Diagnosis of chronic fatigue syndrome using beat-to-beat autonomic measurements, 2025, Kujawski, Morten et al

[Wyva]

Sławomir Kujawski, Hanna Tabisz, Karl J. Morten, Aleksandra Modlińska, Joanna Słomko & Paweł Zalewski

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.

Open access: https://link.springer.com/article/10.1186/s12967-025-07433-y

 

[Wyva]

Fukuda.

 

[Snow Leopard]

Heart rate variation is *extremely* non specific, this method would pick up **many** people who do not have ME/CFS.

It also doesn't imply the sympathetic nervous system aspects the authors are trying to imply - it is a poor marker for parasympathetic/sympathetic balance as well.

 

[Jonathan Edwards]

Maybe it picks out the autonomic patterns of people who think they are patients rather than people who think they are healthy?

 

[Eleanor]

Maybe it picks out the autonomic patterns of people who think they are patients rather than people who think they are healthy?

Yes, they need a cohort of people who don't know whether or not they are ill (possibly because they're in a coma).

 

[Trish]

They also need comparitor groups with other diseases. It may be simply picking up people who feel ill.