SELECTION BIAS IN CLINICAL RESEARCH WHEN SUBJECTS ARE EXCLUDED BECAUSE OF FAILURE TO ESTIMATE LEFT-VENTRICULAR MASS BY ECHOCARDIOGRAPHY

Quantitative echocardiographic imaging often is used In clinical resea rch, but the method is not successful in all patients. The goat of thi s study were as follows: (1) to determine the frequency with which lef t ventricular mass (LVM) could be estimated by echocardiography, (2) t o characterize persons in whom LVM could not be estimated from echocar diography, and (3) to determine whether failure to estimate LVM was in dependently related to any common cardiovascular risk factors, thereby suggesting a potential for selection bias. This research was part of a community-based case-control study of risk factors in hypertension, Five hundred hypertensive subjects and 506 normotensive age- and sex-m atched control subjects were studied prospectively. The mean age was 6 1 years (range: 39 to 76 years), the mean weight was 74 kt:(range: 37 to 127 kg), 46% of the subjects were men, and 56% had a history of smo king, Two-dimensionally guided M-mode echocardiography was attempted i n all Subjects for determination of LVM (Penn convention). LVM was est imated when the endocardial borders could be accurately identified fro m a true radial cavity diameter. LVM was estimated in 803 subjects (79 .8%). Subjects in whom estimates of LVM could not be obtained were old er and had the following characteristics: higher systolic blood pressu re; larger chest, waist, and abdominal circumferences; higher waist/hi p ratio; and larger body mass index (all P < .02). In multivariate ana lysis the only independent predictors of failure to estimate LVM by ec hocardiography were advanced age (P = .0001), larger chest size (P = . 0001), and history of smoking (P = .004). The Inability to estimate LV M in 20% of subjects has: important implications to our understanding the strength of association between potential risk factors and LVM, an independent measure of cardiovascular risk. LVM is more likely to be missing in older subjects who have a large chest and a history of smok ing. The effects of such a selection bias should be considered in any analysis, and a characterization of the excluded subjects should be de veloped.