Induction of Atrial Depolarization Abnormality during Dipyridamole Stress Imaging: A Cautionary Note

Cardiology ◽  
2008 ◽  
Vol 110 (1) ◽  
pp. 35-38 ◽  
Author(s):  
Laxman Prajapat ◽  
Vignendra Ariyarajah ◽  
David H. Spodick
Keyword(s):  
Cautionary Note ◽  
Stress Imaging ◽  
Dipyridamole Stress

Prevalence and correlates of increased lung/heart ratio of thallium-201 during dipyridamole stress imaging for suspected coronary artery disease

1990 ◽  
Vol 66 (19) ◽  
pp. 1324-1328 ◽  
Author(s):  
Flordeliza S. Villanueva ◽  
Sanjiv Kaul ◽  
William H. Smith ◽  
Denny D. Watson ◽  
Shailendra K. Varma ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Suspected Coronary Artery Disease ◽  
Thallium 201 ◽  
Stress Imaging ◽  
Artery Disease ◽  
Dipyridamole Stress

317 Fast dipyridamole stress echocardiography

1999 ◽  
Vol 1 ◽  
pp. S53-S53
Author(s):  
E ROSSI ◽  
A MEO ◽  
F PENNESTRI ◽  
A LOMBARDO ◽  
F LOPERFIDO
Keyword(s):  
Stress Echocardiography ◽  
Dipyridamole Stress Echocardiography ◽  
Dipyridamole Stress

A Cautionary Note on Incremental Fit Indices Reported by LISREL

Methodology ◽  
2005 ◽  
Vol 1 (2) ◽  
pp. 81-85 ◽  
Author(s):  
Stefan C. Schmukle ◽  
Jochen Hardt
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Null Model ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Fit Indices ◽  
Cautionary Note ◽  
Confirmatory Factor

Abstract. Incremental fit indices (IFIs) are regularly used when assessing the fit of structural equation models. IFIs are based on the comparison of the fit of a target model with that of a null model. For maximum-likelihood estimation, IFIs are usually computed by using the χ2 statistics of the maximum-likelihood fitting function (ML-χ2). However, LISREL recently changed the computation of IFIs. Since version 8.52, IFIs reported by LISREL are based on the χ2 statistics of the reweighted least squares fitting function (RLS-χ2). Although both functions lead to the same maximum-likelihood parameter estimates, the two χ2 statistics reach different values. Because these differences are especially large for null models, IFIs are affected in particular. Consequently, RLS-χ2 based IFIs in combination with conventional cut-off values explored for ML-χ2 based IFIs may lead to a wrong acceptance of models. We demonstrate this point by a confirmatory factor analysis in a sample of 2449 subjects.

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