PhD Scientific Days 2022
Budapest, 6-7 July 2022
Clinical Medicine VI. (Poster discussion will take place on the terrace of the room during the Coffee Break)
A novel machine learning based algorithm to predict 1-year mortality after ventricular tachycardia ablation
Előadó neve
Dr. Vamosi, Peter
Előadó munkahelye
Semmelweis University Heart and Vascular Center, Department of Cardiology
Előadó telefonszáma
+36206632048
Előadó e-mail címe
vamosi.peter@semmelweis-univ.hu
Az előadás címe
A novel machine learning based algorithm to predict 1-year mortality after ventricular tachycardia ablation
Szerző(k) neve és munkahelye
Peter Vamosi1, Marton Tokodi1, Patrik Toth1, Ferenc Komlosi1, Istvan Osztheimer1, Peter Perge1, Katalin Piros1, Zoltan Sallo1, Nandor Szegedi1, Bela Merkely1, Laszlo Geller1, Klaudia Vivien Nagy1
1 Semmelweis University Heart and Vascular Center, Department of Cardiology, Budapest, Hungary
Bemutatás módja
Szóbeli
Szekció
Clinical Medicine VI. (Poster discussion will take place on the terrace of the room during the Coffee Break)
Language of the presentation
Hungarian
Preferred session
Clinical Medicine
Összefoglaló szövege
Background: Catheter ablation constitutes an established therapeutic option in patients with monomorphic ventricular tachycardia (VT). VT patients have high comorbidity burden and high mortality, however there is big heterogenity among individual patients. Therefore, effective postprocedural risk assessment systems, prediciting the mortality of patients undergoing VT ablation are highly needed.
Purpose: We aimed to develop a risk stratification algorithm predicting the 1-year all-cause mortality of patients undergoing VT ablation, and to identify the most important input factors of the model.
Methods: Between 2005 and 2020, 272 consecutive patients underwent VT ablation due to sustained monomorphic VT at our institution. We processed their procedural, demographic and medical history data, in addition to their laboratory and echocardiographic findings. For the training of different supervised learning models, we used 63 pre-procedural and procedural variables. We performed 5-fold cross validation and calculated the area under the receiver operating characteristic (ROC) curve (AUC), to assess the performance of the models. Finally, with calculating Shapley values we determined the most important factors of the mortality prediction for the best performing model.
Results: After a follow up of one year, total all-cause mortality was 22% (59). In predicting 1-year mortality, the best performance was shown by the random forest model [AUC: 0,73 (0,68-0,78)] among the machine learning models we had trained. This model significantly outperformed the traditional score systems like I-VT [AUC: 0,63 (0,55-0,70) vs. 0,73 (0,68-0,78), p<0,001] and PAINESD [AUC: 0,63 (0,55-0,71) vs. 0,73 (0,68-0,78), p=0,009] in our dataset. The predictive factors with the biggest effect on mortality were mitral E wave deceleration time, presence of cardiac resynchronization therapy, age, electric storm and hemoglobin concentration.
Conclusion: We were able to establish a supervised machine learning based system which managed to predict the 1-year mortality of VT ablation patients with high accuracy and turned out to be superior when compared to the use of existing risk scores. This enables us to identify the patients in need of a more thorough follow-up, which could reduce their mortality.
Funding: NVKP_16-1–2016-0017, KDP-2021.
University and Doctoral School
Semmelweis University, Doctoral School of Theoretical and Translational Medicine
Supervisor
Dr. Klaudia Vivien Nagy
Publication of my abstract
I do not give consent to the publication of my abstract on the website of the congress.
Kind
Szabad
Status
elfogadva
Accepted presentation method
szóbeli
Előadás fájl jóváhagyás
nem rendelkezett róla
Előadó
6929
Start
10:00
End
10:15
Authors (legacy)
Peter Vamosi1, Marton Tokodi1, Patrik Toth1, Ferenc Komlosi1, Istvan Osztheimer1, Peter Perge1, Katalin Piros1, Zoltan Sallo1, Nandor Szegedi1, Bela Merkely1, Laszlo Geller1, Klaudia Vivien Nagy1
1 Semmelweis University Heart and Vascular Center, Department of Cardiology, Budapest, Hungary