Specificity of Doppler echocardiography for the assessment of changes in valvular regurgitation: comparison of side-by-side versus serial interpretation

Plus disease in retinopathy of prematurity: quantitative analysis of vascular change

PURPOSE

To examine the relationship between rate of vascular change and plus disease diagnosis.

DESIGN

Retrospective observational case-control study.

METHODS

Wide-angle images were taken bilaterally from 37 infants at 31 to 33 weeks and 35 to 37 weeks postmenstrual age (PMA). The semi-automated Retinal Image multiScale Analysis system was used to measure parameters for all arteries and veins: integrated curvature, diameter, and tortuosity index. A reference standard diagnosis (plus vs not plus) was defined for each eye by consensus of 5 experts at 35 to 37 weeks PMA. Weekly rate of change in parameters was compared in eyes with plus vs not plus disease. Receiver operating characteristic area under the curve (AUC) was calculated for plus disease detection based on 1) weekly rates of parameter change between 31 to 33 weeks and 35 to 37 weeks PMA and 2) parameter values at 35 to 37 weeks only.

RESULTS

Weekly rates of change in all venous parameters were significantly different in eyes with plus vs not plus disease, particularly for tortuosity index (P < .0004) and diameter (P = .018). Using weekly rate of change, AUC for plus disease detection was highest for venous tortuosity index (0.819) and venous diameter (0.712). Using the 35 to 37-week PMA image only, AUC was highest for venous integrated curvature (0.952) and diameter (0.789).

CONCLUSION

Rate of change in venous, but not arterial, parameters is correlated with plus disease development in this data set. This did not appear to contribute information beyond analysis of an image at 35 to 37 weeks PMA only.

Echocardiographical characteristics of healthy young subjects with physiological mitral regurgitation

We aimed to analyse the echocardiographical characteristics of healthy subjects and determine the causal mechanism of the development of physiological mitral regurgitation (PMR). A total of 130 healthy subjects were divided into two groups according to whether or not PMR was detected. There were no statistical differences between the two groups in terms of the mean values of the systolic and diastolic left ventricular internal diameters and the left atrial diameter. The mean values of the length and thickness of the anterior mitral valve (AMV) and the mitral annular diameter (MAD) from subjects in group 1 (with PMR) were statistically different from those of group 2 (without PMR). In conclusion, the AMV was slightly longer and thicker and the MAD was smaller in subjects with PMR than in subjects without PMR. The changes observed in the AMV and the mitral annulus may play a causal role in the development of PMR.

A simple model to predict coronary disease in patients undergoing operation for mitral regurgitation

BACKGROUND

Coexistent coronary disease can be identified in a third of patients with mitral valve disease. This study aims to evaluate candidate selection strategy using risk factor identification and logistic regression and to develop an additive model for the prediction of coexistent coronary disease.

METHODS

The sample is a consecutive series of patients who had mitral repair from 1987 to 1999. Sensitivities and specificities were calculated for each risk factor. Variables for prediction of coronary disease were entered into a univariate analysis, and predictors were entered into a forward and backward stepwise multivariate logistic regression model to form a predictive score. An additive model was derived from transformation of the logistic model. Receiver operating characteristic curves were used to compare discrimination and precision quantified by the Hosmer-Lemeshow statistic.

RESULTS

The American Heart Association and American College of Cardiology risk factor identification selection criteria for the 359 patients who had screening coronary angiography yielded 100% sensitivity and 1% specificity. Risk prediction with our logistic model produced a receiver operating characteristic curve area of 0.91 and Hosmer-Lemeshow score of 3.4 (p = 0.9). Similar discriminating ability for our patients was achieved by the Cleveland Clinic logistic model (receiver operator characteristic curve area of 0.79; Hosmer-Lemeshow score of 12; p = 0.1). Our five-item additive model produced receiver operating characteristic curve area of 0.91 and Hosmer-Lemeshow score of 3.81 (p = 0.80).

CONCLUSIONS

Simple risk factor identification has excellent sensitivity but is limited by specificity. Logistic regression modeling is an accurate risk prediction method but is difficult to apply at the bedside. Simplicity and accuracy may be achieved by the logistic regression-derived simple additive model.