Sol atriyal apendiks straini ve P dalga dispersiyonu: paroksismal atriyal fibrilasyonun elektro-mekanik belirteçleri

Abstract

Giriş ve Amaç: Paroksismal atriyal fibrilasyon (PAF), aralıklı doğası ve erken tespitinin zorluğu nedeniyle önemli bir klinik sorundur. Sol atriyal apendiks (LAA) fonksiyonu, atriyal mekanikte kritik bir rol oynarken, P dalga dispersiyonu (PWD) elektriksel inhomojeniteyi yansıtır. Bu çalışmada, LAA straini ve PWD’nin PAF tespitindeki öngörücü değerini değerlendirmeyi ve mekanik ile elektrofizyolojik parametreleri entegre eden bir prediktif model oluşturmayı amaçladık. Yöntemler: Gerçekleştirilen retrospektif analizde, 91 PAF ve 100 sinüs ritmindeki (SR) hasta olmak üzere toplam 191 hasta değerlendirildi. LAA fonksiyonu speckle-tracking ekokardiyografi ile, PWD ise 12 derivasyonlu EKG’den dijital olarak ölçüldü. Çok değişkenli lojistik regresyon modelleri oluşturuldu: Model 1’de klinik parametreler, Model 2’de ek olarak PWD, Model 3’te ise LAA strain rezervuar (LAA-Sr) eklendi. Bulgular: PAF hastaları, belirgin şekilde daha düşük LAA-Sr (%14,7 [12,2–18,0] vs. %21,6 [19,1–25,3], p < 0.001) ve daha yüksek PWD (30,3 [27,6–34,5] ms vs. 20,9 [17,3–26,6] ms, p < 0.001) değerlerine sahipti. Çok değişkenli analizde, LAA-Sr (OR: 1,315, %95 CI: 1,201–1,439, p < 0.001) ve PWD (OR: 1,128, %95 CI: 1,054–1,215, p = 0.038) bağımsız PAF prediktörleri olarak saptandı. Model 3, en iyi prediktif performansı gösterdi (AUC: 0.983, duyarlılık: %74, özgüllük: %72) ve Model 1 (AUC: 0.890) ile Model 2’ye (AUC: 0.950) kıyasla üstün bulundu. Sonuç: Çalışmamız, LAA straini ve PWD’nin PAF için güçlü ve bağımsız öngörücüler olduğunu ortaya koymaktadır. Mekanik ve elektrofizyolojik belirteçlerin kombinasyonu, AF risk sınıflandırmasını ve erken tespiti güçlendirebilir. Bu bulguların doğrulanması ve PAF risk değerlendirme stratejilerinin optimize edilmesi için gelecekte prospektif, çok merkezli çalışmalar gerekmektedir.

Keywords

• Paroksismal atriyal fibrilasyon
• Sol atriyal apendiks straini
• P dalga dispersiyonu
• Speckle-tracking ekokardiyografi
• Atriyal fibrilasyon öngörüsü

Left atrial appendage strain and P-wave dispersion: electro-mechanical markers of paroxysmal atrial fibrillation

Abstract

Aims: Paroxysmal atrial fibrillation (PAF) is a major clinical challenge due to its intermittent nature and the difficulty of early detection. Left atrial appendage (LAA) function plays a crucial role in atrial mechanics, while P-wave dispersion (PWD) reflects electrical inhomogeneity. We hypothesized that both parameters would independently and synergistically predict PAF and aimed to develop an integrative electro-mechanical model to enhance risk stratification. Methods: We retrospectively analyzed 191 patients, including 91 with PAF and 100 in sinus rhythm (SR). LAA function was assessed using speckle-tracking echocardiography, and PWD was measured digitally from 12-lead electrocardiography. Multivariable logistic regression models were constructed: model 1 included clinical parameters, model 2 incorporated PWD, and Model 3 further added LAA strain reservoir (LAA-Sr) Results: PAF patients exhibited significantly lower LAA-Sr (14.7 % [12.2-18.0] vs. 21.6% [19.1-25.3], p<0.001) and higher PWD (30.3 [27.6-34.5] ms vs. 20.9 [17.3-26.6] ms, p<0.001). In multivariable analysis, LAA-Sr (OR: 1.315, 95% confidence interval [CI]: 1.201-1.439, p<0.001) and PWD (OR: 1.128, 95% CI: 1.054-1.215, p=0.038) were independent PAF predictors. Model 3, which included both parameters, demonstrated the best predictive performance (AUC: 0.983, sensitivity: 92.8%, specificity: 79.4%) compared to model 1 (AUC: 0.890) and model 2 (AUC: 0.950). Conclusion: Our study highlights LAA strain and PWD as robust, independent predictors of PAF. The combination of mechanical and electrophysiological markers enhances AF risk stratification and early detection. Future prospective, multicenter studies are warranted to validate these findings and optimize risk assessment strategies for PAF.

Keywords

• Atrial fibrillation
• left atrial appendage
• transesophageal echocardiography
• electrocardiography
• prediction algorithms

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