Bowei Zhang1, Wenbo Zhao1, Chuanjie Wu1, Longfei Wu1, Chengbei Hou2, Kara Klomparens3, Yuchuan Ding3, Chuanhui Li4, Jian Chen5, Jiangang Duan4, Yunzhou Zhang1, Hong Chang1, Xunming Ji5. 1. Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China. 2. Center for Evidence-Based Medicine, Xuanwu Hospital of Capital Medical University, Beijing, China. 3. Department of Neurosurgery, Wayne State University School of Medicine, Detroit, MI, United States. 4. Department of Emergency, Xuanwu Hospital of Capital Medical University, Beijing, China. 5. Department of Neurosurgery, Xuanwu Hospital of Capital Medical University, Beijing, China.
Abstract
Objective: This study aimed to develop and validate a novel index to predict SAP for AIS patients who underwent endovascular treatment. Methods: A study was conducted in an advanced comprehensive stroke center from January 2013 to December 2019 aiming to develop and validate a novel index to predict SAP for AIS patients who underwent endovascular treatment. This cohort consisted of a total of 407 consecutively registered AIS patients who underwent endovascular therapy, which was divided into derivation and validation cohorts. Multiple blood parameters as well as demographic features, vascular risk factors, and clinical features were carefully evaluated in the derivation cohort. The independent predictors were obtained using multivariable logistic regression. The scoring system was generated based on the β-coefficients of each independent risk factor. Results: Ultimately, a novel predictive model: the SDL index (stroke history, dysphagia, lymphocyte count < 1.00 × 103/μL) was developed. The SDL index showed good discrimination both in the derivation cohort (AUROC: 0.739, 95% confidence interval, 0.678-0.801) and the validation cohort (AUROC: 0.783, 95% confidence interval, 0.707-0.859). The SDL index was well-calibrated (Hosmer-Lemeshow test) in the derivation cohort (P = 0.389) and the validation cohort (P = 0.692). We therefore divided our population into low (SDL index = 0), medium (SDL index = 1), and high (SDL index ≥ 2) risk groups for SAP. The SDL index showed good discrimination when compared with two existing SAP prediction models. Conclusions: The SDL index is a novel feasible tool to predict SAP risk in acute ischemic stroke patients post endovascular treatment.
Objective: This study aimed to develop and validate a novel index to predict SAP for AISpatients who underwent endovascular treatment. Methods: A study was conducted in an advanced comprehensive stroke center from January 2013 to December 2019 aiming to develop and validate a novel index to predict SAP for AISpatients who underwent endovascular treatment. This cohort consisted of a total of 407 consecutively registered AISpatients who underwent endovascular therapy, which was divided into derivation and validation cohorts. Multiple blood parameters as well as demographic features, vascular risk factors, and clinical features were carefully evaluated in the derivation cohort. The independent predictors were obtained using multivariable logistic regression. The scoring system was generated based on the β-coefficients of each independent risk factor. Results: Ultimately, a novel predictive model: the SDL index (stroke history, dysphagia, lymphocyte count < 1.00 × 103/μL) was developed. The SDL index showed good discrimination both in the derivation cohort (AUROC: 0.739, 95% confidence interval, 0.678-0.801) and the validation cohort (AUROC: 0.783, 95% confidence interval, 0.707-0.859). The SDL index was well-calibrated (Hosmer-Lemeshow test) in the derivation cohort (P = 0.389) and the validation cohort (P = 0.692). We therefore divided our population into low (SDL index = 0), medium (SDL index = 1), and high (SDL index ≥ 2) risk groups for SAP. The SDL index showed good discrimination when compared with two existing SAP prediction models. Conclusions: The SDL index is a novel feasible tool to predict SAP risk in acute ischemic strokepatients post endovascular treatment.
Authors: Craig J Smith; Amit K Kishore; Andy Vail; Angel Chamorro; Javier Garau; Stephen J Hopkins; Mario Di Napoli; Lalit Kalra; Peter Langhorne; Joan Montaner; Christine Roffe; Anthony G Rudd; Pippa J Tyrrell; Diederik van de Beek; Mark Woodhead; Andreas Meisel Journal: Stroke Date: 2015-06-25 Impact factor: 7.914
Authors: Edward C Jauch; Jeffrey L Saver; Harold P Adams; Askiel Bruno; J J Buddy Connors; Bart M Demaerschalk; Pooja Khatri; Paul W McMullan; Adnan I Qureshi; Kenneth Rosenfield; Phillip A Scott; Debbie R Summers; David Z Wang; Max Wintermark; Howard Yonas Journal: Stroke Date: 2013-01-31 Impact factor: 7.914
Authors: Yousef Hannawi; Bashar Hannawi; Chethan P Venkatasubba Rao; Jose I Suarez; Eric M Bershad Journal: Cerebrovasc Dis Date: 2013-05-31 Impact factor: 2.762