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 (...
Elmentve itt :
| Szerzők: | |
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| Dokumentumtípus: | Cikk |
| Megjelent: |
2023
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| Sorozat: | SCIENTIFIC REPORTS
13 No. 1 |
| Tárgyszavak: | |
| doi: | 10.1038/s41598-023-47092-x |
| mtmt: | 34401643 |
| Online Access: | http://publicatio.bibl.u-szeged.hu/28958 |
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| 100 | 1 | |a Schwertner Walter Richard | |
| 245 | 1 | 0 | |a Phenogrouping and risk stratification of patients undergoing cardiac resynchronization therapy upgrade using topological data analysis |h [elektronikus dokumentum] / |c Schwertner Walter Richard |
| 260 | |c 2023 | ||
| 300 | |a 13 | ||
| 490 | 0 | |a SCIENTIFIC REPORTS |v 13 No. 1 | |
| 520 | 3 | |a 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 ). | |
| 650 | 4 | |a Klinikai orvostan | |
| 700 | 0 | 1 | |a Tokodi Márton |e aut |
| 700 | 0 | 1 | |a Veres Boglárka |e aut |
| 700 | 0 | 1 | |a Behon Anett |e aut |
| 700 | 0 | 1 | |a Merkel Eperke Dóra |e aut |
| 700 | 0 | 1 | |a Masszi Richárd |e aut |
| 700 | 0 | 1 | |a Kuthi Luca |e aut |
| 700 | 0 | 1 | |a Szijártó Ádám |e aut |
| 700 | 0 | 1 | |a Kovács Attila |e aut |
| 700 | 0 | 1 | |a Osztheimer István |e aut |
| 700 | 0 | 1 | |a Zima Endre |e aut |
| 700 | 0 | 1 | |a Gellér László |e aut |
| 700 | 0 | 1 | |a Vámos Máté |e aut |
| 700 | 0 | 1 | |a Sághy László |e aut |
| 700 | 0 | 1 | |a Merkely Béla |e aut |
| 700 | 0 | 1 | |a Kosztin Annamária |e aut |
| 700 | 0 | 1 | |a Becker Dávid |e aut |
| 856 | 4 | 0 | |u http://publicatio.bibl.u-szeged.hu/28958/1/saghy.pdf |z Dokumentum-elérés |