The American Journal of Respiratory and Critical Care Medicine has posted a new abstract authored by David Systrom et al, which evaluated invasive CPET testing. The test includes iCPET results from 438 ME/CFS patients, 73 Long Covid patients, and 43 symptomatic but otherwise normal controls.
The measurement method is illustrated here from their previous 2023 report [TBA]
An image extract from the article is shown below, showing correlation of pVO2 between each cohort and outcomes, and remarkably similar results from ME and Long Covid:
Follow-up project to DecodeME will analysye complete Genome sequences of 9000 ME/CFS existing samples to help pinpoint biology of ME and hopefully lead to treatments. Loads of detail in this article by Simon McGrath about how the DNA will be sequenced etc. using Oxford Nanopore technology.
It is ME Awareness Day and I’ve had a tough one, so I’ll write a bit more soon… But didn’t want to forget documenting here what seems a major step in the right direction.
Released as version 1.0 today, a new and outstandingly comprehensive reference guide for both Doctors and Health Care Workers. The reference guide we needed 20 years ago! This certainly feels like an essential training reference:
“A Roadmap to Better Care: Clinical Care Guide for ME/CFS, Long COVID & Infection-Associated Chronic Conditions
Developed by the OMF-supported Medical Education Resource Center (MERC) at Bateman Horne Center, this resource offers a practical path forward—one grounded in clinical expertise, research, and the lived experience of patients”
Helen Pidd discusses what life is like during and after Long Covid. Emma Gore-Lloyd shares her continuing journey in search of a cure for her partner James, whilst Georgina tells her story of how she got better.
YouTube links are provided here when available as well as Spotify, as they don’t currently require a subscription to watch / listen. Listening via Spotify makes it easier to listen whilst doing something else on your browser or phone.
Suzanne D. Vernon PhD, Tianyu Zheng MS, Hyungrok Do PhD, Vincent C. Marconi MD, Leonard A. Jason PhD, Nora G. Singer MD, Benjamin H. Natelson MD, Zaki A. Sherif PhD, Hector Fabio Bonilla MD, Emily Taylor MA, Janet M. Mullington PhD, Hassan Ashktorab PhD, Adeyinka O. Laiyemo MD, Hassan Brim PhD, Thomas F. Patterson MD, Teresa T. Akintonwa BA, Anisha Sekar BA, Michael J. Peluso MD, Nikita Maniar MD, Lucinda Bateman MD, Leora I. Horwitz MD & Rachel Hess MD on behalf of the NIH Researching COVID to Enhance Recovery (RECOVER) Consortium
Abstract
Background
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) may occur after infection. How often people develop ME/CFS after SARS-CoV-2 infection is unknown.
Objective
To determine the incidence and prevalence of post-COVID-19 ME/CFS among adults enrolled in the Researching COVID to Enhance Recovery (RECOVER-Adult) study.
Design, Setting, and Participants
RECOVER-Adult is a longitudinal observational cohort study conducted across the U.S. We included participants who had a study visit at least 6 months after infection and had no pre-existing ME/CFS, grouped as (1) acute infected, enrolled within 30 days of infection or enrolled as uninfected who became infected (n=4515); (2) post-acute infected, enrolled greater than 30 days after infection (n=7270); and (3) uninfected (1439).
Measurements
Incidence rate and prevalence of post-COVID-19 ME/CFS based on the 2015 Institute of Medicine ME/CFS clinical diagnostic criteria.
Results
The incidence rate of ME/CFS in participants followed from time of SARS-CoV-2 infection was 2.66 (95% CI 2.63–2.70) per 100 person-years while the rate in matched uninfected participants was 0.93 (95% CI 0.91–10.95) per 100 person-years: a hazard ratio of 4.93 (95% CI 3.62–6.71). The proportion of all RECOVER-Adult participants that met criteria for ME/CFS following SARS-CoV-2 infection was 4.5% (531 of 11,785) compared to 0.6% (9 of 1439) in uninfected participants. Post-exertional malaise was the most common ME/CFS symptom in infected participants (24.0%, 2830 of 11,785). Most participants with post-COVID-19 ME/CFS also met RECOVER criteria for long COVID (88.7%, 471 of 531).
Limitations
The ME/CFS clinical diagnostic criteria uses self-reported symptoms. Symptoms can wax and wane.
Conclusion
ME/CFS is a diagnosable sequela that develops at an increased rate following SARS-CoV-2 infection. RECOVER provides an unprecedented opportunity to study post-COVID-19 ME/CFS.
Annie Aitken1; Abbey Sawyer; Akiko Iwasaki; Harlan M. Krumholz; Rory Preston; Harry Leeming; Jenna Tosto-Mancuso; Amy Proal; Michael A. Osborne; David Putrino
Version 1 posted 29 Nov, 2024
Abstract
Background: Complex chronic conditions like Long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome involve energy limitations and changes in heart rate variability (HRV) and resting heart rate (HR). Mobile health technologies now offer real-time, valid measurements of HRV and HR, advancing symptom monitoring and management. Using a high-density dataset from an observational longitudinal study, we aimed to describe, quantify, and predict within-person co-variations in daily biometric data and subsequent crash, fatigue, and brain fog symptom occurrences.
Methods: Leveraging data collected through a mobile health app (n=4,244), we developed predictive models using mixed-effects linear regression and logistic regression to explore how within-person fluctuations in biometrics (HR, HRV, and respiratory rate) predict dynamic change in symptomology (crash, fatigue, and brain fog). Predictive performance was assessed using 5-fold stratified cross-validation and compared to a 20% holdout set to evaluate model generalizability to new observations and individuals.
Results: Across all symptom domains, within-person changes in HRV and HR consistently emerged as key predictors of symptom change across all models, with higher HR and lower HRV conferring risk for crashes, fatigue, and brain fog. Moreover, 7-day biometric stability (or variable dispersion) was a robust predictor of symptom occurrence and severity. Models trained solely on biometric features achieved moderate predictive performance in the stratified cross-validation set; however, incorporating random effects to capture individual-specific variations and prior-day symptom reports substantially enhanced model accuracy, with AUC values reaching .91.
Discussion and Conclusion: This study is the first to use data-driven models to predict everyday symptom experiences in individuals with complex chronic illnesses based on biometric fluctuations. Findings demonstrate the potential utility of mobile health tools for real-time monitoring of symptoms and highlight the need for further research to refine these predictive models and integrate them into clinical decision-making processes.
The #ThereForME team have published the first version of their UK Ecosystem Report for ME and Long Covid, created in collaboration with CrunchME. This report maps out key stakeholders and initiatives across the UK, aiming to inform advocacy efforts, policymakers, and potential funders of research and care.
The report tracks active and future ME/CFS research projects within the UK. Following each overview slide like the one below, are detailed additional slides showing the technical nature of the work and interventions being trialled.
Following slides go in to show research projects for Long Covid within the UK, Clinics and Clinicians, Biotech resource, and forthcoming conferences.
