Literature DB >> 12844197

Evaluation of left ventricular enlargement in the lateral position of the chest using the Hoffman and Rigler sign.

V Freeman1, C Mutatiri, M Pretorius, A Doubell.   

Abstract

OBJECTIVE: To evaluate left ventricular enlargement in the lateral projection of the chest using the Hoffman and Rigler sign.
BACKGROUND: The Hoffman and Rigler sign for determining left ventricular enlargement was suggested as early as 1965 before the routine use of echocardiography.
METHODS: We studied 136 patients who had had cardiac ultrasound and chest X-rays with lateral projections. We assessed left ventricular size on the lateral projection using the Hoffman and Rigler method (measurement A) and compared this measurement to the value obtained by cardiac ultrasound. The effect of right ventricular size on this measurement was also evaluated. RESULTS The average value of measurement A in all patients with echocardiographic evidence of left vetricular enlargement (LVED above 59 mm) was 19 mm (SD +/- 4.03) (95% CI 17.96 to 20.04). Of the 48 patients with a normal size left ventricle on echocardiography, 25.58% had measurement A 18 mm and above, and 13.95% had a value 19 mm and above. Of the 19 patients with right ventricular enlargement (normal left ventricle) on echocardiography, 36.84% had measurement A18 mm and above, whereas 21.05% had this value 19 mm and above. Measurement A in patients with left ventricular enlargement compared with those with right ventricular enlargement showed a significant difference (p < 0.05).
CONCLUSIONS: When the crossing of the inferior vena cava and the left ventricle can be adequately visualised, the Hoffman and Rigler sign of evaluating left ventricular enlargement in the lateral projection of the chest is a valuable alternative where cardiac ultrasound is not readily available.

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Year:  2003        PMID: 12844197

Source DB:  PubMed          Journal:  Cardiovasc J S Afr


  1 in total

1.  Automatic prediction of left cardiac chamber enlargement from chest radiographs using convolutional neural network.

Authors:  Ju Gang Nam; Jinwook Kim; Keonwoo Noh; Hyewon Choi; Da Som Kim; Seung-Jin Yoo; Hyun-Lim Yang; Eui Jin Hwang; Jin Mo Goo; Eun-Ah Park; Hye Young Sun; Min-Soo Kim; Chang Min Park
Journal:  Eur Radiol       Date:  2021-05-03       Impact factor: 5.315

  1 in total

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