Improved differential diagnosis of intracardiac and extracardiac shunts using acoustic intensity mapping of saline contrast studies. (19th July 2019)

• Record Type: Journal Article
• Authors: Rasalingam, Ravi; Novak, Eric; Rifkin, Robert D
• Is Part Of: European heart journal. Volume 21:Number 3(2020)
• Page Start: 307
• Page End: 317
• Publication Date: 2019-07-19
• Subjects: acoustic intensity mapping -- saline contrast echocardiography -- patent foramen ovale -- pulmonary arteriovenous malformation; Cardiovascular system -- Imaging -- Periodicals; Heart -- Imaging -- Periodicals; 616.10754
• DOI: 10.1093/ehjci/jez129
• Languages: English
• ISSNs: 2047-2404

Abstract

Abstract: Aims: The aim of this study was to test the hypothesis that temporal patterns of saline contrast entry into, and exit from the left heart are significantly different in intra- and extra-cardiac shunts and can be used to differentiate the shunt mechanism when Valsalva manoeuvre cannot be performed, or is of uncertain quality. We propose a novel approach of mapping the temporal changes in acoustic intensity (AI) within the left and right heart to identify and define these unique patterns. Methods and results: We screened cases of right to left shunting on resting agitated saline contrast echocardiograms with clinical criteria that identified the origin of shunting as either a patent foramen ovale or pulmonary arteriovenous malformation. Acoustic time-intensity curves were generated from the right and left heart chambers that reflected the change in saline contrast density over time. Several novel pre-specified parameters were measured from these curves, in addition to the standard heartbeat counting method, to characterize the entrance (wash-in) and exit (wash-out) patterns of saline contrast in the left heart. Statistical analysis showed that AI mapping provided superior differentiation of the two populations than did the traditional beat counting method. Conclusion: Diagnosis of shunt mechanism from saline contrast studies can be improved over current methods through the use of AI mapping to define the rapidity that peak contrast effect develops, the speed that the contrast effect decays, and the contrast intensity late in the recording.