Kardiol Pol. 2021 Oct 27. doi: 10.33963/KP.a2021.0142. Online ahead of print.
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.
PMID:34704605 | DOI:10.33963/KP.a2021.0142
