Phenogrouping and risk stratification of patients undergoing cardiac resynchronization therapy upgrade using topological data analysis

Choosing the optimal device during cardiac resynchronization therapy (CRT) upgrade can be challenging. Therefore, we sought to provide a solution for identifying patients in whom upgrading to a CRT-defibrillator (CRT-D) is associated with better long-term survival than upgrading to a CRT-pacemaker (...

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Bibliographic Details
Main Authors: Schwertner Walter Richard
Tokodi Márton
Veres Boglárka
Behon Anett
Merkel Eperke Dóra
Masszi Richárd
Kuthi Luca
Szijártó Ádám
Kovács Attila
Osztheimer István
Zima Endre
Gellér László
Vámos Máté
Sághy László
Merkely Béla
Kosztin Annamária
Becker Dávid
Format: Article
Published: 2023
Series:SCIENTIFIC REPORTS 13 No. 1
Subjects:
doi:10.1038/s41598-023-47092-x

mtmt:34401643
Online Access:http://publicatio.bibl.u-szeged.hu/28958
Description
Summary:Choosing the optimal device during cardiac resynchronization therapy (CRT) upgrade can be challenging. Therefore, we sought to provide a solution for identifying patients in whom upgrading to a CRT-defibrillator (CRT-D) is associated with better long-term survival than upgrading to a CRT-pacemaker (CRT-P). To this end, we first applied topological data analysis to create a patient similarity network using 16 clinical features of 326 patients without prior ventricular arrhythmias who underwent CRT upgrade. Then, in the generated circular network, we delineated three phenogroups exhibiting significant differences in clinical characteristics and risk of all-cause mortality. Importantly, only in the high-risk phenogroup was upgrading to a CRT-D associated with better survival than upgrading to a CRT-P (hazard ratio: 0.454 (0.228–0.907), p = 0.025). Finally, we assigned each patient to one of the three phenogroups based on their location in the network and used this labeled data to train multi-class classifiers to enable the risk stratification of new patients. During internal validation, an ensemble of 5 multi-layer perceptrons exhibited the best performance with a balanced accuracy of 0.898 (0.854–0.942) and a micro-averaged area under the receiver operating characteristic curve of 0.983 (0.980–0.986). To allow further validation, we made the proposed model publicly available ( https://github.com/tokmarton/crt-upgrade-risk-stratification ).
Physical Description:13
ISSN:2045-2322