long covid assessment – American ME and CFS Society

Characterizing Long COVID in Children and Adolescents

Key Points:

Question What prolonged symptoms experienced by youth are most associated with SARS-CoV-2 infection?

Findings Among 5367 participants in the RECOVER-Pediatrics cohort study, 14 symptoms in both school-age children (6-11 years) and adolescents (12-17 years) were more common in those with vs without SARS-CoV-2 infection history, with 4 additional symptoms in school-age children only and 3 in adolescents only. Empirically derived indices for PASC research and associated clustering patterns were developed.

Meaning This study developed research indices for characterizing pediatric PASC. Symptom patterns were similar but distinguishable between school-age children and adolescents, highlighting the importance of characterizing PASC separately in different age groups.

Abstract

Importance Most research to understand postacute sequelae of SARS-CoV-2 infection (PASC), or long COVID, has focused on adults, with less known about this complex condition in children. Research is needed to characterize pediatric PASC to enable studies of underlying mechanisms that will guide future treatment.

Objective To identify the most common prolonged symptoms experienced by children (aged 6 to 17 years) after SARS-CoV-2 infection, how these symptoms differ by age (school-age [6-11 years] vs adolescents [12-17 years]), how they cluster into distinct phenotypes, and what symptoms in combination could be used as an empirically derived index to assist researchers to study the likely presence of PASC.

Design, Setting, and Participants Multicenter longitudinal observational cohort study with participants recruited from more than 60 US health care and community settings between March 2022 and December 2023, including school-age children and adolescents with and without SARS-CoV-2 infection history.

Exposure SARS-CoV-2 infection.

Main Outcomes and Measures PASC and 89 prolonged symptoms across 9 symptom domains.

Results A total of 898 school-age children (751 with previous SARS-CoV-2 infection [referred to as infected] and 147 without [referred to as uninfected]; mean age, 8.6 years; 49% female; 11% were Black or African American, 34% were Hispanic, Latino, or Spanish, and 60% were White) and 4469 adolescents (3109 infected and 1360 uninfected; mean age, 14.8 years; 48% female; 13% were Black or African American, 21% were Hispanic, Latino, or Spanish, and 73% were White) were included. Median time between first infection and symptom survey was 506 days for school-age children and 556 days for adolescents. In models adjusted for sex and race and ethnicity, 14 symptoms in both school-age children and adolescents were more common in those with SARS-CoV-2 infection history compared with those without infection history, with 4 additional symptoms in school-age children only and 3 in adolescents only. These symptoms affected almost every organ system. Combinations of symptoms most associated with infection history were identified to form a PASC research index for each age group; these indices correlated with poorer overall health and quality of life. The index emphasizes neurocognitive, pain, and gastrointestinal symptoms in school-age children but change or loss in smell or taste, pain, and fatigue/malaise–related symptoms in adolescents. Clustering analyses identified 4 PASC symptom phenotypes in school-age children and 3 in adolescents.

Conclusions and Relevance This study developed research indices for characterizing PASC in children and adolescents. Symptom patterns were similar but distinguishable between the 2 groups, highlighting the importance of characterizing PASC separately for these age ranges.

The long COVID evidence gap in England

Introduction:

The term long COVID, also known as post-COVID-19 condition, was coined in spring, 2020, by individuals with ongoing symptoms following COVID-19 in response to unsatisfactory recognition of this emerging syndrome by health-care practitioners.

In September to November, 2020, clinical codes for persistent post-COVID-19 condition and related referrals were introduced and became available for use by health-care practitioners to record details of clinical encounters in electronic health records (EHRs) in England. EHRs, which cover a large proportion of individuals living in England, are increasingly used to help understand the epidemiology of disease alongside the effectiveness and safety of interventions.

Many factors influence the completeness of information in EHRs, including help-seeking behaviour of patients and the discretion and data-recording behaviour of practitioners. Longitudinal population-based studies often include participant self-reports of illness; hence, these studies might be subject to reporting and participation biases. Comparing reported illness in studies to recorded illness in the EHRs of the same individuals might be helpful in understanding the epidemiology and clinical recognition of emerging conditions such as long COVID.

Source: Knuppel A, Boyd A, Macleod J, Chaturvedi N, Williams DM. The long COVID evidence gap in England. Lancet. 2024 May 6:S0140-6736(24)00744-X. doi: 10.1016/S0140-6736(24)00744-X. Epub ahead of print. PMID: 38729195. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24)00744-X/fulltext (Full text)

Exploring Cognitive Dysfunction in Long COVID Patients: Eye Movement Abnormalities and Frontal-Subcortical Circuits Implications via Eye-Tracking and Machine Learning

Abstract:

Background: Cognitive dysfunction is regarded as one of the most severe aftereffects following coronavirus disease 2019 (COVID-19). Eye movements, controlled by various brain regions, including the dorsolateral prefrontal cortex and frontal-thalamic circuits, offer a potential metric for evaluating cognitive dysfunction. We aimed to examine the utility of eye movement measurements in identifying cognitive impairments in long COVID patients.

Methods: We recruited 40 long COVID patients experiencing subjective cognitive complaints and 40 healthy controls and used a certified eye-tracking medical device to record saccades and antisaccades. Machine learning was applied to enhance the analysis of eye movement data.

Results: Patients did not differ from the healthy controls regarding age, sex, and years of education. However, the patients’ Montreal Cognitive Assessment total score was significantly lower than healthy controls. Most eye movement parameters were significantly worse in patients: the latencies, gain, and velocity of visually and memory-guided saccades, the number of correct memory saccades, the latencies and duration of reflexive saccades, and the number of errors in the antisaccade test. Machine learning permitted distinguishing between long COVID patients experiencing subjective cognitive complaints and healthy controls.

Conclusion: Our findings suggest impairments in frontal subcortical circuits in long COVID patients experiencing subjective cognitive complaints. Eye-tracking, combined with machine learning, offers a novel, efficient way to assess and monitor long COVID patients’ cognitive dysfunctions, suggesting its utility in clinical settings for early detection and personalized treatment strategies. Further research is needed to determine the long-term implications of these findings and the reversibility of cognitive dysfunctions.

Source: Benito-León J, Lapeña J, García-Vasco L, Cuevas C, Viloria-Porto J, Calvo-Córdoba A, Arrieta-Ortubay E, Ruiz-Ruigómez M, Sánchez-Sánchez C, García-Cena C. Exploring Cognitive Dysfunction in Long COVID Patients: Eye Movement Abnormalities and Frontal-Subcortical Circuits Implications via Eye-Tracking and Machine Learning. Am J Med. 2024 Apr 5:S0002-9343(24)00217-1. doi: 10.1016/j.amjmed.2024.04.004. Epub ahead of print. PMID: 38583751. https://pubmed.ncbi.nlm.nih.gov/38583751/

Long COVID: Molecular Mechanisms and Detection Techniques

Abstract:

Long COVID, also known as post-acute sequelae of SARS-CoV-2 infection (PASC), has emerged as a significant health concern following the COVID-19 pandemic. Molecular mechanisms underlying the occurrence and progression of long COVID include viral persistence, immune dysregulation, endothelial dysfunction, and neurological involvement, and highlight the need for further research to develop targeted therapies for this condition. While a clearer picture of the clinical symptomatology is shaping, many molecular mechanisms are yet to be unraveled, given their complexity and high level of interaction with other metabolic pathways.

This review summarizes some of the most important symptoms and associated molecular mechanisms that occur in long COVID, as well as the most relevant molecular techniques that can be used in understanding the viral pathogen, its affinity towards the host, and the possible outcomes of host-pathogen interaction.

Source: Constantinescu-Bercu A, Lobiuc A, Căliman-Sturdza OA, Oiţă RC, Iavorschi M, Pavăl N-E, Șoldănescu I, Dimian M, Covasa M. Long COVID: Molecular Mechanisms and Detection Techniques. International Journal of Molecular Sciences. 2024; 25(1):408. https://doi.org/10.3390/ijms25010408 https://www.mdpi.com/1422-0067/25/1/408 (Full text)

Smartphone-based evaluation of static balance and mobility in long-lasting COVID-19 patients

Abstract:

Background: SARS-CoV-2 infection can lead to a variety of persistent sequelae, collectively known as long COVID-19. Deficits in postural balance have been reported in patients several months after COVID-19 infection. The purpose of this study was to evaluate the static balance and balance of individuals with long COVID-19 using inertial sensors in smartphones.

Methods: A total of 73 participants were included in this study, of which 41 had long COVID-19 and 32 served as controls. All participants in the long COVID-19 group reported physical complaints for at least 7 months after SARS-CoV-2 infection. Participants were evaluated using a built-in inertial sensor of a smartphone attached to the low back, which recorded inertial signals during a static balance and mobility task (timed up and go test). The parameters of static balance and mobility obtained from both groups were compared.

Results: The groups were matched for age and BMI. Of the 41 participants in the long COVID-19 group, 22 reported balance impairment and 33 had impaired balance in the Sharpened Romberg test. Static balance assessment revealed that the long COVID-19 group had greater postural instability with both eyes open and closed than the control group. In the TUG test, the long COVID-19 group showed greater acceleration during the sit-to-stand transition compared to the control group.

Conclusion: The smartphone was feasible to identify losses in the balance motor control and mobility of patients with long-lasting symptomatic COVID-19 even after several months or years. Attention to the balance impairment experienced by these patients could help prevent falls and improve their quality of life, and the use of the smartphone can expand this monitoring for a broader population.

Source: Corrêa BDC, Santos EGR, Belgamo A, Pinto GHL, Xavier SS, Silva CC, Dias ÁRN, Paranhos ACM, Cabral ADS, Callegari B, Costa E Silva AA, Quaresma JAS, Falcão LFM, Souza GS. Smartphone-based evaluation of static balance and mobility in long-lasting COVID-19 patients. Front Neurol. 2023 Dec 11;14:1277408. doi: 10.3389/fneur.2023.1277408. PMID: 38148981; PMCID: PMC10750373. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10750373/ (Full text)

Low handgrip strength is associated with worse functional outcomes in long-Covid

Abstract:

The diagnosis of long-Covid is troublesome, even when functional limitations are present. Dynapenia is a decrease in muscle strength and power production and may explain in part these limitations. This study aimed to identify the distribution and possible association of dynapenia with functional assessment in patients with long-Covid.

A total of 113 inpatients with COVID-19 were evaluated by functional assessment 120 days post-acute severe disease. Body composition, respiratory muscle strength, spirometry, six-minute walk test (6MWT) and hand-grip strength (HGS) were assessed.

Dynapenia was defined as HGS < 30kg/f (men), and < 20kg/f (women). Twenty-five (22%) participants were dynapenic, presenting lower muscle mass (p < 0.001), worse forced expiratory volume in the first second (FEV₁) (p = 0.0001), lower forced vital capacity (p < 0.001), and inspiratory (p = 0.007) and expiratory (p = 0.002) peek pressures, as well as worse 6MWT performance (p < 0.001). Dynapenia was associated with worse FEV₁, MEP, and 6MWT, independent of age (p < 0.001).

Patients with dynapenia had higher ICU admission rates (p = 0.01) and need for invasive mechanical ventilation (p = 0.007) during hospitalization. The HGS is a simple, reliable, and low-cost measurement that can be performed in outpatient clinics in low- and middle-income countries. Thus, HGS may be used as a proxy indicator of functional impairment in this population.

Source: Camila Miriam Suemi Sato Barros do Amaral AMARAL, Cássia da Luz Goulart GOULART, Bernardo Maia da Silva SILVA et al. Low handgrip strength is associated with worse functional outcomes in long-Covid, 11 December 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3695556/v1] https://www.researchsquare.com/article/rs-3695556/v1 (Full text)

Assessing symptoms of long/post COVID and chronic fatigue syndrome using the DePaul symptom questionnaire-2: a validation in a German-speaking population

Abstract:

Objective: A subset of Covid-19 survivors will develop persisting health sequelae (i.e. Long Covid/LC or Post Covid/PC) similar to Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). In the absence of a reliable biomarker to diagnose LC/PC and ME/CFS, their classification based on symptoms becomes indispensable. Hence, we translated and validated the DePaul Symptom Questionnaire−2 (DSQ-2), to offer a screening tool for the German-speaking population.

Methods: A sample of healthy adults, and adults with ME/CFS and LC/PC (N = 502) completed a reduced-item version of the DSQ-2 and SF-36 questionnaire online. We performed an exploratory factor analysis, assessed construct validity, diagnostic accuracy and compared the symptom profiles of individuals with ME/CFS versus LC/PC versus healthy adults.

Results: Exploratory factor analysis revealed a 10-factor solution with excellent internal consistencies. The sensitivity of the DSQ-2 was excellent. The specificity was moderate with moderate inter-rater reliability. Construct validity of the DSQ-2 was supported by strong negative correlations with physical health subscales of the SF-36. A visual comparison of the symptom profiles of individuals with ME/CFS versus LC/PC revealed a comparable pattern.

Conclusion: Despite lower symptom severity, individuals with LC/PC reported significantly stronger limitations in general health and physical functioning and were more likely to meet ME/CFS diagnostic criteria with ongoing sickness duration, suggesting that ME/CFS can be considered a long-term sequela of LC/PC. This study offers a translated and validated version of the reduced-item DSQ-2 that can guide medical evaluation and aid physicians in identifying a ME/CFS-like subtype of LC/PC.

Source: Nina Buntić, Leonard A. Jason, Jochen Schneider, Marc Schlesser & André Schulz (2023) Assessing symptoms of long/post COVID and chronic fatigue syndrome using the DePaul symptom questionnaire-2: a validation in a German-speaking population, Fatigue: Biomedicine, Health & Behavior, DOI: 10.1080/21641846.2023.2295419 https://www.tandfonline.com/doi/full/10.1080/21641846.2023.2295419 (Full text)

Long Covid Clinical Severity Types Based on Symptoms and Functional Disability: A Longitudinal Evaluation

Abstract:

Background: Long Covid (LC) is a multisystem clinical syndrome with Functional Disability (FD) and compromised Overall Health (OH). There is a lack of distinct clinical symptom clusters (phenotypes) identified in LC so far but there is emerging information on LC clinical severity types. This study explores the consistency of these clinical severity types over time and the relationship between Symptom Severity (SS), FD, and OH in the context of the clinical severity types in a prospective sample.

Methods: A purposive sample of LC patients recruited to the LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS (LOCOMOTION) study were assessed using the modified COVID-19 Yorkshire Rehabilitation Scale (C19-YRSm) at two assessment time points. A cluster analysis for clinical severity types was undertaken at both time points using k-means partition using two, three, and four initial clusters and different starting values. Cluster analysis was also carried out to assess the presence of symptom phenotypes (symptom clusters).

Findings: Cross-sectional data was available for 759 patients with 356 patients completing C19-YRSm at the two assessment points. Mean age was 46·8 years (SD = 12·7), 69·4% were females, and median duration of LC symptoms at first assessment was 360 days (IQR 217 to 703 days). Cluster analysis revealed three distinct SS and FD clinical severity types – mild (N=96), moderate (N=422), and severe (N=241) – with no distinct symptom phenotypes. The three-level clinical severity pattern remained consistent over time between the two assessments, with 51% of patients switching the clinical severity type between the assessments. The fluctuation was independent of the LC severity and time between the assessments.

Interpretation: This is the first study in the literature to show the consistency of the three clinical severity types over time with patients also switching between severity types indicating the fluctuating nature of LC.

Source: Sivan, Manoj and Smith, Adam B. and Osborne, Thomas and Goodwin, Madeline and Lawrence, Román Rocha and Baley, Sareeta and Williams, Paul and Lee, Cassie and Davies, Helen and Balasundaram, Kumaran and Greenwood, Darren C., Long Covid Clinical Severity Types Based on Symptoms and Functional Disability: A Longitudinal Evaluation. Available at SSRN: https://ssrn.com/abstract=4642650 or http://dx.doi.org/10.2139/ssrn.4642650 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4642650 (Full text available as PDF file)

A retrospective cohort analysis leveraging augmented intelligence to characterize long COVID in the electronic health record: A precision medicine framework

Abstract:

Physical and psychological symptoms lasting months following an acute COVID-19 infection are now recognized as post-acute sequelae of COVID-19 (PASC). Accurate tools for identifying such patients could enhance screening capabilities for the recruitment for clinical trials, improve the reliability of disease estimates, and allow for more accurate downstream cohort analysis.

In this retrospective cohort study, we analyzed the EHR of hospitalized COVID-19 patients across three healthcare systems to develop a pipeline for better identifying patients with persistent PASC symptoms (dyspnea, fatigue, or joint pain) after their SARS-CoV-2 infection. We implemented distributed representation learning powered by the Machine Learning for modeling Health Outcomes (MLHO) to identify novel EHR features that could suggest PASC symptoms outside of typical diagnosis codes. MLHO applies an entropy-based feature selection and boosting algorithms for representation mining. These improved definitions were then used for estimating PASC among hospitalized patients.

30,422 hospitalized patients were diagnosed with COVID-19 across three healthcare systems between March 13, 2020 and February 28, 2021. The mean age of the population was 62.3 years (SD, 21.0 years) and 15,124 (49.7%) were female.

We implemented the distributed representation learning technique to augment PASC definitions. These definitions were found to have positive predictive values of 0.73, 0.74, and 0.91 for dyspnea, fatigue, and joint pain, respectively.

We estimated that 25 percent (CI 95%: 6-48), 11 percent (CI 95%: 6-15), and 13 percent (CI 95%: 8-17) of hospitalized COVID-19 patients will have dyspnea, fatigue, and joint pain, respectively, 3 months or longer after a COVID-19 diagnosis. We present a validated framework for screening and identifying patients with PASC in the EHR and then use the tool to estimate its prevalence among hospitalized COVID-19 patients.

Source: Strasser ZH, Dagliati A, Shakeri Hossein Abad Z, Klann JG, Wagholikar KB, Mesa R, Visweswaran S, Morris M, Luo Y, Henderson DW, Samayamuthu MJ; Consortium for Clinical Characterization of COVID-19 by EHR (4CE); Omenn GS, Xia Z, Holmes JH, Estiri H, Murphy SN. A retrospective cohort analysis leveraging augmented intelligence to characterize long COVID in the electronic health record: A precision medicine framework. PLOS Digit Health. 2023 Jul 25;2(7):e0000301. doi: 10.1371/journal.pdig.0000301. PMID: 37490472; PMCID: PMC10368277. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10368277/ (Full text)

Neuropsychological measures of post-COVID-19 cognitive status

Abstract:

Background: COVID-19 may result in persistent symptoms in the post-acute phase, including cognitive and neurological ones. The aim of this study is to investigate the cognitive and neurological features of patients with a confirmed diagnosis of COVID-19 evaluated in the post-acute phase through a direct neuropsychological evaluation.

Methods: Individuals recovering from COVID-19 were assessed in an out-patient practice with a complete neurological evaluation and neuropsychological tests (Mini-Mental State Examination; Rey Auditory Verbal Test, Multiple Feature Target Cancellation Test, Trial Making Test, Digit Span Forward and Backward, and Frontal Assessment Battery). Pre- and post-COVID-19 global and mental health status was assessed along with the history of the acute phase of infection. Post-COVID-19 cognitive status was modeled by combining persistent self-reported COVID-related cognitive symptoms and pathologic neuropsychological tests.

Results: A total of 406 individuals (average age 54.5 ± 15.1 years, 45.1% women) were assessed on average at 97.8 ± 48.0 days since symptom onset. Persistent self-reported neurological symptoms were found in the areas of sleep (32%), attention (31%), and memory (22%). The MMSE mean score was 28.6. In total, 84 subjects (20.7%) achieved pathologic neuropsychological test results. A high prevalence of failed tests was found in digit span backward (18.7%), trail making (26.6%), and frontal assessment battery (10.9%). Cognitive status was associated with a number of factors including cardiovascular disease history, persistent fatigue, female sex, age, anxiety, and mental health stress.

Conclusion: COVID-19 is capable of eliciting persistent measurable neurocognitive alterations particularly relevant in the areas of attention and working memory. These neurocognitive disorders have been associated with some potentially treatable factors and others that may stratify risk at an early stage.

Source: Lauria A, Carfì A, Benvenuto F, Bramato G, Ciciarello F, Rocchi S, Rota E, Salerno A, Stella L, Tritto M, Di Paola A, Pais C, Tosato M, Janiri D, Sani G, Lo Monaco R, Pagano FC, Fantoni M, Bernabei R, Landi F, Bizzarro A; Gemelli Against COVID-19 Post-acute Care Group. Neuropsychological measures of post-COVID-19 cognitive status. Front Psychol. 2023 Jul 10;14:1136667. doi: 10.3389/fpsyg.2023.1136667. PMID: 37492442; PMCID: PMC10363721. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10363721/ (Full text)