Abstract
BACKGROUND: Mild non-disabling ischemic stroke (MNDIS) is increasingly treated with intravenous thrombolysis, yet a substantial proportion of patients still experience poor functional outcomes, and robust tools for individualized risk prediction are lacking. METHODS: In this retrospective cohort study, we analyzed 713 consecutive MNDIS patients who received intravenous thrombolysis within 4.5 h of symptom onset at an advanced stroke center between January 1, 2022 and December 31, 2024. Poor outcome was defined as a 90-day modified Rankin Scale (mRS) score ≥2. Candidate predictors, including demographic, clinical, laboratory, hemodynamic and imaging variables, were first screened by univariable analysis and then entered into a stepwise multivariable logistic regression model (entry p < 0.05, removal p > 0.10). A nomogram incorporating independent predictors was constructed in R, and its performance was evaluated using receiver operating characteristic (ROC) analysis, bootstrap calibration, and decision curve analysis. RESULTS: Of the 713 patients, 91 (12.8%) had poor 90-day outcomes (mRS 2-6) and 622 (87.2%) had good outcomes (mRS 0-1). Admission NIHSS score (OR 1.37; 95% CI 1.10-1.72), 24-h NIHSS score (OR 1.78; 95% CI 1.52-2.10), diastolic blood pressure (OR 1.02 per mmHg; 95% CI 1.00-1.05), and coronary heart disease (OR 1.88; 95% CI 1.05-3.35) were independently associated with poor outcome. The resulting nomogram showed good discrimination (AUC 0.835; 95% CI 0.805-0.861; sensitivity 71.4%; specificity 84.1%), excellent calibration (bootstrap mean absolute error 0.014), and provided positive net clinical benefit across a wide range of risk thresholds (0.03-0.89). CONCLUSION: Admission and 24-h NIHSS scores, diastolic blood pressure, and coronary heart disease are key predictors of poor 90-day outcomes after thrombolysis in patients with MNDIS. The derived nomogram offers accurate, well-calibrated, and clinically useful individualized risk estimation, and may assist clinicians in early post-thrombolysis risk stratification and tailoring the intensity of monitoring and follow-up.