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Patient-Specific Computational Simulations for LAA Closure
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- Preprocedural use of cardiac CT-based computer simulations resulted in improved procedural outcomes with a trend towards a lower LAA patency rate and a significantly higher rate of complete LAA closure.
- There was also a significantly improved procedural efficiency with use of fewer devices and fewer device repositionings in cases prepared with preprocedure simulations.
- Future studies are indicated to assess whether integration of computational models intraprocedurally or preprocedurally by means of fusion imaging can further improve procedural efficiency and outcomes including longer-term outcomes.
Study Questions:
What is the impact of preprocedural computational modeling on procedural efficiency and outcomes of transcatheter left atrial appendage (LAA) closure?
Methods:
The investigators conducted the PREDICT-LAA trial (clinicaltrials.gov: NCT04180605), a prospective, multicenter, randomized trial, in which 200 patients were 1:1 randomized to standard planning versus cardiac computed tomography (CT)-simulation-based planning of LAA closure with Amplatzer AmuletTM (Abbott, USA). The artificial intelligence (AI)-enabled CT-based anatomical analyses and computer simulations were provided by FEops (Belgium). The PREDICT-LAA study was designed and powered to detect a difference in post-procedural imaging endpoints comparing two different planning strategies. The Fisher’s exact probability test and Student’s t-test were used to compare categorical variables (proportions) and continuous variables between both groups, respectively.
Results:
All patients had a preprocedural cardiac CT, 197 patients underwent LAA closure, and 181 of these had a post-procedural CT scan (Standard, n = 91; CT + Simulation, n = 90). The composite primary endpoint, defined as contrast leakage distal of the Amulet lobe and/or presence of device-related thrombus was observed in 41.8% in the Standard group versus 28.9% in the CT + Simulation group (relative risk [RR], 0.69; 95% confidence interval [CI], 0.46-1.04; p = 0.08). Complete LAA closure with no residual leak and no disc retraction into the LAA was observed in 44.0% vs. 61.1%, respectively (RR, 1.44; 95% CI, 1.05-1.98; p = 0.03). In addition, use of computer simulations resulted in improved procedural efficiency with use of fewer Amulet devices (103 vs. 118; p < 0.001) and device repositionings (104 vs. 195; p < 0.001) in the CT + Simulation group.
Conclusions:
The authors report that the PREDICT-LAA trial demonstrates the possible added value of AI-enabled CT-based computational modeling when planning for transcatheter LAA closure, leading to improved procedural efficiency and a trend towards better procedural outcomes.
Perspective:
This study reports that preprocedural use of cardiac CT-based computer simulations resulted in improved procedural outcomes with a trend towards a lower LAA patency rate and a significantly higher rate of complete LAA closure. Furthermore, there was a significantly improved procedural efficiency with use of fewer devices and fewer device repositionings in cases prepared with preprocedure simulations. Future studies are indicated to assess whether integration of computational models intraprocedurally by means of fusion imaging can further improve procedural efficiency and outcomes including longer-term outcomes.
Clinical Topics: Arrhythmias and Clinical EP, Noninvasive Imaging, Prevention, Implantable Devices, SCD/Ventricular Arrhythmias, Atrial Fibrillation/Supraventricular Arrhythmias, Computed Tomography, Nuclear Imaging
Keywords: Arrhythmias, Cardiac, Artificial Intelligence, Atrial Appendage, Computer Simulation, Diagnostic Imaging, Risk, Secondary Prevention, Thrombosis, Tomography, X-Ray Computed
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