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.