Long Covid: A Systematic Review and Meta-Analysis of 120,970 Patients

Gennaro, Francesco Di; Belati, Alessandra; Tulone, Ottavia; Diella, Lucia; Bavaro, Davide Fiore; Bonica, Roberta; Genna, Vincenzo; Smıth, Lee; Trott, Mike; Bruyère, Olivier; Mirarchi, Luigi; Cusumano, C.; Barbagallo, Mario; Rodriguez, Ligia Juliana Dominguez; Saracino, Annalisa; Veronese, Nicola

doi:10.2139/ssrn.4099429

Cited by 6 publications

(9 citation statements)

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“…Our transformer-based entity extraction tool, called UmlsBERT, outperformed Stanza and UMLS MetaMap (+AMIA) for extraction of symptoms and conditions (SyCo) from both Twitter and Reddit. The outcome of our NLP pipeline, normalized and mapped SyCo terms, were comparable with the outcomes of peer-reviewed articles relevant to long COVID symptoms [3], [31], [32]. In particular, there was very high agreement between our findings and those of three different studies investigating long COVID [3], [31], [32].…”

Section: Principle Resultssupporting

confidence: 73%

“…The outcome of our NLP pipeline, normalized and mapped SyCo terms, were comparable with the outcomes of peer-reviewed articles relevant to long COVID symptoms [3], [31], [32]. In particular, there was very high agreement between our findings and those of three different studies investigating long COVID [3], [31], [32]. Our analysis confirms prior findings that COVID-19 infection is associated with long-term effects that are represented by diverse symptoms and conditions affecting multiple organ systems.…”

Section: Principle Resultsmentioning

confidence: 71%

“…The closer the proximity metric to 0, the closer the model's predictions to the human-annotated benchmark. Validity and reliability of the extracted and normalized symptoms, a subset of SyCo terms, were evaluated compared with an online survey including 3762 participants from 56 countries [3], [31], [32].…”

Section: Discussionmentioning

confidence: 99%

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Using Social Media to Help Understand Long COVID Patient Reported Health Outcomes: A Natural Language Processing Approach

Dolatabadi

Moyano

Bales

et al. 2022

Preprint

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Background: There remains significant uncertainty in the definition of the long COVID disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most clinicians. Objective: We aim to determine the validity and effectiveness of advanced NLP approaches built to derive insight into Long COVID-related patient-reported health outcomes from social media platforms. Methodology: We use Transformer-based BERT models to extract and normalize long COVID Symptoms and Conditions (SyCo) from English posts on Twitter and Reddit. Furthermore, we estimate the occurrence and co-occurrence of SyCo terms at any point or across time and locations. Finally, we compare the extracted health outcomes with human annotations and highly utilized clinical outcomes grounded in the medical literature. Result: Based on our findings, the top three most commonly occurring groups of long COVID symptoms are systemic (such as "Fatigue"), neuropsychiatric (such as "Anxiety" and "Brain fog"), and respiratory (such as "Shortness of breath"). Regarding the co-occurring symptoms, the pair of "Fatigue & Headaches" is most common. In addition, we show that other conditions, such as infection, hair loss, and weight loss, as well as mentions of other diseases, such as flu, cancer, or Lyme disease, are among the top reported terms by social media users. Conclusion: The outcome of our social media-derived pipeline is comparable with the outcomes of peer-reviewed articles relevant to long COVID symptoms. Overall, this study provides unique insights into patient-reported health outcomes from long COVID and valuable information about the patient's journey that can help healthcare providers anticipate future needs.

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Section: Principle Resultssupporting

confidence: 73%

Section: Principle Resultsmentioning

confidence: 71%

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Using Social Media to Help Understand Long COVID Patient Reported Health Outcomes: A Natural Language Processing Approach

Dolatabadi

Moyano

Bales

et al. 2022

Preprint

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“…The characteristic symptom of long-COVID syndrome is brain fog, which includes a set of neurological symptoms that are a consequence of COVID-19. According to the literature, up to one in seven patients may be affected by this condition [30,31]. Despite observations, its mechanisms are still unknown, though one hypothesis is that damage to the nervous system in response to ongoing inflammation alters brain function, particularly in areas related to cognitive function [32,33].…”

Section: Discussionmentioning

confidence: 99%

Long-COVID Clinical Features and Risk Factors: A Retrospective Analysis of Patients from the STOP-COVID Registry of the PoLoCOV Study

Chudzik

Babicki

Kapusta

et al. 2022

Viruses

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Despite recovering from the acute phase of coronavirus disease (COVID-19), many patients report continuing symptoms that most commonly include fatigue, cough, neurologic problems, hair loss, headache, and musculoskeletal pain, a condition termed long-COVID syndrome. Neither its etiopathogenesis, nor its clinical presentation or risk factors are fully understood. Therefore, the purpose of this study was to retrospectively evaluate the most common symptoms of long-COVID among patients from the STOP COVID registry of the PoLoCOV study, and to search for risk factors for development of the syndrome. The registry includes patients who presented to the medical center for persistent clinical symptoms following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The analysis included data from initial presentation and at three-month follow-up. Of the 2218 patients, 1569 (70.7%) reported having at least one symptom classified as long-COVID syndrome three months after recovery from the initial SARS-CoV-2 infection. The most common symptoms included chronic fatigue (35.6%\), cough (23.0%), and a set of neurological symptoms referred to as brain fog (12.1%). Risk factors for developing long-COVID syndrome included female gender (odds ratio [OR]: 1.48, 95% confidence intervals [CI] [1.19–1.84]), severe COVID-19 (OR: 1.56, CI: 1.00–2.42), dyspnea (OR: 1.31, CI: 1.02–1.69), and chest pain (OR: 1.48, CI: 1.14–1.92). Long-COVID syndrome represents a significant clinical and social problem. The most common clinical manifestations are chronic fatigue, cough, and brain fog. Given the still-limited knowledge of long-COVID syndrome, further research and observation are needed to better understand the mechanisms and risk factors of the disease.

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“…Patients have reported headaches and dizziness, as well as psychiatric disorders and motor discoordination (11)(12)(13). In a period of 7 months after viral infection, some patients have presented conditions that are mainly related to neuropsychiatric and neurological deficits, with a prevalence of 19.7% to 36% (4,14,15). The characteristic symptoms of these alterations are anosmia, hypogeusia, partial or total hyposmia (16,17), myalgia, cerebral inflammation, cerebrovascular strokes (18), acute encephalopathy, seizures, Guillain-Barrésyndrome (19), neurocognitive disorders, sleep disorders, delirium, memory deficit, concentration deficit, depression, psychosis, hallucinations, paranoia (20), chronic fatigue and partial or total apraxia (21).…”

Section: Introductionmentioning

confidence: 99%

The relationship between chronic immune response and neurodegenerative damage in long COVID-19

et al. 2022

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In the past two years, the world has faced the pandemic caused by the severe acute respiratory syndrome 2 coronavirus (SARS-CoV-2), which by August of 2022 has infected around 619 million people and caused the death of 6.55 million individuals globally. Although SARS-CoV-2 mainly affects the respiratory tract level, there are several reports, indicating that other organs such as the heart, kidney, pancreas, and brain can also be damaged. A characteristic observed in blood serum samples of patients suffering COVID-19 disease in moderate and severe stages, is a significant increase in proinflammatory cytokines such as interferon-α (IFN-α), interleukin-1β (IL-1β), interleukin-2 (IL-2), interleukin-6 (IL-6) and interleukin-18 (IL-18), as well as the presence of autoantibodies against interferon-α (IFN-α), interferon-λ (IFN-λ), C-C motif chemokine ligand 26 (CCL26), CXC motif chemokine ligand 12 (CXCL12), family with sequence similarity 19 (chemokine (C-C motif)-like) member A4 (FAM19A4), and C-C motif chemokine ligand 1 (CCL1). Interestingly, it has been described that the chronic cytokinemia is related to alterations of blood-brain barrier (BBB) permeability and induction of neurotoxicity. Furthermore, the generation of autoantibodies affects processes such as neurogenesis, neuronal repair, chemotaxis and the optimal microglia function. These observations support the notion that COVID-19 patients who survived the disease present neurological sequelae and neuropsychiatric disorders. The goal of this review is to explore the relationship between inflammatory and humoral immune markers and the major neurological damage manifested in post-COVID-19 patients.

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Long Covid: A Systematic Review and Meta-Analysis of 120,970 Patients

Cited by 6 publications

References 0 publications

Using Social Media to Help Understand Long COVID Patient Reported Health Outcomes: A Natural Language Processing Approach

Using Social Media to Help Understand Long COVID Patient Reported Health Outcomes: A Natural Language Processing Approach

Long-COVID Clinical Features and Risk Factors: A Retrospective Analysis of Patients from the STOP-COVID Registry of the PoLoCOV Study

The relationship between chronic immune response and neurodegenerative damage in long COVID-19

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