Dataset Information

Echocardiographic Criteria to Differentiate Constrictive Pericarditis From Restrictive Cardiomyopathy: A Meta-analysis.

ABSTRACT:

Background

To assess the diagnostic accuracy of the Mayo Clinic echocardiographic criteria for differentiating between constrictive pericarditis and restrictive cardiomyopathy.

Methods

We searched electronic databases for the date range from their inception to July 1, 2022. The index tests were the Mayo Clinic echocardiographic criteria. We performed a bivariate random-effects model to estimate the pooled sensitivity and specificity, each with 95% confidence interval (CI). The area under the curve of the summary receiver operator characteristic curves, with 95% CI, was also calculated.

Results

We included 17 case-control studies involving 889 patients. The pooled sensitivity and specificity (95% CI), respectively, were as follows: ventricular septal shift, 82% (60%-94%) and 78% (65%-87%); respiratory variation in mitral inflow ≥ 14.6%, 71% (51%-85%) and 82% (66%-91%); septal e' velocity ≥ 8 cm/s, 83% (80%-87%) and 90% (83%-95%); septal e' velocity/lateral e' velocity ≥ 0.88, 74% (64%-82%) and 81% (70%-88%); and hepatic vein ratio in expiration ≥ 0.79, 73% (65%-81%) and 71% (19%-96%). The area under the curve of the summary receiver operator characteristic curves varied from 0.75 to 0.85, with overlapping CIs across index tests.

Conclusions

Our meta-analysis suggests that all echocardiographic parameters from the Mayo Clinic criteria have good diagnostic accuracy for differentiating between constrictive pericarditis and restrictive cardiomyopathy.

SUBMITTER: Diaz-Arocutipa C

PROVIDER: S-EPMC10516719 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Publications

Echocardiographic Criteria to Differentiate Constrictive Pericarditis From Restrictive Cardiomyopathy: A Meta-analysis.

Diaz-Arocutipa Carlos C Chumbiauca María M Medina Hector M HM Hernandez Adrian V AV Vicent Lourdes L

CJC open 20230609 9

<h4>Background</h4>To assess the diagnostic accuracy of the Mayo Clinic echocardiographic criteria for differentiating between constrictive pericarditis and restrictive cardiomyopathy.<h4>Methods</h4>We searched electronic databases for the date range from their inception to July 1, 2022. The index tests were the Mayo Clinic echocardiographic criteria. We performed a bivariate random-effects model to estimate the pooled sensitivity and specificity, each with 95% confidence interval (CI). The are ...[more]

PMID: 37744658

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Project description:BackgroundAssociating a patient's profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography data sets derived from patients with known constrictive pericarditis and restrictive cardiomyopathy.Methods and resultsClinical and echocardiographic data of 50 patients with constrictive pericarditis and 44 with restrictive cardiomyopathy were used for developing an associative memory classifier-based machine-learning algorithm. The speckle tracking echocardiography data were normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve of the associative memory classifier was evaluated for differentiating constrictive pericarditis from restrictive cardiomyopathy. Using only speckle tracking echocardiography variables, associative memory classifier achieved a diagnostic area under the curve of 89.2%, which improved to 96.2% with addition of 4 echocardiographic variables. In comparison, the area under the curve of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63.7%, respectively. Furthermore, the associative memory classifier demonstrated greater accuracy and shorter learning curves than other machine-learning approaches, with accuracy asymptotically approaching 90% after a training fraction of 0.3 and remaining flat at higher training fractions.ConclusionsThis study demonstrates feasibility of a cognitive machine-learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine-learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience.

Dataset Information

Echocardiographic Criteria to Differentiate Constrictive Pericarditis From Restrictive Cardiomyopathy: A Meta-analysis.

Background

Methods

Results

Conclusions

Publications

Echocardiographic Criteria to Differentiate Constrictive Pericarditis From Restrictive Cardiomyopathy: A Meta-analysis.

Similar Datasets

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