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STOP-stroke: STroke Outcome Prediction in the Acute Treatment Setting

NCT06534645 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 250

Last updated 2026-05-07

No results posted yet for this study

Summary

The STOP-stroke project aims at improving prediction of outcome early after stroke. In order to achieve this, we need to understand reasons (important variables) for prediction in a real clinical prognostication process.

We aim to:

1. Test the predictive performance of stroke neurologists for outcome prediction (NIHSS at 24 hours and 3 months and mRS at 3 months after stroke onset) prospectively and in a real clinical setting, and to explore the most important baseline variables in their prognostication process.
2. Test the prediction performance of our DL models when being provided with structured clinical and/or imaging information from the same patients as the neurologists; and to discover most relevant features of the input data.
3. Use the information gained from our experiments for improving our DL algorithm. This will include an error analysis on the missclassifications of models and neurologists to understand the pitfalls of both approaches. We anticipate to develop a robust, reliable and clinically feasible application ready for testing in a prospective, observational trial.

Conditions

  • Stroke Outcome Prediction Supported by Deep Learning Algorithm

Sponsors & Collaborators

  • University of Zurich

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-10-29
Primary Completion
2027-07-30
Completion
2027-07-30

Countries

  • Switzerland

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06534645 on ClinicalTrials.gov