TY - JOUR
T1 - Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke
T2 - Potential Value of Machine Learning Algorithms
AU - van Os, Hendrikus J. A.
AU - Ramos, Lucas A.
AU - Hilbert, Adam
AU - van Leeuwen, Matthijs
AU - van Walderveen, Marianne A. A.
AU - Kruyt, Nyika D.
AU - Dippel, Diederik W. J.
AU - Steyerberg, Ewout W.
AU - van der Schaaf, Irene C.
AU - Lingsma, Hester F.
AU - Schonewille, Wouter J.
AU - Majoie, Charles B. L. M.
AU - Olabarriaga, Silvia D.
AU - Zwinderman, Koos H.
AU - Venema, Esmee
AU - Marquering, Henk A.
AU - Wermer, Marieke J. H.
AU - MR CLEAN Registry Investigators
AU - van Oostenbrugge, Robert Jan
AU - van Zwam, Wim
PY - 2018/9/25
Y1 - 2018/9/25
N2 - Background: Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables.Methods: We included patients from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry, an observational cohort of LVO patients treated with EVT. We applied the following machine learning algorithms: Random Forests, Support Vector Machine, Neural Network, and Super Learner and compared their predictive value with classic logistic regression models using various variable selection methodologies. Outcome variables were good reperfusion (post-mTICI >= 2b) and functional independence (modified Rankin ScaleResults: We included 1,383 EVT patients, with good reperfusion in 531 (38%) and functional independence in 525 (38%) patients. Machine learning and logistic regression models all performed poorly in predicting good reperfusion (range mean AUC: 0.53-0.57), and moderately in predicting 3-months functional independence (range mean AUC: 0.77-0.79) using only baseline variables. All models performed well in predicting 3-months functional independence using both baseline and treatment variables (range mean AUC: 0.88-0.91) with a negligible difference of mean AUC (0.01; 95% CI: 0.00-0.01) between best performing machine learning algorithm (Random Forests) and best performing logistic regression model (based on prior knowledge).Conclusion: In patients with LVO machine learning algorithms did not outperform logistic regression models in predicting reperfusion and 3-months functional independence after endovascular treatment. For all models at time of admission radiological outcome was more difficult to predict than clinical outcome.
AB - Background: Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables.Methods: We included patients from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry, an observational cohort of LVO patients treated with EVT. We applied the following machine learning algorithms: Random Forests, Support Vector Machine, Neural Network, and Super Learner and compared their predictive value with classic logistic regression models using various variable selection methodologies. Outcome variables were good reperfusion (post-mTICI >= 2b) and functional independence (modified Rankin ScaleResults: We included 1,383 EVT patients, with good reperfusion in 531 (38%) and functional independence in 525 (38%) patients. Machine learning and logistic regression models all performed poorly in predicting good reperfusion (range mean AUC: 0.53-0.57), and moderately in predicting 3-months functional independence (range mean AUC: 0.77-0.79) using only baseline variables. All models performed well in predicting 3-months functional independence using both baseline and treatment variables (range mean AUC: 0.88-0.91) with a negligible difference of mean AUC (0.01; 95% CI: 0.00-0.01) between best performing machine learning algorithm (Random Forests) and best performing logistic regression model (based on prior knowledge).Conclusion: In patients with LVO machine learning algorithms did not outperform logistic regression models in predicting reperfusion and 3-months functional independence after endovascular treatment. For all models at time of admission radiological outcome was more difficult to predict than clinical outcome.
KW - ischemic stroke
KW - prediction
KW - machine learning
KW - endovascular treatment
KW - functional outcome
KW - reperfusion
KW - TRAUMATIC BRAIN-INJURY
KW - BREAST-CANCER
KW - SUPER LEARNER
KW - REGRESSION
KW - SELECTION
KW - CHEMOTHERAPY
KW - MODELS
KW - TRIALS
KW - SCORE
KW - RISK
U2 - 10.3389/fneur.2018.00784
DO - 10.3389/fneur.2018.00784
M3 - Article
C2 - 30319525
SN - 1664-2295
VL - 9
JO - Frontiers in Neurology
JF - Frontiers in Neurology
M1 - 784
ER -