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Updated: May 5, 2026

Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
Published on: January 18, 2018
Investigating discharge predictors for stroke patients with active cancer after endovascular therapy
Yasaman Pirahanchi1, Constance McGraw2, Russell Bartt3
1Department of Neurology, Swedish Medical Center, Englewood, CO, USA.
Objective:
Current cancer diagnosis is not an exclusion from treatment with endovascular therapy (EVT) in patients with acute ischemic stroke (AIS). There are insufficient studies to determine whether outcomes, based on modified Rankin Scale (mRS), differ for cancer and non-cancer patients, and what factors affect a favorable outcome. This study aims to identify predictors of discharge outcome in AIS patients with active cancer who have undergone EVT.
Methods:
This retrospective cohort study included patients (age ≥ 18) admitted from 07/01/2018-10/01/2020 with AIS and treated with EVT. Patients were grouped according to the presence or absence of active cancer diagnosis. Multivariable logistic regression determined independent predictors of favorable outcomes (discharge mRS 0-2) in patients with and without active cancer. The predictive utility of admission National Institutes of Health Stroke Scale (NIHSS) was further explored using receiver operating characteristic (ROC) curve analysis to determine area under the curve (AUC) and optimal cut points for favorable outcomes.
Results:
Of 463 patients who received EVT, 10 % had cancer. Patients with cancer had significantly higher rates of hypercoagulation-related stroke mechanisms, prior clots, renal failure, and thromboembolic events during hospitalization (all p < 0.01), compared to patients without cancer. Favorable discharge outcomes did not differ significantly between groups (24 % vs. 35 %, p = 0.13). In patients with cancer after adjustment, admission NIHSS independently predicted favorable discharge outcomes (adjusted odds ratio (AOR): 0.81, 95 % confidence interval (CI) 0.69-0.99, p = 0.01), with a 19 % decrease in odds per 1-unit increase in NIHSS. The optimal threshold for NIHSS was 6, with strong fit (AUC: 0.88, p = 0.002). For non-cancer patients, NIHSS (AOR: 0.91, 95 % CI 0.88-0.93, p < 0.001), age, and diabetes history were independent predictors, with a 9 % decrease in odds per unit increase for NIHSS. The threshold for NIHSS in non-cancer patients was 21, with moderate fit (AUC: 0.77, p < 0.001).
Conclusion:
Admission NIHSS is an important predictor of favorable discharge outcomes in AIS patients with active cancer treated with EVT. Incorporating NIHSS into risk stratification, alongside patients' medical history, may improve the ability to assess the likelihood of favorable discharge outcomes.
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