Cargando…

Identification of Predictors for Hemorrhagic Transformation in Patients with Acute Ischemic Stroke After Endovascular Therapy Using the Decision Tree Model

PURPOSE: 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 enr...

Descripción completa

Detalles Bibliográficos
Autores principales: Feng, Xin, Ye, Gengfan, Cao, Ruoyao, Qi, Peng, Lu, Jun, Chen, Juan, Wang, Daming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7490069/
https://www.ncbi.nlm.nih.gov/pubmed/32982196
http://dx.doi.org/10.2147/CIA.S257931
_version_ 1783581973944467456
author Feng, Xin
Ye, Gengfan
Cao, Ruoyao
Qi, Peng
Lu, Jun
Chen, Juan
Wang, Daming
author_facet Feng, Xin
Ye, Gengfan
Cao, Ruoyao
Qi, Peng
Lu, Jun
Chen, Juan
Wang, Daming
author_sort Feng, Xin
collection PubMed
description PURPOSE: 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.
format Online
Article
Text
id pubmed-7490069
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Dove
record_format MEDLINE/PubMed
spelling pubmed-74900692020-09-24 Identification of Predictors for Hemorrhagic Transformation in Patients with Acute Ischemic Stroke After Endovascular Therapy Using the Decision Tree Model Feng, Xin Ye, Gengfan Cao, Ruoyao Qi, Peng Lu, Jun Chen, Juan Wang, Daming Clin Interv Aging Original Research PURPOSE: 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. Dove 2020-09-08 /pmc/articles/PMC7490069/ /pubmed/32982196 http://dx.doi.org/10.2147/CIA.S257931 Text en © 2020 Feng et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Feng, Xin
Ye, Gengfan
Cao, Ruoyao
Qi, Peng
Lu, Jun
Chen, Juan
Wang, Daming
Identification of Predictors for Hemorrhagic Transformation in Patients with Acute Ischemic Stroke After Endovascular Therapy Using the Decision Tree Model
title Identification of Predictors for Hemorrhagic Transformation in Patients with Acute Ischemic Stroke After Endovascular Therapy Using the Decision Tree Model
title_full Identification of Predictors for Hemorrhagic Transformation in Patients with Acute Ischemic Stroke After Endovascular Therapy Using the Decision Tree Model
title_fullStr Identification of Predictors for Hemorrhagic Transformation in Patients with Acute Ischemic Stroke After Endovascular Therapy Using the Decision Tree Model
title_full_unstemmed Identification of Predictors for Hemorrhagic Transformation in Patients with Acute Ischemic Stroke After Endovascular Therapy Using the Decision Tree Model
title_short Identification of Predictors for Hemorrhagic Transformation in Patients with Acute Ischemic Stroke After Endovascular Therapy Using the Decision Tree Model
title_sort identification of predictors for hemorrhagic transformation in patients with acute ischemic stroke after endovascular therapy using the decision tree model
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7490069/
https://www.ncbi.nlm.nih.gov/pubmed/32982196
http://dx.doi.org/10.2147/CIA.S257931
work_keys_str_mv AT fengxin identificationofpredictorsforhemorrhagictransformationinpatientswithacuteischemicstrokeafterendovasculartherapyusingthedecisiontreemodel
AT yegengfan identificationofpredictorsforhemorrhagictransformationinpatientswithacuteischemicstrokeafterendovasculartherapyusingthedecisiontreemodel
AT caoruoyao identificationofpredictorsforhemorrhagictransformationinpatientswithacuteischemicstrokeafterendovasculartherapyusingthedecisiontreemodel
AT qipeng identificationofpredictorsforhemorrhagictransformationinpatientswithacuteischemicstrokeafterendovasculartherapyusingthedecisiontreemodel
AT lujun identificationofpredictorsforhemorrhagictransformationinpatientswithacuteischemicstrokeafterendovasculartherapyusingthedecisiontreemodel
AT chenjuan identificationofpredictorsforhemorrhagictransformationinpatientswithacuteischemicstrokeafterendovasculartherapyusingthedecisiontreemodel
AT wangdaming identificationofpredictorsforhemorrhagictransformationinpatientswithacuteischemicstrokeafterendovasculartherapyusingthedecisiontreemodel