Developing a Long COVID Phenotype for Postacute COVID-19 in a National Primary Care Sentinel Cohort: Observational Retrospective Database Analysis

Phenotype execution and modeling architecture to support disease surveillance and real-world evidence studies: English sentinel network evaluation

Objective

To evaluate Phenotype Execution and Modelling Architecture (PhEMA), to express sharable phenotypes using Clinical Quality Language (CQL) and intensional Systematised Nomenclature of Medicine (SNOMED) Clinical Terms (CT) Fast Healthcare Interoperability Resources (FHIR) valuesets, for exemplar chronic disease, sociodemographic risk factor, and surveillance phenotypes.

Method

We curated 3 phenotypes: Type 2 diabetes mellitus (T2DM), excessive alcohol use, and incident influenza-like illness (ILI) using CQL to define clinical and administrative logic. We defined our phenotypes with valuesets, using SNOMED's hierarchy and expression constraint language, and CQL, combining valuesets and adding temporal elements where needed. We compared the count of cases found using PhEMA with our existing approach using convenience datasets. We assessed our new approach against published desiderata for phenotypes.

Results

The T2DM phenotype could be defined as 2 intensionally defined SNOMED valuesets and a CQL script. It increased the prevalence from 7.2% to 7.3%. Excess alcohol phenotype was defined by valuesets that added qualitative clinical terms to the quantitative conceptual definitions we currently use; this change increased prevalence by 58%, from 1.2% to 1.9%. We created an ILI valueset with SNOMED concepts, adding a temporal element using CQL to differentiate new episodes. This increased the weekly incidence in our convenience sample (weeks 26-38) from 0.95 cases to 1.11 cases per 100 000 people.

Conclusions

Phenotypes for surveillance and research can be described fully and comprehensibly using CQL and intensional FHIR valuesets. Our use case phenotypes identified a greater number of cases, whilst anticipated from excessive alcohol this was not for our other variable. This may have been due to our use of SNOMED CT hierarchy. Our new process fulfilled a greater number of phenotype desiderata than the one that we had used previously, mostly in the modeling domain. More work is needed to implement that sharing and warehousing domains.

Impact of Pre-Infection COVID-19 Vaccination on the Incidence and Severity of Post-COVID Syndrome: A Systematic Review and Meta-Analysis

This systematic review critically evaluated the impact of a pre-infection COVID-19 vaccination on the incidence and severity of post-COVID-19 syndrome and aimed to assess the potential protective effect across different vaccines and patient demographics. This study hypothesized that vaccination before infection substantially reduces the risk and severity of post-COVID-19 syndrome. In October 2023, a comprehensive literature search was conducted across three databases, PubMed, Embase, and Scopus, focusing on studies published up to that date. Utilizing a wide array of keywords, the search strategy adhered to the PRISMA guidelines and was registered in the Open Science Framework. The inclusion criteria comprised studies focusing on patients with a breakthrough SARS-CoV-2 infection who developed post-COVID-19 syndrome. We included a total of 13 articles that met the inclusion criteria, analyzing more than 10 million patients with a mean age of 50.6 years, showing that the incidence of intensive care unit (ICU) admissions post-vaccination was as low as 2.4%, with a significant reduction in mortality risk (OR 0.66, 95% CI 0.58-0.74). The prevalence of post-COVID-19 syndrome symptoms was lower in vaccinated individuals (9.5%) compared to unvaccinated (14.6%), with a notable decrease in activity-limiting symptoms (adjusted OR 0.59, 95% CI 0.48-0.73). Vaccinated patients also showed a quicker recovery and return to work (HR 1.37, 95% CI 1.04-1.79). The pooled odds ratio of 0.77 indicates that vaccination is associated with a 23% reduction in the risk of developing post-COVID-19 syndrome (95% CI 0.75-0.79). Despite the protective effects observed, a substantial heterogeneity among the studies was noted. In conclusion, a pre-infection COVID-19 vaccination is associated with a significant reduction in the risk and severity of post-COVID-19 syndrome. However, the observed heterogeneity across studies suggests a need for further research with standardized methods to fully comprehend vaccine efficacy against long COVID.

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

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

A hybrid register and questionnaire study of Covid-19 and post-acute sick leave in Denmark

Post-acute sick leave is an underexplored indicator of the societal burden of SARS-CoV-2. Here,  we report findings about self-reported sick leave and risk factors thereof from a hybrid survey and register study, which include 37,482 RT-PCR confirmed SARS-CoV-2 cases and 51,336 test-negative controls who were tested during the index- and alpha-dominant waves. We observe that an additional 33 individuals per 1000 took substantial sick leave following acute infection compared to persons with no known history of infection, where substantial sick leave is defined as >1 month of sick leave within the period 1-9 months after the RT-PCR test date. Being female, 50-65 years, or having certain pre-existing health conditions such as obesity, chronic lung diseases, and fibromyalgia each increase risk for taking substantial sick leave. Altogether, these results may help motivate improved diagnostic and treatment options for persons living with post-Covid conditions.

Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study

Background

Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal attributes, and definitions. Scalable characterization of PASC sub-phenotypes can enhance screening capacities, disease management, and treatment planning.

Methods

We conducted a retrospective multi-centre observational cohort study, leveraging longitudinal electronic health record (EHR) data of 30,422 patients from three healthcare systems in the Consortium for the Clinical Characterization of COVID-19 by EHR (4CE). From the total cohort, we applied a deductive approach on 12,424 individuals with follow-up data and developed a distributed representation learning process for providing augmented definitions for PASC sub-phenotypes.

Findings

Our framework characterized seven PASC sub-phenotypes. We estimated that on average 15.7% of the hospitalized COVID-19 patients were likely to suffer from at least one PASC symptom and almost 5.98%, on average, had multiple symptoms. Joint pain and dyspnea had the highest prevalence, with an average prevalence of 5.45% and 4.53%, respectively.

Interpretation

We provided a scalable framework to every participating healthcare system for estimating PASC sub-phenotypes prevalence and temporal attributes, thus developing a unified model that characterizes augmented sub-phenotypes across the different systems.

Funding

Authors are supported by National Institute of Allergy and Infectious Diseases, National Institute on Aging, National Center for Advancing Translational Sciences, National Medical Research Council, National Institute of Neurological Disorders and Stroke, European Union, National Institutes of Health, National Center for Advancing Translational Sciences.

Advancing the Management of Long COVID by Integrating into Health Informatics Domain: Current and Future Perspectives

The ongoing COVID-19 pandemic has profoundly affected millions of lives globally, with some individuals experiencing persistent symptoms even after recovering. Understanding and managing the long-term sequelae of COVID-19 is crucial for research, prevention, and control. To effectively monitor the health of those affected, maintaining up-to-date health records is essential, and digital health informatics apps for surveillance play a pivotal role. In this review, we overview the existing literature on identifying and characterizing long COVID manifestations through hierarchical classification based on Human Phenotype Ontology (HPO). We outline the aspects of the National COVID Cohort Collaborative (N3C) and Researching COVID to Enhance Recovery (RECOVER) initiative in artificial intelligence (AI) to identify long COVID. Through knowledge exploration, we present a concept map of clinical pathways for long COVID, which offers insights into the data required and explores innovative frameworks for health informatics apps for tackling the long-term effects of COVID-19. This study achieves two main objectives by comprehensively reviewing long COVID identification and characterization techniques, making it the first paper to explore incorporating long COVID as a variable risk factor within a digital health informatics application. By achieving these objectives, it provides valuable insights on long COVID's challenges and impact on public health.

Antihistamines improve cardiovascular manifestations and other symptoms of long-COVID attributed to mast cell activation

Introduction

Long-COVID is a broadly defined condition and there are no effective therapies. Cardiovascular manifestations of long-COVID include high heart rate, postural tachycardia, and palpitations. Previous studies have suggested that mast cell activation (MCA) may play a role in the pathophysiology of long-COVID, including in the mechanisms of its cardiovascular manifestations. The present study aimed to evaluate the effectiveness of a treatment with blockers of histamine receptors in patients with long-COVID who did not respond to other therapies.

Methods

In all, 14 patients (F/M = 9/5; 49.5 ± 11.5 years) and 13 controls (F/M = 8/5; 47.3 ± 8.0 years) with long-COVID symptoms attributed to MCA were evaluated. Patients were treated with fexofenadine (180 mg/day) and famotidine (40 mg/day). Fatigue, brain fog, abdominal disorders, and increased heart rate were evaluated in treated and untreated patients at baseline and 20 days later.

Results

Long-COVID symptoms disappeared completely in 29% of treated patients. There was a significant improvement in each of the considered symptoms (improved or disappeared) in all treated patients, and the improvement grade was significantly greater in treated patients compared to controls. No significant differences in the outcomes were observed in the controls.

Conclusions

Our data confirm that histamine receptors blockade may be an effective target to successfully treat long-COVID. Our finding supports the underlying role of MCA in the pathophysiology of long-COVID.

Protective effect of COVID-19 vaccination against long COVID syndrome: A systematic review and meta-analysis

BACKGROUND

The relationship between coronavirus disease 2019 (COVID-19) vaccination and long COVID has not been firmly established. We conducted a systematic review and meta-analysis to evaluate the association between COVID-19 vaccination and long COVID.

METHODS

PubMed and EMBASE databases were searched on September 2022 without language restrictions (CRD42022360399) to identify prospective trials and observational studies comparing patients with and without vaccination before severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. We also included studies reporting symptomatic changes of ongoing long COVID following vaccination among those with a history of SARS-CoV-2 infection. Odds ratios (ORs) for each outcome were synthesized using a random-effects model. Symptomatic changes after vaccination were synthesized by a one-group meta-analysis.

RESULTS

Six observational studies involving 536,291 unvaccinated and 84,603 vaccinated (before SARS-CoV-2 infection) patients (mean age, 41.2-66.6; female, 9.0-67.3%) and six observational studies involving 8,199 long COVID patients (mean age, 40.0 to 53.5; female, 22.2-85.9%) who received vaccination after SARS-CoV-2 infection were included. Two-dose vaccination was associated with a lower risk of long COVID compared to no vaccination (OR, 0.64; 95% confidence interval [CI], 0.45-0.92) and one-dose vaccination (OR, 0.60; 95% CI, 0.43-0.83). Two-dose vaccination compared to no vaccination was associated with a lower risk of persistent fatigue (OR, 0.62; 95% CI, 0.41-0.93) and pulmonary disorder (OR, 0.50; 95% CI, 0.47-0.52). Among those with ongoing long COVID symptoms, 54.4% (95% CI, 34.3-73.1%) did not report symptomatic changes following vaccination, while 20.3% (95% CI, 8.1-42.4%) experienced symptomatic improvement after two weeks to six months of COVID-19 vaccination.

CONCLUSIONS

COVID-19 vaccination before SARS-CoV-2 infection was associated with a lower risk of long COVID, while most of those with ongoing long COVID did not experience symptomatic changes following vaccination.