Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model

被引:8
|
作者
Nunez-Garcia, Jean C. [1 ]
Sanchez-Puente, Antonio [1 ,2 ]
Sampedro-Gomez, Jesus [1 ,2 ]
Vicente-Palacios, Victor [1 ,3 ]
Jimenez-Navarro, Manuel [4 ,5 ]
Oterino-Manzanas, Armando [1 ]
Jimenez-Candil, Javier [1 ,2 ,6 ]
Dorado-Diaz, P. Ignacio [1 ,2 ]
Sanchez, Pedro L. [1 ,2 ,6 ]
机构
[1] Hosp Univ Salamanca IBSAL, Dept Cardiol, Salamanca 37007, Spain
[2] Inst Salud Carlos III, CIBERCV, Ctr Invest Biomed Red Enfermedades Cardiovasc, Monforte Lemos 3-5,Pabellon 11,Planta 0, Madrid 28029, Spain
[3] Philips Healthcare, Madrid 28050, Spain
[4] Hosp Virgen Victoria IBIMA, Dept Cardiol, Malaga 29010, Spain
[5] Univ Malaga, Fac Med, Malaga 29071, Spain
[6] Univ Salamanca, Dept Med, Salamanca 37007, Spain
关键词
machine-learning; electrical cardioversion; atrial fibrillation; rhythm control; pharmacologic cardioversion; SINUS RHYTHM; SUCCESS RATE; PREDICTION; RECURRENCE; MAINTENANCE; RISK;
D O I
10.3390/jcm11092636
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: The integrated approach to electrical cardioversion (EC) in atrial fibrillation (AF) is complex; candidates can resolve spontaneously while waiting for EC, and post-cardioversion recurrence is high. Thus, it is especially interesting to avoid the programming of EC in patients who would restore sinus rhythm (SR) spontaneously or present early recurrence. We have analyzed the whole elective EC of the AF process using machine-learning (ML) in order to enable a more realistic and detailed simulation of the patient flow for decision making purposes. Methods: The dataset consisted of electronic health records (EHRs) from 429 consecutive AF patients referred for EC. For analysis of the patient outcome, we considered five pathways according to restoring and maintaining SR: (i) spontaneous SR restoration, (ii) pharmacologic-cardioversion, (iii) direct-current cardioversion, (iv) 6-month AF recurrence, and (v) 6-month rhythm control. We applied ML classifiers for predicting outcomes at each pathway and compared them with the CHA2DS2-VASc and HATCH scores. Results: With the exception of pathway (iii), all ML models achieved improvements in comparison with CHA2DS2-VASc or HATCH scores (p < 0.01). Compared to the most competitive score, the area under the ROC curve (AUC-ROC) was: 0.80 vs. 0.66 for predicting (i); 0.71 vs. 0.55 for (ii); 0.64 vs. 0.52 for (iv); and 0.66 vs. 0.51 for (v). For a threshold considered optimal, the empirical net reclassification index was: +7.8%, +47.2%, +28.2%, and +34.3% in favor of our ML models for predicting outcomes for pathways (i), (ii), (iv), and (v), respectively. As an example tool of generalizability of ML models, we deployed our algorithms in an open-source calculator, where the model would personalize predictions. Conclusions: An ML model improves the accuracy of restoring and maintaining SR predictions over current discriminators. The proposed approach enables a detailed simulation of the patient flow through personalized predictions.
引用
收藏
页数:19
相关论文
共 50 条
  • [31] Pretreatment with ACE inhibitors improves acute outcome of electrical cardioversion in patients with persistent atrial fibrillation
    Van Noord T.
    Crijns H.J.G.M.
    van den Berg M.P.
    Van Velhuisen D.J.
    Van Gelder I.C.
    BMC Cardiovascular Disorders, 5 (1)
  • [32] Comparing the delay with different anticoagulants before elective electrical cardioversion for atrial fibrillation/flutter
    Wall, Christopher
    Jankowski, Tania
    Naruka, Vinci
    Mota, Paula
    PLOS ONE, 2019, 14 (01):
  • [33] Prevalence and predictors of persistent sinus rhythm after elective electrical cardioversion for atrial fibrillation
    Castrichini, Matteo
    Restivo, Luca
    Fabris, Enrico
    Massa, Laura
    Di Meola, Raffaella
    Beltrame, Daria
    De Luca, Antonio
    Korcova, Renata
    Milo, Marco
    Sinagra, Gianfranco
    JOURNAL OF CARDIOVASCULAR MEDICINE, 2021, 22 (08) : 626 - 630
  • [34] Elective Cardioversion of atrial fibrillation.: Are quinidine and electrical shock really equivalent?: Response
    Paya, VC
    Martín, JV
    Ortuño, FM
    de Burgos, FG
    Garri, FS
    REVISTA ESPANOLA DE CARDIOLOGIA, 2002, 55 (09): : 1003 - 1004
  • [35] Multidimensional Fibrillatory Waves Analysis for Improved Electrical Cardioversion Outcome Prediction in Persistent Atrial Fibrillation
    Cirugeda, Eva M.
    Calero, Sofia
    Plancha, Eva
    Enero, Jose
    Rieta, Jose J.
    Alcaraz, Raul
    2020 INTERNATIONAL CONFERENCE ON E-HEALTH AND BIOENGINEERING (EHB), 2020,
  • [36] Usefulness of risk scores and predictors of atrial fibrillation recurrence after elective electrical cardioversion
    Aguila-Gordo, Daniel
    Jimenez-Diaz, Javier
    Negreira-Caamano, Martin
    Martinez-Del Rio, Jorge
    Ruiz-Pastor, Cristina
    Perez, Ignacio Sanchez
    Piqueras-Flores, Jesus
    ANNALS OF NONINVASIVE ELECTROCARDIOLOGY, 2024, 29 (01)
  • [37] Restoration and maintenance of sinus rhythm after the first elective electrical cardioversion of atrial fibrillation
    Lehto, M
    Kala, R
    EUROPEAN HEART JOURNAL, 2000, 21 : 471 - 471
  • [38] Lipoprotein(a) and inflammation in patients with atrial fibrillation after electrical cardioversion
    Naji, Franjo
    Sabovic, Miso
    JOURNAL OF NEGATIVE RESULTS IN BIOMEDICINE, 2011, 10
  • [39] Pretreatment of patients with atrial fibrillation who undergo electrical cardioversion
    Stabile, G
    Turco, P
    De Simone, A
    CIRCULATION, 2003, 107 (11) : 375 - 375
  • [40] Utilization and Predictors of Electrical Cardioversion in Patients Hospitalized for Atrial Fibrillation
    Rochlani, Yogita M.
    Shah, Nishi N.
    Pothineni, Naga V.
    Paydak, Hakan
    CARDIOLOGY RESEARCH AND PRACTICE, 2016, 2016