Abstract
Background: Chalos et al recently developed the MR PREDICTS@24H model to predict 90 days functional outcomes in ischaemic stroke patients following endovascular treatment (EVT). We aimed to validate this model in the real-world situation of endovascular stroke patients admitted to a tertiary care hospital.
Methods: We conducted a retrospective cohort study including a selection of adult (≥18 years old) ischaemic stroke patients eligible for EVT in a tretiary care center between January 2014 and May 2023. Model performance was assessed using C-statistics for discrimination and calibration plots for goodness of fit.
Results: Among 254 eligible stroke patients, the model demonstrates a strong discriminatory performance for both functional independence (C-statistics 0.92; 95% CI 0.88 to 0.95) and survival (C-statistic 0.83; 95% CI 0.76 to 0.90). Compared with the MR CLEAN Registry, no significant differences were observed in discriminative ability (functional independence: z-score 0.54, p=0.590; survival: z-score -1.66, p=0.0962).
Conclusions: The MR PREDICTS@24H model reliably predicts outcomes in a real-world setting and may help clinicians in the communication with patient relatives.
Methods: We conducted a retrospective cohort study including a selection of adult (≥18 years old) ischaemic stroke patients eligible for EVT in a tretiary care center between January 2014 and May 2023. Model performance was assessed using C-statistics for discrimination and calibration plots for goodness of fit.
Results: Among 254 eligible stroke patients, the model demonstrates a strong discriminatory performance for both functional independence (C-statistics 0.92; 95% CI 0.88 to 0.95) and survival (C-statistic 0.83; 95% CI 0.76 to 0.90). Compared with the MR CLEAN Registry, no significant differences were observed in discriminative ability (functional independence: z-score 0.54, p=0.590; survival: z-score -1.66, p=0.0962).
Conclusions: The MR PREDICTS@24H model reliably predicts outcomes in a real-world setting and may help clinicians in the communication with patient relatives.
| Original language | English |
|---|---|
| Article number | e000973 |
| Number of pages | 3 |
| Journal | BMJ Neurology Open |
| Volume | 7 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 14 Apr 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Fields of science
- 301405 Neuropathology
- 301114 Cell biology
- 302052 Neurology
- 302051 Neurosurgery
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