Coding long COVID: characterizing a new disease through an ICD-10 lens

Comparing neural language models for medical concept representation and patient trajectory prediction

Effective representation of medical concepts is crucial for secondary analyses of electronic health records. Neural language models have shown promise in automatically deriving medical concept representations from clinical data. However, the comparative performance of different language models for creating these empirical representations, and the extent to which they encode medical semantics, has not been extensively studied. This study aims to address this gap by evaluating the effectiveness of three popular language models - word2vec, fastText, and GloVe - in creating medical concept embeddings that capture their semantic meaning. By using a large dataset of digital health records, we created patient trajectories and used them to train the language models. We then assessed the ability of the learned embeddings to encode semantics through an explicit comparison with biomedical terminologies, and implicitly by predicting patient outcomes and trajectories with different levels of available information. Our qualitative analysis shows that empirical clusters of embeddings learned by fastText exhibit the highest similarity with theoretical clustering patterns obtained from biomedical terminologies, with a similarity score between empirical and theoretical clusters of 0.88, 0.80, and 0.92 for diagnosis, procedure, and medication codes, respectively. Conversely, for outcome prediction, word2vec and GloVe tend to outperform fastText, with the former achieving AUROC as high as 0.78, 0.62, and 0.85 for length-of-stay, readmission, and mortality prediction, respectively. In predicting medical codes in patient trajectories, GloVe achieves the highest performance for diagnosis and medication codes (AUPRC of 0.45 and of 0.81, respectively) at the highest level of the semantic hierarchy, while fastText outperforms the other models for procedure codes (AUPRC of 0.66). Our study demonstrates that subword information is crucial for learning medical concept representations, but global embedding vectors are better suited for more high-level downstream tasks, such as trajectory prediction. Thus, these models can be harnessed to learn representations that convey clinical meaning, and our insights highlight the potential of using machine learning techniques to semantically encode medical data.

COVID-19 Vaccination Timing, Relative to Acute COVID-19, and Subsequent Risk of Long COVID

Objectives

Long COVID is a debilitating condition that impacts millions of Americans, but patients and clinicians have little information on how to prevent this disorder. Vaccination is a vital tool in preventing acute COVID-19 and may confer additional protection against Long COVID. There is limited evidence regarding the optimal timing of COVID-19 vaccination (i.e., vaccination schedule) to minimize the risk of Long COVID.

Methods

We applied Longitudinal Targeted Maximum Likelihood Estimation to electronic health record (EHR) data from a retrospective cohort of patients vaccinated against COVID-19 between December 2021 and September 2022. We evaluated the association between binary COVID-19 vaccination status (two or more doses vs. zero doses) and 12-month Long COVID risk among patients diagnosed with acute COVID-19 between December 2021 and September 2022. In addition, we compared the 12-month cumulative risk of Long COVID (ICD-10 code U09.9) among patients diagnosed with acute COVID-19 one to three months after vaccination, three to five months after vaccination, or five to seven months after vaccination while adjusting for relevant high-dimensional baseline and time-dependent covariates.

Results

We analyzed EHR data from a retrospective cohort of 1,558,018 patients. In our binary cohort ( n = 519,980), we found that vaccinated patients had a lower risk of Long COVID than unvaccinated patients (adjusted marginal risk ratio 0.84 (0.81, 0.88)). In our longitudinal cohort ( n = 1,085,291), we did not find a significant difference in Long COVID risk comparing patients who were diagnosed with acute COVID-19 one to three months after vaccination versus patients who were diagnosed with COVID-19 three to five months (adjusted marginal risk ratio 0.93 (95% CI 0.62, 1.41) or 5 to 7 months (adjusted marginal risk ratio 1.06 (95% CI 0.72, 1.56)) after vaccination.

Conclusions

We found that COVID-19 vaccination before SARS-CoV-2 infection was protective against Long COVID, and we did not find that this protection significantly waned within 7 months after vaccination. These findings suggest that COVID-19 vaccination protects against Long COVID.

Identifying commonalities and differences between EHR representations of PASC and ME/CFS in the RECOVER EHR cohort

BACKGROUND

Shared symptoms and biological abnormalities between post-acute sequelae of SARS-CoV-2 infection (PASC) and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) could suggest common pathophysiological bases and would support coordinated treatment efforts. Empirical studies comparing these syndromes are needed to better understand their commonalities and differences.

METHODS

We analyzed electronic health record data from 6.5 million adult patients from the National COVID Cohort Collaborative. PASC and ME/CFS diagnostic groups were defined based on recorded diagnoses, and other recorded conditions within the two groups were used to train separate machine learning-driven computable phenotypes (CPs). The most predictive conditions for each CP were examined and compared, and the overlap of patients labeled by each CP was examined. Condition records from the diagnostic groups were also used to statistically derive condition clusters. Rates of subphenotypes based on these clusters were compared between PASC and ME/CFS groups.

RESULTS

Approximately half of patients labeled by one CP are also labeled by the other. Dyspnea, fatigue, and cognitive impairment are the most-predictive conditions shared by both CPs, whereas other most-predictive conditions are specific to one CP. Recorded conditions separate into cardiopulmonary, neurological, and comorbidity clusters, with the cardiopulmonary cluster showing partial specificity for the PASC groups.

CONCLUSIONS

Data-driven approaches indicate substantial overlap in the condition records associated with PASC and ME/CFS diagnoses. Nevertheless, cardiopulmonary conditions are somewhat more commonly associated with PASC diagnosis, whereas other conditions, such as pain and sleep disturbances, are more associated with ME/CFS diagnosis. These findings suggest that symptom management approaches to these illnesses could overlap.

Cumulative social disadvantage and its impact on long COVID: insights from a U.S. national survey

BACKGROUND

The COVID-19 pandemic has exacerbated health disparities, with long COVID emerging as a major global public health challenge. Although clinical risk factors for long COVID are well-documented, the cumulative burden of adverse social determinants of health (SDoH) remains underexplored. This study aims to investigate the association between cumulative social disadvantage and long COVID.

METHODS

Using data from the 2022 and 2023 National Health Interview Survey cycles (n = 16,446 U.S.adults), cumulative social disadvantage was quantified through 18 SDoH indicators and categorized into quartiles. The highest quartile represents the most disadvantaged individuals. Long COVID was defined as self-reported symptoms persisting for three months or longer. Weighted logistic regression models were used to examine the association, adjusting for demographic and clinical variables.

RESULTS

Adults in the highest quartile of cumulative social disadvantage exhibited an increased odds of experiencing long COVID compared to those in the lowest quartile (AOR = 2.52, 95% Cl: 2.13, 2.98). This association persisted across demographic subgroups, with particularly pronounced effects among women and non-Hispanic Blacks. Hispanics and non-Hispanic Whites showed weaker, but still statistically significant. Key contributors included mental health difficulties, economic instability, and healthcare access barriers. Furthermore, cumulative social disadvantage was linked to fair or poor general health status among individuals with long COVID.

CONCLUSIONS

This study highlights the positive association between cumulative social disadvantage and long COVID. Addressing systemic inequities through integrated public health strategies is essential to mitigate the burden of long COVID and reduce social disparities in health.

Tracking Survivors With Long COVID: Method, Implementation, and Results of an Observational Study

While the coronavirus disease 2019 (COVID-19) pandemic has declined, many survivors continue to suffer debilitating symptoms, such as fatigue, pain, and foggy thoughts. Sustained COVID-19 symptoms, or Long COVID, challenge health care resources and economic recovery. This article describes the methodology, implementation, and results of an observational study investigating how time since diagnosis may affect lingering symptoms among the adult COVID-19 population. The descriptive distribution and overall symptoms experience by individuals' characteristics were examined. Random samples from two patient cohorts (n = 147 in 2020-2021 and n = 137 in 2021-2022) were recruited from a COVID-19 patient registry in mid-Michigan. Samples were drawn from a pool of patients ≥ 3 months following their COVID-19 diagnosis. Overall symptoms experience (number, severity, interference) was self-reported using a comprehensive symptom inventory. The findings showed that 66% of the 2020-2021 cohort and 47% of the 2021-2022 cohort reported ≥ 1 lingering symptom with an average of 11.2 (±3.0) and 8.9 (±3.3) months, respectively, after COVID-19 diagnosis. Females reported significantly more symptoms (p = 0.018), higher symptom severity (p = 0.008) and interference (p = 0.007) compared to males. Compared to patients admitted to emergency departments, outpatients reported significantly lower symptom severity (p = 0.020) and less symptom interference (p = 0.018). Our analyses showed that a moderate proportion (43%) of adults remained symptomatic nearly a year after COVID-19 infection and time since diagnosis did not affect symptom experience in either cohort. Female sex and admission setting are important factors to consider for managing and studying Long COVID.

Maximal oxygen uptake, pulmonary function and walking economy are not impaired in patients diagnosed with long COVID

INTRODUCTION

SARS-CoV-2 may result in the development of new symptoms, known as long COVID, a few months after the original infection.

PURPOSE

It is elusive to what extent physical capacity in patients diagnosed with long COVID is impacted.

METHODS

We compared maximal oxygen uptake (V̇O2max), one of the single most important factors for cardiovascular health and mortality, expired lung volumes and air flow, oxygen cost of walking and 6-min-walking-test (6MWT), in 20 patients diagnosed with long COVID (11 males and 9 females; 44 ± 16 years (SD); 26.7 ± 3.8BMI, duration of acute phase 1.7 ± 1.2 weeks, tested 4 ± 3 months after long COVID diagnosis) with 20 healthy age and sex matched controls (11 males and 9 females; 44 ± 16 years; 25.9 ± 4.0BMI).

RESULTS

Long COVID patients had a V̇O2max of 41.4 ± 16.2 mL∙kg-1∙min-1(men) and 38.2 ± 7.5 (women) and this was not different from controls. Similarly, mean spirometry measures in the patient group (VC; FVC; FEV1; FEV1/FVC) were also not different (85-106%) from predicted healthy values. Finally, inclined treadmill (5%, 4 km∙h-1) walking economy was not different between the groups (long COVID: 15.2 ± 1.1 mL∙kg-1∙min-1; controls: 15.2 ± 1.2 mL∙kg-1∙min-1), while the 6MWT revealed a difference (long COVID: 606 ± 118 m; controls: 685 ± 85 m; p = 0.036).

CONCLUSION

V̇O2max, oxygen cost of walking, and spirometry measurements did not appear to be impaired in patients diagnosed with long COVID with a prior mild to moderate SARS-CoV-2 infection. The typical outcomes in these essential factors for health and longevity implies that while long COVID can present with a range of symptoms, caution should be made when attributing these symptoms directly to compromised pulmonary function or V̇O2max.

Impact of treatment of COVID-19 with sotrovimab on post-acute sequelae of COVID-19 (PASC): an analysis of National COVID Cohort Collaborative (N3C) data

PURPOSE

To assess the impact of early sotrovimab treatment versus no treatment on the risk of developing post-acute sequelae of COVID-19 (PASC; long COVID) in patients (age ≥ 12 years) with COVID-19 at high risk for progression to severe disease.

METHODS

Retrospective cohort study using the US National COVID Cohort Collaborative (N3C) data. Phase 1 identified and assessed multiple definitions of PASC; Phase 2 evaluated the effectiveness of sotrovimab for reducing the risk of PASC, utilizing definitions from Phase 1. Average treatment effect in the treated (ATT)-weighted Cox proportional hazards regression models were used to compare time to event for PASC between high-risk patients who received sotrovimab treatment between May 26, 2021 and April 5, 2022, and high-risk patients with COVID-19 diagnosed between May 26, 2021 and March 26, 2022 who did not receive any treatment for COVID-19 during the acute phase or any pre-exposure prophylaxis against SARS-CoV-2.

RESULTS

A total of 9,504 sotrovimab-treated and 619,668 untreated patients were included in the main analysis. Most baseline characteristics were balanced between the two cohorts after ATT weighting. The doubly robust ATT-weighted hazard ratio (95% confidence interval) was 0.92 (0.89-0.96) (p < 0.001), indicating that sotrovimab use was associated with a significantly lower risk of PASC. Results remained consistent in sensitivity analyses.

CONCLUSION

In patients at high risk for severe COVID-19, the benefits of early sotrovimab treatment may extend beyond the acute phase of COVID-19 and contribute to the prevention of PASC symptoms.

Precision phenotyping for curating research cohorts of patients with unexplained post-acute sequelae of COVID-19

BACKGROUND

Scalable identification of patients with post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms, which has led to suboptimal accuracy, demographic biases, and underestimation of the PASC.

METHODS

In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying cohorts of patients with PASC. We used longitudinal electronic health records data from over 295,000 patients from 14 hospitals and 20 community health centers in Massachusetts. The algorithm employs an attention mechanism to simultaneously exclude sequelae that prior conditions can explain and include infection-associated chronic conditions. We performed independent chart reviews to tune and validate the algorithm.

FINDINGS

The PASC phenotyping algorithm improves precision and prevalence estimation and reduces bias in identifying PASC cohorts compared to the ICD-10-CM code U09.9. The algorithm identified a cohort of over 24,000 patients with 79.9% precision. Our estimated prevalence of PASC was 22.8%, which is close to the national estimates for the region. We also provide in-depth analyses, encompassing identified lingering effects by organ, comorbidity profiles, and temporal differences in the risk of PASC.

CONCLUSIONS

PASC precision phenotyping boasts superior precision and prevalence estimation while exhibiting less bias in identifying patients with PASC. The cohort derived from this algorithm will serve as a springboard for delving into the genetic, metabolomic, and clinical intricacies of PASC, surmounting the constraints of prior PASC cohort studies.

FUNDING

This research was funded by the US National Institute of Allergy and Infectious Diseases (NIAID).

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.

Identification of biomarkers in Alzheimer's disease and COVID-19 by bioinformatics combining single-cell data analysis and machine learning algorithms

BACKGROUND

Since its emergence in 2019, COVID-19 has become a global epidemic. Several studies have suggested a link between Alzheimer's disease (AD) and COVID-19. However, there is little research into the mechanisms underlying these phenomena. Therefore, we conducted this study to identify key genes in COVID-19 associated with AD, and evaluate their correlation with immune cells characteristics and metabolic pathways.

METHODS

Transcriptome analyses were used to identify common biomolecular markers of AD and COVID-19. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed on gene chip datasets (GSE213313, GSE5281, and GSE63060) from AD and COVID-19 patients to identify genes associated with both conditions. Gene ontology (GO) enrichment analysis identified common molecular mechanisms. The core genes were identified using machine learning. Subsequently, we evaluated the relationship between these core genes and immune cells and metabolic pathways. Finally, our findings were validated through single-cell analysis.

RESULTS

The study identified 484 common differentially expressed genes (DEGs) by taking the intersection of genes between AD and COVID-19. The black module, containing 132 genes, showed the highest association between the two diseases according to WGCNA. GO enrichment analysis revealed that these genes mainly affect inflammation, cytokines, immune-related functions, and signaling pathways related to metal ions. Additionally, a machine learning approach identified eight core genes. We identified links between these genes and immune cells and also found a association between EIF3H and oxidative phosphorylation.

CONCLUSION

This study identifies shared genes, pathways, immune alterations, and metabolic changes potentially contributing to the pathogenesis of both COVID-19 and AD.

Healthcare utilization patterns before and after a long COVID diagnosis: a case-control study

BACKGROUND

Documenting Long COVID cases has been challenging partly due to the lack of population-level data and uncertain diagnostic criteria, hindering the ability to ascertain healthcare utilization patterns over time. The objective of this study is to examine the characteristics and healthcare utilization patterns of Long COVID patients in Colorado pre- and post-diagnosis compared to controls.

METHODS

Retrospective, longitudinal case-control study using a 100% sample of Colorado's All-Payer Claims Database. The sample includes individuals 18 or older diagnosed with Long COVID between October 1, 2021, and August 1, 2022, with patients followed until August 2023. Long COVID was identified using the International Classification of Diseases, 10th Revision, U09.9 code in medical insurance claims. Analysis of healthcare utilization required one year of continuous enrollment before and after diagnosis. Controls were matched 2:1 on age group, sex, payer, and index month to account for contemporaneous trends in utilization.

RESULTS

26,358 individuals were ever diagnosed with Long COVID, resulting in a claims-based prevalence of 674 per 100,000 during the study period (population 3,906,402 individuals). Of these, 12,698 individuals had continuous enrollment and a Long COVID diagnosis: mean (SD) age, 59.0 (17.1); 65.3% female; 60.1% white; 83.0% residing in urban areas. The Long COVID sample was matched with 25,376 controls. Before diagnosis, 17% of Long COVID patients were hospitalized at least once, and 40% visited an emergency department on at least one occasion. Within the year following diagnosis, utilization of acute healthcare services significantly decreased relative to controls: hospitalizations, -6.1percentage points (p.p.), emergency department visits, -7.7 p.p., whereas outpatient services and medications increased: office visits, 3.6 p.p.; specialist office visits, 4.7 p.p.; and 5.2 new medications, (controls: 2.8). Changes in diagnoses of some conditions (e.g., metastatic carcinomas and lung cancer) were similar between groups.

CONCLUSIONS AND RELEVANCE

Long COVID patients increased outpatient healthcare utilization following a diagnosis, switching from acute care settings. The change in service settings among this population suggests that diagnosis could lead to better patient management. Healthcare utilization among these patients is high, underscoring the need to understand the Long COVID burden on healthcare systems with population-level data.

Association of glycemic control with Long COVID in patients with type 2 diabetes: findings from the National COVID Cohort Collaborative (N3C)

INTRODUCTION

Elevated glycosylated hemoglobin (HbA1c) in individuals with type 2 diabetes is associated with increased risk of hospitalization and death after acute COVID-19, however the effect of HbA1c on Long COVID is unclear.

OBJECTIVE

Evaluate the association of glycemic control with the development of Long COVID in patients with type 2 diabetes (T2D).

RESEARCH DESIGN AND METHODS

We conducted a retrospective cohort study using electronic health record data from the National COVID Cohort Collaborative. Our cohort included individuals with T2D from eight sites with longitudinal natural language processing (NLP) data. The primary outcome was death or new-onset recurrent Long COVID symptoms within 30-180 days after COVID-19. Symptoms were identified as keywords from clinical notes using NLP in respiratory, brain fog, fatigue, loss of smell/taste, cough, cardiovascular and musculoskeletal symptom categories. Logistic regression was used to evaluate the risk of Long COVID by HbA1c range, adjusting for demographics, body mass index, comorbidities, and diabetes medication. A COVID-negative group was used as a control.

RESULTS

Among 7430 COVID-positive patients, 1491 (20.1%) developed symptomatic Long COVID, and 380 (5.1%) died. The primary outcome of death or Long COVID was increased in patients with HbA1c 8% to <10% (OR 1.20, 95% CI 1.02 to 1.41) and ≥10% (OR 1.40, 95% CI 1.14 to 1.72) compared with those with HbA1c 6.5% to <8%. This association was not seen in the COVID-negative group. Higher HbA1c levels were associated with increased risk of Long COVID symptoms, especially respiratory and brain fog. There was no association between HbA1c levels and risk of death within 30-180 days following COVID-19. NLP identified more patients with Long COVID symptoms compared with diagnosis codes.

CONCLUSION

Poor glycemic control (HbA1c≥8%) in people with T2D was associated with higher risk of Long COVID symptoms 30-180 days following COVID-19. Notably, this risk increased as HbA1c levels rose. However, this association was not observed in patients with T2D without a history of COVID-19. An NLP-based definition of Long COVID identified more patients than diagnosis codes and should be considered in future studies.

The incidence of outpatient care within 24 months from SARS-CoV-2 infection in the general population: a multicenter population-based cohort study

BACKGROUND

The long-term effects of COVID-19, which can vary significantly in type and timing, are considered relevant and impacting on the well-being of individuals. The present study aims to assess the incidence of outpatient care in the post-acute phase of SARS-CoV-2 infection in two Italian regions.

METHODS

The study has a multicentre, population-based, pre-post, repeated measures design to compare the incidence rate of access to outpatient visits and diagnostics before and after SARS-CoV-2 infection, considering a follow-up of 24 months. The study made use of previously recorded large-scale healthcare data available in the administrative databases of the Emilia-Romagna (E-R) and Veneto regions. Analyses were carried out separately in the two regions and results were pooled using random effects meta-analysis.

RESULTS

There were 27,140 subjects in E-R and 22,876 in Veneto who were included in the analysis. The pooled outputs showed an increase in rates of outpatient visits and diagnostics starting from month 2 after SARS-CoV-2 infection (IRR = 1.68, 95% CI = 1.56-1.81) with a peak at month 4 (IRR = 2.05, 95% CI = 1.95-2.15); the increase continued with reduced intensity up to month 15. Stratified analysis revealed that subjects with severe acute COVID-19 had a higher increase in rates (up to IRR = 3.96, 95% CI = 2.89-5.44), as well as patients with no comorbidities (up to IRR = 2.71, 95% CI = 2.60-2.83).

CONCLUSION

Long-term effects of COVID-19 include an increase in the healthcare burden especially in the first months after the acute infection. The increased demand for resources can last up to two years after infection in particular subgroups of patients such as subjects admitted to hospital during the acute phase due to the severe presentation of the disease.

The impact of gut microbiome and diet on post-acute sequelae of SARS-CoV-2 infection

Long COVID, also known as Post COVID-19 condition by the World Health Organization or Post-Acute Sequelae of SARS-CoV-2 infection (PASC), is defined as the development of symptoms such as post-exertional malaise, dysgeusia, and partial or full anosmia three months after initial SARS-CoV-2 infection. The multisystem effects of PASC make it difficult to distinguish from its mimickers. Further, a comprehensive evaluation of the gut microbiome, nutrition, and PASC has yet to be studied. The gut-brain axis describes bidirectional immune, neural, endocrine, and humoral modulatory interactions between the gut microbiome and brain function. We explore recent studies that support an association between alterations in gut microbiome diversity and the severity of acute-phase COVID-19, and how these may be affected by diets rich in antioxidants and fiber. The Mediterranean Diet (MeDi) has demonstrated promising neuroprotective effects through its anti-inflammatory processes. Further, diets rich in fiber increase gut diversity and increase the amount of short-chain fatty acids (SCFAs) within the body-both shown to protect from acute COVID-19 complications. Long-term changes to the gut microbiome persist after acute infection and may increase susceptibility to PASC. This study builds on existing knowledge of determinants of PASC and highlights a relationship between nutrition, gut microbiome, acute-phase COVID-19, and, subsequently, PASC susceptibility.

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.

Tree-based classification model for Long-COVID infection prediction with age stratification using data from the National COVID Cohort Collaborative

Objectives

We propose and validate a domain knowledge-driven classification model for diagnosing post-acute sequelae of SARS-CoV-2 infection (PASC), also known as Long COVID, using Electronic Health Records (EHRs) data.

Materials and Methods

We developed a robust model that incorporates features strongly indicative of PASC or associated with the severity of COVID-19 symptoms as identified in our literature review. The XGBoost tree-based architecture was chosen for its ability to handle class-imbalanced data and its potential for high interpretability. Using the training data provided by the Long COVID Computation Challenge (L3C), which was a sample of the National COVID Cohort Collaborative (N3C), our models were fine-tuned and calibrated to optimize Area Under the Receiver Operating characteristic curve (AUROC) and the F1 score, following best practices for the class-imbalanced N3C data.

Results

Our age-stratified classification model demonstrated strong performance with an average 5-fold cross-validated AUROC of 0.844 and F1 score of 0.539 across the young adult, mid-aged, and older-aged populations in the training data. In an independent testing dataset, which was made available after the challenge was over, we achieved an overall AUROC score of 0.814 and F1 score of 0.545.

Discussion

The results demonstrated the utility of knowledge-driven feature engineering in a sparse EHR data and demographic stratification in model development to diagnose a complex and heterogeneously presenting condition like PASC. The model's architecture, mirroring natural clinician decision-making processes, contributed to its robustness and interpretability, which are crucial for clinical translatability. Further, the model's generalizability was evaluated over a new cross-sectional data as provided in the later stages of the L3C challenge.

Conclusion

The study proposed and validated the effectiveness of age-stratified, tree-based classification models to diagnose PASC. Our approach highlights the potential of machine learning in addressing the diagnostic challenges posed by the heterogeneity of Long-COVID symptoms.

Current update on the neurological manifestations of long COVID: more questions than answers

Since the outbreak of the COVID-19 pandemic, there has been a global surge in patients presenting with prolonged or late-onset debilitating sequelae of SARS-CoV-2 infection, colloquially termed long COVID. This narrative review provides an updated synthesis of the latest evidence on the neurological manifestations of long COVID, discussing its clinical phenotypes, underlying pathophysiology, while also presenting the current state of diagnostic and therapeutic approaches. Approximately one-third of COVID-19 survivors experience prolonged neurological sequelae that persist for at least 12-months post-infection, adversely affecting patients' quality of life. Core neurological manifestations comprise fatigue, post-exertional malaise, cognitive impairment, headache, lightheadedness ('brain fog'), sleep disturbances, taste or smell disorders, dysautonomia, anxiety, and depression. Some of these features overlap substantially with those reported in post-intensive-care syndrome, myalgic encephalomyelitis/chronic fatigue syndrome, fibromyalgia, and postural-orthostatic-tachycardia syndrome. Advances in data-driven research utilizing electronic-health-records combined with machine learning and artificial intelligence have propelled the identification of long COVID sub-phenotypes. Furthermore, the evolving definitions reflect the dynamic conceptualization of long COVID in both research and clinical contexts. Although the underlying pathophysiology remains incompletely elucidated, neuroinflammatory responses, endotheliopathy, and metabolic imbalances, rather than direct viral neuroinvasion, are implicated in neurological sequelae. Genetic susceptibility has also emerged as a potential risk factor. While major limitations remain with existing definitions, collaborative strategies to standardize diagnostic approaches are needed. Current therapeutic paradigms advocate for multimodal approaches, integrating pharmacological and non-pharmacological interventions along with comprehensive rehabilitation programs. Although preliminary evidence of therapeutic efficacy has been provided by a number of clinical trials, methodological constraints limit the generalizability of this evidence. Preventive measures, notably vaccination, have proven integral for reducing the global burden of long COVID. Considering the healthcare and socioeconomic repercussions incurred by long COVID worldwide, international collaborative initiatives are warranted to address the remaining challenges in diagnosing and managing patients presenting with neurological sequelae. See also the graphical abstract(Fig. 1).

A worldwide look into long COVID-19 management: an END-COVID survey

Background

Long COVID is a heterogeneous clinical syndrome characterised by a variety of reported symptoms and signs. Its clinical management is expected to differ significantly worldwide.

Methods

A survey-based study investigating long COVID-related standard operating procedures (SOPs) has been conducted by the European Respiratory Society (ERS) END-COVID clinical research collaboration with the support of other international societies (ALAT, APSR, CHEST, ESCMID and PATS). A global analysis of the results is provided here, alongside sub-population analysis based on continents, national income levels, type of involved healthcare professional and inclusion or exclusion of paediatric patients.

Findings

1015 healthcare professionals from 110 different countries worldwide participated in this study, the majority of them being respiratory physicians (60.6%). A dedicated long COVID programme was present in 55.4% of the investigated institutions, with hospital admission during the acute infection being the main inclusion criteria to access them. Consistent differences in long COVID-related procedures were identified among centres, mainly regarding the multidisciplinary approach, the availability of telemedicine and psychological support, the type of requested exams and the total amount of visits in the centre.

Interpretation

Long COVID management shows important differences related to geographical areas and national income levels. SOPs were significantly different when centres were managed by a pulmonologist or when paediatric patients were included.

Association between malnutrition and post-acute COVID-19 sequelae: A retrospective cohort study

BACKGROUND

Long coronavirus disease consists of health problems people experience after being infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These can be severe and include respiratory, neurological, and gastrointestinal symptoms, with resulting detrimental impacts on quality of life. Although malnutrition has been shown to increase risk of severe disease and death during acute infection, less is known about its influence on post-acute COVID-19 outcomes. We addressed this critical gap in knowledge by evaluating malnutrition's impact on post-COVID-19 sequelae.

METHODS

This study leveraged the National COVID Cohort Collaborative to identify a cohort of patients who were at least 28 days post-acute COVID-19 infection. Multivariable Cox proportional hazard models evaluated the impact of malnutrition on the following postacute sequelae of SARS-CoV-2: (1) death, (2) long COVID diagnosis, (3) COVID-19 reinfection, and (4) other phenotypic abnormalities. A subgroup analysis evaluated these outcomes in a cohort of hospitalized patients with COVID-19 with hospital-acquired (HAC) malnutrition.

RESULTS

The final cohort included 4,372,722 individuals, 78,782 (1.8%) with a history of malnutrition. Individuals with malnutrition had a higher risk of death (adjusted hazard ratio [aHR]: 2.10; 95% CI: 2.04-2.17) and SARS-CoV-2 reinfection (aHR: 1.52; 95% CI: 1.43-1.61) in the postacute period than those without malnutrition. In the subgroup, those with HAC malnutrition had a higher risk of death and long COVID diagnosis.

CONCLUSION

Nutrition screening for individuals with acute SARS-CoV-2 infection may be a crucial step in mitigating life-altering, negative postacute outcomes through early identification and intervention of patients with malnutrition.

Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs

Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease.

Post-acute sequelae of SARS-CoV-2 mimic: An important neurological condition

BACKGROUND AND OBJECTIVES

In 2024, the sequalae of the acute phase of coronavirus disease-19 (COVID-19) infection, which include neurological symptoms and are commonly referred to as long COVID or post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (PASC), continue to be a substantial health concern; however, similar symptoms are observed in individuals with no previous COVID-19 infection.

METHODS

This was a single-center, retrospective, descriptive case series study. Data were obtained from patients who visited our outpatient clinic specializing in PASC between June 1, 2021, and May 31, 2023. We compared antibody test results between patients with confirmed acute phase infection and those without. We compared differences in demographic and clinical characteristics between patients with positive results during the acute phase of COVID-19 infection and positive anti-SARS-CoV-2 antibody tests (true-PASC), and those with neither (PASC-mimic).

RESULTS

Of 437 patients diagnosed with PASC according to World Health Organization criteria, 222 underwent COVID-19 antibody tests. Of these, 193 patients (86.9%) had a history of confirmed acute phase infection, whereas 29 (13.1%) did not. Of the former, 186 patients (96.4%) were seropositive for anti-nucleotide SARS-CoV-2 antibodies (true-PASC), whereas 19 of the latter tested seronegative for anti-nucleotide SARS-CoV-2 antibodies (PASC-mimic). There were no significant differences in symptom characteristics between true-PASC and PASC-mimic participants.

CONCLUSIONS

It was difficult to identify any clinical features to aid in diagnosing PASC without confirmation of acute COVID-19 infection. The findings indicate the existence of a "PASC-mimic" condition that should be acknowledged and excluded in future PASC-related research studies.

Exploring the Effects of Qigong, Tai Chi, and Yoga on Fatigue, Mental Health, and Sleep Quality in Chronic Fatigue and Post-COVID Syndromes: A Systematic Review with Meta-Analysis

Background/Objectives: Chronic fatigue syndrome (CFS) and post-COVID syndrome (PCS) pose a substantial socioeconomic burden. The aim of this systematic review was to assess current evidence regarding the effect of the most popular forms of movement-based mindful exercises, i.e., qigong, tai chi, and yoga, on fatigue and associated symptoms in CFS and PCS. Methods: CINAHL, Embase, PsycINFO, PubMed, Scopus, and the Cochrane Library were searched from inception to October 2023. Randomized controlled trials (RCTs) where qigong, tai chi, or yoga were compared with waitlist, no intervention, or active controls were included. Independent reviewers participated in data extraction, and evaluated risk of bias, spin of information, completeness of intervention description, and certainty of the evidence (GRADE). Meta-analyses were conducted. The primary outcome was the level of fatigue. Secondary measures were the severity of anxiety and depressive symptoms and sleep quality. Results were expressed as mean difference (MD) or standardized mean difference (SMD) with a 95% confidence interval (CI). Results: Thirteen RCTs with 661 participants were included, with most studies presenting a moderate or high risk of bias. Mindful exercises were more effective than control interventions to alleviate fatigue: SMD (95%CI) = -0.44 (-0.63 to -0.25), I2 = 48%, p < 0.0001. Positive effects were also observed for secondary outcomes. The certainty of the evidence was low or very low. Conclusions: Qigong, tai chi, and yoga may be effective to reduce fatigue and improve anxiety, depression, and sleep quality in adults with CFS or PCS. However, serious methodological concerns limit the clinical applicability of these findings.

SSRI use during acute COVID-19 and risk of long COVID among patients with depression

BACKGROUND

Long COVID, also known as post-acute sequelae of COVID-19 (PASC), is a poorly understood condition with symptoms across a range of biological domains that often have debilitating consequences. Some have recently suggested that lingering SARS-CoV-2 virus particles in the gut may impede serotonin production and that low serotonin may drive many Long COVID symptoms across a range of biological systems. Therefore, selective serotonin reuptake inhibitors (SSRIs), which increase synaptic serotonin availability, may be used to prevent or treat Long COVID. SSRIs are commonly prescribed for depression, therefore restricting a study sample to only include patients with depression can reduce the concern of confounding by indication.

METHODS

In an observational sample of electronic health records from patients in the National COVID Cohort Collaborative (N3C) with a COVID-19 diagnosis between September 1, 2021, and December 1, 2022, and a comorbid depressive disorder, the leading indication for SSRI use, we evaluated the relationship between SSRI use during acute COVID-19 and subsequent 12-month risk of Long COVID (defined by ICD-10 code U09.9). We defined SSRI use as a prescription for SSRI medication beginning at least 30 days before acute COVID-19 and not ending before SARS-CoV-2 infection. To minimize bias, we estimated relationships using nonparametric targeted maximum likelihood estimation to aggressively adjust for high-dimensional covariates.

RESULTS

We analyzed a sample (n = 302,626) of patients with a diagnosis of a depressive condition before COVID-19 diagnosis, where 100,803 (33%) were using an SSRI. We found that SSRI users had a significantly lower risk of Long COVID compared to nonusers (adjusted causal relative risk 0.92, 95% CI (0.86, 0.99)) and we found a similar relationship comparing new SSRI users (first SSRI prescription 1 to 4 months before acute COVID-19 with no prior history of SSRI use) to nonusers (adjusted causal relative risk 0.89, 95% CI (0.80, 0.98)).

CONCLUSIONS

These findings suggest that SSRI use during acute COVID-19 may be protective against Long COVID, supporting the hypothesis that serotonin may be a key mechanistic biomarker of Long COVID.

Body Mass Index and Postacute Sequelae of SARS-CoV-2 Infection in Children and Young Adults

Importance

Obesity is associated with increased severity of COVID-19. Whether obesity is associated with an increased risk of post-acute sequelae of SARS-CoV-2 infection (PASC) among pediatric populations, independent of its association with acute infection severity, is unclear.

Objective

To quantify the association of body mass index (BMI) status before SARS-CoV-2 infection with pediatric PASC risk, controlling for acute infection severity.

Design, Setting, and Participants

This retrospective cohort study occurred at 26 US children's hospitals from March 2020 to May 2023 with a minimum follow-up of 179 days. Eligible participants included children and young adults aged 5 to 20 years with SARS-CoV-2 infection. Data analysis was conducted from October 2023 to January 2024.

Exposures

BMI status assessed within 18 months before infection; the measure closest to the index date was selected. The BMI categories included healthy weight (≥5th to <85th percentile for those aged 5-19 years or ≥18.5 to <25 for those aged >19 years), overweight (≥85th to <95th percentile for those aged 5-19 years or ≥25 to <30 for for those aged >19 years), obesity (≥95th percentile to <120% of the 95th percentile for for those aged 5-19 years or ≥30 to <40 for those aged >19 years), and severe obesity (≥120% of the 95th percentile for those aged 5-19 years or ≥40 for those aged >19 years).

Main Outcomes And Measures

To identify PASC, a diagnostic code specific for post-COVID-19 conditions was used and a second approach used clusters of symptoms and conditions that constitute the PASC phenotype. Relative risk (RR) for the association of BMI with PASC was quantified by Poisson regression models, adjusting for sociodemographic, acute COVID severity, and other clinical factors.

Results

A total of 172 136 participants (mean [SD] age at BMI assessment 12.6 [4.4] years; mean [SD] age at cohort entry, 13.1 [4.4] years; 90 187 female [52.4%]) were included. Compared with participants with healthy weight, those with obesity had a 25.4% increased risk of PASC (RR, 1.25; 95% CI, 1.06-1.48) and those with severe obesity had a 42.1% increased risk of PASC (RR, 1.42; 95% CI, 1.25-1.61) when identified using the diagnostic code. Compared with those with healthy weight, there was an increased risk for any occurrences of PASC symptoms and conditions among those with obesity (RR, 1.11; 95% CI, 1.06-1.15) and severe obesity (RR, 1.17; 95% CI, 1.14-1.21), and the association held when assessing total incident occurrences among those with overweight (RR, 1.05; 95% CI, 1.00-1.11), obesity (RR, 1.13; 95% CI, 1.09-1.19), and severe obesity (RR, 1.18; 95% CI, 1.14-1.22).

Conclusions And Relevance

In this cohort study, elevated BMI was associated with a significantly increased PASC risk in a dose-dependent manner, highlighting the need for targeted care to prevent chronic conditions in at-risk children and young adults.

Associations Between Acute COVID-19 Symptom Profiles and Long COVID Prevalence: Population-Based Cross-Sectional Study

Background

Growing evidence suggests that severe acute COVID-19 illness increases the risk of long COVID (also known as post-COVID-19 condition). However, few studies have examined associations between acute symptoms and long COVID onset.

Objective

This study aimed to examine associations between acute COVID-19 symptom profiles and long COVID prevalence using a population-based sample.

Methods

We used a dual mode (phone and web-based) population-based probability survey of adults with polymerase chain reaction-confirmed SARS-CoV-2 between June 2020 and May 2022 in the Michigan Disease Surveillance System to examine (1) how acute COVID-19 symptoms cluster together using latent class analysis, (2) sociodemographic and clinical predictors of symptom clusters using multinomial logistic regression accounting for classification uncertainties, and (3) associations between symptom clusters and long COVID prevalence using modified Poisson regression.

Results

In our sample (n=4169), 15.9% (n=693) had long COVID, defined as new or worsening symptoms at least 90 days post SARS-CoV-2 infection. We identified 6 acute COVID-19 symptom clusters resulting from the latent class analysis, with flu-like symptoms (24.7%) and fever (23.6%) being the most prevalent in our sample, followed by nasal congestion (16.4%), multi-symptomatic (14.5%), predominance of fatigue (10.8%), and predominance of shortness of breath (10%) clusters. Long COVID prevalence was highest in the multi-symptomatic (39.7%) and predominance of shortness of breath (22.4%) clusters, followed by the flu-like symptom (15.8%), predominance of fatigue (14.5%), fever (6.4%), and nasal congestion (5.6%) clusters. After adjustment, females (vs males) had greater odds of membership in the multi-symptomatic, flu-like symptom, and predominance of fatigue clusters, while adults who were Hispanic or another race or ethnicity (vs non-Hispanic White) had greater odds of membership in the multi-symptomatic cluster. Compared with the nasal congestion cluster, the multi-symptomatic cluster had the highest prevalence of long COVID (adjusted prevalence ratio [aPR] 6.1, 95% CI 4.3-8.7), followed by the predominance of shortness of breath (aPR 3.7, 95% CI 2.5-5.5), flu-like symptom (aPR 2.8, 95% CI 1.9-4.0), and predominance of fatigue (aPR 2.2, 95% CI 1.5-3.3) clusters.

Conclusions

Researchers and clinicians should consider acute COVID-19 symptom profiles when evaluating subsequent risk of long COVID, including potential mechanistic pathways in a research context, and proactively screen high-risk patients during the provision of clinical care.

Modeling the burden of long COVID in California with quality adjusted life-years (QALYS)

Individuals infected with SARS-CoV-2 may develop post-acute sequelae of COVID-19 ("long COVID") even after asymptomatic or mild acute illness. Including time varying COVID symptom severity can provide more informative burden estimates for public health response. Using a compartmental model driven by confirmed cases, this study estimated long COVID burden by age group (0-4, 5-17, 18-49, 50-64, 65+) in California as measured by the cumulative and severity-specific proportion of quality-adjusted life years (QALYs) lost. Long COVID symptoms were grouped into severe, moderate, and mild categories based on estimates from the Global Burden of Disease study, and symptoms were assumed to decrease in severity in the model before full recovery. All 10,945,079 confirmed COVID-19 cases reported to the California Department of Public Health between March 1, 2020, and December 31, 2022, were included in the analysis. Most estimated long COVID-specific QALYs [59,514 (range: 10,372-180,257)] lost in California were concentrated in adults 18-49 (31,592; 53.1%). Relative to other age groups, older adults (65+) lost proportionally more QALYs from severe long COVID (1,366/6,984; 20%). Due to changing case ascertainment over time, this analysis might underestimate the actual total burden. In global sensitivity analysis, estimates of QALYs lost were most sensitive to the proportion of individuals that developed long COVID and proportion of cases with each initial level of long COVID symptom severity (mild/moderate/severe). Models like this analysis can help translate observable metrics such as cases and hospitalizations into quantitative estimates of long COVID burden that are currently difficult to directly measure. Unlike the observed relationship between age and incident severe outcomes for COVID-19, this study points to the potential cumulative impact of mild long COVID symptoms in younger individuals.

The prevalence of postacute sequelae of coronavirus disease 2019 in solid organ transplant recipients: Evaluation of risk in the National COVID Cohort Collaborative

Postacute sequelae after the coronavirus disease (COVID) of 2019 (PASC) is increasingly recognized, although data on solid organ transplant (SOT) recipients (SOTRs) are limited. Using the National COVID Cohort Collaborative, we performed 1:1 propensity score matching (PSM) of all adult SOTR and nonimmunosuppressed/immunocompromised (ISC) patients with acute COVID infection (August 1, 2021 to January 13, 2023) for a subsequent PASC diagnosis using International Classification of Diseases, 10th Revision, Clinical Modification codes. Multivariable logistic regression was used to examine not only the association of SOT status with PASC, but also other patient factors after stratifying by SOT status. Prior to PSM, there were 8769 SOT and 1 576 769 non-ISC patients with acute COVID infection. After PSM, 8756 SOTR and 8756 non-ISC patients were included; 2.2% of SOTR (n = 192) and 1.4% (n = 122) of non-ISC patients developed PASC (P value < .001). In the overall matched cohort, SOT was independently associated with PASC (adjusted odds ratio [aOR], 1.48; 95% confidence interval [CI], 1.09-2.01). Among SOTR, COVID infection severity (aOR, 11.6; 95% CI, 3.93-30.0 for severe vs mild disease), older age (aOR, 1.02; 95% CI, 1.01-1.03 per year), and mycophenolate mofetil use (aOR, 2.04; 95% CI, 1.38-3.05) were each independently associated with PASC. In non-ISC patients, only depression (aOR, 1.96; 95% CI, 1.24-3.07) and COVID infection severity were. In conclusion, PASC occurs more commonly in SOTR than in non-ISC patients, with differences in risk profiles based on SOT status.

Association of COVID-19 Vaccination With Risk of Medically Attended Postacute Sequelae of COVID-19 During the Ancestral, Alpha, Delta, and Omicron Variant Eras

Background

Uncertainty exists regarding the effectiveness of COVID-19 vaccine to prevent postacute sequelae of COVID-19 (PASC) following a breakthrough infection. While most studies based on symptom surveys found an association between preinfection vaccination status and PASC symptoms, studies of medically attended PASC are less common and have reported conflicting findings.

Methods

In this retrospective cohort of patients with an initial SARS-CoV-2 infection who were continually empaneled for primary care in a large US health system, the electronic health record was queried for preinfection vaccination status, demographics, comorbidity index, and diagnosed conditions. Multivariable logistic regression was used to model the outcome of a medically attended PASC diagnosis within 6 months of SARS-CoV-2 infection. Likelihood ratio tests were used to assess the interaction between vaccination status and prevalent variant at the time of infection and between vaccination status and hospitalization for SARS-CoV-2 infection.

Results

During the observation period, 6.9% of patients experienced medically attended and diagnosed PASC. A diagnosis of PASC was associated with older age, female sex, hospitalization for the initial infection, and an increased severity-weighted comorbidity index and was inversely associated with infection during the Omicron period. No difference in the development of diagnosed PASC was observed between unvaccinated patients and those vaccinated with either 2 doses of an mRNA vaccine or >2 doses.

Conclusions

We found no association between vaccination status at the time of infection and development of medically diagnosed PASC. Vaccine remains an important measure to prevent SARS-CoV-2 infection and severity. Further research is needed to identify effective measures to prevent and treat PASC.

Insulin Resistance in Long COVID-19 Syndrome

Background: The COVID-19 pandemic has caused severe health issues worldwide and contributed to huge financial losses. Key comorbidities linked to an increased risk of severe COVID-19 and higher mortality rates include cardio-metabolic disorders such as type 1 and type 2 diabetes mellitus (T1DM and T2DM), atherosclerotic cardiovascular disease, chronic kidney disease, hypertension, heart failure, and obesity. The persistence of symptoms even after the acute phase is over is termed long COVID-19 syndrome. This study aimed to evaluate the relationship between long COVID-19 syndrome and the development of insulin resistance in previously non-diabetic patients. Methods: A prospective observational study was performed on 143 non-diabetic patients who had tested positive for SARS-CoV-2 infection by a PCR test and were hospitalized in our hospital between January 2020 and December 2022. The clinical and para-clinical data at 0, 4, and 12 months of hospital admission for post-COVID-19 infection follow-up was collected and labeled as t0, t4, and t12. Blood glucose, insulin, and C-peptide levels were measured at the beginning and further at 2, 5, 10, and 30 min after the intravenous arginine stimulation test. Similarly, BMI was calculated, and hs-CRP and ESR levels were noted. The results obtained were statistically analyzed. Results: More than one-third (30.7%) of the included patients developed long COVID-19 syndrome. It was found that 75% of patients with long COVID-19 hospitalized in our clinic developed diabetes within a year of acute infection with COVID-19; therefore, it can be said that the presence of long COVID-19 is a major risk for an altered metabolic status, which can cause diabetes. When comparing the glycemia levels (106 mg/dL) with the BMI at t0, t4, and t12 time intervals, the p-values were found to be 0.214, 0.042, and 0.058, respectively. Almost 62% of the patients having BMI > 30 kg/m2 were found to have an increase in blood glucose levels at 1 year. Similarly, insulin resistance was noted during this interval. A negative correlation of 0.40 for hsCRP and 0.38 for ESR was noted when compared with acute infection with COVID-19. Conclusions: The association between long COVID-19 and insulin resistance highlights the varied and widespread impacts of SARS-CoV-2 infection. Addressing the complexities of long COVID-19 requires a holistic strategy that encompasses both respiratory and metabolic considerations, which is crucial for enhancing the well-being of those enduring this persistent condition.

EHR-based Case Identification of Pediatric Long COVID: A Report from the RECOVER EHR Cohort

Objective

Long COVID, marked by persistent, recurring, or new symptoms post-COVID-19 infection, impacts children's well-being yet lacks a unified clinical definition. This study evaluates the performance of an empirically derived Long COVID case identification algorithm, or computable phenotype, with manual chart review in a pediatric sample. This approach aims to facilitate large-scale research efforts to understand this condition better.

Methods

The algorithm, composed of diagnostic codes empirically associated with Long COVID, was applied to a cohort of pediatric patients with SARS-CoV-2 infection in the RECOVER PCORnet EHR database. The algorithm classified 31,781 patients with conclusive, probable, or possible Long COVID and 307,686 patients without evidence of Long COVID. A chart review was performed on a subset of patients (n=651) to determine the overlap between the two methods. Instances of discordance were reviewed to understand the reasons for differences.

Results

The sample comprised 651 pediatric patients (339 females, M age = 10.10 years) across 16 hospital systems. Results showed moderate overlap between phenotype and chart review Long COVID identification (accuracy = 0.62, PPV = 0.49, NPV = 0.75); however, there were also numerous cases of disagreement. No notable differences were found when the analyses were stratified by age at infection or era of infection. Further examination of the discordant cases revealed that the most common cause of disagreement was the clinician reviewers' tendency to attribute Long COVID-like symptoms to prior medical conditions. The performance of the phenotype improved when prior medical conditions were considered (accuracy = 0.71, PPV = 0.65, NPV = 0.74).

Conclusions

Although there was moderate overlap between the two methods, the discrepancies between the two sources are likely attributed to the lack of consensus on a Long COVID clinical definition. It is essential to consider the strengths and limitations of each method when developing Long COVID classification algorithms.

Long COVID impacts: the voices and views of diverse Black and Latinx residents in Massachusetts

OBJECTIVE

To understand how Long COVID is impacting the health and social conditions of the Black and Latinx communities.

BACKGROUND

Emerging research on Long COVID has identified three distinct characteristics, including multi-organ damage, persistent symptoms, and post-hospitalization complications. Given Black and Latinx communities experienced significantly higher COVID rates in the first phase of the pandemic they may be disproportionately impacted by Long COVID.

METHODS

Eleven focus groups were conducted in four languages with diverse Black and Latinx individuals (n = 99) experiencing prolonged symptoms of COVID-19 or caring for family members with prolonged COVID-19 symptoms. Data was analyzed thematically.

RESULTS

Most participants in non-English language groups reported they were unfamiliar with the diagnosis of long COVID, despite experiencing symptoms. Long COVID impacts spanned financial and housing stability to physical and mental health impacts. Participants reported challenging encounters with health care providers, a lack of support managing symptoms and difficulty performing activities of daily living including work.

CONCLUSIONS

There is a need for multilingual, accessible information about Long COVID symptoms, improved outreach and healthcare delivery, and increased ease of enrollment in long-term disability and economic support programs.

Multilevel Determinants of Long COVID and Potential for Telehealth Intervention

Background

Post-coronavirus disease 2019 (COVID-19) syndrome, or long COVID, has a variety of symptoms, but little is known about the condition. This study evaluated the association between individual factors, social determinants of health, and the likelihood of long COVID by assessing internet usage as an indicator of viable access to telehealth.

Methods

Data from the 2022 National Health Interview Survey identified adults who (1) reported a previous COVID-19-positive test and/or diagnosis and (2) experienced long COVID. A 2-stage selection model predicted COVID-19 infection in the first stage and long COVID in the second stage. To test the potential use of telehealth, binary dependent variable regression evaluated internet usage among respondents with long COVID.

Results

About 40% (N=10,318) of respondents had tested positive/been diagnosed with COVID-19, but less than 20% of them (N=1797) had long COVID. Although older respondents were less likely to have COVID (odds ratio [OR]=0.48; 95% confidence interval [CI]=0.44, 0.53), they were more likely to experience long COVID (OR=1.63; CI=1.37, 1.93). Relative to White individuals, Black individuals were less likely to have COVID (OR=0.78; CI=0.69, 0.89) but significantly more likely (OR=1.21; CI=1.09, 1.64) to experience long COVID. Long COVID was also more likely among low-income earners (first income-to-poverty ratio quartile OR=1.40, CI=1.14, 1.72; second income-to-poverty ratio OR=1.37, CI=1.14, 1.64) and those without a college degree (OR=1.42; CI=1.01, 1.66). There were no statistically significant differences in internet access between racial, geographic, or income groups.

Conclusion

Long COVID is significantly more likely among Black individuals and low-income households than among their counterparts, but with few recourses available, telehealth service delivery could be a feasible intervention mechanism.

Limited Awareness of Long COVID Despite Common Experience of Symptoms Among African American/Black, Hispanic/Latino, and Indigenous Adults in Arizona

OBJECTIVES

Communities of color might disproportionately experience long-term consequences of COVID-19, known as Long COVID. We sought to understand the awareness of and experiences with Long COVID among African American/Black (AA/B), Hispanic/Latino (H/L), and Indigenous (Native) adults (18 + years of age) in Arizona who previously tested positive for COVID-19.

METHODS

Between December 2022 and April 2023, the Arizona Community Engagement Alliance (AZCEAL) conducted 12 focus groups and surveys with 65 AA/B, H/L and Native community members. Data from focus groups were analyzed using thematic analysis to identify emerging issues. Survey data provided demographic information about participants and quantitative assessments of Long COVID experiences were used to augment focus group data.

RESULTS

Study participants across all three racial/ethnic groups had limited to no awareness of the term Long COVID, yet many described experiencing or witnessing friends and family endure physical symptoms consistent with Long COVID (e.g., brain fog, loss of memory, fatigue) as well as associated mental health issues (e.g., anxiety, worry, post-traumatic stress disorder). Participants identified a need for Long COVID mental health and other health resources, as well as increased access to Long COVID information.

CONCLUSION

To prevent Long COVID health inequities among AA/B, H/L, and Native adults living in AZ, health-related organizations and providers should increase access to culturally relevant, community-based Long COVID-specific information, mental health services, and other health resources aimed at serving these populations.

Antidepressant treatment initiation among children and adolescents with acute versus long COVID: a large retrospective cohort study

BACKGROUND

Child and adolescent antidepressant use increased post-pandemic, but it is unknown if this disproportionally affected those who develop post-acute sequelae of coronavirus disease 2019 (COVID) or long COVID. This study compared the risk of antidepressant initiation among children and adolescents with long COVID with those who had COVID but did not have evidence of long COVID.

METHODS

Our retrospective cohort study of children and adolescents aged 3-17 years at the first evidence of COVID or long COVID from October 1, 2021 through April 4, 2022 was conducted within Komodo's Healthcare Map™ database. The index date was the earliest date of a medical claim associated with a COVID (COVID comparators) or long COVID diagnosis (long COVID cases). The baseline period was six months before the index date. The outcome was antidepressant initiation within twelve months after the index date. Due to the large number of COVID relative to long COVID cases, COVID comparators were randomly selected with a ratio of 2 COVID to 1 long COVID. We used propensity score matching to control for confounding due to imbalances in the baseline covariates. Log-binomial models estimated the relative risk (RR) of antidepressant initiation in the propensity score matched sample. We conducted several sensitivity analyses to test the robustness of our findings to several assumptions.

RESULTS

Our child and adolescent sample included 18 274 with COVID and 9137 with long COVID. Compared with those with COVID, a larger proportion of long COVID children and adolescents had psychiatric disorders, psychotropic use, medical comorbidities, were previously hospitalized, or visited the emergency department. In the propensity score-adjusted analysis, the long COVID group had a statistically significant higher risk of antidepressant initiation relative to the COVID comparator (adjusted-RR: 1.40, 95% CI = 1.20, 1.62). Our findings were robust across sensitivity analyses.

CONCLUSIONS

The increased risk of antidepressant initiation following long COVID warrants further study to better understand the underlying reasons for this higher risk. Emerging evidence of long COVID's impact on child mental health has important implications for prevention and early interventions.

Insights from an N3C RECOVER EHR-based cohort study characterizing SARS-CoV-2 reinfections and Long COVID

BACKGROUND

Although the COVID-19 pandemic has persisted for over 3 years, reinfections with SARS-CoV-2 are not well understood. We aim to characterize reinfection, understand development of Long COVID after reinfection, and compare severity of reinfection with initial infection.

METHODS

We use an electronic health record study cohort of over 3 million patients from the National COVID Cohort Collaborative as part of the NIH Researching COVID to Enhance Recovery Initiative. We calculate summary statistics, effect sizes, and Kaplan-Meier curves to better understand COVID-19 reinfections.

RESULTS

Here we validate previous findings of reinfection incidence (6.9%), the occurrence of most reinfections during the Omicron epoch, and evidence of multiple reinfections. We present findings that the proportion of Long COVID diagnoses is higher following initial infection than reinfection for infections in the same epoch. We report lower albumin levels leading up to reinfection and a statistically significant association of severity between initial infection and reinfection (chi-squared value: 25,697, p-value: <0.0001) with a medium effect size (Cramer's V: 0.20, DoF = 3). Individuals who experienced severe initial and first reinfection were older in age and at a higher mortality risk than those who had mild initial infection and reinfection.

CONCLUSIONS

In a large patient cohort, we find that the severity of reinfection appears to be associated with the severity of initial infection and that Long COVID diagnoses appear to occur more often following initial infection than reinfection in the same epoch. Future research may build on these findings to better understand COVID-19 reinfections.

A Bayesian Survival Analysis on Long COVID and non Long COVID patients: A Cohort Study Using National COVID Cohort Collaborative (N3C) Data

Since the outbreak of COVID-19 pandemic in 2020, numerous researches and studies have focused on the long-term effects of COVID infection. The Centers for Disease Control (CDC) implemented an additional code into the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for reporting 'Post COVID-19 condition, unspecified (U09.9)' effective on October 1st 2021, representing that Long COVID is a real illness with potential chronic conditions. The National COVID Cohort Collaborative (N3C) provides researchers with abundant electronic health records (EHR) data by aggregating and harmonizing EHR data across different clinical organizations in the United States, making it convenient to build up a survival analysis on Long COVID patients and non Long COVID patients among large amounts of COVID positive patients.

Finding Long-COVID: Temporal Topic Modeling of Electronic Health Records from the N3C and RECOVER Programs

Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease.

Understanding the Long Haulers of COVID-19: Mixed Methods Analysis of YouTube Content

BACKGROUND

The COVID-19 pandemic had a devastating global impact. In the United States, there were >98 million COVID-19 cases and >1 million resulting deaths. One consequence of COVID-19 infection has been post-COVID-19 condition (PCC). People with this syndrome, colloquially called long haulers, experience symptoms that impact their quality of life. The root cause of PCC and effective treatments remains unknown. Many long haulers have turned to social media for support and guidance.

OBJECTIVE

In this study, we sought to gain a better understanding of the long hauler experience by investigating what has been discussed and how information about long haulers is perceived on social media. We specifically investigated the following: (1) the range of symptoms that are discussed, (2) the ways in which information about long haulers is perceived, (3) informational and emotional support that is available to long haulers, and (4) discourse between viewers and creators. We selected YouTube as our data source due to its popularity and wide range of audience.

METHODS

We systematically gathered data from 3 different types of content creators: medical sources, news sources, and long haulers. To computationally understand the video content and viewers' reactions, we used Biterm, a topic modeling algorithm created specifically for short texts, to analyze snippets of video transcripts and all top-level comments from the comment section. To triangulate our findings about viewers' reactions, we used the Valence Aware Dictionary and Sentiment Reasoner to conduct sentiment analysis on comments from each type of content creator. We grouped the comments into positive and negative categories and generated topics for these groups using Biterm. We then manually grouped resulting topics into broader themes for the purpose of analysis.

RESULTS

We organized the resulting topics into 28 themes across all sources. Examples of medical source transcript themes were Explanations in layman's terms and Biological explanations. Examples of news source transcript themes were Negative experiences and handling the long haul. The 2 long hauler transcript themes were Taking treatments into own hands and Changes to daily life. News sources received a greater share of negative comments. A few themes of these negative comments included Misinformation and disinformation and Issues with the health care system. Similarly, negative long hauler comments were organized into several themes, including Disillusionment with the health care system and Requiring more visibility. In contrast, positive medical source comments captured themes such as Appreciation of helpful content and Exchange of helpful information. In addition to this theme, one positive theme found in long hauler comments was Community building.

CONCLUSIONS

The results of this study could help public health agencies, policy makers, organizations, and health researchers understand symptomatology and experiences related to PCC. They could also help these agencies develop their communication strategy concerning PCC.

Clinical coding of long COVID in primary care 2020-2023 in a cohort of 19 million adults: an OpenSAFELY analysis

Background

Long COVID is the patient-coined term for the persistent symptoms of COVID-19 illness for weeks, months or years following the acute infection. There is a large burden of long COVID globally from self-reported data, but the epidemiology, causes and treatments remain poorly understood. Primary care is used to help identify and treat patients with long COVID and therefore Electronic Health Records (EHRs) of past COVID-19 patients could be used to help fill these knowledge gaps. We aimed to describe the incidence and differences in demographic and clinical characteristics in recorded long COVID in primary care records in England.

Methods

With the approval of NHS England we used routine clinical data from over 19 million adults in England linked to SARS-COV-2 test result, hospitalisation and vaccination data to describe trends in the recording of 16 clinical codes related to long COVID between November 2020 and January 2023. Using OpenSAFELY, we calculated rates per 100,000 person-years and plotted how these changed over time. We compared crude and adjusted (for age, sex, 9 NHS regions of England, and the dominant variant circulating) rates of recorded long COVID in patient records between different key demographic and vaccination characteristics using negative binomial models.

Findings

We identified a total of 55,465 people recorded to have long COVID over the study period, which included 20,025 diagnoses codes and 35,440 codes for further assessment. The incidence of new long COVID records increased steadily over 2021, and declined over 2022. The overall rate per 100,000 person-years was 177.5 cases in women (95% CI: 175.5-179) and 100.5 in men (99.5-102). The majority of those with a long COVID record did not have a recorded positive SARS-COV-2 test 12 or more weeks before the long COVID record.

Interpretation

In this descriptive study, EHR recorded long COVID was very low between 2020 and 2023, and incident records of long COVID declined over 2022. Using EHR diagnostic or referral codes unfortunately has major limitations in identifying and ascertaining true cases and timing of long COVID.

Funding

This research was supported by the National Institute for Health and Care Research (NIHR) (OpenPROMPT: COV-LT2-0073).

Long COVID awareness and receipt of medical care: a survey among populations at risk for disparities

Introduction

The COVID-19 pandemic has been characterized by disparities in disease burden and medical care provision. Whether these disparities extend to long COVID awareness and receipt of medical care is unknown. We aimed to characterize awareness of long COVID and receipt of medical care for long COVID symptoms among populations who experience disparities in the United States (US).

Methods

We conducted a cross-sectional survey among a national sample of US adults between January 26-February 5, 2023. We surveyed approximately 2,800 adults drawn from the Ipsos probability-based KnowledgePanel® who identify as White, Black, or Hispanic, with over-sampling of Black, Hispanic, and Spanish-proficient adults. Awareness of long COVID was assessed with the question, "Have you heard of long COVID? This is also referred to as post-COVID, Long-haul COVID, Post-acute COVID-19, or Chronic COVID." Respondents reporting COVID-19 symptoms lasting longer than 1 month were classified as having long COVID and asked about receipt of medical care.

Results

Of the 2,828 respondents, the mean age was 50.4 years, 52.8% were female, 40.2% identified as Hispanic, 29.8% as Black, and 26.7% as White. 18% completed the survey in Spanish. Overall, 62.5% had heard of long COVID. On multivariate analysis, long COVID awareness was lower among respondents who identified as Black (OR 0.64; 95% CI 0.51, 0.81), Hispanic and completed the survey in English (OR 0.59; 95% CI 0.46, 0.76), and Hispanic and completed the survey in Spanish (OR 0.31, 95% C.I. 0.23, 0.41), compared to White respondents (overall p < 0.001). Long COVID awareness was also associated with educational attainment, higher income, having health insurance, prior history of COVID-19 infection, and COVID-19 vaccination. Among those reporting symptoms consistent with long COVID (n = 272), 26.8% received medical care. Older age, longer symptom duration and greater symptom impact were associated with receipt of medical care for long COVID symptoms. Of those who received care, most (77.8%) rated it as less than excellent on a 5-point scale.

Discussion

This survey reveals limited awareness of long COVID and marked disparities in awareness according to race, ethnicity, and language. Targeted public health campaigns are needed to raise awareness.

Racial and ethnic disparities post-hospitalization for COVID-19: barriers to access to care for survivors of COVID-19 acute respiratory distress syndrome

Racial and ethnic health disparities in the incidence and severity of Coronavirus Disease 2019 (COVID-19) have been observed globally and in the United States. Research has focused on transmission, hospitalization, and mortality among racial and ethnic minorities, but Long COVID-19 health disparities research is limited. This study retrospectively evaluated 195 adults who survived COVID-19 associated acute respiratory distress syndrome (C-ARDS) in New York City from March-April 2020. Among survivors, 54% met the criteria for Long COVID syndrome. Hispanic/Latinx patients, were more likely to be uninsured (p = 0.027) and were less frequently discharged to rehabilitation facilities (p < 0.001). A cross-sectional telephone survey and interview were conducted with a subset of survivors (n = 69). Among these, 11% reported a lack of follow-up primary care post-discharge and 38% had subsequent emergency room visits. Notably, 38% reported poor treatment within the health care system, with 67% attributing this to racial or ethnic bias. Thematic analysis of interviews identified four perceived challenges: decline in functional status, discrimination during hospitalization, healthcare system inequities, and non-healthcare-related structural barriers. Sources of resilience included survivorship, faith, and family support. This study highlights structural and healthcare-related barriers rooted in perceived racism and poverty as factors impacting post-COVID-19 care.

Characterization of Post-COVID-19 Definitions and Clinical Coding Practices: Longitudinal Study

BACKGROUND

Post-COVID-19 condition (colloquially known as "long COVID-19") characterized as postacute sequelae of SARS-CoV-2 has no universal clinical case definition. Recent efforts have focused on understanding long COVID-19 symptoms, and electronic health record (EHR) data provide a unique resource for understanding this condition. The introduction of the International Classification of Diseases, Tenth Revision (ICD-10) code U09.9 for "Post COVID-19 condition, unspecified" to identify patients with long COVID-19 has provided a method of evaluating this condition in EHRs; however, the accuracy of this code is unclear.

OBJECTIVE

This study aimed to characterize the utility and accuracy of the U09.9 code across 3 health care systems-the Veterans Health Administration, the Beth Israel Deaconess Medical Center, and the University of Pittsburgh Medical Center-against patients identified with long COVID-19 via a chart review by operationalizing the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) definitions.

METHODS

Patients who were COVID-19 positive with either a U07.1 ICD-10 code or positive polymerase chain reaction test within these health care systems were identified for chart review. Among this cohort, we sampled patients based on two approaches: (1) with a U09.9 code and (2) without a U09.9 code but with a new onset long COVID-19-related ICD-10 code, which allows us to assess the sensitivity of the U09.9 code. To operationalize the long COVID-19 definition based on health agency guidelines, symptoms were grouped into a "core" cluster of 11 commonly reported symptoms among patients with long COVID-19 and an extended cluster that captured all other symptoms by disease domain. Patients having ≥2 symptoms persisting for ≥60 days that were new onset after their COVID-19 infection, with ≥1 symptom in the core cluster, were labeled as having long COVID-19 per chart review. The code's performance was compared across 3 health care systems and across different time periods of the pandemic.

RESULTS

Overall, 900 patient charts were reviewed across 3 health care systems. The prevalence of long COVID-19 among the cohort with the U09.9 ICD-10 code based on the operationalized WHO definition was between 23.2% and 62.4% across these health care systems. We also evaluated a less stringent version of the WHO definition and the CDC definition and observed an increase in the prevalence of long COVID-19 at all 3 health care systems.

CONCLUSIONS

This is one of the first studies to evaluate the U09.9 code against a clinical case definition for long COVID-19, as well as the first to apply this definition to EHR data using a chart review approach on a nationwide cohort across multiple health care systems. This chart review approach can be implemented at other EHR systems to further evaluate the utility and performance of the U09.9 code.

Long COVID incidence in adults and children between 2020 and 2023: a real-world data study from the RECOVER Initiative

Estimates of post-acute sequelae of SARS-CoV-2 infection (PASC) incidence, also known as Long COVID, have varied across studies and changed over time. We estimated PASC incidence among adult and pediatric populations in three nationwide research networks of electronic health records (EHR) participating in the RECOVER Initiative using different classification algorithms (computable phenotypes). Overall, 7% of children and 8.5%-26.4% of adults developed PASC, depending on computable phenotype used. Excess incidence among SARS-CoV-2 patients was 4% in children and ranged from 4-7% among adults, representing a lower-bound incidence estimation based on two control groups - contemporary COVID-19 negative and historical patients (2019). Temporal patterns were consistent across networks, with peaks associated with introduction of new viral variants. Our findings indicate that preventing and mitigating Long COVID remains a public health priority. Examining temporal patterns and risk factors of PASC incidence informs our understanding of etiology and can improve prevention and management.

Neurocognitive Impairment in Post-COVID-19 Condition in Adults: Narrative Review of the Current Literature

The severe acute respiratory syndrome coronavirus 2 virus has, up to the time of this article, resulted in >770 million cases of COVID-19 illness worldwide, and approximately 7 million deaths, including >1.1 million in the United States. Although defined as a respiratory virus, early in the pandemic, it became apparent that considerable numbers of people recovering from COVID-19 illness experienced persistence or new onset of multi-system health problems, including neurologic and cognitive and behavioral health concerns. Persistent multi-system health problems are defined as Post-COVID-19 Condition (PCC), Post-Acute Sequelae of COVID-19, or Long COVID. A significant number of those with PCC report cognitive problems. This paper reviews the current state of scientific knowledge on persisting cognitive symptoms in adults following COVID-19 illness. A brief history is provided of the emergence of concerns about persisting cognitive problems following COVID-19 illness and the definition of PCC. Methodologic factors that complicate clear understanding of PCC are reviewed. The review then examines research on patterns of cognitive impairment that have been found, factors that may contribute to increased risk, behavioral health variables, and interventions being used to ameliorate persisting symptoms. Finally, recommendations are made about ways neuropsychologists can improve the quality of existing research.

Precision Phenotyping for Curating Research Cohorts of Patients with Post-Acute Sequelae of COVID-19 (PASC) as a Diagnosis of Exclusion

Scalable identification of patients with the post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms and the suboptimal accuracy, demographic biases, and underestimation of the PASC diagnosis code (ICD-10 U09.9). In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying research cohorts of PASC patients, defined as a diagnosis of exclusion. We used longitudinal electronic health records (EHR) data from over 295 thousand patients from 14 hospitals and 20 community health centers in Massachusetts. The algorithm employs an attention mechanism to exclude sequelae that prior conditions can explain. We performed independent chart reviews to tune and validate our precision phenotyping algorithm. Our PASC phenotyping algorithm improves precision and prevalence estimation and reduces bias in identifying Long COVID patients compared to the U09.9 diagnosis code. Our algorithm identified a PASC research cohort of over 24 thousand patients (compared to about 6 thousand when using the U09.9 diagnosis code), with a 79.9 percent precision (compared to 77.8 percent from the U09.9 diagnosis code). Our estimated prevalence of PASC was 22.8 percent, which is close to the national estimates for the region. We also provide an in-depth analysis outlining the clinical attributes, encompassing identified lingering effects by organ, comorbidity profiles, and temporal differences in the risk of PASC. The PASC phenotyping method presented in this study boasts superior precision, accurately gauges the prevalence of PASC without underestimating it, and exhibits less bias in pinpointing Long COVID patients. The PASC cohort derived from our algorithm will serve as a springboard for delving into Long COVID's genetic, metabolomic, and clinical intricacies, surmounting the constraints of recent PASC cohort studies, which were hampered by their limited size and available outcome data.

Post-COVID-19 cognitive symptoms in patients assisted by a teleassistance service: a retrospective cohort study

Introduction

Four years after the onset of the COVID-19 pandemic, the frequency of long-term post-COVID-19 cognitive symptoms is a matter of concern given the impact it may have on the work and quality of life of affected people.

Objective

To evaluate the incidence of post-acute COVID-19 cognitive symptoms, as well as the associated risk factors.

Methods

Retrospective cohort, including outpatients with laboratory-confirmed COVID-19 and who were assisted by a public telehealth service provided by the Telehealth Network of Minas Gerais (TNMG), during the acute phase of the disease, between December/2020 and March/2022. Data were collected through a structured questionnaire, applied via phone calls, regarding the persistence of COVID-19 symptoms after 12 weeks of the disease. Cognitive symptoms were defined as any of the following: memory loss, problems concentrating, word finding difficulties, and difficulty thinking clearly.

Results

From 630 patients who responded to the questionnaire, 23.7% presented cognitive symptoms at 12 weeks after infection. These patients had a higher median age (33 [IQR 25-46] vs. 30 [IQR 24-42] years-old, p = 0.042) with a higher prevalence in the female sex (80.5% vs. 62.2%, p < 0.001) when compared to those who did not present cognitive symptoms, as well as a lower prevalence of smoking (8.7% vs. 16.2%, p = 0.024). Furthermore, patients with persistent cognitive symptoms were more likely to have been infected during the second wave of COVID-19 rather than the third (31.0% vs. 21.3%, p = 0.014). Patients who needed to seek in-person care during the acute phase of the disease were more likely to report post-acute cognitive symptoms (21.5% vs. 9.3%, p < 0,001). In multivariate logistic regression analysis, cognitive symptoms were associated with female sex (OR 2.24, CI 95% 1.41-3.57), fatigue (OR 2.33, CI 95% 1.19-4.56), depression (OR 5.37, CI 95% 2.19-13.15) and the need for seek in-person care during acute COVID-19 (OR 2.23, CI 95% 1.30-3.81).

Conclusion

In this retrospective cohort of patients with mostly mild COVID-19, cognitive symptoms were present in 23.7% of patients with COVID-19 at 12 weeks after infection. Female sex, fatigue, depression and the need to seek in-person care during acute COVID-19 were the risk factors independently associated with this condition.

Vaccine Effectiveness Against Long COVID in Children

OBJECTIVES

Vaccination reduces the risk of acute coronavirus disease 2019 (COVID-19) in children, but it is less clear whether it protects against long COVID. We estimated vaccine effectiveness (VE) against long COVID in children aged 5 to 17 years.

METHODS

This retrospective cohort study used data from 17 health systems in the RECOVER PCORnet electronic health record program for visits after vaccine availability. We examined both probable (symptom-based) and diagnosed long COVID after vaccination.

RESULTS

The vaccination rate was 67% in the cohort of 1 037 936 children. The incidence of probable long COVID was 4.5% among patients with COVID-19, whereas diagnosed long COVID was 0.8%. Adjusted vaccine effectiveness within 12 months was 35.4% (95 CI 24.5-44.7) against probable long COVID and 41.7% (15.0-60.0) against diagnosed long COVID. VE was higher for adolescents (50.3% [36.6-61.0]) than children aged 5 to 11 (23.8% [4.9-39.0]). VE was higher at 6 months (61.4% [51.0-69.6]) but decreased to 10.6% (-26.8% to 37.0%) at 18-months.

CONCLUSIONS

This large retrospective study shows moderate protective effect of severe acute respiratory coronavirus 2 vaccination against long COVID. The effect is stronger in adolescents, who have higher risk of long COVID, and wanes over time. Understanding VE mechanism against long COVID requires more study, including electronic health record sources and prospective data.

Long COVID: Long-Term Impact of SARS-CoV2

Four years post-pandemic, SARS-CoV-2 continues to affect many lives across the globe. An estimated 65 million people suffer from long COVID, a term used to encapsulate the post-acute sequelae of SARS-CoV-2 infections that affect multiple organ systems. Known symptoms include chronic fatigue syndrome, brain fog, cardiovascular issues, autoimmunity, dysautonomia, and clotting due to inflammation. Herein, we review long COVID symptoms, the proposed theories behind the pathology, diagnostics, treatments, and the clinical trials underway to explore treatments for viral persistence, autonomic and cognitive dysfunctions, sleep disturbances, fatigue, and exercise intolerance.

SCALE-UP II: protocol for a pragmatic randomised trial examining population health management interventions to increase the uptake of at-home COVID-19 testing in community health centres

INTRODUCTION

SCALE-UP II aims to investigate the effectiveness of population health management interventions using text messaging (TM), chatbots and patient navigation (PN) in increasing the uptake of at-home COVID-19 testing among patients in historically marginalised communities, specifically, those receiving care at community health centres (CHCs).

METHODS AND ANALYSIS

The trial is a multisite, randomised pragmatic clinical trial. Eligible patients are >18 years old with a primary care visit in the last 3 years at one of the participating CHCs. Demographic data will be obtained from CHC electronic health records. Patients will be randomised to one of two factorial designs based on smartphone ownership. Patients who self-report replying to a text message that they have a smartphone will be randomised in a 2×2×2 factorial fashion to receive (1) chatbot or TM; (2) PN (yes or no); and (3) repeated offers to interact with the interventions every 10 or 30 days. Participants who do not self-report as having a smartphone will be randomised in a 2×2 factorial fashion to receive (1) TM with or without PN; and (2) repeated offers every 10 or 30 days. The interventions will be sent in English or Spanish, with an option to request at-home COVID-19 test kits. The primary outcome is the proportion of participants using at-home COVID-19 tests during a 90-day follow-up. The study will evaluate the main effects and interactions among interventions, implementation outcomes and predictors and moderators of study outcomes. Statistical analyses will include logistic regression, stratified subgroup analyses and adjustment for stratification factors.

ETHICS AND DISSEMINATION

The protocol was approved by the University of Utah Institutional Review Board. On completion, study data will be made available in compliance with National Institutes of Health data sharing policies. Results will be disseminated through study partners and peer-reviewed publications.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov: NCT05533918 and NCT05533359.

Unraveling Links between Chronic Inflammation and Long COVID: Workshop Report

As COVID-19 continues, an increasing number of patients develop long COVID symptoms varying in severity that last for weeks, months, or longer. Symptoms commonly include lingering loss of smell and taste, hearing loss, extreme fatigue, and "brain fog." Still, persistent cardiovascular and respiratory problems, muscle weakness, and neurologic issues have also been documented. A major problem is the lack of clear guidelines for diagnosing long COVID. Although some studies suggest that long COVID is due to prolonged inflammation after SARS-CoV-2 infection, the underlying mechanisms remain unclear. The broad range of COVID-19's bodily effects and responses after initial viral infection are also poorly understood. This workshop brought together multidisciplinary experts to showcase and discuss the latest research on long COVID and chronic inflammation that might be associated with the persistent sequelae following COVID-19 infection.