Symptoms and coronary risk factors predictive of adverse cardiac events in chest pain patients in an Asian emergency department: the need for a local prediction score

被引:1
|
作者
Lin, Ziwei [1 ]
Lim, Swee Han [2 ]
Yap, Qai Ven [3 ]
Kow, Cheryl Shumin [4 ]
Chan, Yiong Huak [3 ]
Chua, Siang Jin Terrance [5 ]
Venkataraman, Anantharaman [2 ]
机构
[1] Sengkang Gen Hosp, Dept Emergency Med, Sengkang, Singapore
[2] Singapore Gen Hosp, Dept Emergency Med, Outram Rd, Singapore 169608, Singapore
[3] Natl Univ Singapore, Yong Loo Lin Sch Med, Biostat Unit, Singapore, Singapore
[4] Singapore Gen Hosp, Dept Gen Surg, Singapore, Singapore
[5] Natl Heart Ctr, Dept Cardiol, Singapore, Singapore
基金
英国医学研究理事会;
关键词
Acute coronary syndrome; chest pain; major adverse cardiac events; myocardial infarction; risk score; troponin; DIAGNOSIS;
D O I
10.4103/singaporemedj.SMJ-2023-260
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Introduction: Clinical assessment is pivotal in diagnosing acute coronary syndrome. Our study aimed to identify clinical characteristics predictive of major adverse cardiac events (MACE) in an Asian population and to derive a risk score for MACE. Methods: Patients presenting to the emergency department (ED) with chest pain and non-diagnostic 12-lead electrocardiograms were recruited. Clinical history was recorded in a predesigned template. Random glucose and direct low-density lipoprotein measurements were taken, in addition to serial troponin. We derived the age, coronary risk factors (CRF), sex and symptoms (ACSS) risk score based on multivariate analysis results, considering age, CRF, sex and symptoms and classifying patients into very low, low, moderate and high risk for MACE. Comparison was made with the ED Assessment of Chest Pain Score (EDACS) and the history, electrocardiogram, age, risk factors, troponin (HEART) score. We also modified the HEART score with the CRF that we had identified. The outcomes were 30-day and 1-year MACE. Results: There were a total of 1689 patients, with 172 (10.2%) and 200 (11.8%) having 30-day and 1-year MACE, respectively. Symptoms predictive of MACE included central chest pain, radiation to the jaw/neck, associated diaphoresis, and symptoms aggravated by exertion and relieved by glyceryl trinitrate. The ACSS score had an area under the curve of 0.769 (95% confidence interval [CI]: 0.735-0.803) and 0.760 (95% CI: 0.727-0.793) for 30-day and 1-year MACE, respectively, outperforming EDACS. Those in the very-low-risk and low-risk groups had <1% risk of 30-day MACE. Conclusion: The ACSS risk score shows potential for use in the local ED or primary care setting, potentially reducing unnecessary cardiac investigations and admission.
引用
收藏
页码:397 / 404
页数:8
相关论文
共 50 条
  • [1] Risk stratification for prediction of adverse coronary events in emergency department chest pain patients with a machine learning score compared with the TIMI score
    Liu, Nan
    Lee, Marcus Aik Beng
    Ho, Andrew Fu Wah
    Haaland, Benjamin
    Fook-Chong, Stephanie
    Koh, Zhi Xiong
    Pek, Pin Pin
    Chua, Eric Chern-Pin
    Ting, Boon Ping
    Lin, Zhiping
    Ong, Marcus Eng Hock
    INTERNATIONAL JOURNAL OF CARDIOLOGY, 2014, 177 (03) : 1095 - 1097
  • [2] Are cardiac risk factors predictive in low risk emergency department chest pain patients who have chest pain?
    Faroghi, A
    Kontos, MC
    Jesse, RL
    Roberts, CS
    Tatum, JL
    Ornato, JP
    JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2000, 35 (02) : 379A - 379A
  • [3] Gender differences in the predictive value of the HEART score for major adverse cardiac events in patients presenting to the emergency department with acute chest pain
    Bank, I. E. M.
    De Hoog, V. C.
    De Kleijn, D. P. V.
    Pasterkamp, G. P.
    Den Ruijter, H. M.
    Doevendans, P. A. F.
    Wildbergh, T. X.
    Mosterd, A.
    Timmers, L.
    EUROPEAN HEART JOURNAL, 2016, 37 : 988 - 988
  • [4] Predictors of major adverse cardiac events among patients with chest pain and low HEART score in the emergency department
    Ho, Andrew Fu Wah
    Yau, Chun En
    Ho, Jamie Sin-Ying
    Lim, Swee Han
    Ibrahim, Irwani
    Kuan, Win Sen
    Ooi, Shirley Beng Suat
    Chan, Mark Y.
    Sia, Ching-Hui
    Mosterd, Arend
    Gijsberts, Crystel M.
    de Hoog, Vince C.
    Bank, Ingrid E. M.
    Doevendans, Pieter A.
    de Kleijn, Dominique P. V.
    INTERNATIONAL JOURNAL OF CARDIOLOGY, 2024, 395
  • [5] Chest Pain in the Emergency Department: In patients presenting to the Emergency Department with chest pain is the use of the HEART score safe and effective in ruling out major adverse cardiac events (MACE)?
    Beirne, Swanick N.
    Neill, A.
    Eager, R.
    IRISH JOURNAL OF MEDICAL SCIENCE, 2019, 188 : S108 - S109
  • [6] Prediction of adverse cardiac outcomes in high-risk Mexican patients with chest pain in the emergency department
    Leon-Blanchet, Maria F.
    Araiza-Garaygordobil, Diego
    Reynier-Garza, Valeria
    Gopar-Nieto, Rodrigo
    Belderrain-Morales, Nallely
    Sarabia-Chao, Vianney
    Martinez-Amezcua, Pablo
    Cabello-Lopez, Alejandro
    Sandoval-Aguilar, Tomas T.
    Arias-Mendoza, Alexandra
    ARCHIVOS DE CARDIOLOGIA DE MEXICO, 2023, 93 (02): : 183 - 188
  • [7] Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain
    Zhang, Pei-, I
    Hsu, Chien-Chin
    Kao, Yuan
    Chen, Chia-Jung
    Kuo, Ya-Wei
    Hsu, Shu-Lien
    Liu, Tzu-Lan
    Lin, Hung-Jung
    Wang, Jhi-Joung
    Liu, Chung-Feng
    Huang, Chien-Cheng
    SCANDINAVIAN JOURNAL OF TRAUMA RESUSCITATION & EMERGENCY MEDICINE, 2020, 28 (01):
  • [8] Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection
    Nan Liu
    Zhi Xiong Koh
    Junyang Goh
    Zhiping Lin
    Benjamin Haaland
    Boon Ping Ting
    Marcus Eng Hock Ong
    BMC Medical Informatics and Decision Making, 14
  • [9] Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection
    Liu, Nan
    Koh, Zhi Xiong
    Goh, Junyang
    Lin, Zhiping
    Haaland, Benjamin
    Ting, Boon Ping
    Ong, Marcus Eng Hock
    BMC MEDICAL INFORMATICS AND DECISION MAKING, 2014, 14
  • [10] Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain
    Pei-I Zhang
    Chien-Chin Hsu
    Yuan Kao
    Chia-Jung Chen
    Ya-Wei Kuo
    Shu-Lien Hsu
    Tzu-Lan Liu
    Hung-Jung Lin
    Jhi-Joung Wang
    Chung-Feng Liu
    Chien-Cheng Huang
    Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 28