PhD Scientific Days 2020

Clinical Medicine IV. Lectures

Sex-Specific Patterns of Mortality Predictors among Patients undergoing Cardiac Resynchronization Therapy: A Machine Learning Approach

Előadó neve

Dr. Tokodi, Márton

Előadó munkahelye

Heart and Vascular Center, Semmelweis University

Előadó telefonszáma

+36304652356

Előadó e-mail címe

tokmarton@gmail.com

Az előadás címe

Sex-Specific Patterns of Mortality Predictors among Patients undergoing Cardiac Resynchronization Therapy: A Machine Learning Approach

Szerző(k) neve és munkahelye

Márton Tokodi1, Anett Behon1, Eperke Dóra Merkel1, Attila Kovács1, Zoltán Tősér2, András Sárkány2, Máté Csákvári2, Bálint Károly Lakatos1, Walter Richard Schwertner1, Annamária Kosztin1, Béla Merkely1
1 Heart and Vascular Center, Semmelweis University, Budapest, Hungary
2 Argus Cognitive, Inc., Lebanon, NH, USA

Szekció

Clinical Medicine IV. Lectures

Language of the presentation

English

Section, first choice

Clinical Medicine

Section, second choice

Clinical Medicine

Összefoglaló szövege

Introduction: The relative importance of variables explaining sex differences in outcomes is scarcely explored in patients undergoing cardiac resynchronization therapy (CRT).
Aims: We sought to implement and evaluate machine learning (ML) algorithms for the prediction of 1- and 3-year all-cause mortality in CRT patients. We also aimed to assess the sex-specific differences in predictors of mortality using ML.
Methods: Using a retrospective registry of 2191 CRT patients, ML models were implemented in 6 partially overlapping patient subsets (all patients, females, or males with 1- or 3-year follow-up). Each cohort was randomly split into training (80%) and test sets (20%). After hyperparameter tuning in the training set, the best performing algorithm was evaluated in the test set. Model discrimination was quantified using the area under the receiver-operating characteristic curves (AUC). The most important predictors were identified using the permutation feature importances method.
Results: Conditional inference random forest exhibited the best performance with AUCs of 0.728 [0.645 – 0.802] and 0.732 [0.681 – 0.784] for the prediction of 1- and 3-year mortality, respectively. Etiology of heart failure, NYHA class, left ventricular ejection fraction, and QRS morphology had higher predictive power, whereas hemoglobin was less important in females compared to males. The importance of atrial fibrillation and age increased, while serum creatinine decreased from 1- to 3-year follow-up in both sexes.
Conclusions: Using ML techniques in combination with easily obtainable clinical features, our models effectively predicted 1- and 3-year all-cause mortality in CRT patients. Gender-specific patterns of predictors were identified, showing dynamic variation over time.

Additional Information

Supervisor: Attila Kovács
E-mail: kovatti@gmail.com

Bemutatás módja

Szóbeli

Kind

Szabad

Status

elfogadva

Accepted presentation method

szóbeli

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

4173

Start

18:00

End

18:15

Authors (legacy)

Márton Tokodi1, Anett Behon1, Eperke Dóra Merkel1, Attila Kovács1, Zoltán Tősér2, András Sárkány2, Máté Csákvári2, Bálint Károly Lakatos1, Walter Richard Schwertner1, Annamária Kosztin1, Béla Merkely1
1 Heart and Vascular Center, Semmelweis University, Budapest, Hungary
2 Argus Cognitive, Inc., Lebanon, NH, USA