External validation of risk prediction models for post-stroke mortality in Berlin

Area of Science:

• Neurology
• Epidemiology
• Biostatistics

Background:

• External validation of post-stroke mortality prediction models is crucial for assessing their real-world applicability.
• Existing models often lack validation in diverse geographical settings, limiting their generalizability.

Purpose of the Study:

• To evaluate the performance of two established post-stroke mortality prediction models in a German cohort.
• To assess the calibration and discrimination of these models for predicting 30-day and in-hospital mortality.

Main Methods:

• Utilized data from the Berlin-SPecific Acute Treatment in Ischaemic or hAemorrhagic stroke with Long-term follow-up (B-SPATIAL) registry.
• Validated Bray et al.'s 30-day and Smith et al.'s in-hospital mortality prediction models using calibration and discrimination metrics.
• Included adult patients diagnosed with ischemic stroke, hemorrhagic stroke, or transient ischemic attack.

Main Results:

• Bray et al.'s model (n=7879) showed a c-statistic of 0.865, with 9.7% observed vs. 8.6% predicted 30-day mortality.
• Smith et al.'s model (n=1931) achieved a c-statistic of 0.891, with 5.4% observed vs. 4.8% predicted in-hospital mortality.
• Both models demonstrated underestimation of mortality risk in high-risk patient groups.

Conclusions:

• The evaluated models exhibit good discrimination and acceptable calibration for low-to-medium risk stroke patients in Berlin.
• The findings suggest that routinely collected data can support valid post-stroke mortality predictions, though high-risk stratification requires further refinement.
• External validation is essential for ensuring the reliable application of prediction models in clinical practice.