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

## TL;DR

This study identifies a combination of biomarkers to detect atrial cardiopathy in cryptogenic stroke patients, helping personalize their treatment.

## Contribution

A novel multiparametric model using NT-proBNP and LASct to estimate atrial cardiopathy probability in cryptogenic stroke patients.

## Key findings

- NT-proBNP ≥ 469 pg/mL and LASct ≥ -10.2% best identified cardioembolic stroke etiology (AUC = 0.995).
- 30% of cryptogenic stroke patients had high AC probability, showing older age and more severe strokes.
- Patients with high AC probability had a higher incidence of atrial fibrillation during follow-up.

## Abstract

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.

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.

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).

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.

Graphical abstract