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Related Experiment Video

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Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke
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PRERISK: A Personalized, Artificial Intelligence-Based and Statistically-Based Stroke Recurrence Predictor for

Giorgio Colangelo1,2, Marc Ribo1,3, Estefanía Montiel2

  • 1Vall d'Hebron Research Institute, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera).

Stroke
|March 28, 2024
PubMed
Summary
This summary is machine-generated.

Predicting stroke recurrence is challenging. PRERISK, a new machine learning tool, offers personalized stroke recurrence risk prediction, potentially improving patient self-care and outcomes.

Keywords:
artificial intelligenceawarenessmachine learningprognosisrecurrencestrokesurvivors

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Area of Science:

  • Neurology
  • Data Science
  • Public Health

Background:

  • Individualized stroke recurrence prediction is difficult but crucial for patient self-care.
  • A novel statistical and machine learning classifier, PRERISK, was developed to address this challenge.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting individual stroke recurrence risk over time.
  • To compare the performance of machine learning models against traditional Cox regression.

Main Methods:

  • Analysis of clinical and socioeconomic data from 41,975 patients diagnosed with stroke in Catalonia, Spain (2014-2020).
  • Development of supervised machine learning models to predict early, late, and long-term stroke recurrence.
  • Evaluation of model accuracy using C statistics and area under the receiver operating characteristic curve (AUC).

Main Results:

  • 16.21% of patients experienced stroke recurrence within a median follow-up of 2.69 years.
  • Key predictors identified include time from previous stroke, Barthel Index, atrial fibrillation, dyslipidemia, age, diabetes, and sex.
  • Machine learning models demonstrated statistically significant improvements over Cox regression for predicting recurrence at 90 days, 91-365 days, and >365 days (AUCs: 0.76, 0.60, 0.71 respectively).

Conclusions:

  • PRERISK offers a novel, personalized, and accurate approach to predicting stroke recurrence risk.
  • The model's ability to incorporate dynamic risk factor control holds potential for improved patient management.