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Updated: Jan 31, 2026
Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
Published on: January 18, 2018
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.
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 causes and personalizing treatment.
Area of Science:
- Cardiology
- Neurology
- Biomarker Research
Background:
- Cryptogenic stroke (CS) is a heterogeneous condition with unclear etiology.
- Atrial cardiopathy (AC) is increasingly recognized as a significant factor in CS and atrial fibrillation (AF).
- Current diagnostic tools for early and accurate AC identification are lacking.
Purpose of the Study:
- To develop and validate a tool for early and accurate identification of AC in CS patients.
- To differentiate between cardioembolic and non-cardioembolic stroke etiologies in CS.
- To guide personalized clinical management strategies for CS patients.
Main Methods:
- Prospective study including patients with cardioembolic stroke due to AF (CES-AF), non-cardioembolic stroke (NCES), and CS.
- Assessment of left atrial strain (LAS) via speckle-tracking echocardiography and serum biomarkers (NT-proBNP).
- 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 identification.
- Approximately 30% of CS patients exhibited a predicted AC probability >0.5.
- CS patients with high predicted AC probability were older, experienced more severe strokes, and had a higher incidence of AF during follow-up.
Conclusions:
- NT-proBNP levels and biplanar LASct serve as highly sensitive and specific biomarkers for AC.
- The developed multiparametric model enables individualized AC probability estimation in CS patients.
- This model can aid in discriminating cardioembolic from non-cardioembolic etiologies and inform personalized management.
Introduction:
Cryptogenic stroke (CS) represents a heterogeneous group in terms of etiology. Atrial cardiopathy (AC) has emerged as a relevant underlying substrate for both stroke and atrial fibrillation (AF) in these patients. However, no reliable tools are currently available for the early and accurate identification of AC.
Material And Methods:
We conducted a prospective study including consecutive patients with cardioembolic stroke due to AF (CES-AF), non-cardioembolic stroke (NCES) and cryptogenic stroke (CS). Left atrial strain (LAS) assessed by speckle-tracking echocardiography, and serum markers of AC were evaluated in CES-AF versus NCES patients using ROC curve analysis. Based on these results, we developed a logistic regression model to calculate the probability of AC in CS patients, aiming to discriminate between cardioembolic and non-cardioembolic etiology. Clinical characteristics were compared between CS patients with high (>0.5) and low (<0.5) predicted probability of AC.
Results:
A total of 136 patients were included: 44 with CES-AF, 52 with NCES, and 40 with CS. The combination of N-terminal pro-brain natriuretic peptide (NT-proBNP) levels ⩾ 469 pg/mL and biplanar LAS during the contraction phase (LASct) ⩾ -10.2% demonstrated the best-performing AC biomarker combination among those evaluated for identifying cardioembolic etiology (AUC = 0.995). Based on this combination, 30% of CS patients had a predicted probability > 0.5 for AC. These patients were older (77.3 ± 8 vs 68.8 ± 10 years; p = 0.011), had more severe strokes (NIHSS score 10.1 ± 7.5 vs 4.6 ± 5.2; p = 0.024) and showed a higher incidence of AF during follow-up (6 vs 0 cases; p = 0.029).
Conclusions:
The combination of NT-proBNP levels and biplanar LASct provides highly sensitive and specific biomarkers of AC. This multiparametric model allows for individualized estimation of AC probability in CS patients, supporting its potential utility in discriminating cardioembolic from non-cardioembolic etiologies and guiding personalized clinical management.
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