Appl Clin Inform 2016; 07(02): 477-488
DOI: 10.4338/ACI-2015-12-RA-0178
Research Article
Schattauer GmbH

Visual assessment of the similarity between a patient and trial population

Is This Clinical Trial Applicable to My Patient?
Amos Cahan
1   IBM T.J. Watson Research Center, Yorktown Heights, NY
2   National Library of Medicine, Bethesda, MD; Informatics Institute
,
James J Cimino
3   University of Alabama at Birmingham, Birmingham, AL
4   National Institutes of Health Clinical Center, Bethesda, MD
› Institutsangaben
This project was supported in part by an appointment to the Research Participation Program for the Centers for Disease Control and Prevention: National Center for Environmental Health, Division of Laboratory Sciences, administered by the Oak Ridge Institute for Science and Education through an agreement between the Department of Energy and DLS. Dr. Cimino was supported in part by research funds from the National Library of Medicine and the NIH Clinical Center.
Weitere Informationen

Publikationsverlauf

received: 15. Dezember 2015

accepted: 23. März 2016

Publikationsdatum:
16. Dezember 2017 (online)

Summary

Background

A critical consideration when applying the results of a clinical trial to a particular patient is the degree of similarity of the patient to the trial population. However, similarity assessment rarely is practical in the clinical setting. Here, we explore means to support similarity assessment by clinicians.

Methods

A scale chart was developed to represent the distribution of reported clinical and demographic characteristics of clinical trial participant populations. Constructed for an individual patient, the scale chart shows the patient’s similarity to the study populations in a graphical manner. A pilot test case was conducted using case vignettes assessed by clinicians. Two pairs of clinical trials were used, each addressing a similar clinical question. Scale charts were manually constructed for each simulated patient. Clinicians were asked to estimate the degree of similarity of each patient to the populations of a pair of trials. Assessors relied on either the scale chart, a summary table (aligning characteristics of 2 trial populations), or original trial reports. Assessment time and between-assessor agreement were compared. Population characteristics considered important by assessors were recorded.

Results

Six assessors evaluated 6 cases each. Using a visual scale chart, agreement between physicians was higher and the time required for similarity assessment was comparable

Conclusion

We suggest that further research is warranted to explore visual tools facilitating the choice of the most applicable clinical trial to a specific patient. Automating patient and trial population characteristics extraction is key to support this effort.

 
  • References

  • 1 Alper BS, Hand JA, Elliott SG, Kinkade S, Hauan MJ, Onion DK. et al. How much effort is needed to keep up with the literature relevant for primary care?. J Med Libr Assoc JMLA 2004; 92 (04) 429-437.
  • 2 Williamson JW, German PS, Weiss R, Skinner EA, Bowes F. Health science information management and continuing education of physicians. A survey of U.S. primary care practitioners and their opinion leaders. Ann Intern Med 1989; 110 (02) 151-160.
  • 3 Gupta K, Hooton TM, Naber KG, Wullt B, Colgan R, Miller LG. et al. International clinical practice guidelines for the treatment of acute uncomplicated cystitis and pyelonephritis in women: A 2010 update by the Infectious Diseases Society of America and the European Society for Microbiology and Infectious Diseases. Clin Infect Dis Off Publ Infect Dis Soc Am 2011; 52 (05) e103-e120 doi:10.1093/cid/ciq257.
  • 4 McDonald CJ. Medical heuristics: the silent adjudicators of clinical practice. Ann Intern Med 1996; 124 (1 Pt 1): 56-62.
  • 5 Tversky A, Kahneman D. Judgment under Uncertainty: Heuristics and Biases. Science 1974; 185 (4157): 1124-1131 doi:10.1126/science.185.4157.1124.
  • 6 Gaissmaier W, Wegwarth O, Skopec D, Müller A-S, Broschinski S, Politi MC. Numbers can be worth a thousand pictures: individual differences in understanding graphical and numerical representations of health-related information. Health Psychol Off J Div Health Psychol Am Psychol Assoc 2012; 31 (03) 286-296 doi:10.1037/a0024850.
  • 7 Tait AR, Voepel-Lewis T, Zikmund-Fisher BJ, Fagerlin A. Presenting research risks and benefits to parents: does format matter?. Anesth Analg 2010; 111 (03) 718-723 doi:10.1213/ANE.0b013e3181e8570a.
  • 8 Hawley ST, Zikmund-Fisher B, Ubel P, Jancovic A, Lucas T, Fagerlin A. The impact of the format of graphical presentation on health-related knowledge and treatment choices. Patient Educ Couns 2008; 73 (03) 448-455 doi:10.1016/j.pec.2008.07.023.
  • 9 Edwards A, Thomas R, Williams R, Ellner AL, Brown P, Elwyn G. Presenting risk information to people with diabetes: evaluating effects and preferences for different formats by a web-based randomised controlled trial. Patient Educ Couns 2006; 63 (03) 336-349 doi:10.1016/j.pec.2005.12.016.
  • 10 Bauer DT, Guerlain S, Brown PJ. The design and evaluation of a graphical display for laboratory data. J Am Med Inform Assoc JAMIA 2010; 17 (04) 416-424 doi:10.1136/jamia.2009.000505.
  • 11 Henry JB, Kelly KC. Comprehensive graphic-based display of clinical pathology laboratory data. Am J Clin Pathol 2003; 119 (03) 330-336.
  • 12 Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR. et al. Enterotypes of the human gut microbiome. Nature 2011; 473 (7346): 174-180 doi:10.1038/nature09944.
  • 13 Shiffman RN. Representation of Clinical Practice Guidelines in Conventional and Augmented Decision Tables. J Am Med Inform Assoc 1997; 04 (05) 382-393.
  • 14 Plaisant C, Mushlin R, Snyder A, Li J, Heller D, Shneiderman B. LifeLines: using visualization to enhance navigation and analysis of patient records. Proc AMIA Annu Symp AMIA Symp 1998; 76-80.
  • 15 Rathod RH, Farias M, Friedman KG, Graham D, Fulton DR, Newburger JW. et al. A novel approach to gathering and acting on relevant clinical information: SCAMPs. Congenit Heart Dis 2010; 05 (04) 343-353 doi:10.1111/j.1747-0803.2010.00438.x.
  • 16 Pickering BW, Herasevich V, Ahmed A, Gajic O. Novel Representation of Clinical Information in the ICU. Appl Clin Inform 2010; 01 (02) 116-131 doi:10.4338/ACI-2009-12-CR-0027.
  • 17 Weng C, Li Y, Ryan P, Zhang Y, Liu F, Gao J. et al. A distribution-based method for assessing the differences between clinical trial target populations and patient populations in electronic health records. Appl Clin Inform 2014; 05 (02) 463-479 doi:10.4338/ACI-2013-12-RA-0105.
  • 18 Rubins HB, Robins SJ, Collins D, Fye CL, Anderson JW, Elam MB. et al. Gemfibrozil for the secondary prevention of coronary heart disease in men with low levels of high-density lipoprotein cholesterol. Veterans Affairs High-Density Lipoprotein Cholesterol Intervention Trial Study Group. N Engl J Med 1999; 341 (06) 410-418 doi:10.1056/NEJM199908053410604.
  • 19 ACCORD Study Group. Elam MB, Lovato LC, Crouse JR, Leiter LA. et al. Effects of combination lipid therapy in type 2 diabetes mellitus. N Engl J Med 2010; 362 (17) 1563-1574 doi:10.1056/NEJMoa1001282.
  • 20 Metsalu T, Vilo J. ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Res 2015; 43 9W1): W566-W570 doi:10.1093/nar/gkv468.
  • 21 Albo Y, Lanir J, Bak P, Rafaeli S. Off the Radar: Comparative Evaluation of Radial Visualization Solutions for Composite Indicators. IEEE Trans Vis Comput Graph 2016; 22 (01) 569-578 doi:10.1109/TVCG.2015.2467322.