Clinical Interventions in Aging (Sep 2020)
Identification of Predictors for Hemorrhagic Transformation in Patients with Acute Ischemic Stroke After Endovascular Therapy Using the Decision Tree Model
Abstract
Xin Feng,1,* Gengfan Ye,1,* Ruoyao Cao,1 Peng Qi,2 Jun Lu,2 Juan Chen,2 Daming Wang1 1Department of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences; Graduate School of Peking Union Medical College, Beijing, 100730, People’s Republic of China; 2Department of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People’s Republic of China*These authors contributed equally to this workCorrespondence: Daming WangDepartment of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences; Graduate School of Peking Union Medical College, No. 1 DaHua Road, Dong Dan, Beijing 100730, People’s Republic of ChinaTel +86 10-85136281Fax +86 10-85132621Email [email protected]: This study aimed to identify independent predictors for the risk of hemorrhagic transformation (HT) in arterial ischemic stroke (AIS) patients.Methods: Consecutive patients with AIS due to large artery occlusion in the anterior circulation treated with mechanical thrombectomy (MT) were enrolled in a tertiary stroke center. Demographic and medical history data, admission lab results, and Circle of Willis (CoW) variations were collected from all patients.Results: Altogether, 90 patients were included in this study; among them, 34 (37.8%) had HT after MT. The final pruned decision tree (DT) model consisted of collateral score and platelet to lymphocyte ratios (PLR) as predictors. Confusion matrix analysis showed that 82.2% (74/90) were correctly classified by the model (sensitivity, 79.4%; specificity, 83.9%). The area under the ROC curve (AUC) was 81.7%. The DT model demonstrated that participants with collateral scores of 2– 4 had a 75.0% probability of HT. For participants with collateral scores of 0– 1, if PLR at admission was < 302, participants had a 13.0% probability of HT; otherwise, participants had an 75.0% probability of HT. The final adjusted multivariate logistic regression analysis indicated that collateral score 0– 1 (OR, 10.186; 95% CI, 3.029– 34.248; p < 0.001), PLR (OR, 1.005; 95% CI, 1.001– 1.010; p = 0.040), and NIHSS at admission (OR, 1.106; 95% CI, 1.014– 1.205; p = 0.022) could be used to predict HT. The AUC for the model was 0.855, with 83.3% (75/90) were correctly classified (sensitivity, 79.4%; specificity, 87.3%). Less patients with HT achieved independent outcomes (mRS, 0– 2) in 90 days (20.6% vs. 64.3%, p < 0.001). Rate of poor outcomes (mRS, 4– 6) was significantly higher in patients with HT (73.5% vs. 19.6%; p < 0.001).Conclusion: Both the DT model and multivariate logistic regression model confirmed that the lower collateral status and the higher PLR were significantly associated with an increased risk for HT in AIS patients after MT. PLR may be one of the cost-effective and practical predictors for HT. Further prospective multicenter studies are needed to validate our findings.Keywords: acute ischemic stroke, mechanical thrombectomy; MT, hemorrhagic transformation; HT, decision tree model; DT
