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Multiparametric assessment of atrial cardiopathy in cryptogenic stroke patients: Implications for personalized

Iria López-Dequidt1, Sonia Eiras-Penas2,3, Adrián González-Maestro2,3

  • 1Neurology Department, University Clinical Hospital of A Coruña, A Coruña, Galicia, Spain.

European Stroke Journal
|September 24, 2025
PubMed

Insights

A new model combining N-terminal pro-brain natriuretic peptide (NT-proBNP) and left atrial strain (LAS) accurately identifies atrial cardiopathy (AC) in cryptogenic stroke (CS) patients. This aids in distinguishing cardioembolic from non-cardioembolic stroke causes.

Area of Science:

  • Cardiology
  • Neurology
  • Medical Diagnostics

Background:

  • Cryptogenic stroke (CS) has heterogeneous causes, with atrial cardiopathy (AC) increasingly recognized as a significant factor.
  • Current diagnostic tools for early and accurate AC identification in CS patients are lacking.
  • Atrial cardiopathy is a key substrate for both stroke and atrial fibrillation (AF).

Purpose of the Study:

  • To develop and validate a diagnostic model for identifying atrial cardiopathy (AC) in patients with cryptogenic stroke (CS).
  • To differentiate between cardioembolic and non-cardioembolic etiologies of stroke using biomarkers.
  • To assess the clinical utility of a multiparametric model in guiding personalized patient management.

Main Methods:

  • Prospective study involving patients with cardioembolic stroke due to AF (CES-AF), non-cardioembolic stroke (NCES), and CS.
  • Evaluation of left atrial strain (LAS) via speckle-tracking echocardiography and serum AC markers.
  • Development of a logistic regression model using ROC analysis to predict AC probability in CS patients.

Main Results:

  • The combination of NT-proBNP levels (≥469 pg/mL) and biplanar LAS during contraction (LASct) (≥-10.2%) showed high accuracy (AUC=0.995) for AC detection.
  • Approximately 30% of CS patients exhibited a predicted AC probability >0.5.
  • CS patients with high predicted AC probability were older, had more severe strokes, and a higher incidence of AF during follow-up.

Conclusions:

  • The combined NT-proBNP and biplanar LASct model provides highly sensitive and specific biomarkers for AC.
  • This multiparametric approach enables individualized AC probability estimation in CS patients.
  • The model shows potential utility in differentiating stroke etiologies and informing personalized clinical strategies.
Abstract

Keywords:
NT-proBNPSpeckle-tracking echocardiographyStrokeatrial cardiopathyatrial fibrillationbiomarkerscryptogenic strokeleft atrial strainmultiparametric modelpersonalized medicine

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