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Is neural network better than logistic regression in death prediction in patients after ST-segment elevation myocardial infarction?

Jacek T Niedziela12, Daniel Cieśla3, Wojciech Wojakowski4, Marek Gierlotka5, Dariusz Dudek6, Adam Witkowski7, Tomasz Zdrojewski8, Maciej Lesiak9, Paweł Buszman10, Mariusz Gąsior12
DOI: 10.33963/KP.a2021.0142
·
Pubmed: 34704605
Affiliations
  1. 3rd Department of Cardiology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Poland
  2. 3rd Department of Cardiology, Silesian Centre for Heart Disease, Zabrze, Poland
  3. Department of Science, Education and New Medical Technologies, Silesian Centre for Heart Disease in Zabrze, Poland
  4. Department of Cardiology and Structural Heart Diseases, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland
  5. Department of Cardiology, University Hospital, Institute of Medicine, University of Opole, Opole, Poland
  6. 2nd Department of Clinical Cardiology and Cardiovascular Interventions, Institute of Cardiology, Jagiellonian University Medical College, Kraków, Poland
  7. Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warszawa, Poland
  8. Department of Preventive Medicine and Education, Medical University of Gdansk, Gdańsk, Poland
  9. Department of Cardiology, Poznan University of Medical Sciences, Poznań, Poland
  10. American Heart of Poland, Katowice, Poland

open access

Online first
Original article
Published online: 2021-10-25

Abstract

Background: There is a need to develop patient classification methods to adjust post-discharge care, improving survival after ST-segment elevation myocardial infarction (STEMI).
Aims: The study aimed to determine whether a neural network (NN) is better than logistic regression (LR) in mortality prediction in STEMI patients.
Material and methods: The study included patients from the Polish Registry of Acute Coronary Syndromes (PL-ACS). Patients with the first anterior STEMI treated with the primary percutaneous coronary intervention (pPCI) of the left anterior descending (LAD) artery between 2009 and 2015 and discharged alive were included in the study. Patients were randomly divided into three groups: learning (60%), validation (20%), and test group (20%). Two models (LR and NN) were developed to predict 6-month all-cause mortality. The predictive values of LR and NN were evaluated with the Area Under the Receiver Operating Characteristics Curve (AUROC), and the comparison of AUROC for learning and test groups was performed. Validation of both methods was performed in the same group.
Results: Out of 175,895 patients with acute coronary syndrome, 17 793 were included in the study. The all-cause 6-month mortality was 5.9%. Both NN and LR had good predictive values. Better results were obtained in the NN approach regarding the models' statistical quality — AUROC 0.8422 vs 0.8137 for LR (P <0.0001). AUROC in the test groups were 0.8103 and 0.7939, respectively (P = 0.037).
Conclusions: The neural network may have a better predictive value than logistic regression in patients after the first STEMI.

Abstract

Background: There is a need to develop patient classification methods to adjust post-discharge care, improving survival after ST-segment elevation myocardial infarction (STEMI).
Aims: The study aimed to determine whether a neural network (NN) is better than logistic regression (LR) in mortality prediction in STEMI patients.
Material and methods: The study included patients from the Polish Registry of Acute Coronary Syndromes (PL-ACS). Patients with the first anterior STEMI treated with the primary percutaneous coronary intervention (pPCI) of the left anterior descending (LAD) artery between 2009 and 2015 and discharged alive were included in the study. Patients were randomly divided into three groups: learning (60%), validation (20%), and test group (20%). Two models (LR and NN) were developed to predict 6-month all-cause mortality. The predictive values of LR and NN were evaluated with the Area Under the Receiver Operating Characteristics Curve (AUROC), and the comparison of AUROC for learning and test groups was performed. Validation of both methods was performed in the same group.
Results: Out of 175,895 patients with acute coronary syndrome, 17 793 were included in the study. The all-cause 6-month mortality was 5.9%. Both NN and LR had good predictive values. Better results were obtained in the NN approach regarding the models' statistical quality — AUROC 0.8422 vs 0.8137 for LR (P <0.0001). AUROC in the test groups were 0.8103 and 0.7939, respectively (P = 0.037).
Conclusions: The neural network may have a better predictive value than logistic regression in patients after the first STEMI.

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Keywords

myocardial infarction, neural network, prediction, STEMI

About this article
Title

Is neural network better than logistic regression in death prediction in patients after ST-segment elevation myocardial infarction?

Journal

Kardiologia Polska (Polish Heart Journal)

Issue

Online first

Article type

Original article

Published online

2021-10-25

DOI

10.33963/KP.a2021.0142

Pubmed

34704605

Keywords

myocardial infarction
neural network
prediction
STEMI

Authors

Jacek T Niedziela
Daniel Cieśla
Wojciech Wojakowski
Marek Gierlotka
Dariusz Dudek
Adam Witkowski
Tomasz Zdrojewski
Maciej Lesiak
Paweł Buszman
Mariusz Gąsior

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