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

  • Mi Yeon Eun
  • , Eun Tae Jeon
  • , Kwon Duk Seo
  • , Dongwhane Lee
  • , Jin Man Jung

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

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.

Original languageEnglish
Article number105742
JournalJournal of Stroke and Cerebrovascular Diseases
Volume30
Issue number6
DOIs
StatePublished - Jun 2021

Keywords

  • Cancer
  • Machine learning
  • Meta-analysis
  • Reperfusion
  • Stroke

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