Data science applied to chronic fatigue syndrome

AbstractMyalgic Encephalomyelitis, or Chronic Fatigue Syndrome (ME/CFS), is a chronic, debilitating, multifaceteddisease. Its heterogeneous onset and clinical presentation with additional co-morbidities makediagnosis difficult. There is no evidence of diagnostic tests or biomarkers alone to establish a diagnosis.Current treatment guidelines focus on symptom management. The causative mechanism is unknown.Research lines are heterogeneous. A consensus is needed to pool resources and find solid results. Clinicaltrials are urgently needed to identify effective treatments.One limiting factor is the lack of objective measures of efficacy. These are currently based on subjectivequestionnaires answered by the patients themselves. The need to use objective measures is one of themain conclusions. This research provides two biomarkers that can be used for this purpose: peak oxygenconsumption in the stress test and the CPT3 test result to measure cognitive impairment.Exercise test oxygen consumptionIt is shown that the physical status of the ME/CFS patient can be determined by measuring the peakoxygen consumption in the exercise test. Furthermore, screening can be performed using an artificialintelligence-based model that predicts patients requiring stress testing due to limiting physical status.The CPT3 Test for the detection of cognitive impairmentThe CPT3 test has been shown to measure cognitive impairment in ME/CFS patients and may beused to measure treatment efficacy in future clinical trials.Knowledge TransferAn application using the SF-36 questionnaire as input data will be developed that provides multidisciplinaryanalysis and predicts the physical risk of a ME/CFS patient. This application not only favorsthe early detection of physical deterioration and the suggestion of referral to a specialized unit, but wouldalso favor a greater detection of the syndrome and provide knowledge to the medical community in thisfield.