In silico thrombectomy trials for acute ischemic stroke. (January 2023)

• Record Type: Journal Article
• Authors: Miller, Claire; Konduri, Praneeta; Bridio, Sara; Luraghi, Giulia; Arrarte Terreros, Nerea; Boodt, Nikki; Samuels, Noor; Rodriguez Matas, Jose F.; Migliavacca, Francesco; Lingsma, Hester; van der Lugt, Aad; Roos, Yvo; Dippel, Diederik; Marquering, Henk; Majoie, Charles; Hoekstra, Alfons
• Is Part Of: Computer methods and programs in biomedicine. Volume 228(2023)
• Publication Date: 2023-01
• Subjects: In silico trials -- Acute ischemic stroke -- Computational biology -- Simulation frameworks; 87.85.G- -- 07.05.Tp; 92C10 -- 92C50 -- 92C32; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107244
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: In silico trial to evaluate treatment device performance on virtual stroke patients. A surrogate thrombectomy model enabled in silico trials on large virtual populations. High fibrin clots were harder to retrieve, which has also been observed in the clinic. In silico trial compared performance of two devices on the same virtual population. Abstract: Background and objective: In silico trials aim to speed up the introduction of new devices in clinical practice by testing device design and performance in different patient scenarios and improving patient stratification for optimizing clinical trials. In this paper, we demonstrate an in silico trial framework for thrombectomy treatment of acute ischemic stroke and apply this framework to compare treatment outcomes in different subpopulations and with different thrombectomy stent-retriever devices. We employ a novel surrogate thrombectomy model to evaluate the thrombectomy success in the in silico trial. Methods: The surrogate thrombectomy model, built using data from a fine-grained finite-element model, is a device-specific binary classifier (logistic regression), to estimate the probability of successful recanalization, the outcome of interest. We incorporate this surrogate model within our previously developed in silico trial framework and demonstrate its use with three examples of in silico clinical trials. The first trial is a validation trial for the surrogate thrombectomy model. We then present two exploratory trials: one evaluating the performance of a commercially available device based on the fibrin composition in the occluding thrombus and one comparing the performance of two commercially available stent retrievers. Results: The Validation Trial showed the surrogate thrombectomy model was able to reproduce a similar recanalization rate as the real-life MR CLEAN trial ( p = 0.6 ). Results from the first exploratory trial showed that the chance of successful thrombectomy increases with higher blood cell concentrations in the thrombi, which is in line with observations from clinical data. The second exploratory trial showed improved recanalization success with a newer stent retriever device; however, these results require further investigation as the surrogate model for the newer stent retriever device has not yet been validated. Conclusions: In this novel study, we have shown that in silico trials have the potential to help inform medical device developers on the performance of a new device and may also be used to select populations of interest for a clinical trial. This would reduce the time and costs involved in device development and traditional clinical trials.