Prediction of Major Adverse Cardiac Events After Transcatheter Aortic Valve Implantation: A Machine Learning Approach with GRACE Score

Objectives: Predictive risk scores have a significant impact on patient selection and assessing the likelihood of complications following interventions in patients with severe aortic stenosis (AS). This study aims to explore the utility of machine learning (ML) techniques in predicting 30-day major adverse cardiac events (MACE) by analyzing parameters, including the Global Registry of Acute Coronary Events (GRACE) score.
Methods: This retrospective, multi-center, observational study enrolled 453 consecutive patients diagnosed with severe AS who underwent transcatheter aortic valve implantation (TAVI) from April 2020 to January 2023. The primary outcome was defined as a composition of MACE comprising periprocedural myocardial infarction (MI), cerebrovascular events (CVE), and all-cause mortality during the 1-month follow-up period after the procedure. Conventional binomial logistic regression and ML models were utilized and compared for prediction purposes.
Results: The study population had a mean age of 76.1, with 40.8% being male. The primary endpoint was observed in 7.5% of cases. Among the individual components of the primary endpoint, the rates of all-cause mortality, MI, and CVE were reported as 4.2%, 2.4%, and 1.9%, respectively. The ML-based Extreme Gradient Boosting (XGBoost) model with the GRACE score demonstrated superior discriminative performance in predicting the primary endpoint, compared to both the ML model without the GRACE score and the conventional regression model [Area Under the Curve (AUC)= 0.98 (0.91-0.99), AUC= 0,87 (0.80-0.98), AUC= 0.84 (0.79-0.96)]. Conclusion: ML techniques hold the potential to enhance outcomes in clinical practice, especially when utilized alongside established clinical tools such as the GRACE score.

Transkateter Aort Kapak Ýmplantasyonu Sonrasý Majör Advers Kardiyak Olaylarýn Tahmini: GRACE Skoru ile Yapýlan Makine Öðrenimi Yaklaþýmý

Amaç: Öngörücü risk skorlarý, ciddi aort stenozu (AS) olan hastalarda hastane seçiminde ve müdahale sonrasý komplikasyon olasýlýðýný deðerlendirmede önemli bir etkiye sahiptir. Bu çalýþma, Global Registry of Acute Coronary Events (GRACE) skoru dahil olmak üzere öngörücü parametreleri analiz ederek, makine öðrenimi (ML) tekniklerinin 30 günlük major advers kardiyak olaylarý (MACE) tahmin etme konusundaki kullanýlabilirliðini keþfetmeyi amaçlamaktadýr.
Metod: Bu retrospektif, çok merkezli, gözlemsel çalýþma, Nisan 2020 ile Ocak 2023 tarihleri arasýnda transkateter aort kapak implantasyonu (TAVI) uygulanan ciddi AS tanýsý almýþ 453 ardýþýk hastayý içermektedir. Birincil sonuç, prosedür sonrasý 1 aylýk takip döneminde meydana gelen MACE'yi içeren bir bileþim olarak tanýmlandý. Geleneksel binomiyal lojistik regresyon ve ML modelleri tahmin amaçlý kullanýldý ve karþýlaþtýrýldý.
Bulgular: Çalýþma popülasyonunun ortalama yaþý 76.1'dir ve %40.8'i erkektir. Birincil sonuç, vakalarýn %7.5'inde gözlendi. Birincil sonuç bileþenlerinden, tüm nedenlere baðlý ölüm, prosedür sonrasý miyokard enfarktüsü (MI), serebrovasküler olaylar (CVE) oranlarý sýrasýyla %4.2, %2.4 ve %1.9 olarak rapor edildi. GRACE skoru ile birlikte kullanýlan ML tabanlý Extreme Gradient Boosting (XGBoost) modeli, birincil sonucu tahmin etmede, GRACE skoru olmadan ML modeli ve geleneksel regresyon modeline kýyasla üstün ayýrt edici performans gösterdi [Eðri Altýndaki Alan (AUC)= 0.98 (0.91-0.99), AUC= 0.87 (0.80-0.98), AUC= 0.84 (0.79-0.96)].
Sonuç: ML teknikleri, özellikle GRACE skoru gibi kanýtlanmýþ klinik parametrelerle birlikte kullanýldýðýnda, klinik uygulamalardaki sonuçlarý öngörmede önemli bir potansiyele sahiptir. (SETB-2024-01-028)