Tag: vagal impairment

Diagnosis of chronic fatigue syndrome using beat-to-beat autonomic measurements

Abstract:

Background: An artificial intelligence (AI) pipeline was used to differentiate patients suffering from Chronic Fatigue Syndrome (CFS) from healthy controls (HC) based on high-frequency, large-scale data obtained using beat-to-beat measurement of the autonomic nervous system (ANS) and cardiovascular function.

Methods: This prospective, case-control study included a cohort of 112 CFS patients and 61 HCs examined. Heart rate (HR), high-frequency R-to-R interval (HF RRI), diastolic blood pressure (dBP), stroke volume (SV), and SV index (SV/FFM) were measured using the Task Force Monitor. A novel sequential learning approach was applied: first, a Transformer model was trained, followed by an XGBoost classifier that learned from the errors of the Transformer. Matthews correlation coefficient (MCC), accuracy, and Area Under the Receiver Operating Characteristic Curve (ROC AUC) were assessed. Model classifications were explained globally.

Results: The applied classifier achieved a subject-level accuracy of 0.89, an MCC of 0.79, and an AUC of 1.00. Lower values of beat-to-beat difference in HR and raw HF RRI (indicating reduced cardiac vagal tone) and higher values of dBP difference (more beat-to-beat increases, indicating higher sympathetic vascular tone) were related to being more likely classified as CFS patients. Low values of SV difference and low values of SV/FFM (both indicating less effective cardiac hemodynamics) were related to being more likely classified as CFS patients.

Conclusions: The AI-driven classifier demonstrates remarkable proficiency in distinguishing between patients with CFS and HC. By leveraging this automated pipeline, beat-to-beat measurements of the ANS can significantly enhance the objective assessment of CFS diagnosis.

Source: Kujawski S, Tabisz H, Morten KJ, Modlińska A, Słomko J, Zalewski P. Diagnosis of chronic fatigue syndrome using beat-to-beat autonomic measurements. J Transl Med. 2025 Dec 23;23(1):1413. doi: 10.1186/s12967-025-07433-y. PMID: 41437251; PMCID: PMC12729017. https://pmc.ncbi.nlm.nih.gov/articles/PMC12729017/ (Full text)

Impaired Vagal Activity in Long-COVID-19 Patients

Abstract:

Long-COVID-19 refers to the signs and symptoms that continue or develop after the “acute COVID-19” phase. These patients have an increased risk of multiorgan dysfunction, readmission, and mortality. In Long-COVID-19 patients, it is possible to detect a persistent increase in D-Dimer, NT-ProBNP, and autonomic nervous system dysfunction.

To verify the dysautonomia hypothesis in Long-COVID-19 patients, we studied heart rate variability using 12-lead 24-h ECG monitoring in 30 Long-COVID-19 patients and 20 No-COVID patients. Power spectral analysis of heart rate variability was lower in Long-COVID-19 patients both for total power (7.46 ± 0.5 vs. 8.08 ± 0.6; p < 0.0001; Cohens-d = 1.12) and for the VLF (6.84 ± 0.8 vs. 7.66 ± 0.6; p < 0.0001; Cohens-d = 1.16) and HF (4.65 ± 0.9 vs. 5.33 ± 0.9; p = 0.015; Cohens-d = 0.76) components. The LF/HF ratio was significantly higher in Long-COVID-19 patients (1.46 ± 0.27 vs. 1.23 ± 0.13; p = 0.001; Cohens-d = 1.09). On multivariable analysis, Long-COVID-19 is significantly correlated with D-dimer (standardized β-coefficient = 0.259), NT-ProBNP (standardized β-coefficient = 0.281), HF component of spectral analysis (standardized β-coefficient = 0.696), and LF/HF ratio (standardized β-coefficient = 0.820).

Dysautonomia may explain the persistent symptoms in Long COVID-19 patients. The persistence of a procoagulative state and an elevated myocardial strain could explain vagal impairment in these patients. In Long-COVID-19 patients, impaired vagal activity, persistent increases of NT-ProBNP, and a prothrombotic state require careful monitoring and appropriate intervention.

Source: Acanfora D, Nolano M, Acanfora C, Colella C, Provitera V, Caporaso G, Rodolico GR, Bortone AS, Galasso G, Casucci G. Impaired Vagal Activity in Long-COVID-19 Patients. Viruses. 2022 May 13;14(5):1035. doi: 10.3390/v14051035. PMID: 35632776; PMCID: PMC9147759. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9147759/ (Full text)