitalized patients (hospitalization > 7 days) and correlations between serological and cytokines.

RESULTS

Patients had a significantly higher SARS-CoV-2 seroprevalence (67.85 % [95% CI 62.20-73.02]) than hospital caregivers (27.08% [95% CI 21.29-33.77]), and had significantly higher global SARS-CoV-2 titers (F = 29.40, df = 2, p < 0.0001). Moreover, patients had a 2.51-fold (95% CI 1.95-3.20) higher SARS-CoV-2 exposure risk compared to hospital caregivers (Fisher's exact test, P < 0.0001). No difference was found in SARS-CoV-2 seroprevalence and titers between patient subgroups. Patients could be differentiated most accurately from hospital caregivers by their higher Spike protein titers (OR 136.54 [95% CI 43.08-481.98], P < 0.0001), lower S1 (OR 0.06 [95% CI 0.02-0.15], P < 0.0001) titers and higher IL-6 (OR 3.41 [95% CI 1.73-7.24], P < 0.0001) and TNF-α (OR 34.29 [95% CI 5.00-258.87], P < 0.0001) and lower titers of IL-8 (OR 0.13 [95% CI 0.05-0.30], P < 0.0001). Seropositive patients had significantly higher SARS-COV-2 antibody titers compared to seropositive hospital caregivers (F = 19.53, df = 2, P < 0.0001), while titers were not different in seronegative individuals. Pro-inflammatory cytokine concentrations were not associated with serological status.

CONCLUSION

Our work demonstrated a very high unrecognized exposure to SARS-CoV-2 among newly admitted and hospitalized psychiatric inpatients, which is cause for concern in the context of highly robust evidence of adverse outcomes following COVID-19 in psychiatric patients. Attention should be directed toward monitoring and mitigating exposure to infectious agents within psychiatric hospitals.

SARS-CoV-2 and the DNA damage response

The recent coronavirus disease 2019 (COVID-19) pandemic was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is characterized by respiratory distress, multiorgan dysfunction and, in some cases, death. The virus is also responsible for post-COVID-19 condition (commonly referred to as 'long COVID'). SARS-CoV-2 is a single-stranded, positive-sense RNA virus with a genome of approximately 30 kb, which encodes 26 proteins. It has been reported to affect multiple pathways in infected cells, resulting, in many cases, in the induction of a 'cytokine storm' and cellular senescence. Perhaps because it is an RNA virus, replicating largely in the cytoplasm, the effect of SARS-Cov-2 on genome stability and DNA damage responses (DDRs) has received relatively little attention. However, it is now becoming clear that the virus causes damage to cellular DNA, as shown by the presence of micronuclei, DNA repair foci and increased comet tails in infected cells. This review considers recent evidence indicating how SARS-CoV-2 causes genome instability, deregulates the cell cycle and targets specific components of DDR pathways. The significance of the virus's ability to cause cellular senescence is also considered, as are the implications of genome instability for patients suffering from long COVID.

Unified Protocol for the transdiagnostic treatment of emotional disorders in people with post COVID-19 condition: study protocol for a multiple baseline n-of-1 trial

Background

Post COVID-19 syndrome, defined as the persistence of COVID-19 symptoms beyond 3 months, is associated with a high emotional burden. Post COVID-19 patients frequently present comorbid anxiety, depressive and related disorders (emotional disorders, EDs) which have an important impact on their quality of life. Unfortunately, psychological interventions to manage these EDs are rarely provided to post COVID-19 patients. Also importantly, most psychological interventions do not address comorbidity, namely simultaneous EDs present in COVID-19 patients. This study will explore the clinical utility and acceptability of a protocol-based cognitive-behavioral therapy called the Unified Protocol for the transdiagnostic treatment of EDs in patients suffering post COVID-19 condition.

Methods

A multiple baseline n-of-1 trial will be used, as it allows participants to be their own comparison control. Sample will be composed of 60 patients diagnosed with post COVID-19 conditions and comorbid EDs from three Spanish hospitals. After meeting the eligibility criteria, participants will answer the pre-assessment protocol and then they will be randomly assigned to three different baseline conditions (6, 8, or 10 days of assessments before the intervention). Participants and professionals will be unblinded to participants' allocation. Once the baseline assessment has been completed, participants will receive the online psychological individual intervention through video-calls. The Unified Protocol intervention will comprise 8 sessions of a 1 h duration each. After the intervention, participants will answer the post-assessment protocol. Additional follow-up assessments will be conducted at one, three, six, and twelve months after the intervention. Primary outcomes will be anxiety and depressive symptoms. Secondary outcomes include quality of life, emotion dysregulation, distress tolerance, and satisfaction with the programme. Data analyses will include between-group and within-group differences and visual analysis of patients' progress.

Discussion

Results from this study will be disseminated in scientific journals. These findings may help to provide valuable information in the implementation of psychological interventions for patients suffering post COVID-19 conditions.

Clinical trial registration

https://clinicaltrials.gov, identifier (NCT05581277).

Immunological profiling in long COVID: overall low grade inflammation and T-lymphocyte senescence and increased monocyte activation correlating with increasing fatigue severity

Background

Many patients with SARS-CoV-2 infection develop long COVID with fatigue as one of the most disabling symptoms. We performed clinical and immune profiling of fatigued and non-fatigued long COVID patients and age- and sex-matched healthy controls (HCs).

Methods

Long COVID symptoms were assessed using patient-reported outcome measures, including the fatigue assessment scale (FAS, scores ≥22 denote fatigue), and followed up to one year after hospital discharge. We assessed inflammation-related genes in circulating monocytes, serum levels of inflammation-regulating cytokines, and leukocyte and lymphocyte subsets, including major monocyte subsets and senescent T-lymphocytes, at 3-6 months post-discharge.

Results

We included 37 fatigued and 36 non-fatigued long COVID patients and 42 HCs. Fatigued long COVID patients represented a more severe clinical profile than non-fatigued patients, with many concurrent symptoms (median 9 [IQR 5.0-10.0] vs 3 [1.0-5.0] symptoms, p<0.001), and signs of cognitive failure (41%) and depression (>24%). Immune abnormalities that were found in the entire group of long COVID patients were low grade inflammation (increased inflammatory gene expression in monocytes, increased serum pro-inflammatory cytokines) and signs of T-lymphocyte senescence (increased exhausted CD8+ TEMRA-lymphocytes). Immune profiles did not significantly differ between fatigued and non-fatigued long COVID groups. However, the severity of fatigue (total FAS score) significantly correlated with increases of intermediate and non-classical monocytes, upregulated gene levels of CCL2, CCL7, and SERPINB2 in monocytes, increases in serum Galectin-9, and higher CD8+ T-lymphocyte counts.

Conclusion

Long COVID with fatigue is associated with many concurrent and persistent symptoms lasting up to one year after hospitalization. Increased fatigue severity associated with stronger signs of monocyte activation in long COVID patients and potentially point in the direction of monocyte-endothelial interaction. These abnormalities were present against a background of immune abnormalities common to the entire group of long COVID patients.

Predictive models of long COVID

BACKGROUND

The cause and symptoms of long COVID are poorly understood. It is challenging to predict whether a given COVID-19 patient will develop long COVID in the future.

METHODS

We used electronic health record (EHR) data from the National COVID Cohort Collaborative to predict the incidence of long COVID. We trained two machine learning (ML) models - logistic regression (LR) and random forest (RF). Features used to train predictors included symptoms and drugs ordered during acute infection, measures of COVID-19 treatment, pre-COVID comorbidities, and demographic information. We assigned the 'long COVID' label to patients diagnosed with the U09.9 ICD10-CM code. The cohorts included patients with (a) EHRs reported from data partners using U09.9 ICD10-CM code and (b) at least one EHR in each feature category. We analysed three cohorts: all patients (n = 2,190,579; diagnosed with long COVID = 17,036), inpatients (149,319; 3,295), and outpatients (2,041,260; 13,741).

FINDINGS

LR and RF models yielded median AUROC of 0.76 and 0.75, respectively. Ablation study revealed that drugs had the highest influence on the prediction task. The SHAP method identified age, gender, cough, fatigue, albuterol, obesity, diabetes, and chronic lung disease as explanatory features. Models trained on data from one N3C partner and tested on data from the other partners had average AUROC of 0.75.

INTERPRETATION

ML-based classification using EHR information from the acute infection period is effective in predicting long COVID. SHAP methods identified important features for prediction. Cross-site analysis demonstrated the generalizability of the proposed methodology.

FUNDING

NCATS U24 TR002306, NCATS UL1 TR003015, Axle Informatics Subcontract: NCATS-P00438-B, NIH/NIDDK/OD, PSR2015-1720GVALE_01, G43C22001320007, and Director, Office of Science, Office of Basic Energy Sciences of the U.S. Department of Energy Contract No. DE-AC02-05CH11231.

Long Covid symptoms and diagnosis in primary care: A cohort study using structured and unstructured data in The Health Improvement Network primary care database

BACKGROUND

Long Covid is a widely recognised consequence of COVID-19 infection, but little is known about the burden of symptoms that patients present with in primary care, as these are typically recorded only in free text clinical notes.

AIMS

To compare symptoms in patients with and without a history of COVID-19, and investigate symptoms associated with a Long Covid diagnosis.

METHODS

We used primary care electronic health record data until the end of December 2020 from The Health Improvement Network (THIN), a Cegedim database. We included adults registered with participating practices in England, Scotland or Wales. We extracted information about 89 symptoms and 'Long Covid' diagnoses from free text using natural language processing. We calculated hazard ratios (adjusted for age, sex, baseline medical conditions and prior symptoms) for each symptom from 12 weeks after the COVID-19 diagnosis.

RESULTS

We compared 11,015 patients with confirmed COVID-19 and 18,098 unexposed controls. Only 20% of symptom records were coded, with 80% in free text. A wide range of symptoms were associated with COVID-19 at least 12 weeks post-infection, with strongest associations for fatigue (adjusted hazard ratio (aHR) 3.46, 95% confidence interval (CI) 2.87, 4.17), shortness of breath (aHR 2.89, 95% CI 2.48, 3.36), palpitations (aHR 2.59, 95% CI 1.86, 3.60), and phlegm (aHR 2.43, 95% CI 1.65, 3.59). However, a limited subset of symptoms were recorded within 7 days prior to a Long Covid diagnosis in more than 20% of cases: shortness of breath, chest pain, pain, fatigue, cough, and anxiety / depression.

CONCLUSIONS

Numerous symptoms are reported to primary care at least 12 weeks after COVID-19 infection, but only a subset are commonly associated with a GP diagnosis of Long Covid.

Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach

BACKGROUND

While scientific knowledge of post-COVID-19 condition (PCC) is growing, there remains significant uncertainty in the definition of the disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most clinicians.

OBJECTIVE

In this study, we aimed to determine the validity and effectiveness of advanced natural language processing approaches built to derive insight into PCC-related patient-reported health outcomes from social media platforms Twitter and Reddit. We extracted PCC-related terms, including symptoms and conditions, and measured their occurrence frequency. We compared the outputs with human annotations and clinical outcomes and tracked symptom and condition term occurrences over time and locations to explore the pipeline's potential as a surveillance tool.

METHODS

We used bidirectional encoder representations from transformers (BERT) models to extract and normalize PCC symptom and condition terms from English posts on Twitter and Reddit. We compared 2 named entity recognition models and implemented a 2-step normalization task to map extracted terms to unique concepts in standardized terminology. The normalization steps were done using a semantic search approach with BERT biencoders. We evaluated the effectiveness of BERT models in extracting the terms using a human-annotated corpus and a proximity-based score. We also compared the validity and reliability of the extracted and normalized terms to a web-based survey with more than 3000 participants from several countries.

RESULTS

UmlsBERT-Clinical had the highest accuracy in predicting entities closest to those extracted by human annotators. Based on our findings, the top 3 most commonly occurring groups of PCC symptom and condition terms were systemic (such as fatigue), neuropsychiatric (such as anxiety and brain fog), and respiratory (such as shortness of breath). In addition, we also found novel symptom and condition terms that had not been categorized in previous studies, such as infection and pain. Regarding the co-occurring symptoms, the pair of fatigue and headaches was among the most co-occurring term pairs across both platforms. Based on the temporal analysis, the neuropsychiatric terms were the most prevalent, followed by the systemic category, on both social media platforms. Our spatial analysis concluded that 42% (10,938/26,247) of the analyzed terms included location information, with the majority coming from the United States, United Kingdom, and Canada.

CONCLUSIONS

The outcome of our social media-derived pipeline is comparable with the results of peer-reviewed articles relevant to PCC symptoms. Overall, this study provides unique insights into patient-reported health outcomes of PCC and valuable information about the patient's journey that can help health care providers anticipate future needs.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1101/2022.12.14.22283419.