Predicting Long-Term Outcomes After Poor-Grade Aneurysmal Subarachnoid Hemorrhage Using Decision Tree Modeling
| Autor: | Xianxi Tan, Xianzhong Guo, Yunjun Yang, Ye Xiong, Bing Zhao, Jinjin Liu, Ming Zhong |
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| Rok vydání: | 2021 |
| Předmět: |
Adult
Male medicine.medical_specialty Subarachnoid hemorrhage Clinical Decision-Making Decision tree Logistic regression Machine Learning 03 medical and health sciences 0302 clinical medicine Modified Rankin Scale medicine Humans 030212 general & internal medicine Aged Receiver operating characteristic business.industry Decision Trees Glasgow Coma Scale Middle Aged Subarachnoid Hemorrhage Prognosis medicine.disease Logistic Models Treatment Outcome ROC Curve Emergency medicine Female Observational study Surgery Neurology (clinical) business 030217 neurology & neurosurgery Decision tree model |
| Zdroj: | Neurosurgery. 89:S7-S7 |
| ISSN: | 1524-4040 0148-396X |
| Popis: | Background Despite advances in the treatment of poor-grade aneurysmal subarachnoid hemorrhage (aSAH), predicting the long-term outcome of aSAH remains challenging, although essential. Objective To predict long-term outcomes after poor-grade aSAH using decision tree modeling. Methods This was a retrospective analysis of a prospective multicenter observational registry of patients with poor-grade aSAH with a World Federation of Neurosurgical Societies (WFNS) grade IV or V. Outcome was assessed by the modified Rankin Scale (mRS) at 12 mo, and an unfavorable outcome was defined as an mRS of 4 or 5 or death. Long-term prognostic models were developed using multivariate logistic regression and decision tree algorithms. An additional independent testing dataset was collected for external validation. Overall accuracy, sensitivity, specificity, and area under receiver operating characteristic curves (AUC) were used to assess model performance. Results Of the 266 patients, 139 (52.3%) had an unfavorable outcome. Older age, absence of pupillary reactivity, lower Glasgow coma score (GCS), and higher modified Fisher grade were independent predictors of unfavorable outcome. Modified Fisher grade, pupillary reactivity, GCS, and age were used in the decision tree model, which achieved an overall accuracy of 0.833, sensitivity of 0.821, specificity of 0.846, and AUC of 0.88 in the internal test. There was similar predictive performance between the logistic regression and decision tree models. Both models achieved a high overall accuracy of 0.895 in the external test. Conclusion Decision tree model is a simple tool for predicting long-term outcomes after poor-grade aSAH and may be considered for treatment decision-making. |
| Databáze: | OpenAIRE |
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