Predicting atrial fibrillation in comorbid patients with arterial hypertension and chronic obstructive pulmonary disease using laboratory research methods: a machine learning approach

Cover Page


Cite item

Full Text

Abstract

BACKGROUND: Arterial hypertension and chronic obstructive pulmonary disease have a deleterious effect on the structure of the heart, leading to the development of atrial fibrillation, which remains the leading cause of cerebral stroke and premature death [1]. Consequently, the early identification of atrial fibrillation risk factors in patients with arterial hypertension and chronic obstructive pulmonary disease is of paramount importance for the prevention of such conditions. This is why predictive cardiology employs machine learning methods, which are demonstrably superior to classical statistical methods of prediction [2–4].

AIM: The study aimed to develop a prognostic model of atrial fibrillation in comorbid patients with arterial hypertension and chronic obstructive pulmonary disease based on multilayer perceptron.

MATERIALS AND METHODS: The study included 419 patients treated at the University Clinical Hospital No. 4 of the I.M. Sechenov First Moscow State Medical University. Group 1 consisted of 91 (21.7%) patients with a verified diagnosis of atrial fibrillation, while Group 2 comprised 328 (78.3%) patients without atrial fibrillation. The random forest machine learning algorithm was used to identify predictors, which were then utilized to develop a neural network of the multilayer perceptron type. This consisted of two layers: an input layer of 12 neurons with the ReLU activation function and an output layer that receives input data from the previous layer and transmits them to one output with the sigmoid activation function. The threshold value, sensitivity, specificity, and diagnostic efficiency of the obtained model were determined using receiver operating characteristic analysis with the calculation of the area under the curve (AUC).

RESULTS: By the first stage of prognostic model development, the most significant predictors of atrial fibrillation development were selected by the random forest machine learning algorithm. The model was developed using three variables: C-reactive protein concentration (odds ratio, OR 1.04; 95% confidence interval, CI 1.015–1.067; p=0.002), erythrocyte sedimentation rate (OR 1.04; 95% CI 1.019–1.069; p=0.002), and creatinine concentration (OR 1.03; 95% CI 1.011–1.042; p <0.001). These variables were used to train a multilayer perceptron model on a test sample for 500 epochs.

Following training, the developed model exhibited a sensitivity of 85%, a specificity of 80%, and a diagnostic efficiency of 79.6%. AUC amounted to 0.900.

CONCLUSIONS: The study resulted in the development of a prognostic model based on the application of machine learning methods, which exhibited favorable metrics. This model may be considered a valuable tool for clinical practice.

Full Text

BACKGROUND: Arterial hypertension and chronic obstructive pulmonary disease have a deleterious effect on the structure of the heart, leading to the development of atrial fibrillation, which remains the leading cause of cerebral stroke and premature death [1]. Consequently, the early identification of atrial fibrillation risk factors in patients with arterial hypertension and chronic obstructive pulmonary disease is of paramount importance for the prevention of such conditions. This is why predictive cardiology employs machine learning methods, which are demonstrably superior to classical statistical methods of prediction [2–4].

AIM: The study aimed to develop a prognostic model of atrial fibrillation in comorbid patients with arterial hypertension and chronic obstructive pulmonary disease based on multilayer perceptron.

MATERIALS AND METHODS: The study included 419 patients treated at the University Clinical Hospital No. 4 of the I.M. Sechenov First Moscow State Medical University. Group 1 consisted of 91 (21.7%) patients with a verified diagnosis of atrial fibrillation, while Group 2 comprised 328 (78.3%) patients without atrial fibrillation. The random forest machine learning algorithm was used to identify predictors, which were then utilized to develop a neural network of the multilayer perceptron type. This consisted of two layers: an input layer of 12 neurons with the ReLU activation function and an output layer that receives input data from the previous layer and transmits them to one output with the sigmoid activation function. The threshold value, sensitivity, specificity, and diagnostic efficiency of the obtained model were determined using receiver operating characteristic analysis with the calculation of the area under the curve (AUC).

RESULTS: By the first stage of prognostic model development, the most significant predictors of atrial fibrillation development were selected by the random forest machine learning algorithm. The model was developed using three variables: C-reactive protein concentration (odds ratio, OR 1.04; 95% confidence interval, CI 1.015–1.067; p=0.002), erythrocyte sedimentation rate (OR 1.04; 95% CI 1.019–1.069; p=0.002), and creatinine concentration (OR 1.03; 95% CI 1.011–1.042; p <0.001). These variables were used to train a multilayer perceptron model on a test sample for 500 epochs.

Following training, the developed model exhibited a sensitivity of 85%, a specificity of 80%, and a diagnostic efficiency of 79.6%. AUC amounted to 0.900.

CONCLUSIONS: The study resulted in the development of a prognostic model based on the application of machine learning methods, which exhibited favorable metrics. This model may be considered a valuable tool for clinical practice.

×

About the authors

Evgeniya V. Kazantseva

The First Sechenov Moscow State Medical University

Author for correspondence.
Email: kazantseva_ev@inbox.ru
ORCID iD: 0000-0001-8552-5364
SPIN-code: 4018-8734
Russian Federation, Moscow

Aleksander A. Ivannikov

The First Sechenov Moscow State Medical University

Email: ivannikov_a95@mail.ru
ORCID iD: 0000-0002-9738-1801
Russian Federation, Moscow

Aida I. Tarzimanova

The First Sechenov Moscow State Medical University

Email: tarzimanova_a_i@staff.sechenov.ru
Russian Federation, Moscow

Valeriy I. Podzolkov

The First Sechenov Moscow State Medical University

Email: podzolkov_v_i@staff.sechenov.ru
Russian Federation, Moscow

References

  1. Shapkina MYu, Ryabikov AN, Titarenko AV, et al. Arterial hypertension, blood pressure levels and risk of atrial fibrillation in Siberian residents. Ateroscleroz. 2023;19(3):258–261. (In Russ). doi: 10.52727/2078-256X-2023-19-3-258-261
  2. Mishkin IA, Kontsevaya AV, Gusev AV, Drapkina OM. Prediction of cardiovascular events using proportional risk models and machine learning models: A systematic review. Current problems of health care and medical statistics. 2023;(2):804–829. EDN: PQZXOT doi: 10.24412/2312-2935-2023-2-804-829
  3. Seetharam K, Balla S, Bianco C, et al. Applications of machine learning in cardiology. Cardiology and Therapy. 2022;11(3):355–368. doi: 10.1007/s40119-022-00273-7
  4. Johnson KW, Torres Soto J, Glicksberg BS, et al. Artificial intelligence in cardiology. Journal of the American College of Cardiology. 2018;71(23):2668–2679. doi: 10.1016/j.jacc.2018.03.521

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2024 Eco-Vector

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ: серия ПИ № ФС 77 - 79539 от 09 ноября 2020 г.


This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies