Development of a Clinical Risk Score for Prediction of Life-Threatening Arrhythmia Events in Patients with ST Elevated Acute Coronary Syndrome after Primary Percutaneous Coronary Intervention

Author:

Wongthida ThanutornORCID,Lumkul Lalita,Patumanond Jayanton,Wongtheptian Wattana,Piyayotai Dilok,Phinyo PhichayutORCID

Abstract

ST-elevated acute coronary syndrome (STEACS) is a serious condition requiring timely treatment. Reperfusion with primary percutaneous coronary intervention (pPCI) is recommended and preferred over fibrinolysis. Despite its efficacy, lethal complications, such as life-threatening arrhythmia (LTA), are common in post-PCI patients. Although various risk assessment tools were developed, only a few focus on LTA prediction. This study aimed to develop a risk score to predict LTA events after pPCI. A risk score was developed using a retrospective cohort of consecutive patients with STEACS who underwent pPCI at Chiangrai Prachanukroh Hospital from January 2012 to December 2016. LTA is defined as the occurrence of malignant arrhythmia that requires advanced cardiovascular life support (ACLS) within 72 h after pPCI. Logistic regression was used for model derivation. Among 273 patients, 43 (15.8%) developed LTA events. Seven independent predictors were identified: female sex, hemoglobin < 12 gm/dL, pre- and intra-procedural events (i.e., respiratory failure and pulseless arrest), IABP insertion, intervention duration > 60 min, and desaturation after pPCI. The LTA score showed an AuROC of 0.93 (95%CI 0.90, 0.97). The score was categorized into three risk categories: low (<2.5), moderate (2.5–4), and high risk (>4) for LTA events. The LTA score demonstrated high predictive performance and potential clinical utility for predicting LTA events after pPCI.

Publisher

MDPI AG

Subject

Health, Toxicology and Mutagenesis,Public Health, Environmental and Occupational Health

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. ECG-Based Heartbeat Classification for Arrhythmia Detection Using Artificial Neural Networks;Computational Science and Its Applications – ICCSA 2022;2022

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