Predictive model of electrical therapy failure in paroxysmal atrial fibrillation

Authors

Keywords:

atrial fibrillation, predictive model, therapeutics.

Abstract

Introduction: Atrial fibrillation is the most common recurrent arrhythmia in clinical practice. Its prevalence is multiplying in the current population and has different pathophysiological causes that make it a global pandemic.
Objectives: To design a predictive model for failure of electrical therapy in patients with paroxysmal atrial fibrillation.
Methods: A case-control study was carried out with 33 cases, and 66 controls. Predictor variables: age, ejection fraction ≤ 40%, left atrial volume ≥ 34 mL/m2. From logistic regression, a model was obtained in which the positive predictive value, negative predictive value, sensitivity and specificity were included.
Results: The predictive risk factors were: age ≥ 55 years (p= 0.013; odds ratio (OR)= 3.58; 95% confidence interval -CI-: 1.33-9.67); left ventricular ejection fraction (LVEF) ≤ 40% was observed in 20 patients (22.7%) (p= 0.004; OR= 4.45; 95% CI: 1.54-12.8); elevated left atrial pressure, elevated left atrial volume (p= 0.004; OR= 3.11; 95% CI: 1.24-8.77), according to the logistic regression model. Internal validation was carried out by data division; It was confirmed that the model predicts very well those who will be successful in the therapeutic result.
Conclusions: The predictive model developed is composed of the predictors age > 55 years, LVEF; left atrial volume; It presents a good fit and discriminating power, especially positive predictive value.

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Author Biographies

Yoandro Rosabal Garcia, Centro de Cirugia Cardiovascular y Cardiologia Santiago de Cuba

jefe de departamento de eccardiografia del centro de cardiologia y cirugia cardiovascular santiago de cuba

Eddy Alberto Rosales Guibert, Hospital Joaquin Castillo Duany

Jefe del Servicio de Cardiologia Hospital Joquian Castillo Duany

lorchen Torres Quiñones, universidad ciencias medicas stgo

profesora del departamento de medicina general integral

References

1. Guerra García D, Valladares Carvajal F, Bernal-Valladares E, Díaz-Quiñones J. Factores de riesgo asociados a ictus cardioembólico en pacientes con fibrilación auricular no valvular. Revista Finlay. 2018 [acceso: 11/02/2023]; 8(1):10-11. Disponible en: https://revfinlay.sld.cu/index.php/finlay/article/view/577

2. Kotalczyk A, Lip GY, Calkins H. The 2020 ESC Guidelines on the Diagnosis and Management of Atrial Fibrillation. Arrhythmia & Electrophysiology Review. 2021; 10(2):65-7. DOI: 10.15420/aer.2021.07

3. Forero-Gómez JE, Moreno JM, Agudelo CA, Rodríguez-Arias EA, Sánchez-Moscoso PA. Fibrilación auricular: enfoque para el médico no cardiólogo. Iatreia. 2017 [acceso 08/10/2023]; 30(4):404-22. Disponible en: http://www.scielo.org.co/pdf/iat/v30n4/0121-0793- iat-30-04-00404.pdf

4. Hernández Velazquez F, Carcasés Lamorú S, Lamorú-Turro R, Rodríguez-Camacho A. Caracterización clínica y tratamiento de pacientes con fibrilación auricular. Rev Cubana Med Milit. 2022 [acceso: 16/03/2023];51(4):3-4. Disponible en: https://revmedmilitar.sld.cu/index.php/mil/article/view/1829

5. Ministerio de Salud Pública. Anuario Estadístico 2020. La Habana: MINSAP; 2021. [acceso: 19/01/2023]. Disponible en: https://files.sld.cu/bvscuba/files/2021/08/Anuario-Estadistico-Español-2020-Definitivo.pdf

6. Álvarez-Aliaga A, Maceo-Gómez L. Índices de predicción, algunos aspectos metodológicos para su construcción y validación. MULTIMED. 2014 [acceso 28/10/ 2023]; 18(2):[aprox. 19 p.]. Disponible en: https://revmultimed.sld.cu/index.php/mtm/article/view/45

7. Sagaró-del-Campo N, Zamora-Matamoros L. ¿Cómo aplicar el análisis estadístico implicativo en los estudios de causalidad en salud? Revista Electrónica Dr. Zoilo E. Marinello Vidaurreta. 2020 [acceso 28/10/2023]; 45(1):[aprox. 8 pág.]. Disponible en: https://revzoilomarinello.sld.cu/index.php/zmv/article/view/1960

8. Soto A, Cvetkovich Vega A. Estudios de casos y controles. Revista Facultad Medicina Humana. 2020; 20(1):138-43. DOI: 10.25176/RFMH.v20i1.2555

9. Cerda J, Cifuentes L. Uso de curvas ROC en investigación clínica: Aspectos teórico-prácticos. Revista Chilena Infectología. 2012; 29(2): 138-141. DOI: 10.4067/S0716-10182012000200003

10. Varela-García R. Cardioversión eléctrica como modalidad terapéutica en pacientes con fibrilación auricular. MEDISAN. 2018 [acceso: 11/02/2023]; 22(7):762. Disponible en: https://medisan.sld.cu/index.php/san/article/view/1296

11. Ayaviri Dilean E, Orellana Arnez S, Arispe Gutierrez J, Salinas Gil AS, Orellana Aguilar ML. Cardioversión eléctrica y cardioversión farmacológica en pacientes con fibrilación auricular. Revista UNITEPC. 2020 [acceso: 27/10/2023]; 7(1):32-42. Disponible en: http://www.scielo.org.bo/scielo.php?script=sci_arttext&pid=S2520-98252020000100004&lng=es

12. Vásquez JP, Leiria Tiago LL, Kruse Marcelo L, Pires LM, Lima Gustavo GD. Recurrencia de fibrilación auricular posablación de venas pulmonares. Identificación de factores predictores. Rev. Urug. Cardiol. 2020; 35(1):231-248. DOI: 10.29277/cardio.35.1.12

13. Cruz-Cardentey M, Pérez-Rivera T, Méndez--Rosabal A. Factores predictores de éxito de la cardioversión eléctrica en la fibrilación auricular. CorSalud. 2014 [acceso: 28/10/2023]; 6(4):[aprox. 7 p.]. Disponible en: https://revcorsalud.sld.cu/index.php/cors/article/view/115

14. Miao Y, Xu M, Zhang C, Liu H, Shao X, Wang Y, et al. An echocardiographic model for predicting the recurrence of paroxysmal atrial fibrillation after circumferential pulmonary vein ablation. Clinical Cardiology. 2021; 44(11):1506-15. DOI: 10.1002/clc.23712

15. Kranert M, Shchetynska-marinova T, Liebe V, Doesch C, Papavassiliu T, Akin I, et al. Recurrence of Atrial Fibrillation in Dependence of Left Atrial Volume Index. In Vivo. 2020; 34(2):889-96. DOI: 10.21873/invivo.11854

16. Lyngbakken MN, Rønningen PS, Solberg MG, Berge T, Brynildsen J, Aagaard EN, et al. Prediction of incident atrial fibrillation with cardiac biomarkers and left atrial volumes. Heart. 2023; 109(5):356-363. DOI: 10.1136/heartjnl-2022-321608

17. Gawałko M, Lodziński P, Budnik M, Tymińska A, Wancerz A, Ozierański K, et al. Vascular disease in patients with atrial fibrillation. A report from Polish participants in the EORP‐AF General Long‐Term Registry. International Journal of Clinical Practice. 2020; 75:9. DOI: 10.1111/ijcp.13701

18. Gudiño A, Chediak C. Epidemiology, pathogenesis and genetics of atrial fibrillation. Medwave Año XII. 2012; 3:e5337-e5337. DOI: 10.5867/medwave.2012.03.5337

19. Mora-Llabata V, Dubois-Marqués D, Roldán-Torres I, Mateu-Navarro C, Sanz-García JJ, Moreno-Ballester V, et al. Prevalencia de fibrilación auricular y características de la fibrilación auricular no valvular en la población general. Registro AFINVA. Revista Colombiana de Cardiología. 2017 [acceso: 17/02/2023]; 24(1): 26-33.Disponible en: https://www.sciencedirect.com/science/article/pii/S0120563316300079

20. Kramer CM, DiMarco JP, Kolm P, Ho CY, Desai MY, Kwong RY, et al. Predictors of Major Atrial Fibrillation Endpoints in the National Heart, Lung, and Blood Institute HCMR. JACC: Clinical Electrophysiology. 2021; 7(11):1376-86. DOI 10.1016/j.jacep.2021.04.004

21. Collazo Rodríguez PM, Rodríguez Leyva D, Pérez Martín O, Cruz Cardentey M, Mengana Betancourt A, Prohías Martínez J, et al. Índice predictivo clínico, electrocardiográfico y ecocardiográfico de las recurrencias de la fibrilación auricular paroxística. CorSalud. 2017 [acceso: 20/01/2023]; 9(2):59-69. Disponible en: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S207871702017000200002&lng=es

22. Núñez García JC, Sánchez Puente A, Sampedro-Gómez J, Vicente-Palacios, V, Jiménez-Navarro, M, Oterino-Manzanas et al. Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model. Journal of clinical medicine. 2022; 11(9):2636. DOI: 10.3390/jcm11092636

23. Niederdöckl J, Simon A, Cacioppo F, Buchtele N, Merrelaar A, Schütz N, et al. Predicting spontaneous conversion to sinus rhythm in symptomatic atrial fibrillation: The ReSinus score. European Journal of Internal Medicine. 2021 [acceso: 19/03/2023]; 83:45-53. Disponible en: https://www.sciencedirect.com/science/article/pii/S0953620520303150

24. Díaz-Narváez VP. Regresión logística y decisiones clínicas. Nutrición Hospitalaria. 2017; 34(6):1505-1505. DOI: 10.20960/nh.1468

25. Domínguez Dueñas L, Goode-Romero G, Aguayo-Ortiza R. Relaciones cuantitativas estructura-actividad/propiedad en dos dimensiones empleando el programa R. Educación Química. 2019; 930(2):27. DOI: 10.22201/fq.18708404e.2019.2.67211

26. Bermúdez Yera G, Barreto Fiu E, Chaljub-Bravo E, López de la Cruz Y, Naranjo Ugalde A, Rabassa- López -Calleja M, et. Diseño y validación de la escala pronostica cubana PREDICMED para estratificar el riesgo de mediastinitis postoperatoria. Corsalud 2020 [acceso: 17/11/2023];12(4) [aprox. 9 pag.] disponible en https://revcorsalud.sld.cu/index.php/cors/article/view/734

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Published

2023-12-11

How to Cite

1.
Rosabal Garcia Y, Rosales Guibert EA, Torres Quiñones lorchen. Predictive model of electrical therapy failure in paroxysmal atrial fibrillation. Rev Cubana Med Milit [Internet]. 2023 Dec. 11 [cited 2025 Apr. 20];52(4):e02302831. Available from: https://revmedmilitar.sld.cu/index.php/mil/article/view/2831

Issue

Vol. 52 No. 4 (2023): October - december

Section

Research Article

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