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.
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页数:19
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