scholarly journals 67 Echocardiography vs. computed tomography and cardiac magnetic resonance for the detection of left heart thrombosis: a systematic review and meta-analysis

2020 ◽  
Vol 22 (Supplement_N) ◽  
pp. N28-N44
Author(s):  
Alberto Aimo ◽  
Georgios Ntritsos ◽  
Pier-Giorgio Masci ◽  
Stefano Figliozzi ◽  
Dimitrios Klettas ◽  
...  

Abstract Aims Accurate and reproducible diagnostic techniques are essential to detect left-sided cardiac thrombi (either in the left ventricle [LV] or in the left atrial appendage [LAA]) and to guide the onset and duration of antithrombotic treatment while minimizing the risk for thromboembolic and hemorrhagic events. Methods and results We conducted a systematic review and meta-analysis aiming to compare the diagnostic performance of transthoracic echocardiography (TTE) vs. cardiac magnetic resonance (CMR) for the detection of LV thrombi, and transesophageal echocardiography (TEE) vs. computed tomography (CT) for the detection of LAA thrombi. Six studies were included in the first meta-analysis. Pooled sensitivity and specificity values were 62% (95% confidence interval [CI], 37-81%) and 97% (95% CI, 94-99%). The shape of the hierarchical summary receiver operating characteristic (HSROC) curve and the area under the curve (AUC) of 0.96 suggested a high accuracy. Ten studies were included in the meta-analysis of the diagnostic accuracy of CT vs. TEE. The pooled values of sensitivity and specificity were 97% (95% CI, 77-100%) and 94% (95% CI, 87-98%). The pooled DOR was 500 (95% CI, 52-4810), and the pooled LR+ and LR- values were 17% (95% CI, 7-40%) and 3% (95% CI, 0-28%). The shape of the HSROC curve and the 0.99 AUC suggested a high accuracy of CT vs. TEE. Conclusion TTE is a valid alternative to DE-CMR for the identification of LV thrombi, and CT has a good accuracy compared to TEE for the detection of LAA thrombosis.

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shelly Soffer ◽  
Eyal Klang ◽  
Orit Shimon ◽  
Yiftach Barash ◽  
Noa Cahan ◽  
...  

AbstractComputed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803–0.927) and 0.86 (95% CI 0.756–0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms.


2016 ◽  
Vol 26 (11) ◽  
pp. 3771-3780 ◽  
Author(s):  
Vivan J. M. Baggen ◽  
Tim Leiner ◽  
Marco C. Post ◽  
Arie P. van Dijk ◽  
Jolien W. Roos-Hesselink ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document