News from the CDC: chronic fatigue syndrome (CFS) and standardized patient videos - a novel approach to educating medical students about CFS

Assessment of ME/CFS (Myalgic Encephalomyelitis/Chronic Fatigue Syndrome): A Case Study for Health Care Providers

INTRODUCTION

Over a million adults in the U.S. are affected by chronic fatigue syndrome (CFS), also known as myalgic encephalomyelitis (ME). A debilitating illness, ME/CFS is accompanied by profound fatigue that is not relieved by rest and affects daily activities. Symptoms include postexertional malaise, cognitive problems, unrefreshing sleep, and pain. This course provides tools to teach medical, physician assistant, and nursing students to recognize, diagnose, and manage patients with ME/CFS.

METHODS

The student is expected to view the provider-to-provider video and the PowerPoint slide curriculum, which describe the steps for evaluation. The video depicts two physicians conferring about ME/CFS and addresses the challenges of making a diagnosis. The slide curriculum shows the different ME/CFS symptoms and case definitions and how to apply them using a case study. Medical, physician assistant, and nursing students were recruited to evaluate the course and viewed it either online or in a campus education center.

RESULTS

Results showed that students met learning objectives and showed increases from pre- to posttest evaluation in their understanding of the difficulties of diagnosing ME/CFS and managing the illness. In addition to knowledge, student attitudes towards ME/CFS changed, with more empathy towards patients and the awareness that ME/CFS requires frequent communication and reevaluation of patients' symptoms.

DISCUSSION

Strategies of how to manage ME/CFS for the patient and manage time constraints during visits with ME/CFS patients are discussed.

A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data

BACKGROUND

In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for the association studies of disease susceptibility. In this work, our goal was to compare computational tools with and without feature selection for predicting chronic fatigue syndrome (CFS) using genetic factors such as single nucleotide polymorphisms (SNPs).

METHODS

We employed the dataset that was original to the previous study by the CDC Chronic Fatigue Syndrome Research Group. To uncover relationships between CFS and SNPs, we applied three classification algorithms including naive Bayes, the support vector machine algorithm, and the C4.5 decision tree algorithm. Furthermore, we utilized feature selection methods to identify a subset of influential SNPs. One was the hybrid feature selection approach combining the chi-squared and information-gain methods. The other was the wrapper-based feature selection method.

RESULTS

The naive Bayes model with the wrapper-based approach performed maximally among predictive models to infer the disease susceptibility dealing with the complex relationship between CFS and SNPs.

CONCLUSION

We demonstrated that our approach is a promising method to assess the associations between CFS and SNPs.