Browsing by Subject "Long COVID"

Now showing 1 - 8 of 8
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    A Comparative Experimental and Computational Study on the Nature of the Pangolin-CoV and COVID-19 Omicron
    (MDPI, 2024-07-09) Wei, Lai; Song, Lihua; Dunker, A. Keith; Foster, James A.; Uversky, Vladimir N.; Goh, Gerard Kian-Meng; Biochemistry and Molecular Biology, School of Medicine
    The relationship between pangolin-CoV and SARS-CoV-2 has been a subject of debate. Further evidence of a special relationship between the two viruses can be found by the fact that all known COVID-19 viruses have an abnormally hard outer shell (low M disorder, i.e., low content of intrinsically disordered residues in the membrane (M) protein) that so far has been found in CoVs associated with burrowing animals, such as rabbits and pangolins, in which transmission involves virus remaining in buried feces for a long time. While a hard outer shell is necessary for viral survival, a harder inner shell could also help. For this reason, the N disorder range of pangolin-CoVs, not bat-CoVs, more closely matches that of SARS-CoV-2, especially when Omicron is included. The low N disorder (i.e., low content of intrinsically disordered residues in the nucleocapsid (N) protein), first observed in pangolin-CoV-2017 and later in Omicron, is associated with attenuation according to the Shell-Disorder Model. Our experimental study revealed that pangolin-CoV-2017 and SARS-CoV-2 Omicron (XBB.1.16 subvariant) show similar attenuations with respect to viral growth and plaque formation. Subtle differences have been observed that are consistent with disorder-centric computational analysis.
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    A machine learning-based phenotype for long COVID in children: an EHR-based study from the RECOVER program
    (Cold Spring Harbor Laboratory, 2022-12-26) Lorman, Vitaly; Razzaghi, Hanieh; Song, Xing; Morse, Keith; Utidjian, Levon; Allen, Andrea J.; Rao, Suchitra; Rogerson, Colin; Bennett, Tellen D.; Morizono, Hiroki; Eckrich, Daniel; Jhaveri, Ravi; Huang, Yungui; Ranade, Daksha; Pajor, Nathan; Lee, Grace M.; Forrest, Christopher B.; Bailey, L. Charles; Pediatrics, School of Medicine
    Background: As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data. Methods and findings: In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS-CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach. We used an XGboost model, with hyperparameters selected through cross-validated grid search, and model performance was assessed using 5-fold cross-validation. Model predictions and feature importance were evaluated using Shapley Additive exPlanation (SHAP) values. Conclusions: The model provides a tool for identifying patients with PASC and an approach to characterizing PASC using diagnosis, medication, laboratory, and procedure features in health systems data. Using appropriate threshold settings, the model can be used to identify PASC patients in health systems data at higher precision for inclusion in studies or at higher recall in screening for clinical trials, especially in settings where PASC diagnosis codes are used less frequently or less reliably. Analysis of how specific features contribute to the classification process may assist in gaining a better understanding of features that are associated with PASC diagnoses.
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    A Study on the Nature of SARS-CoV-2 Using the Shell Disorder Models: Reproducibility, Evolution, Spread, and Attenuation
    (MDPI, 2022-09-23) Goh, Gerard Kian-Meng; Dunker, A. Keith; Foster, James A.; Uversky, Vladimir N.; Biochemistry and Molecular Biology, School of Medicine
    The basic tenets of the shell disorder model (SDM) as applied to COVID-19 are that the harder outer shell of the virus shell (lower PID-percentage of intrinsic disorder-of the membrane protein M, PIDM) and higher flexibility of the inner shell (higher PID of the nucleocapsid protein N, PIDN) are correlated with the contagiousness and virulence, respectively. M protects the virion from the anti-microbial enzymes in the saliva and mucus. N disorder is associated with the rapid replication of the virus. SDM predictions are supported by two experimental observations. The first observation demonstrated lesser and greater presence of the Omicron particles in the lungs and bronchial tissues, respectively, as there is a greater level of mucus in the bronchi. The other observation revealed that there are lower viral loads in 2017-pangolin-CoV, which is predicted to have similarly low PIDN as Omicron. The abnormally hard M, which is very rarely seen in coronaviruses, arose from the fecal-oral behaviors of pangolins via exposure to buried feces. Pangolins provide an environment for coronavirus (CoV) attenuation, which is seen in Omicron. Phylogenetic study using M shows that COVID-19-related bat-CoVs from Laos and Omicron are clustered in close proximity to pangolin-CoVs, which suggests the recurrence of interspecies transmissions. Hard M may have implications for long COVID-19, with immune systems having difficulty degrading viral proteins/particles.
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    Age-dependent phenotypes of cognitive impairment as sequelae of SARS-CoV-2 infection
    (Frontiers Media, 2025-01-07) Gonzalez Aleman, Gabriela; Vavougios, George D.; Tartaglia, Carmela; Uvais, Nalakath A.; Guekht, Alla; Hosseini, Akram A.; Lo Re, Vincenzina; Ferreccio, Catterina; D'Avossa, Giovanni; Zamponi, Hernan P.; Figueredo Aguiar, Mariana; Yecora, Agustin; Ul Haq Katshu, Mohammad Zia; Stavrou, Vasileios T.; Boutlas, Stylianos; Gourgoulianis, Konstantinos I.; Botero, Camila; González Insúa, Francisco; Perez-Lloret, Santiago; Zinchuk, Mikhail; Gersamija, Anna; Popova, Sofya; Bryzgalova, Yulia; Sviatskaya, Ekaterina; Russelli, Giovanna; Avorio, Federica; Wang, Sophia; Edison, Paul; Niimi, Yoshiki; Sohrabi, Hamid R.; Mukaetova Ladinska, Elizabeta B.; Neidre, Daria; de Erausquin, Gabriel A.; Psychiatry, School of Medicine
    Cognitive changes associated with PASC may not be uniform across populations. We conducted individual-level pooled analyses and meta-analyses of cognitive assessments from eight prospective cohorts, comprising 2,105 patients and 1,432 controls from Argentina, Canada, Chile, Greece, India, Italy, Russia, and the UK. The meta-analysis found no differences by country of origin. The profile and severity of cognitive impairment varied by age, with mild attentional impairment observed in young and middle-aged adults, but memory, language, and executive function impairment in older adults. The risk of moderate to severe impairment doubled in older adults. Moderately severe or severe impairment was significantly associated with infection diagnoses (chi-square = 26.57, p ≤ 0.0001) and the severity of anosmia (chi-square = 31.81, p ≤ 0.0001). We found distinct age-related phenotypes of cognitive impairment in patients recovering from COVID-19. We identified the severity of acute illness and the presence of olfactory dysfunction as the primary predictors of dementia-like impairment in older adults.
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    Characteristics of Chemosensory Perception in Long COVID and COVID Reinfection
    (MDPI, 2023-05-22) Jaramillo, Mikki; Thyvalikakath, Thankam P.; Eckert, George; Srinivasan, Mythily; Oral Pathology, Medicine and Radiology, School of Dentistry
    Emerging data suggest an increasing prevalence of persistent symptoms in individuals affected by coronavirus disease-19 (COVID-19). The objective of this study was to determine the relative frequency of altered taste and smell in COVID reinfection (multiple COVID positive tests) and long COVID (one COVID positive test). We sent an electronic survey to patients in the Indiana University Health COVID registry with positive COVID test results, querying if they were experiencing symptoms consistent with long COVID including altered chemosensory perceptions. Among the 225 respondents, a greater long COVID burden and COVID reinfection was observed in women. Joint pain was reported as the most common symptom experienced by 18% of individuals in the long COVID cohort. In the COVID reinfection cohort >20% of individuals reported headache, joint pain, and cough. Taste perception worse than pre-COVID was reported by 29% and 42% of individuals in the long COVID and COVID reinfection cohorts, respectively. Smell perception worse than pre-COVID was reported by 37% and 46% of individuals in long COVID and COVID reinfection cohorts, respectively. Further, Chi-square test suggested significant association between pre-COVID severity of taste/smell perception and headache in both cohorts. Our findings highlight the prevalence of persistent chemosensory dysfunction for two years and longer in long COVID and COVID reinfection.
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    EHR-based Case Identification of Pediatric Long COVID: A Report from the RECOVER EHR Cohort
    (medRxiv, 2024-05-23) Botdorf, Morgan; Dickinson, Kimberley; Lorman, Vitaly; Razzaghi, Hanieh; Marchesani, Nicole; Rao, Suchitra; Rogerson, Colin; Higginbotham, Miranda; Mejias, Asuncion; Salyakina, Daria; Thacker, Deepika; Dandachi, Dima; Christakis, Dimitri A.; Taylor, Emily; Schwenk, Hayden; Morizono, Hiroki; Cogen, Jonathan; Pajor, Nate M.; Jhaveri, Ravi; Forrest, Christopher B.; Bailey, L. Charles; RECOVER Consortium; Pediatrics, School of Medicine
    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.
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    Leadership Changes to Support Healthcare Providers with Long COVID Care
    (2025-04) Weideman, Angela Katherine; Czabanowska, Katarzyna; Bigatti, Silvia; Modji, Komi
    Objective: To explore how public health leaders and long COVID stakeholders from the Midwest United States can best support healthcare providers in providing long COVID care and how they can help ease the burdens of such care. Data sources and study setting: Qualitative interviews with 34 long COVID stakeholders from the Midwest region of the United States, from 15 different stakeholder groups, were collected from December 2024 to February 2025. Study Design: The research design was a qualitative study in which key informant interviews were conducted with stakeholders of long COVID using a semi-structured interview. Data collection: Semi-structured, one-on-one interviews were conducted virtually using Microsoft teams, and interviews were audio recorded and transcribed using Microsoft teams. Interviews were then coded using NVivo for inductive coding, used to identify and describe themes. Principal findings: The challenges identified related to diagnosis, access to care, provider concern, communication, and treatment. Stakeholders identified that they have impact and influence related to helping patients get to diagnosis and treatment more quickly, setting or supporting policies, making long COVID a priority in their organizations, making it easier for people to access long COVID care, and to support the people who are giving long COVID care. Numerous strategies were offered by stakeholders for supporting healthcare providers who are providing long COVID care. These include better characterization of the disease, increased treatment options, increased prevention efforts, advocacy, communication strategies, support for providers, collaboration with stakeholders, policy development, and learning from the success of advancement of care for other chronic conditions. Conclusion: Long COVID is a novel condition that requires change leadership support especially in the areas of diagnosis, access to care, communication, treatment, and provider knowledge and relations. It will take a multidisciplinary approach from a variety of stakeholders to create and implement a plan to support healthcare professionals who provide long COVID care. If these changes can be implemented and maintained, those who are suffering with long COVID symptoms can get to diagnosis and treatment more quickly and receive better quality care and treatment. This will have a positive impact on long COVID patients, their family members, employers, schools, medical professionals, health systems, governments, and the economy.
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    Prolonged Gastrointestinal Manifestations After Recovery From COVID-19
    (Elsevier, 2023) Elmunzer, B. Joseph; Palsson, Olafur S.; Forbes , Nauzer; Zakaria , Ali; Davis, Christian; Canakis, Andrew; Qayed, Emad; Bick, Benjamin; Pawa, Swati; Tierney, William M.; McLeod, Caroline G.; Taylor, Jason; Patel, Harsh; Mendelsohn, Robin B.; Bala, Gokul; Sloan, Ian; Merchant, Ambreen A.; Smith, Zachary L.; Sendzischew Shane, Morgan A.; Aroniadis, Olga C.; Ordiah, Collins O.; Ruddy, Johannah M.; Simren, Magnus; Tack, Jan; Drossman, Douglas; Medicine, School of Medicine
    Background & Aims Acute enteric infections are well known to result in long-term gastrointestinal (GI) disorders. Although COVID-19 is principally a respiratory illness, it demonstrates significant GI tropism, possibly predisposing to prolonged gut manifestations. We aimed to examine the long-term GI impact of hospitalization with COVID-19. Methods Nested within a large-scale observational cohort study of patients hospitalized with COVID-19 across North America, we performed a follow-up survey of 530 survivors 12–18 months later to assess for persistent GI symptoms and their severity, and for the development of disorders of gut-brain interaction (DGBIs). Eligible patients were identified at the study site level and surveyed electronically. The survey instrument included the Rome IV Diagnostic Questionnaire for DGBI, a rating scale of 24 COVID-related symptoms, the Gastrointestinal Symptoms Rating Scale, and the Impact of Events–Revised trauma symptom questionnaire (a measure of posttraumatic stress associated with the illness experience). A regression analysis was performed to explore the factors associated with GI symptom severity at follow-up. Results Of the 530 invited patients, 116 responded (52.6% females; mean age, 55.2 years), and 73 of those (60.3%) met criteria for 1 or more Rome IV DGBI at follow-up, higher than the prevalence in the US general population (P < .0001). Among patients who experienced COVID-related GI symptoms during the index hospitalization (abdominal pain, nausea, vomiting, or diarrhea), 42.1% retained at least 1 of these symptoms at follow-up; in comparison, 89.8% of respondents retained any (GI or non-GI) COVID-related symptom. The number of moderate or severe GI symptoms experienced during the initial COVID-19 illness by self-report correlated with the development of DGBI and severity of GI symptoms at follow-up. Posttraumatic stress disorder (Impact of Events–Revised score ≥33) related to the COVID-19 illness experience was identified in 41.4% of respondents and those individuals had higher DGBI prevalence and GI symptom severity. Regression analysis revealed that higher psychological trauma score (Impact of Events–Revised) was the strongest predictor of GI symptom severity at follow-up. Conclusions In this follow-up survey of patients 12–18 months after hospitalization with COVID-19, there was a high prevalence of DGBIs and persistent GI symptoms. Prolonged GI manifestations were associated with the severity of GI symptoms during hospitalization and with the degree of psychological trauma related to the illness experience.