Critical Review of Current Approaches for Echocardiographic Reproducibility and Reliability Assessment in Clinical Research.
Abstract
<h4>Background</h4>There is no broadly accepted standard method for assessing the
quality of echocardiographic measurements in clinical research reports, despite the
recognized importance of this information in assessing the quality of study results.<h4>Methods</h4>Twenty
unique clinical studies were identified reporting echocardiographic data quality for
determinations of left ventricular (LV) volumes (n = 13), ejection fraction (n = 12),
mass (n = 9), outflow tract diameter (n = 3), and mitral Doppler peak early velocity
(n = 4). To better understand the range of possible estimates of data quality and
to compare their utility, reported reproducibility measures were tabulated, and de
novo estimates were then calculated for missing measures, including intraclass correlation
coefficient (ICC), 95% limits of agreement, coefficient of variation (CV), coverage
probability, and total deviation index, for each variable for each study.<h4>Results</h4>The
studies varied in approaches to reproducibility testing, sample size, and metrics
assessed and values reported. Reported metrics included mean difference and its SD
(n = 7 studies), ICC (n = 5), CV (n = 4), and Bland-Altman limits of agreement (n = 4).
Once de novo estimates of all missing indices were determined, reasonable reproducibility
targets for each were identified as those achieved by the majority of studies. These
included, for LV end-diastolic volume, ICC > 0.95, CV < 7%, and coverage probability > 0.93
within 30 mL; for LV ejection fraction, ICC > 0.85, CV < 8%, and coverage probability > 0.85
within 10%; and for LV mass, ICC > 0.85, CV < 10%, and coverage probability > 0.60
within 20 g.<h4>Conclusions</h4>Assessment of data quality in echocardiographic clinical
research is infrequent, and methods vary substantially. A first step to standardizing
echocardiographic quality reporting is to standardize assessments and reporting metrics.
Potential benefits include clearer communication of data quality and the identification
of achievable targets to benchmark quality improvement initiatives.
Type
Journal articleSubject
Image EnhancementEchocardiography
Sensitivity and Specificity
Reproducibility of Results
Evidence-Based Medicine
Biomedical Research
Quality Assurance, Health Care
Practice Guidelines as Topic
Data Accuracy
Permalink
https://hdl.handle.net/10161/22519Published Version (Please cite this version)
10.1016/j.echo.2016.08.006Publication Info
Crowley, Anna Lisa; Yow, Eric; Barnhart, Huiman X; Daubert, Melissa A; Bigelow, Robert;
Sullivan, Daniel C; ... Douglas, Pamela S (2016). Critical Review of Current Approaches for Echocardiographic Reproducibility and Reliability
Assessment in Clinical Research. Journal of the American Society of Echocardiography : official publication of the
American Society of Echocardiography, 29(12). pp. 1144-1154.e7. 10.1016/j.echo.2016.08.006. Retrieved from https://hdl.handle.net/10161/22519.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
Collections
More Info
Show full item recordScholars@Duke
Huiman Xie Barnhart
Professor of Biostatistics & Bioinformatics
My research interests include both statistical methodology and disease-specific clinical
research biostatistics. My statistical research areas include methods for assessing
reliability/agreement between methods or raters, evaluating performance of new medical
diagnostic tests, missing data, correlated categorical data and methods for clinical
trials. My collaborative research include the following clinical areas: cardiovascular
imaging, radiology imaging, cardiovascular disease, renal disea
Anna Lisa Chamis
Associate Professor of Medicine
Melissa Anne Daubert
Associate Professor of Medicine
Pamela Susan Douglas
Ursula Geller Distinguished Professor of Research in Cardiovascular Diseases
Pamela S Douglas MD is the Ursula Geller Professor of Research in Cardiovascular Diseases
in the Department of Medicine at Duke University and Director of the Multimodality
Imaging Program at Duke Clinical Research Institute. During her 30+ years of experience
she has led several landmark multicenter government studies and pivotal industry clinical
trials along with outcomes research studies. She is renowned for her scientific and
policy work in improving the quality and appropriateness
Michael J Pencina
Professor of Biostatistics & Bioinformatics
Michael J. Pencina, PhD Chief Data Scientist, Duke Health Vice Dean for Data Science
Director, Duke AI Health Professor, Biostatistics & Bioinformatics Duke University
School of Medicine
Michael J. Pencina, PhD, is Duke Health's chief data scientist and serves as vice
dean for data science, director of Duke AI Health, and professor of biostatistics
and bioinformatics at the Duke University School of Medicine. His work bridges the
fiel
Daniel Carl Sullivan
Professor Emeritus of Radiology
Research interests are in oncologic imaging, especially the clinical evaluation and
validation of imaging biomarkers for therapeutic response assessment.
Alphabetical list of authors with Scholars@Duke profiles.

Articles written by Duke faculty are made available through the campus open access policy. For more information see: Duke Open Access Policy
Rights for Collection: Scholarly Articles
Works are deposited here by their authors, and represent their research and opinions, not that of Duke University. Some materials and descriptions may include offensive content. More info