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This analysis investigates how prognostic factors for stroke outcomes evolve over time after thrombolysis treatment.

December 8, 2025PLoS ONEOpen Access

Temporal shifts in prognostic factors for 90- and 180-day outcomes after stroke thrombolysis: A machine learning analysis

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This analysis investigates how prognostic factors for stroke outcomes evolve over time after thrombolysis treatment.
Retrospective analysis of acute ischemic stroke patients undergoing thrombolysis
Feature selection from pre-therapy data using LASSO regression
Developed machine learning models including Logistic Regression and Random Forest
Assessment through AUC-ROC and Decision Curve Analysis
18.8% of patients had an unfavorable outcome at 90 days
Key predictors transitioned from fibrinogen at 90 days to white blood cell count at 180 days
Logistic Regression had AUC of 0.757 and Random Forest had AUC of 0.833 for 90- and 180-day outcomes, respectively

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Implication

Retrospective analysis demonstrates shifts in prognostic factors at 90 and 180 days after thrombolysis for acute ischemic stroke, suggesting the importance of tailored models.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/69362f364fa91c937236d2ce https://doi.org/10.1371/journal.pone.0338011

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Implication

Retrospective analysis demonstrates shifts in prognostic factors at 90 and 180 days after thrombolysis for acute ischemic stroke, suggesting the importance of tailored models.

Temporal shifts in prognostic factors for 90- and 180-day outcomes after stroke thrombolysis: A machine learning analysis

Key Points

Discussion

Implication

Cite This Study

Temporal shifts in prognostic factors for 90- and 180-day outcomes after stroke thrombolysis: A machine learning analysis

Key Points

Discussion

Implication

Cite This Study

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