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PRERISK

PRERISK: A Personalized, Artificial Intelligence–Based and Statistically–Based Stroke Recurrence Predictor for Recurrent Stroke

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Year of Publication: 2024

Authors: Giorgio Colangelo, DS, PhD; Marc Ribo, ..., PhD; Marta Rubiera

Journal: Stroke

Citation: Stroke. 2024;55:1200–1209. DOI: 10.1161/STROKEAHA.123.043691

Link: https://doi.org/10.1161/STROKEAHA.123.043691

PDF: https://www.ahajournals.org/doi/epub/10....EAHA.123.043691

Clinical Question

Can routinely collected clinical and socioeconomic data be used to build accurate statistical and machine-learning models (PRERISK) that predict early, late, and long-term stroke recurrence in individual patients after a first-ever stroke?

Bottom Line

In a large, population-based cohort, PRERISK machine-learning models achieved AUROC 0.76 (early), 0.60 (late), and 0.71 (long-term) and outperformed Cox regression; a simplified model using key predictors had similar performance.

        Major Points
        Population-based dataset: 41,975 stroke admissions from 88 public health centers (Catalonia, 2014–2020); analysis cohort 36,118 first-ever IS/ICH cases; 16.21% (5,932/36,114) had recurrence
Outcomes predicted at three windows: early (≤90 days), late (91–365 days), long-term (>365 days)
Model performance (ML AUROC): 0.76 (95% CI 0.74–0.77) early; 0.60 (0.58–0.61) late; 0.71 (0.69–0.72) long-term
Comparator performance (Cox AUROC): 0.73 (0.72–0.75) early; 0.59 (0.57–0.61) late; 0.67 (0.66–0.70) long-term
Key predictors: time since previous stroke, Barthel Index, atrial fibrillation, dyslipidemia, age, diabetes, sex; simplified model with modifiable risk factors showed similar accuracy
Median follow-up was 2.69 years

      

Design

Study Type: Population-based cohort analysis with statistical (Cox) and supervised machine-learning models

Randomization:

Enrollment Period: 2014–2020

Follow-up Duration: Median 2.69 years

Centers: 88

Countries: Spain

Sample Size: 36114

Analysis: Supervised ML (Random Forest, AdaBoost, XGBoost) compared to Cox regression; performance assessed by AUROC/C-statistic; permutation importance for predictor contribution

Inclusion Criteria

First-ever ischemic stroke (IS) or intracerebral hemorrhage (ICH) identified via ICD-9/10 codes
Admission within the Catalonia public healthcare system (2014–2020)
Survival ≥7 days after index stroke

Exclusion Criteria

Transient ischemic attack (TIA) not included in analysis cohort
Death within 7 days of index stroke
Recurrent stroke diagnoses within 24 hours of index event (considered fluctuation, not recurrence)

Baseline Characteristics

Characteristic	Control	Active

Arms

Field	Population-based cohort
Intervention	Supervised machine-learning models (Random Forest, AdaBoost, XGBoost) and Cox regression applied to routinely collected clinical + socioeconomic data to predict early (≤90 d), late (91–365 d), and long-term (>365 d) stroke recurrence.
Duration	Up to long-term follow-up (>1 year)

Outcomes

Outcome	Type	Control	Intervention
Discrimination (AUROC) for prediction of stroke recurrence at early (≤90 d), late (91–365 d), and long-term (>365 d) windows	Primary	Cox AUROC: 0.73 (0.72–0.75); 0.59 (0.57–0.61); 0.67 (0.66–0.70)	ML AUROC: 0.76 (0.74–0.77); 0.60 (0.58–0.61); 0.71 (0.69–0.72)
Recurrence proportion and follow-up	Secondary		16.21% (5,932/36,114) recurrences; median follow-up 2.69 years
Predictor importance (modifiable risk factors)	Secondary		Modifiable risk factors accounted for ~16%–39% of permutation importance across ML models
Retrospective model-building study	Adverse		Retrospective AI-based recurrence prediction study - no AE data

Subgroup Analysis

A simplified model using key predictors (time since stroke, Barthel Index, AF, dyslipidemia, age, diabetes, sex) achieved similar performance to full ML models.

Criticisms

Observational design using administrative/registry data susceptible to coding and selection bias
Late-window performance (AUROC 0.60) indicates modest discriminative ability
Generalizability may be limited to similar healthcare systems and data availability

Funding

Fundación Instituto Carlos III (PI20/01768); Ministerio de Asuntos Económicos y Transformación Digital (MIA.2021.M02.0005); European Commission (as listed in the article).

Based on: PRERISK (Stroke, 2024)