Availability of patient-specific computational modeling data significantly increased the proportion of cardiologists recommending intervention for a borderline case of coarctation of the aorta compared to conventional imaging alone (94.3% vs. 72.2%, RR 1.31).
RCT (n=178)
Open-label
Web-based survey platform (Qualtrics) generated random allocation sequence
Yes
Does patient-specific computational modelling data influence therapeutic decision making in paediatric cardiologists evaluating coarctation of the aorta?
Relative Risk: 1.31 (95% CI 1.14–1.5)
Absolute Event Rate: 94.3% vs 72.2%
p-value: p=0.00
Abstract Computational modelling has made significant progress towards clinical application in recent years. In addition to providing detailed diagnostic data, these methods have the potential to simulate patient-specific interventions and to predict their outcome. Our objective was to evaluate to which extent patient-specific modelling influences treatment decisions in coarctation of the aorta (CoA), a common congenital heart disease. We selected three cases with CoA, two of which had borderline indications for intervention according to current clinical guidelines. The third case was not indicated for intervention according to guidelines. For each case, we generated two separate datasets. First dataset included conventional diagnostic parameters (echocardiography and magnetic resonance imaging). In the second, we added modelled parameters (pressure fields). For the two cases with borderline indications for intervention, the second dataset also included pressure fields after virtual stenting simulations. All parameters were computed by modelling methods that were previously validated. In an online-administered, invitation-only survey, we randomized 178 paediatric cardiologists to view either conventional (control) or add-on modelling (experimental) datasets. Primary endpoint was the proportion of participants recommending different therapeutic options: (1) surgery or catheter lab (collectively, “intervention”) or (2) no intervention (follow-up with or without medication). Availability of data from computational predictive modelling influenced therapeutic decision making in two of three cases. There was a statistically significant association between group assignment and the recommendation of an intervention for one borderline case and one non-borderline case: 94.3% vs. 72.2% (RR: 1.31, 95% CI: 1.14–1.50, p = 0.00) and 18.8% vs. 5.1% (RR: 3.09, 95% CI: 1.17–8.18, p = 0.01) of participants in the experimental and control groups respectively recommended an intervention. For the remaining case, there was no difference between the experimental and control group and the majority of participants recommended intervention. In sub-group analyses, findings were not affected by the experience level of participating cardiologists. Despite existing clinical guidelines, the therapy recommendations of the participating physicians were heterogeneous. Validated patient-specific computational modelling has the potential to influence treatment decisions. Future studies in broader areas are needed to evaluate whether differences in decisions result in improved outcomes (Trial Registration: NCT02700737).
Naci et al. (Tue,) conducted a rct in Coarctation of the aorta (clinical decision making) (n=178). Patient-specific computational modeling data (CFD and virtual stenting) added to conventional imaging vs. Conventional imaging parameters alone (echocardiography and MRI) was evaluated on Proportion of participants recommending intervention (surgery or catheter lab) for Case 1 (borderline indication) (RR 1.31, 95% CI 1.14-1.50, p=0.00). Availability of patient-specific computational modeling data significantly increased the proportion of cardiologists recommending intervention for a borderline case of coarctation of the aorta compared to conventional imaging alone (94.3% vs. 72.2%, RR 1.31).