Aurora Semerano, MD
Kamel H, Navi BB, Parikh NS, Merkler AE, Okin PM, Devereux RB, Weinsaft JW, Kim J, Cheung JW, Kim LK, et al. Machine Learning Prediction of Stroke Mechanism in Embolic Strokes of Undetermined Source. Stroke. 2020;51:e203–e210.
In 2014, when the concept of embolic stroke of undetermined source (ESUS) was proposed,1 confidence existed that ESUS could represent a single entity which would have benefitted from a unified treatment. However, after two randomized clinical trials did not show benefit of direct oral anticoagulation for secondary prevention of ESUS patients,2,3 it is now common opinion that these patients rather represent a heterogeneous population and are likely to benefit from tailored, personalized therapies. Today, ESUS represents a useful definition to identify patients deserving extended diagnostic workup, while prevention therapy for these patients remains elusive, and clinical stroke recurrence is still an issue. Both subgroup analyses from the above-mentioned clinical trials and new research studies have been developed or are ongoing, to better understand the pathophysiology of ESUS and help in patient selection.
In such a phenotypically heterogeneous population, one big effort is to identify patient subsets with a single or group of underlying mechanisms likely to respond to an established treatment. With this right purpose of uncover the “hidden structure” in a complex scenario, the recent study from Kamel et al.4 employs a machine learning approach. Firstly, a supervised machine-learning algorithm was developed to distinguish cardioembolic versus non-cardioembolic strokes in a population of 1083 patients with known stroke etiology, by entering data about demographics, comorbidities, vitals, laboratory results, and echocardiograms. After the learning process, the system finally resulted to distinguish cardioembolic from non-cardioembolic strokes with excellent accuracy (area under the curve, AUC=0.85).