Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta‐Analysis
ABSTRACT Introduction Stroke patients are at high risk of developing cerebral edema, which can have severe consequences. However, there are currently few effective tools for early identification or prediction of this risk. As machine learning (ML) is increasingly used in clinical practice, its effec...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
|
| Series: | Brain and Behavior |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/brb3.70198 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832582638966145024 |
|---|---|
| author | Qi Deng Yu Yang Hongyu Bai Fei Li Wenluo Zhang Rong He Yuming Li |
| author_facet | Qi Deng Yu Yang Hongyu Bai Fei Li Wenluo Zhang Rong He Yuming Li |
| author_sort | Qi Deng |
| collection | DOAJ |
| description | ABSTRACT Introduction Stroke patients are at high risk of developing cerebral edema, which can have severe consequences. However, there are currently few effective tools for early identification or prediction of this risk. As machine learning (ML) is increasingly used in clinical practice, its effectiveness in predicting cerebral edema risk in stroke patients has been explored. Nonetheless, the lack of systematic evidence on its predictive value challenges the update of simple and user‐friendly risk assessment tools. Therefore, we conducted a systematic review to evaluate the predictive utility of ML for cerebral edema in stroke patients. Methods We searched PubMed, Embase, Web of Science, and the Cochrane Database up to February 21, 2024. The risk of bias in selected studies was assessed using a bias assessment tool for predictive models. Meta‐analysis synthesized results from validation sets. Results We included 22 studies with 25,096 stroke patients and 25 models, which were constructed using common and interpretable clinical features. In the validation cohort, the models achieved a concordance index (c‐index) of 0.840 (95% CI: 0.810–0.871) for predicting poststroke cerebral edema, with a sensitivity of 0.76 (95% CI: 0.72–0.79) and a specificity of 0.87 (95% CI: 0.83–0.90). Conclusion ML models are significant in predicting poststroke cerebral edema, providing clinicians with a powerful prognostic tool. However, radiomics‐based research was not included. We anticipate advancements in radiomics research to enhance the predictive power of ML for poststroke cerebral edema. |
| format | Article |
| id | doaj-art-4cd2f15275934d63ab7618f6bda7898e |
| institution | Kabale University |
| issn | 2162-3279 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Brain and Behavior |
| spelling | doaj-art-4cd2f15275934d63ab7618f6bda7898e2025-01-29T13:36:39ZengWileyBrain and Behavior2162-32792025-01-01151n/an/a10.1002/brb3.70198Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta‐AnalysisQi Deng0Yu Yang1Hongyu Bai2Fei Li3Wenluo Zhang4Rong He5Yuming Li6Department of NeurologyTianjin Kanghui HospitalTianjinChinaDepartment of RespiratoryTianjin Kanghui HospitalTianjinChinaDepartment of General SurgeryTianjin Kanghui HospitalTianjinChinaDepartment of NeurologyTianjin Kanghui HospitalTianjinChinaDepartment of NeurologyPKUCare Rehabilitation HospitalBeijingChinaDepartment of NeurologyPKUCare Rehabilitation HospitalBeijingChinaDepartment of NeurologyTianjin Kanghui HospitalTianjin ChinaABSTRACT Introduction Stroke patients are at high risk of developing cerebral edema, which can have severe consequences. However, there are currently few effective tools for early identification or prediction of this risk. As machine learning (ML) is increasingly used in clinical practice, its effectiveness in predicting cerebral edema risk in stroke patients has been explored. Nonetheless, the lack of systematic evidence on its predictive value challenges the update of simple and user‐friendly risk assessment tools. Therefore, we conducted a systematic review to evaluate the predictive utility of ML for cerebral edema in stroke patients. Methods We searched PubMed, Embase, Web of Science, and the Cochrane Database up to February 21, 2024. The risk of bias in selected studies was assessed using a bias assessment tool for predictive models. Meta‐analysis synthesized results from validation sets. Results We included 22 studies with 25,096 stroke patients and 25 models, which were constructed using common and interpretable clinical features. In the validation cohort, the models achieved a concordance index (c‐index) of 0.840 (95% CI: 0.810–0.871) for predicting poststroke cerebral edema, with a sensitivity of 0.76 (95% CI: 0.72–0.79) and a specificity of 0.87 (95% CI: 0.83–0.90). Conclusion ML models are significant in predicting poststroke cerebral edema, providing clinicians with a powerful prognostic tool. However, radiomics‐based research was not included. We anticipate advancements in radiomics research to enhance the predictive power of ML for poststroke cerebral edema.https://doi.org/10.1002/brb3.70198cerebral edemamachine learningpredictive modelingstroke |
| spellingShingle | Qi Deng Yu Yang Hongyu Bai Fei Li Wenluo Zhang Rong He Yuming Li Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta‐Analysis Brain and Behavior cerebral edema machine learning predictive modeling stroke |
| title | Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta‐Analysis |
| title_full | Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta‐Analysis |
| title_fullStr | Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta‐Analysis |
| title_full_unstemmed | Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta‐Analysis |
| title_short | Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta‐Analysis |
| title_sort | predictive value of machine learning models for cerebral edema risk in stroke patients a meta analysis |
| topic | cerebral edema machine learning predictive modeling stroke |
| url | https://doi.org/10.1002/brb3.70198 |
| work_keys_str_mv | AT qideng predictivevalueofmachinelearningmodelsforcerebraledemariskinstrokepatientsametaanalysis AT yuyang predictivevalueofmachinelearningmodelsforcerebraledemariskinstrokepatientsametaanalysis AT hongyubai predictivevalueofmachinelearningmodelsforcerebraledemariskinstrokepatientsametaanalysis AT feili predictivevalueofmachinelearningmodelsforcerebraledemariskinstrokepatientsametaanalysis AT wenluozhang predictivevalueofmachinelearningmodelsforcerebraledemariskinstrokepatientsametaanalysis AT ronghe predictivevalueofmachinelearningmodelsforcerebraledemariskinstrokepatientsametaanalysis AT yumingli predictivevalueofmachinelearningmodelsforcerebraledemariskinstrokepatientsametaanalysis |