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Comparison of Conventional Logistic Regression and Machine Learning Methods for Predicting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage: A Multicentric Observational Cohort Study

BACKGROUND: Timely and accurate prediction of delayed cerebral ischemia is critical for improving the prognosis of patients with aneurysmal subarachnoid hemorrhage. Machine learning (ML) algorithms are increasingly regarded as having a higher prediction power than conventional logistic regression (L...

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Autores principales: Hu, Ping, Li, Yuntao, Liu, Yangfan, Guo, Geng, Gao, Xu, Su, Zhongzhou, Wang, Long, Deng, Gang, Yang, Shuang, Qi, Yangzhi, Xu, Yang, Ye, Liguo, Sun, Qian, Nie, Xiaohu, Sun, Yanqi, Li, Mingchang, Zhang, Hongbo, Chen, Qianxue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9247265/
https://www.ncbi.nlm.nih.gov/pubmed/35783143
http://dx.doi.org/10.3389/fnagi.2022.857521
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author Hu, Ping
Li, Yuntao
Liu, Yangfan
Guo, Geng
Gao, Xu
Su, Zhongzhou
Wang, Long
Deng, Gang
Yang, Shuang
Qi, Yangzhi
Xu, Yang
Ye, Liguo
Sun, Qian
Nie, Xiaohu
Sun, Yanqi
Li, Mingchang
Zhang, Hongbo
Chen, Qianxue
author_facet Hu, Ping
Li, Yuntao
Liu, Yangfan
Guo, Geng
Gao, Xu
Su, Zhongzhou
Wang, Long
Deng, Gang
Yang, Shuang
Qi, Yangzhi
Xu, Yang
Ye, Liguo
Sun, Qian
Nie, Xiaohu
Sun, Yanqi
Li, Mingchang
Zhang, Hongbo
Chen, Qianxue
author_sort Hu, Ping
collection PubMed
description BACKGROUND: Timely and accurate prediction of delayed cerebral ischemia is critical for improving the prognosis of patients with aneurysmal subarachnoid hemorrhage. Machine learning (ML) algorithms are increasingly regarded as having a higher prediction power than conventional logistic regression (LR). This study aims to construct LR and ML models and compare their prediction power on delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH). METHODS: This was a multicenter, retrospective, observational cohort study that enrolled patients with aneurysmal subarachnoid hemorrhage from five hospitals in China. A total of 404 aSAH patients were prospectively enrolled. We randomly divided the patients into training (N = 303) and validation cohorts (N = 101) according to a ratio of 75–25%. One LR and six popular ML algorithms were used to construct models. The area under the receiver operating characteristic curve (AUC), accuracy, balanced accuracy, confusion matrix, sensitivity, specificity, calibration curve, and Hosmer–Lemeshow test were used to assess and compare the model performance. Finally, we calculated each feature of importance. RESULTS: A total of 112 (27.7%) patients developed DCI. Our results showed that conventional LR with an AUC value of 0.824 (95%CI: 0.73–0.91) in the validation cohort outperformed k-nearest neighbor, decision tree, support vector machine, and extreme gradient boosting model with the AUCs of 0.792 (95%CI: 0.68–0.9, P = 0.46), 0.675 (95%CI: 0.56–0.79, P < 0.01), 0.677 (95%CI: 0.57–0.77, P < 0.01), and 0.78 (95%CI: 0.68–0.87, P = 0.50). However, random forest (RF) and artificial neural network model with the same AUC (0.858, 95%CI: 0.78–0.93, P = 0.26) were better than the LR. The accuracy and the balanced accuracy of the RF were 20.8% and 11% higher than the latter, and the RF also showed good calibration in the validation cohort (Hosmer-Lemeshow: P = 0.203). We found that the CT value of subarachnoid hemorrhage, WBC count, neutrophil count, CT value of cerebral edema, and monocyte count were the five most important features for DCI prediction in the RF model. We then developed an online prediction tool (https://dynamic-nomogram.shinyapps.io/DynNomapp-DCI/) based on important features to calculate DCI risk precisely. CONCLUSIONS: In this multicenter study, we found that several ML methods, particularly RF, outperformed conventional LR. Furthermore, an online prediction tool based on the RF model was developed to identify patients at high risk for DCI after SAH and facilitate timely interventions. CLINICAL TRIAL REGISTRATION: http://www.chictr.org.cn, Unique identifier: ChiCTR2100044448.
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spelling pubmed-92472652022-07-02 Comparison of Conventional Logistic Regression and Machine Learning Methods for Predicting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage: A Multicentric Observational Cohort Study Hu, Ping Li, Yuntao Liu, Yangfan Guo, Geng Gao, Xu Su, Zhongzhou Wang, Long Deng, Gang Yang, Shuang Qi, Yangzhi Xu, Yang Ye, Liguo Sun, Qian Nie, Xiaohu Sun, Yanqi Li, Mingchang Zhang, Hongbo Chen, Qianxue Front Aging Neurosci Aging Neuroscience BACKGROUND: Timely and accurate prediction of delayed cerebral ischemia is critical for improving the prognosis of patients with aneurysmal subarachnoid hemorrhage. Machine learning (ML) algorithms are increasingly regarded as having a higher prediction power than conventional logistic regression (LR). This study aims to construct LR and ML models and compare their prediction power on delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH). METHODS: This was a multicenter, retrospective, observational cohort study that enrolled patients with aneurysmal subarachnoid hemorrhage from five hospitals in China. A total of 404 aSAH patients were prospectively enrolled. We randomly divided the patients into training (N = 303) and validation cohorts (N = 101) according to a ratio of 75–25%. One LR and six popular ML algorithms were used to construct models. The area under the receiver operating characteristic curve (AUC), accuracy, balanced accuracy, confusion matrix, sensitivity, specificity, calibration curve, and Hosmer–Lemeshow test were used to assess and compare the model performance. Finally, we calculated each feature of importance. RESULTS: A total of 112 (27.7%) patients developed DCI. Our results showed that conventional LR with an AUC value of 0.824 (95%CI: 0.73–0.91) in the validation cohort outperformed k-nearest neighbor, decision tree, support vector machine, and extreme gradient boosting model with the AUCs of 0.792 (95%CI: 0.68–0.9, P = 0.46), 0.675 (95%CI: 0.56–0.79, P < 0.01), 0.677 (95%CI: 0.57–0.77, P < 0.01), and 0.78 (95%CI: 0.68–0.87, P = 0.50). However, random forest (RF) and artificial neural network model with the same AUC (0.858, 95%CI: 0.78–0.93, P = 0.26) were better than the LR. The accuracy and the balanced accuracy of the RF were 20.8% and 11% higher than the latter, and the RF also showed good calibration in the validation cohort (Hosmer-Lemeshow: P = 0.203). We found that the CT value of subarachnoid hemorrhage, WBC count, neutrophil count, CT value of cerebral edema, and monocyte count were the five most important features for DCI prediction in the RF model. We then developed an online prediction tool (https://dynamic-nomogram.shinyapps.io/DynNomapp-DCI/) based on important features to calculate DCI risk precisely. CONCLUSIONS: In this multicenter study, we found that several ML methods, particularly RF, outperformed conventional LR. Furthermore, an online prediction tool based on the RF model was developed to identify patients at high risk for DCI after SAH and facilitate timely interventions. CLINICAL TRIAL REGISTRATION: http://www.chictr.org.cn, Unique identifier: ChiCTR2100044448. Frontiers Media S.A. 2022-06-17 /pmc/articles/PMC9247265/ /pubmed/35783143 http://dx.doi.org/10.3389/fnagi.2022.857521 Text en Copyright © 2022 Hu, Li, Liu, Guo, Gao, Su, Wang, Deng, Yang, Qi, Xu, Ye, Sun, Nie, Sun, Li, Zhang and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Aging Neuroscience
Hu, Ping
Li, Yuntao
Liu, Yangfan
Guo, Geng
Gao, Xu
Su, Zhongzhou
Wang, Long
Deng, Gang
Yang, Shuang
Qi, Yangzhi
Xu, Yang
Ye, Liguo
Sun, Qian
Nie, Xiaohu
Sun, Yanqi
Li, Mingchang
Zhang, Hongbo
Chen, Qianxue
Comparison of Conventional Logistic Regression and Machine Learning Methods for Predicting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage: A Multicentric Observational Cohort Study
title Comparison of Conventional Logistic Regression and Machine Learning Methods for Predicting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage: A Multicentric Observational Cohort Study
title_full Comparison of Conventional Logistic Regression and Machine Learning Methods for Predicting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage: A Multicentric Observational Cohort Study
title_fullStr Comparison of Conventional Logistic Regression and Machine Learning Methods for Predicting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage: A Multicentric Observational Cohort Study
title_full_unstemmed Comparison of Conventional Logistic Regression and Machine Learning Methods for Predicting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage: A Multicentric Observational Cohort Study
title_short Comparison of Conventional Logistic Regression and Machine Learning Methods for Predicting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage: A Multicentric Observational Cohort Study
title_sort comparison of conventional logistic regression and machine learning methods for predicting delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage: a multicentric observational cohort study
topic Aging Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9247265/
https://www.ncbi.nlm.nih.gov/pubmed/35783143
http://dx.doi.org/10.3389/fnagi.2022.857521
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