Tag: long covid research

Machine learning algorithms for detection of visuomotor neural control differences in individuals with PASC and ME

Abstract:

The COVID-19 pandemic has affected millions worldwide, giving rise to long-term symptoms known as post-acute sequelae of SARS-CoV-2 (PASC) infection, colloquially referred to as long COVID. With an increasing number of people experiencing these symptoms, early intervention is crucial. In this study, we introduce a novel method to detect the likelihood of PASC or Myalgic Encephalomyelitis (ME) using a wearable four-channel headband that collects Electroencephalogram (EEG) data. The raw EEG signals are processed using Continuous Wavelet Transform (CWT) to form a spectrogram-like matrix, which serves as input for various machine learning and deep learning models. We employ models such as CONVLSTM (Convolutional Long Short-Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional Long short-term memory). Additionally, we test the dataset on traditional machine learning models for comparative analysis.

Our results show that the best-performing model, CNN-LSTM, achieved an accuracy of 83%. In addition to the original spectrogram data, we generated synthetic spectrograms using Wasserstein Generative Adversarial Networks (WGANs) to augment our dataset. These synthetic spectrograms contributed to the training phase, addressing challenges such as limited data volume and patient privacy. Impressively, the model trained on synthetic data achieved an average accuracy of 93%, significantly outperforming the original model.

These results demonstrate the feasibility and effectiveness of our proposed method in detecting the effects of PASC and ME, paving the way for early identification and management of the condition. The proposed approach holds significant potential for various practical applications, particularly in the clinical domain. It can be utilized for evaluating the current condition of individuals with PASC or ME, and monitoring the recovery process of those with PASC, or the efficacy of any interventions in the PASC and ME populations. By implementing this technique, healthcare professionals can facilitate more effective management of chronic PASC or ME effects, ensuring timely intervention and improving the quality of life for those experiencing these conditions.

Source: Harit Ahuja, Smriti Badhwar, Heather Edgell, Lauren E. Sergio, Marin Litoiu. Machine learning algorithms for detection of visuomotor neural control differences in individuals with PASC and ME. Front. Hum. Neurosci. Sec. Brain-Computer Interfaces, Volume 18 – 2024 | doi: 10.3389/fnhum.2024.1359162 https://www.frontiersin.org/articles/10.3389/fnhum.2024.1359162/full (Full text)

Core outcome measurement instruments for use in clinical and research settings for adults with post-COVID-19 condition: an international Delphi consensus study

Abstract:

Post-COVID-19 condition (also known as long COVID) is a new, complex, and poorly understood disorder. A core outcome set (COS) for post-COVID-19 condition in adults has been developed and agreement is now required on the most appropriate measurement instruments for these core outcomes.

We conducted an international consensus study involving multidisciplinary experts and people with lived experience of long COVID. The study comprised a literature review to identify measurement instruments for the core outcomes, a three-round online modified Delphi process, and an online consensus meeting to generate a core outcome measurement set (COMS). 594 individuals from 58 countries participated.

The number of potential instruments for the 12 core outcomes was reduced from 319 to 19. Consensus was reached for inclusion of the modified Medical Research Council Dyspnoea Scale for respiratory outcomes. Measures for two relevant outcomes from a previously published COS for acute COVID-19 were also included: time until death, for survival, and the Recovery Scale for COVID-19, for recovery. Instruments were suggested for consideration for the remaining nine core outcomes: fatigue or exhaustion, pain, post-exertion symptoms, work or occupational and study changes, and cardiovascular, nervous system, cognitive, mental health, and physical outcomes; however, consensus was not achieved for instruments for these outcomes.

The recommended COMS and instruments for consideration provide a foundation for the evaluation of post-COVID-19 condition in adults, which should help to optimise clinical care and accelerate research worldwide. Further assessment of this COMS is warranted as new data emerge on existing and novel measurement instruments.

Source: Gorst SL, Seylanova N, Dodd SR, Harman NL, O’Hara M, Terwee CB, Williamson PR, Needham DM, Munblit D, Nicholson TR; PC-COS study group. Core outcome measurement instruments for use in clinical and research settings for adults with post-COVID-19 condition: an international Delphi consensus study. Lancet Respir Med. 2023 Nov 2:S2213-2600(23)00370-3. doi: 10.1016/S2213-2600(23)00370-3. Epub ahead of print. PMID: 37926103. https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(23)00370-3/fulltext (Full text)

How methodological pitfalls have created widespread misunderstanding about long COVID

Key messages:

  • The existing epidemiological research on long COVID has suffered from overly broad case definitions and a striking absence of control groups, which have led to distortion of risk.

  • The unintended consequences of this may include, but are not limited to, increased societal anxiety and healthcare spending, a failure to diagnose other treatable conditions misdiagnosed as long COVID and diversion of funds and attention from those who truly suffer from chronic conditions secondary to COVID-19.

  • Future research should include properly matched control groups, sufficient follow-up time after infection and internationally-established diagnostic or inclusion and exclusion criteria.

Source: Høeg TB, Ladhani S, Prasad V. How methodological pitfalls have created widespread misunderstanding about long COVID. BMJ Evid Based Med. 2023 Sep 25:bmjebm-2023-112338. doi: 10.1136/bmjebm-2023-112338. Epub ahead of print. PMID: 37748921. https://ebm.bmj.com/content/early/2023/08/10/bmjebm-2023-112338 (Full text)

The importance of patient-partnered research in addressing long COVID: Takeaways for biomedical research study design from the RECOVER Initiative’s Mechanistic Pathways taskforce

Abstract:

The NIH-funded RECOVER study is collecting clinical data on patients who experience a SARS-CoV-2 infection. As patient representatives of the RECOVER Initiative’s Mechanistic Pathways task force, we offer our perspectives on patient motivations for partnering with researchers to obtain results from mechanistic studies. We emphasize the challenges of balancing urgency with scientific rigor. We recognize the importance of such partnerships in addressing post-acute sequelae of SARS-CoV-2 infection (PASC), which includes ‘long COVID,’ through contrasting objective and subjective narratives.

Long COVID’s prevalence served as a call to action for patients like us to become actively involved in efforts to understand our condition. Patient-centered and patient-partnered research informs the balance between urgency and robust mechanistic research. Results from collaborating on protocol design, diverse patient inclusion, and awareness of community concerns establish a new precedent in biomedical research study design. With a public health matter as pressing as the long-term complications that can emerge after SARS-CoV-2 infection, considerate and equitable stakeholder involvement is essential to guiding seminal research. Discussions in the RECOVER Mechanistic Pathways task force gave rise to this commentary as well as other review articles on the current scientific understanding of PASC mechanisms.

Source: Kim C, Chen B, Mohandas S, Rehman J, Sherif ZA, Coombs K; RECOVER Mechanistic Pathways Task Force; RECOVER Initiative. The importance of patient-partnered research in addressing long COVID: Takeaways for biomedical research study design from the RECOVER Initiative’s Mechanistic Pathways taskforce. Elife. 2023 Sep 22;12:e86043. doi: 10.7554/eLife.86043. PMID: 37737716; PMCID: PMC10516599. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516599/ (Full text)

Etiopathogenic theories about long COVID

Abstract:

The main etiopathogenic theories of long coronavirus disease (COVID) are listed and a conjunction of them is carried out with the objective of deciphering the pathophysiology of the entity, finally the main lines of treatment existing in real life are discussed (Paxlovid, use of antibiotics in dysbiosis, triple anticoagulant therapy, temelimab).

Source: Del Carpio-Orantes L. Etiopathogenic theories about long COVID. World J Virol. 2023 Jun 25;12(3):204-208. doi: 10.5501/wjv.v12.i3.204. PMID: 37396704; PMCID: PMC10311581. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311581/ (Full text)

Detecting anti-SARS-CoV-2 antibodies in urine samples: A noninvasive and sensitive way to assay COVID-19 immune conversion

Abstract:

Serum-based ELISA (enzyme-linked immunosorbent assay) has been widely used to detect anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies. However, to date, no study has investigated patient urine as a biological sample to detect SARS-CoV-2 virus-specific antibodies. An in-house urine-based ELISA was developed using recombinant SARS-CoV-2 nucleocapsid protein.

The presence of SARS-CoV-2 antibodies in urine was established, with 94% sensitivity and 100% specificity for the detection of anti-SARS-CoV-2 antibodies with the urine-based ELISA and 88% sensitivity and 100% specificity with a paired serum-based ELISA. The urine-based ELISA that detects anti-SARS-CoV-2 antibodies is a noninvasive method with potential application as a facile COVID-19 immunodiagnostic platform, which can be used to report the extent of exposure at the population level and/or to assess the risk of infection at the individual level.

Source: Ludolf F, Ramos FF, Bagno FF, Oliveira-da-Silva JA, Reis TAR, Christodoulides M, Vassallo PF, Ravetti CG, Nobre V, da Fonseca FG, Coelho EAF. Detecting anti-SARS-CoV-2 antibodies in urine samples: A noninvasive and sensitive way to assay COVID-19 immune conversion. Sci Adv. 2022 May 13;8(19):eabn7424. doi: 10.1126/sciadv.abn7424. Epub 2022 May 13. PMID: 35559681; PMCID: PMC9106288. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9106288/ (Full text)

De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository

Abstract:

Machine learning (ML)-driven computable phenotypes are among the most challenging to share and reproduce. Despite this difficulty, the urgent public health considerations around Long COVID make it especially important to ensure the rigor and reproducibility of Long COVID phenotyping algorithms such that they can be made available to a broad audience of researchers. As part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, researchers with the National COVID Cohort Collaborative (N3C) devised and trained an ML-based phenotype to identify patients highly probable to have Long COVID. Supported by RECOVER, N3C and NIH’s All of Us study partnered to reproduce the output of N3C’s trained model in the All of Us data enclave, demonstrating model extensibility in multiple environments. This case study in ML-based phenotype reuse illustrates how open-source software best practices and cross-site collaboration can de-black-box phenotyping algorithms, prevent unnecessary rework, and promote open science in informatics.

Source: Pfaff ER, Girvin AT, Crosskey M, Gangireddy S, Master H, Wei WQ, Kerchberger VE, Weiner M, Harris PA, Basford M, Lunt C, Chute CG, Moffitt RA, Haendel M; N3C and RECOVER Consortia. De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository. J Am Med Inform Assoc. 2023 May 22:ocad077. doi: 10.1093/jamia/ocad077. Epub ahead of print. PMID: 37218289. https://pubmed.ncbi.nlm.nih.gov/37218289/

Long COVID and the cardiovascular system-elucidating causes and cellular mechanisms in order to develop targeted diagnostic and therapeutic strategies: a joint Scientific Statement of the ESC Working Groups on Cellular Biology of the Heart and Myocardial and Pericardial Diseases

Abstract:

Long COVID has become a world-wide, non-communicable epidemic, caused by long-lasting multiorgan symptoms that endure for weeks or months after SARS-CoV-2 infection has already subsided. This scientific document aims to provide insight into the possible causes and therapeutic options available for the cardiovascular manifestations of long COVID.

In addition to chronic fatigue, which is a common symptom of long COVID, patients may present with chest pain, ECG abnormalities, postural orthostatic tachycardia, or newly developed supraventricular or ventricular arrhythmias. Imaging of the heart and vessels has provided evidence of chronic, post-infectious perimyocarditis with consequent left or right ventricular failure, arterial wall inflammation, or microthrombosis in certain patient populations.

Better understanding of the underlying cellular and molecular mechanisms of long COVID will aid in the development of effective treatment strategies for its cardiovascular manifestations. A number of mechanisms have been proposed, including those involving direct effects on the myocardium, microthrombotic damage to vessels or endothelium, or persistent inflammation.

Unfortunately, existing circulating biomarkers, coagulation, and inflammatory markers, are not highly predictive for either the presence or outcome of long COVID when measured 3 months after SARS-CoV-2 infection. Further studies are needed to understand underlying mechanisms, identify specific biomarkers, and guide future preventive strategies or treatments to address long COVID and its cardiovascular sequelae.

Source: Selvakumar J, Havdal LB, Drevvatne M, Brodwall EM, Lund Berven L, Stiansen-Sonerud T, Einvik G, Leegaard TM, Tjade T, Michelsen AE, Mollnes TE, Lund-Johansen F, Holmøy T, Zetterberg H, Blennow K, Sandler CX, Cvejic E, Lloyd AR, Wyller VBB. Prevalence and Characteristics Associated With Post-COVID-19 Condition Among Nonhospitalized Adolescents and Young Adults. JAMA Netw Open. 2023 Mar 1;6(3):e235763. doi: 10.1001/jamanetworkopen.2023.5763. PMID: 36995712; PMCID: PMC10064252. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10064252/ (Full text)

Understanding pediatric long COVID using a tree-based scan statistic approach: an EHR-based cohort study from the RECOVER Program

Abstract:

Objectives: Post-acute sequalae of SARS-CoV-2 infection (PASC) is not well defined in pediatrics given its heterogeneity of presentation and severity in this population. The aim of this study is to use novel methods that rely on data mining approaches rather than clinical experience to detect conditions and symptoms associated with pediatric PASC.

Materials and methods: We used a propensity-matched cohort design comparing children identified using the new PASC ICD10CM diagnosis code (U09.9) (N = 1309) to children with (N = 6545) and without (N = 6545) SARS-CoV-2 infection. We used a tree-based scan statistic to identify potential condition clusters co-occurring more frequently in cases than controls.

Results: We found significant enrichment among children with PASC in cardiac, respiratory, neurologic, psychological, endocrine, gastrointestinal, and musculoskeletal systems, the most significant related to circulatory and respiratory such as dyspnea, difficulty breathing, and fatigue and malaise.

Discussion: Our study addresses methodological limitations of prior studies that rely on prespecified clusters of potential PASC-associated diagnoses driven by clinician experience. Future studies are needed to identify patterns of diagnoses and their associations to derive clinical phenotypes.

Conclusion: We identified multiple conditions and body systems associated with pediatric PASC. Because we rely on a data-driven approach, several new or under-reported conditions and symptoms were detected that warrant further investigation.

Source: Lorman V, Rao S, Jhaveri R, Case A, Mejias A, Pajor NM, Patel P, Thacker D, Bose-Brill S, Block J, Hanley PC, Prahalad P, Chen Y, Forrest CB, Bailey LC, Lee GM, Razzaghi H. Understanding pediatric long COVID using a tree-based scan statistic approach: an EHR-based cohort study from the RECOVER Program. JAMIA Open. 2023 Mar 14;6(1):ooad016. doi: 10.1093/jamiaopen/ooad016. PMID: 36926600; PMCID: PMC10013630. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013630/ (Full text)

An Exploratory Factor Analysis of Long Covid

Abstract:
An exploratory factor analysis (EFA) can provide a window into the latent dimensions of a disease, such as Long COVID.
Discovering the latent factors of Long COVID enables researchers and clinicians to better conceptualize, study and treat
this disease.
In this study, participants were recruited from social media sites dedicated to COVID and Long COVID. Among the 480 participants, those who completed at least 90% of the survey, reported symptoms for two or more months since COVID-19 symptom onset, and had not been hospitalized for COVID were used in the EFA. The mean duration since initial symptom onset was 74.0 (37.3) weeks.
A new questionnaire called The DePaul Symptom Questionnaire-COVID was used to assess self-reports of the frequency and severity of 38 Long COVID symptoms experienced over the most recent month. The most burdensome symptoms were “Symptoms that get worse after physical or mental activities (also known as Post-Exertional Malaise),” “Fatigue/extreme tiredness,” “Difficulty thinking and/or concentrating,” “Sleep problems,” and “Muscle aches.” The EFA resulted in a three-factor model with factors labeled General, PEM/Fatigue/Cognitive Dysfunction, and Psychological, consisting of 16, 6, and 3 items respectively (25 items in total).
The reliability of the items in the EFA was .90 using a split-half reliability test. Finally, participant self-reported level of
functional impairment was analyzed across the three EFA factors. Interpretations and applications to research and
practice are provided.
Source: Joseph A. Dorri1 and Leonard A. Jason. An exploratory factor analysis of long covid. Central Asian Journal of Medical Hypotheses and Ethics. 2/14/23 https://www.researchgate.net/publication/368502945_AN_EXPLORATORY_FACTOR_ANALYSIS_OF_LONG_COVID (Full text)