Yao Hao Teo1, Isis Claire Z Y Lim1, Fan Shuen Tseng1, Yao Neng Teo1, Cheryl Shumin Kow1, Zi Hui Celeste Ng1, Nyein Chan Ko Ko1, Ching-Hui Sia1, Aloysius S T Leow1, Wesley Yeung1, Wan Yee Kong2, Bernard P L Chan3, Vijay K Sharma1,3, Leonard L L Yeo4,5, Benjamin Y Q Tan1,3. 1. Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. 2. Department of Neurology, Detroit Medical Centre, Michigan, USA. 3. Division of Neurology, Department of Medicine, National University Health System, 1E Kent Ridge Road, NUHS Tower Block Level 11, 119228, Singapore, Singapore. 4. Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. Leonard_ll_yeo@nuhs.edu.sg. 5. Division of Neurology, Department of Medicine, National University Health System, 1E Kent Ridge Road, NUHS Tower Block Level 11, 119228, Singapore, Singapore. Leonard_ll_yeo@nuhs.edu.sg.
Abstract
PURPOSE: Conventional predictive models are based on a combination of clinical and neuroimaging parameters using traditional statistical approaches. Emerging studies have shown that the machine learning (ML) prediction models with multiple pretreatment clinical variables have the potential to accurately prognosticate the outcomes in acute ischemic stroke (AIS) patients undergoing thrombectomy, and hence identify patients suitable for thrombectomy. This article summarizes the published studies on ML models in large vessel occlusion AIS patients undergoing thrombectomy. METHODS: We searched electronic databases including PubMed from 1 January 2000 up to 14 October 2019 for studies that evaluated ML algorithms for the prediction of outcomes in stroke patients undergoing thrombectomy. We then used random-effects bivariate meta-analysis models to summarize the studies. RESULTS: We retained a total of five studies that evaluated ML (4 support vector machine, 1 decision tree model) with a combined cohort of 802 patients. The prevalence of good functional outcome defined by 90-day mRS of 0-2 when available. Random effects model demonstrated that the AUC was 0.846 (95% confidence interval, CI 0.686-0.902). A pooled diagnostic odds ratio of 12.6 was computed. The pooled sensitivity and specificity were 0.795 (95% CI 0.651-0.889) and 0.780 (95% CI 0.634-0.879), respectively. CONCLUSION: ML may be useful as an adjunct to clinical assessment to predict functional outcomes in AIS patients undergoing thrombectomy, and hence identify suitable patients for treatment. Further studies validating ML models in large multicenter cohorts are necessary to explore this further.
PURPOSE: Conventional predictive models are based on a combination of clinical and neuroimaging parameters using traditional statistical approaches. Emerging studies have shown that the machine learning (ML) prediction models with multiple pretreatment clinical variables have the potential to accurately prognosticate the outcomes in acute ischemic stroke (AIS) patients undergoing thrombectomy, and hence identify patients suitable for thrombectomy. This article summarizes the published studies on ML models in large vessel occlusion AIS patients undergoing thrombectomy. METHODS: We searched electronic databases including PubMed from 1 January 2000 up to 14 October 2019 for studies that evaluated ML algorithms for the prediction of outcomes in stroke patients undergoing thrombectomy. We then used random-effects bivariate meta-analysis models to summarize the studies. RESULTS: We retained a total of five studies that evaluated ML (4 support vector machine, 1 decision tree model) with a combined cohort of 802 patients. The prevalence of good functional outcome defined by 90-day mRS of 0-2 when available. Random effects model demonstrated that the AUC was 0.846 (95% confidence interval, CI 0.686-0.902). A pooled diagnostic odds ratio of 12.6 was computed. The pooled sensitivity and specificity were 0.795 (95% CI 0.651-0.889) and 0.780 (95% CI 0.634-0.879), respectively. CONCLUSION: ML may be useful as an adjunct to clinical assessment to predict functional outcomes in AIS patients undergoing thrombectomy, and hence identify suitable patients for treatment. Further studies validating ML models in large multicenter cohorts are necessary to explore this further.
Authors: Mingxue Jing; Joshua Y P Yeo; Staffan Holmin; Tommy Andersson; Fabian Arnberg; Paul Bhogal; Cunli Yang; Anil Gopinathan; Tian Ming Tu; Benjamin Yong Qiang Tan; Ching Hui Sia; Hock Luen Teoh; Prakash R Paliwal; Bernard P L Chan; Vijay Sharma; Leonard L L Yeo Journal: Clin Neuroradiol Date: 2021-10-28 Impact factor: 3.649
Authors: Shon Thomas; Paula de la Pena; Liam Butler; Oguz Akbilgic; Daniel M Heiferman; Ravi Garg; Rick Gill; Joseph C Serrone Journal: J Clin Neurosci Date: 2021-07-30 Impact factor: 2.116