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Reflect, Collaborate and Listen
‘Reflect, Collaborate and Listen’ looks at why doctors don’t listen and the urgent need to rebalance the power dynamic in the patient – doctor relationship.
See the full article here in the Lancet – It is free to download (after registration, which is very straightforward).
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Why Doctors don’t Reflect, collaborate, and listen
Dr Rageshri Dhairyawan writes “Reflect, Collaborate and Listen” which examines why doctors don’t listen, and the urgent need to rebalance the power dynamic in the patient – doctor relationship.”
See the full article here in the Lancet – It is free to download (after registration, which is quite easy).
The abstract continues: “Anxieties about malingering or feigned illness are at least a thousand years old in the West”, argued public health ethicist Daniel S Goldberg in a paper on the history of “malingerers”. He gives several examples including Arnau de Vilanova who in the 13th century was so worried that patients were fooling him, by passing off other people’s urine samples as their own, that he wrote 19 pieces of advice for other physicians to spot the fraudulent. In this way, Goldberg shows how physicians have doubted the testimonies of patients for a very long time.
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Preprint – Incidence and Prevalence of Post-COVID-19 Myalgic Encephalomyelitis: A Report from the Observational RECOVER-Adult Study
Published: 13 January 2025
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.
Read the full preprint article here: https://link.springer.com/article/10.1007/s11606-024-09290-9
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Preprint – Smartphone-based monitoring of heart rate variability and resting heart rate predicts variability in symptom exacerbations in people with complex chronic illness
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.
Read the full preprint article here: https://www.researchsquare.com/article/rs-5423422/v1
