Literature DB >> 33780696

Reperfusion Therapy in Acute Ischemic Stroke with Active Cancer: A Meta-Analysis Aided by Machine Learning.

Mi-Yeon Eun1, Eun-Tae Jeon2, Kwon-Duk Seo3, Dongwhane Lee4, Jin-Man Jung5.   

Abstract

OBJECTIVES: While the prevalence of active cancer patients experiencing acute stroke is increasing, the effects of active cancer on reperfusion therapy outcomes are inconclusive. Thus, we aimed to compare the safety and outcomes of reperfusion therapy in acute stroke patients with and without active cancer.
MATERIALS AND METHODS: A comprehensive literature search was conducted for studies comparing the effects of intravenous thrombolysis (IVT) or endovascular treatment (EVT) in ischemic stroke patients with and without active cancer. The literature was screened using both a manual and machine learning algorithm approach. The outcomes evaluated were symptomatic intracerebral hemorrhage (sICH), all-type intracerebral hemorrhage (aICH), successful recanalization, favorable outcomes (modified Rankin Scale, 0-2), and mortality. We calculated the pooled odds ratio (OR) and 95% confidence interval (CI) using the random-effects model from the included studies.
RESULTS: Seven studies were analyzed in this meta-analysis. IVT (n = 1012) was associated with an increased risk of sICH (OR, 9.80; 95% CI, 3.19-30.13) in the active cancer group. However, no significant differences in aICH, favorable outcomes, and mortality were found between groups. Although sICH and successful recanalization in the EVT group (n = 2496) were similar, we observed fewer favorable outcomes (OR, 0.55; 95% CI, 0.33-0.93) and a high prevalence of mortality (OR, 2.91; 95% CI, 1.89-4.47) in the active cancer group.
CONCLUSIONS: Reperfusion therapy may benefit selected patients with acute ischemic stroke with active cancer, considering the comparable clinical outcomes of IVT and procedure-related outcomes of EVT. These results should be cautiously interpreted and confirmed in future well-designed large-scale studies.
Copyright © 2021 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Cancer; Machine learning; Meta-analysis; Reperfusion; Stroke

Year:  2021        PMID: 33780696     DOI: 10.1016/j.jstrokecerebrovasdis.2021.105742

Source DB:  PubMed          Journal:  J Stroke Cerebrovasc Dis        ISSN: 1052-3057            Impact factor:   2.136


  3 in total

Review 1.  Cancer and stroke: commonly encountered by clinicians, but little evidence to guide clinical approach.

Authors:  Malin Woock; Nicolas Martinez-Majander; David J Seiffge; Henriette Aurora Selvik; Annika Nordanstig; Petra Redfors; Erik Lindgren; Mayte Sanchez van Kammen; Alexandros Rentzos; Jonathan M Coutinho; Karen Doyle; Halvor Naess; Jukka Putaala; Katarina Jood; Turgut Tatlisumak
Journal:  Ther Adv Neurol Disord       Date:  2022-06-28       Impact factor: 6.430

Review 2.  Mechanical Thrombectomy for Acute Ischemic Stroke in Patients with Malignancy: A Systematic Review.

Authors:  Athina-Maria Aloizou; Daniel Richter; Jeyanthan Charles James; Carsten Lukas; Ralf Gold; Christos Krogias
Journal:  J Clin Med       Date:  2022-08-11       Impact factor: 4.964

3.  Stacking ensemble learning model to predict 6-month mortality in ischemic stroke patients.

Authors:  Lee Hwangbo; Yoon Jung Kang; Hoon Kwon; Jae Il Lee; Han-Jin Cho; Jun-Kyeung Ko; Sang Min Sung; Tae Hong Lee
Journal:  Sci Rep       Date:  2022-10-17       Impact factor: 4.996

  3 in total

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