Omid Shirvani, Stefanie Warnat-Herresthal, Ivan Savchuk, Felix J. Bode, Louisa Nitsch, Sebastian Stösser, Taraneh Ebrahimi, Niklas von Danwitz, Hannah Asperger, Julia Layer, Julius Meissner, Christian Thielscher, Franziska Dorn, Nils Lehnen, Joachim L. Schultze, Gabor C. Petzold, Johannes M. Weller, the GSR-ET Investigators
{"title":"用于大血管前端闭塞血栓切除术结果预测的机器学习模型。","authors":"Omid Shirvani, Stefanie Warnat-Herresthal, Ivan Savchuk, Felix J. Bode, Louisa Nitsch, Sebastian Stösser, Taraneh Ebrahimi, Niklas von Danwitz, Hannah Asperger, Julia Layer, Julius Meissner, Christian Thielscher, Franziska Dorn, Nils Lehnen, Joachim L. Schultze, Gabor C. Petzold, Johannes M. Weller, the GSR-ET Investigators","doi":"10.1002/acn3.52185","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Objective</h3>\n \n <p>Predicting long-term functional outcomes shortly after a stroke is challenging, even for experienced neurologists. Therefore, we aimed to evaluate multiple machine learning models and the importance of clinical/radiological parameters to develop a model that balances minimal input data with reliable predictions of long-term functional independency.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Our study utilized data from the German Stroke Registry on patients with large anterior vessel occlusion who underwent endovascular treatment. We trained seven machine learning models using 30 parameters from the first day postadmission to predict a modified Ranking Scale of 0–2 at 90 days poststroke. Model performance was assessed using a 20-fold cross-validation and one-sided Wilcoxon rank-sum tests. Key features were identified through backward feature selection.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>We included 7485 individuals with a median age of 75 years and a median NIHSS score at admission of 14 in our analysis. Our Deep Neural Network model demonstrated the best performance among all models including data from 24 h postadmission. Backward feature selection identified the seven most important features to be NIHSS after 24 h, age, modified Ranking Scale after 24 h, premorbid modified Ranking Scale, intracranial hemorrhage within 24 h, intravenous thrombolysis, and NIHSS at admission. Narrowing the Deep Neural Network model's input data to these features preserved the high performance with an AUC of 0.9 (CI: 0.89–0.91).</p>\n </section>\n \n <section>\n \n <h3> Interpretation</h3>\n \n <p>Our Deep Neural Network model, trained on over 7000 patients, predicts 90-day functional independence using only seven clinical/radiological features from the first day postadmission, demonstrating both high accuracy and practicality for clinical implementation on stroke units.</p>\n </section>\n </div>","PeriodicalId":126,"journal":{"name":"Annals of Clinical and Translational Neurology","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acn3.52185","citationCount":"0","resultStr":"{\"title\":\"Machine learning models for outcome prediction in thrombectomy for large anterior vessel occlusion\",\"authors\":\"Omid Shirvani, Stefanie Warnat-Herresthal, Ivan Savchuk, Felix J. Bode, Louisa Nitsch, Sebastian Stösser, Taraneh Ebrahimi, Niklas von Danwitz, Hannah Asperger, Julia Layer, Julius Meissner, Christian Thielscher, Franziska Dorn, Nils Lehnen, Joachim L. Schultze, Gabor C. Petzold, Johannes M. Weller, the GSR-ET Investigators\",\"doi\":\"10.1002/acn3.52185\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>Predicting long-term functional outcomes shortly after a stroke is challenging, even for experienced neurologists. Therefore, we aimed to evaluate multiple machine learning models and the importance of clinical/radiological parameters to develop a model that balances minimal input data with reliable predictions of long-term functional independency.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>Our study utilized data from the German Stroke Registry on patients with large anterior vessel occlusion who underwent endovascular treatment. We trained seven machine learning models using 30 parameters from the first day postadmission to predict a modified Ranking Scale of 0–2 at 90 days poststroke. Model performance was assessed using a 20-fold cross-validation and one-sided Wilcoxon rank-sum tests. Key features were identified through backward feature selection.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>We included 7485 individuals with a median age of 75 years and a median NIHSS score at admission of 14 in our analysis. Our Deep Neural Network model demonstrated the best performance among all models including data from 24 h postadmission. Backward feature selection identified the seven most important features to be NIHSS after 24 h, age, modified Ranking Scale after 24 h, premorbid modified Ranking Scale, intracranial hemorrhage within 24 h, intravenous thrombolysis, and NIHSS at admission. Narrowing the Deep Neural Network model's input data to these features preserved the high performance with an AUC of 0.9 (CI: 0.89–0.91).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Interpretation</h3>\\n \\n <p>Our Deep Neural Network model, trained on over 7000 patients, predicts 90-day functional independence using only seven clinical/radiological features from the first day postadmission, demonstrating both high accuracy and practicality for clinical implementation on stroke units.</p>\\n </section>\\n </div>\",\"PeriodicalId\":126,\"journal\":{\"name\":\"Annals of Clinical and Translational Neurology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acn3.52185\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Clinical and Translational Neurology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/acn3.52185\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Clinical and Translational Neurology","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/acn3.52185","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Machine learning models for outcome prediction in thrombectomy for large anterior vessel occlusion
Objective
Predicting long-term functional outcomes shortly after a stroke is challenging, even for experienced neurologists. Therefore, we aimed to evaluate multiple machine learning models and the importance of clinical/radiological parameters to develop a model that balances minimal input data with reliable predictions of long-term functional independency.
Methods
Our study utilized data from the German Stroke Registry on patients with large anterior vessel occlusion who underwent endovascular treatment. We trained seven machine learning models using 30 parameters from the first day postadmission to predict a modified Ranking Scale of 0–2 at 90 days poststroke. Model performance was assessed using a 20-fold cross-validation and one-sided Wilcoxon rank-sum tests. Key features were identified through backward feature selection.
Results
We included 7485 individuals with a median age of 75 years and a median NIHSS score at admission of 14 in our analysis. Our Deep Neural Network model demonstrated the best performance among all models including data from 24 h postadmission. Backward feature selection identified the seven most important features to be NIHSS after 24 h, age, modified Ranking Scale after 24 h, premorbid modified Ranking Scale, intracranial hemorrhage within 24 h, intravenous thrombolysis, and NIHSS at admission. Narrowing the Deep Neural Network model's input data to these features preserved the high performance with an AUC of 0.9 (CI: 0.89–0.91).
Interpretation
Our Deep Neural Network model, trained on over 7000 patients, predicts 90-day functional independence using only seven clinical/radiological features from the first day postadmission, demonstrating both high accuracy and practicality for clinical implementation on stroke units.
期刊介绍:
Annals of Clinical and Translational Neurology is a peer-reviewed journal for rapid dissemination of high-quality research related to all areas of neurology. The journal publishes original research and scholarly reviews focused on the mechanisms and treatments of diseases of the nervous system; high-impact topics in neurologic education; and other topics of interest to the clinical neuroscience community.