data collection – American ME and CFS Society

mapMECFS: a portal to enhance data discovery across biological disciplines and collaborative sites

Abstract:

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease which involves multiple body systems (e.g., immune, nervous, digestive, circulatory) and research domains (e.g., immunology, metabolomics, the gut microbiome, genomics, neurology). Despite several decades of research, there are no established ME/CFS biomarkers available to diagnose and treat ME/CFS. Sharing data and integrating findings across these domains is essential to advance understanding of this complex disease by revealing diagnostic biomarkers and facilitating discovery of novel effective therapies.

Methods: The National Institutes of Health funded the development of a data sharing portal to support collaborative efforts among an initial group of three funded research centers. This was subsequently expanded to include the global ME/CFS research community. Using the open-source comprehensive knowledge archive network (CKAN) framework as the base, the ME/CFS Data Management and Coordinating Center developed an online portal with metadata collection, smart search capabilities, and domain-agnostic data integration to support data findability and reusability while reducing the barriers to sustainable data sharing.

Results: We designed the mapMECFS data portal to facilitate data sharing and integration by allowing ME/CFS researchers to browse, share, compare, and download molecular datasets from within one data repository. At the time of publication, mapMECFS contains data curated from public data repositories, peer-reviewed publications, and current ME/CFS Research Network members.

Conclusions: mapMECFS is a disease-specific data portal to improve data sharing and collaboration among ME/CFS researchers around the world. mapMECFS is accessible to the broader research community with registration. Further development is ongoing to include novel systems biology and data integration methods.

Source: Mathur R, Carnes MU, Harding A, Moore A, Thomas I, Giarrocco A, Long M, Underwood M, Townsend C, Ruiz-Esparza R, Barnette Q, Brown LM, Schu M. mapMECFS: a portal to enhance data discovery across biological disciplines and collaborative sites. J Transl Med. 2021 Nov 8;19(1):461. doi: 10.1186/s12967-021-03127-3. PMID: 34749736. https://pubmed.ncbi.nlm.nih.gov/34749736/

Clinical History Segment Extraction From Chronic Fatigue Syndrome Assessments to Model Disease Trajectories

Abstract:

Chronic fatigue syndrome (CFS) is a long-term illness with a wide range of symptoms and condition trajectories. To improve the understanding of these, automated analysis of large amounts of patient data holds promise. Routinely documented assessments are useful for large-scale analysis, however relevant information is mainly in free text. As a first step to extract symptom and condition trajectories, natural language processing (NLP) methods are useful to identify important textual content and relevant information. In this paper, we propose an agnostic NLP method of extracting segments of patients’ clinical histories in CFS assessments. Moreover, we present initial results on the advantage of using these segments to quantify and analyse the presence of certain clinically relevant concepts.

Source: Priou S, Viani N, Vernugopan V, et al. Clinical History Segment Extraction from Chronic Fatigue Syndrome Assessments to Model Disease Trajectories. Stud Health Technol Inform. 2020;270:98-102. doi:10.3233/SHTI200130 https://pubmed.ncbi.nlm.nih.gov/32570354/

Minimum data elements for research reports on CFS

Abstract:

Chronic fatigue syndrome (CFS) is a debilitating condition that has received increasing attention from researchers in the past decade. However, it has become difficult to compare data collected in different laboratories due to the variability in basic information regarding descriptions of sampling methods, patient characteristics, and clinical assessments. The issue of variability in CFS research was recently highlighted at the NIH’s 2011 State of the Knowledge of CFS meeting prompting researchers to consider the critical information that should be included in CFS research reports.

To address this problem, we present our consensus on the minimum data elements that should be included in all CFS research reports, along with additional elements that are currently being evaluated in specific research studies that show promise as important patient descriptors for subgrouping of CFS. These recommendations are intended to improve the consistency of reported methods and the interpretability of reported results. Adherence to minimum standards and increased reporting consistency will allow for better comparisons among published CFS articles, provide guidance for future research and foster the generation of knowledge that can directly benefit the patient.

Source: Jason LA, Unger ER, Dimitrakoff JD, Fagin AP, Houghton M, Cook DB, Marshall GD Jr, Klimas N, Snell C. Minimum data elements for research reports on CFS. Brain Behav Immun. 2012 Mar;26(3):401-6. doi: 10.1016/j.bbi.2012.01.014. Epub 2012 Jan 28. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4643273/ (Full article)

Monitoring and assessing symptoms of chronic fatigue syndrome: use of time series regression

Abstract:

Chronic Fatigue Syndrome’s principal symptoms are severe and include prolonged fatigue and a number of other minor symptoms. Behavioral data collection methods were used in a case study to show some of the benefits that can be derived from monitoring symptoms hourly and daily. Using time series regression, several statistically significant correlates of fatigue were found both within days and between days. Perceived energy, physical exertion, and mental exertion were significantly related to fatigue in both analyses. Collection of such data may help resolve a number of theoretical and methodological problems in research on the Chronic Fatigue Syndrome.

Source: Jason LA, Tryon WW, Taylor RR, King C, Frankenberry EL, Jordan KM. Monitoring and assessing symptoms of chronic fatigue syndrome: use of time series regression. Psychol Rep. 1999 Aug;85(1):121-30. http://www.ncbi.nlm.nih.gov/pubmed/10575979

Chronic fatigue complaints in primary care: incidence and diagnostic patterns

Abstract:

The complaint of chronic fatigue is ubiquitous in the primary care setting. Because of the nonspecific nature of chronic fatigue, practitioners do not focus on this complaint. Furthermore, most physicians use a problem-based approach. Such a prematurely narrowed focus could overlook the chronic fatigue complaint. Omissions in the data collection process would prove this oversight.

Therefore, we postulated that a retrospective review of evaluations for chronic fatigue would demonstrate significant categorical deficiencies. These deficiencies would indicate a problem focus different than the chronic fatigue complaint itself.

The authors reviewed the current literature to establish historical, physical, and laboratory findings pertinent to the evaluation of chronic fatigue. Six major categories and the associated data elements were identified for use in analyzing patient records. The patient records from the preceding 6 months were reviewed to find those containing a complaint of chronic fatigue. These records were analyzed to determine if a complete data set had been sought and if an associated diagnosis was made.

A total of 425 consecutive charts from an academic family practice clinic were retrospectively reviewed; 9.9% (42) mentioned chronic fatigue. Physicians were lax in performing the mental status and physical examinations; taking the patient’s psychiatric and sleep history, as well as the history of chief complaint; and ordering laboratory evaluations. The physician diagnoses included: depression (40.4%), nonspecific fatigue (35.7%), general medical disorders (16.6%), chronic fatigue syndrome (2.4%), fibromyalgia (2.4%), and sleep apnea (2.4%).

From these data, the investigators conclude that the workup for chronic fatigue is often incomplete or lacks documentation. This oversight is likely due to a problem focus not directed at the chronic fatigue complaints. Also complicating the evaluation process are the multiple associated disorders, the prevalence of the complaint, and cost/benefit issues facing the primary care physician.

Source: Ward MH, DeLisle H, Shores JH, Slocum PC, Foresman BH. Chronic fatigue complaints in primary care: incidence and diagnostic patterns. J Am Osteopath Assoc. 1996 Jan;96(1):34-46, 41. http://www.ncbi.nlm.nih.gov/pubmed/8626230