Visual prediction of outcomes in patients undergoing intravenous thrombolysis

Qing Liang, Tao Qie and Yinglei Li

PLOS ONE, 2025, vol. 20, issue 12, 1-15

Abstract: Background: This research presents a novel visual predictive model aimed at the early identification of patients at elevated risk of poor prognosis following intravenous thrombolysis, assessed six months post-acute ischemic stroke. Methods: A retrospective cohort of patients who underwent intravenous thrombolysis at advanced stroke centers was analyzed. The latest Least Absolute Shrinkage and Selection Operator (LASSO) regression technique was employed to select relevant variables and develop nomograms. The model’s performance was evaluated through receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis, culminating in an assessment of the model’s reliability. Results: We identified five principal predictors that are significantly associated with a 6-month adverse prognosis in patients undergoing intravenous thrombolysis. These predictors include door-to-needle time (DNT), homocysteine (HCY) levels, lactate dehydrogenase (LDH) levels, the post-thrombolysis National Institutes of Health Stroke Scale (NIHSS) score (P-NIHSS), and the monocyte to high-density lipoprotein cholesterol (MHR) ratio. The nomogram’s AUC-ROC was 0.914 (95% CI: 0.899–0.939) for the training cohort and 0.892 (95% CI: 0.852–0.932) for the validation cohort. Conclusion: This straightforward visual prediction model effectively identifies factors linked to poor prognosis 6 months post-intravenous thrombolytic therapy for acute ischemic stroke, aiding early treatment and resource allocation.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0336226

DOI: 10.1371/journal.pone.0336226

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