Development of a nomogram prediction model for thrombolytic outcomes in acute ischemic stroke

As stroke is a leading global cause of disability and death, and acute ischemic stroke (AIS) constitutes ~ 80% of strokes, prognostic assessment for intravenous thrombolysis (IVT) remains challenging. This study will evaluate pre-treatment predictors of 90-day outcomes in AIS patients receiving IVT and establish a predictive model to enhance prognostic accuracy.

This retrospective cohort study included patients with AIS who received recombinant tissue plasminogen activator (rt-PA) treatment. Data collected encompassed demographic, clinical, and outcome measures, including neurological scores, blood tests, and medical history. A favorable outcome was defined as a modified Rankin Score (mRS) of ≤ 2 at 90 days post-thrombolysis. Independent risk factors were identified using univariate and multivariate logistic regression. A nomogram was created based on these factors, and its predictive performance was evaluated with receiver operating characteristic (ROC) curves and DeLong validation, along with decision curve analysis (DCA).

Among 175 patients undergoing rt-PA thrombolysis for AIS, 28.57% (50/175) had unfavorable outcomes, while 71.43% experienced favorable outcomes. Elevated blood glucose levels, systolic blood pressure (SBP), the Fazekas scale, and the DRAGON score were identified as independent predictors of unfavorable outcomes. The nomogram achieved an area under the curve (AUC) of 0.84 (95% CI: 0.71 to 0.89), with a calibration curve closely aligning with the ideal curve. DCA indicated that the nomogram provides substantial net clinical benefits, outperforming univariate models.

A predictive model and nomogram for assessing post-thrombolytic outcomes in ischemic stroke patients treated with rt-PA have been developed. This model shows strong performance and can effectively identify patients at increased risk for unfavorable outcomes.