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CLINICAL STUDIES

Epidemiological approach to quality assessment in echocardiographic diagnosis

Alan K. Berger, MD^*,1, John S. Gottdiener, MD^*, Mary Anne Yohe, MBA^* and Jose L. Guerrero, PhD

^* Division of Cardiology, Georgetown University Medical Center, Washington, DC, USA
School of Business, Georgetown University, Washington, DC, USA

Manuscript received December 31, 1998; revised manuscript received July 13, 1999, accepted August 12, 1999.

Reprint requests and correspondence: Dr. John S. Gottdiener, Cardiology Research, St. Francis Hospital, 100 Port Washington Boulevard, Roslyn, New York 11576.
gottdien{at}ziplink.net

OBJECTIVES

This study sought to determine whether statistical analysis of a computerized clinical diagnostic database can be used as a tool for quality assessment by determining the contribution of reader bias to variance in diagnostic output.

BACKGROUND

In industry, measurement of product uniformity is a key component of quality assessment. In echocardiography, quality assessment has focused on review of small numbers of cases, or prospective determination of reader variability in selected and relatively small subsets. However, diagnostic biases in clinical practice might be discerned utilizing large computerized databases to determine interreader differences in diagnostic prevalence and, with use of appropriate statistical methods, to determine the association of reader selection with diagnostic prevalence independently of other covariates.

METHODS

We analyzed 6,026 echocardiograms in a computerized database, read by one of three level 3 (American Society of Echocardiography) readers, for differences in frequency among four coded echocardiographic diagnoses: mitral valve prolapse, valvular vegetations, left ventricular (LV) thrombus, and LV regional wall-motion abnormality.

RESULTS

Significant differences (up to fourfold) were found between readers, which persisted after statistical adjustment for those population characteristics, which differed slightly between readers. The low population prevalence of these conditions would have made it unlikely that these interreader differences could be detected by nonstatistical methods. Additionally, chamber dimensions differed between readers and were not normally distributed.

CONCLUSIONS

Statistically based quality assessment analysis of computerized clinical databases facilitates ongoing monitoring of interreader bias despite low diagnostic prevalence, and targets opportunities for subsequent quality improvement.

Abbreviations and Acronyms   ANOVA = analysis of variance   EF = ejection fraction   LV = left ventricle, left ventricular   MVP = mitral valve prolapse

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