Purpose: Despite receiving standard-of-care treatments, a significant proportion of patients with acute ischemic stroke (AIS) are left with long-term functional impairment. Therefore, an easy-to-use tool for predicting of unfavorable outcome following AIS plays an important role in clinical practice. This study was aimed to develop a dynamic nomogram to predict the 3-month unfavorable outcome for AIS patients.
Methods: This was a prospective observational study conducted in consecutive patients with AIS admitted to our stroke center between September 2019 and June 2020. Baseline demographic, clinical, and laboratory information were obtained. The primary outcome was evaluated with modified Rankin Scale (mRS) scores at 3 months. Least absolute shrinkage and selection operator regression was used to select the optimal predictive factors. Multiple logistics regression was performed to establish the nomogram. Decision curve analysis (DCA) was applied to assess the clinical utility of the nomogram. The calibration and discrimination property of the nomogram was validated by calibration plots and concordance index.
Results: A total of 93 eligible patients were enrolled: 28 (30.1%) patients had unfavorable outcome (mRS > 2). Glycosylated hemoglobin (OR, 1.541; 95% CI, 1.051– 2.261), the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) (OR, 0.635; 95% CI, 0.463– 0.871), and National Institute of Health Stroke Scale (NIHSS) (OR 1.484; 95% CI, 1.155– 1.907) were significant predictors of the poor outcome of patients with AIS and included into the nomogram model. The nomogram showed good calibration and discrimination. C-index was 0.891 (95% CI, 0.854– 0.928). DCA confirmed the clinical usefulness of the model. The dynamic nomogram can be obtained at the website: https://odywong.shinyapps.io/DBT_21/.
Conclusion: The dynamic nomogram, comprised of glycosylated hemoglobin, ASPECTS, and NIHSS score at day 14, may be able to predict the 3-month unfavorable outcome for AIS patients.
Keywords: acute ischemic stroke, unfavorable outcome, dynamic nomogram, predictive model, LASSO regression