Treatment Efficacy Analysis in Acute Ischemic Stroke Patients Using In Silico Modeling Based on Machine Learning: A Proof-of-Principle.

Anthony Winder,Nils D Forkert,Matthias Wilms,Jens Fiehler

doi:10.3390/biomedicines9101357

Abstract

Interventional neuroradiology is characterized by engineering- and experience-driven device development with design improvements every few months. However, clinical validation of these new devices requires lengthy and expensive randomized controlled trials. This contribution proposes a machine learning-based in silico study design to evaluate new devices more quickly with a small sample size. Acute diffusion- and perfusion-weighted MRI, segmented one-week follow-up imaging, and clinical variables were available for 90 acute ischemic stroke patients. Three treatment option-specific random forest models were trained to predict the one-week follow-up lesion segmentation for (1) patients successfully recanalized using intra-arterial mechanical thrombectomy, (2) patients successfully recanalized using intravenous thrombolysis, and (3) non-recanalizing patients as an analogue for conservative treatment for each patient in the sample, independent of the true group membership. A repeated-measures analysis of the three predicted follow-up lesions for each patient revealed significantly larger lesions for the non-recanalizing group compared to the successful intravenous thrombolysis treatment group, which in turn showed significantly larger lesions compared to the successful mechanical thrombectomy treatment group (p < 0.001). A groupwise comparison of the true follow-up lesions for the three treatment options showed the same trend but did not reach statistical significance (p = 0.19). We conclude that the proposed machine learning-based in silico trial design leads to clinically feasible results and can support new efficacy studies by providing additional power and potential early intermediate results.

Highlights

Stroke is a major cause of death and disability in the world
We propose the use of machine learning models, which have been used previously to model the evolution of acute ischemic stroke under particular treatment conditions [15,16,17,18,19,20,21,22,23]
The repeated-measures analysis, made possible by the proposed in silico trial design, revealed that the three sets of predicted lesion volumes were significantly different (p < 0.001)

Summary

Introduction

Stroke is a major cause of death and disability in the world. For example, the annual cost for stroke patients amounts to approximately CAD 2.8 billion in the Canadian health care system [1], USD 28 billion for the US health care system [2], and EUR 60 billion in the European health care systems [3]. Even a small improvement in clinical stroke outcomes could improve the daily life of many patients and result in significant cost savings in the health care system. The majority of all strokes are caused by an artery blockage due to a blood clot, the so-called ischemic stroke [4]. Such a blockage leads to a partial or complete restriction of blood flow to the brain tissue supplied by this artery and may result in irreversible damage to the brain cells in that region. Intravenous tissue plasminogen activator (IV tPA) therapy was the only acute ischemic stroke patient treatment together with stroke unit care.

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