胸痛风险评分在急性胸痛患者危险分层中应用的研究进展
Research Progress on the Application of Chest Pain Risk Score in Risk Stratification of Patients with Acute Chest Pain
摘要: 急性胸痛患者在急诊门诊就诊的患者中占比较大,早期明确诊断并进行危险分层有利于改善患者预后。胸痛病因复杂,分为心源性胸痛和非心源性胸痛,其中较常见的为急性冠脉综合征。目前指南推荐的胸痛风险评分包括HEART评分、EDACS评分、ADAPT评分、hs-cTn评分、TIMI评分和GRACE评分,这些评分各有优劣,但均缺少我国人群数据支持,且大多数建立在动态监测肌钙蛋白基础上,未来有待进一步验证评分效能或创建基于我国人群的危险分层模型。
Abstract: Acute chest pain accounts for a large proportion of emergency outpatient patients, and early diagnosis and risk stratification are beneficial to improve patient prognosis. The etiology of chest pain is complex, including cardiogenic chest pain and non-cardiogenic chest pain, of which acute coronary syndrome is the most common. The chest pain risk scores recommended in the current guidelines include HEART score, EDACS score, ADAPT score, hs-cTn score, TIMI score, and GRACE score. Each of these scores has its advantages and disadvantages, but all of them lack the support of Chinese population data, and most of them are based on dynamic monitoring of troponin. In the future, it is necessary to further validate the scoring efficacy or create a risk stratification model based on Chinese population.
文章引用:于菁怡, 邢家璇, 徐雅妮. 胸痛风险评分在急性胸痛患者危险分层中应用的研究进展[J]. 临床医学进展, 2024, 14(5): 1828-1835. https://doi.org/10.12677/acm.2024.1451623

参考文献

[1] Fallon, E.M. and Roques, J. (1997) Acute Chest Pain. AACN Clinical Issues, 8, 383-397. [Google Scholar] [CrossRef] [PubMed]
[2] Gulati, M., Levy, P.D., Mukherjee, D., et al. (2021) 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation, 144, e368-e454. [Google Scholar] [CrossRef
[3] Amsterdam, E.A., Wenger, N.K., Brindis, R.G., et al. (2014) 2014 AHA/ACC Guideline for the Management of Patients with Non-ST-Elevation Acute Coronary Syndromes: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation, 130, e344-e426.
[4] Sgarbossa, E.B., Pinski, S.L., Gates, K.B., et al. (1996) Early Electrocardiographic Diagnosis of Acute Myocardial Infarction in the Presence of Ventricular Paced Rhythm. GUSTO-I Investigators. The American Journal of Cardiology, 77, 423-424. [Google Scholar] [CrossRef
[5] Launbjerg, J., Fruergaard, P., Hesse, B., et al. (1996) Long-Term Risk of Death, Cardiac Events and Recurrent Chest Pain in Patients with Acute Chest Pain of Different Origin. Cardiology, 87, 60-66. [Google Scholar] [CrossRef
[6] 中国胸痛中心联盟, 中国心血管健康联盟, 苏州工业园区心血管健康研究院, 等. 《中国胸痛中心质控报告(2021)》概要[J]. 中国介入心脏病学杂志, 2022, 30(5): 321-327.
[7] Delaney, M.C., Neth, M. and Thomas, J.J. (2017) Chest Pain Triage: Current Trends in the Emergency Departments in the United States. Journal of Nuclear Cardiology, 24, 2004-2011. [Google Scholar] [CrossRef] [PubMed]
[8] Lindsell, C.J., Anantharaman, V., Diercks, D., et al. (2006) The Internet Tracking Registry of Acute Coronary Syndromes (I*TrACS): A Multicenter Registry of Patients with Suspicion of Acute Coronary Syndromes Reported Using the Standardized Reporting Guidelines for Emergency Department Chest Pain Studies. Annals of Emergency Medicine, 48, 666-77, 677.e1-9. [Google Scholar] [CrossRef] [PubMed]
[9] Ibanez, B., James, S., Agewall, S., et al. (2018) 2017 ESC Guidelines for the Management of Acute Myocardial Infarction in Patients Presenting with ST-Segment Elevation: The Task Force for the Management of Acute Myocardial Infarction in Patients Presenting with ST-Segment Elevation of the European Society of Cardiology (ESC). European Heart Journal, 39, 119-177. [Google Scholar] [CrossRef] [PubMed]
[10] Silaschi, M., Byrne, J. and Wendler, O. (2017) Aortic Dissection: Medical, Interventional and Surgical Management. Heart, 103, 78-87. [Google Scholar] [CrossRef] [PubMed]
[11] Maret-Ouda, J., Markar, S.R. and Lagergren, J. (2020) Gastroesophageal Reflux Disease. JAMA, 324, 2565. [Google Scholar] [CrossRef] [PubMed]
[12] Mandrekar, S., Venkatesan, P. and Nagaraja, R. (2021) Prevalence of Musculoskeletal Chest Pain in the Emergency Department: A Systematic Review and Meta-Analysis. Scandinavian Journal of Pain, 21, 434-444. [Google Scholar] [CrossRef] [PubMed]
[13] 《中国心血管健康与疾病报告2022》编写组. 《中国心血管健康与疾病报告2022》要点解读[J]. 中国心血管杂志, 2023, 28(4): 297-312.
[14] Mahler, S.A., Riley, R.F., Hiestand, B.C., et al. (2015) The HEART Pathway Randomized Trial: Identifying Emergency Department Patients with Acute Chest Pain for Early Discharge. Circulation: Cardiovascular Quality and Outcomes, 8, 195-203. [Google Scholar] [CrossRef
[15] Than, M.P., Pickering, J.W., Aldous, S.J., et al. (2016) Effectiveness of EDACS versus ADAPT Accelerated Diagnostic Pathways for Chest Pain: A Pragmatic Randomized Controlled Trial Embedded within Practice. Annals of Emergency Medicine, 68, 93-102.E1. [Google Scholar] [CrossRef] [PubMed]
[16] Than, M., Aldous, S., Lord, S.J., et al. (2014) A 2-Hour Diagnostic Protocol for Possible Cardiac Chest Pain in the Emergency Department: A Randomized Clinical Trial. JAMA Internal Medicine, 174, 51-58. [Google Scholar] [CrossRef] [PubMed]
[17] Fanaroff, A.C., Rymer, J.A., Goldstein, S.A., et al. (2015) Does This Patient with Chest Pain Have Acute Coronary Syndrome? The Rational Clinical Examination Systematic Review. JAMA, 314, 1955-1965. [Google Scholar] [CrossRef] [PubMed]
[18] Mueller, C., Giannitsis, E., Christ, M., et al. (2016) Multicenter Evaluation of a 0-Hour/1-Hour Algorithm in the Diagnosis of Myocardial Infarction with High-Sensitivity Cardiac Troponin T. Annals of Emergency Medicine, 68, 76-87.E4. [Google Scholar] [CrossRef] [PubMed]
[19] Roffi, M. and Patrono, C. (2016) CardioPulse: ‘Ten Commandments’ of 2015 European Society of Cardiology Guidelines for the Management of Acute Coronary Syndromes in Patients Presenting Without Persistent ST-Segment Elevation (NSTE-ACS). European Heart Journal, 37, 208.
[20] Twerenbold, R., Costabel, J.P., Nestelberger, T., et al. (2019) Outcome of Applying the ESC 0/1-Hour Algorithm in Patients with Suspected Myocardial Infarction. Journal of the American College of Cardiology, 74, 483-494. [Google Scholar] [CrossRef] [PubMed]
[21] Collet, J.P., Thiele, H., Barbato, E., et al. (2021) 2020 ESC Guidelines for the Management of Acute Coronary Syndromes in Patients Presenting without Persistent ST-Segment Elevation. European Heart Journal, 42, 1289-1367. [Google Scholar] [CrossRef] [PubMed]
[22] Ekelund, U. and De Capretz, P.O. (2022) Moving forward with Machine Learning Models in Acute Chest Pain. The Lancet Digital Health, 4, E291-E292. [Google Scholar] [CrossRef
[23] Simms, A.D., Reynolds, S., Pieper, K., et al. (2013) Evaluation of the NICE Mini-GRACE Risk Scores for Acute Myocardial Infarction Using the Myocardial Ischaemia National Audit Project (MINAP) 2003-2009: National Institute for Cardiovascular Outcomes Research (NICOR). Heart, 99, 35-40. [Google Scholar] [CrossRef] [PubMed]
[24] Herrett, E., Smeeth, L., Walker, L., et al. (2010) The Myocardial Ischaemia National Audit Project (MINAP). Heart, 96, 1264-1267. [Google Scholar] [CrossRef] [PubMed]
[25] Qing, P., Yang, Y.M., Hu, L.T., et al. (2022) [The Predictive Value of the CHA2DS2-VASc Score for In-Hospital Outcomes in Patients with Acute Myocardial Infarction: China PEACE-Retrospective Acute Myocardial Infarction Study]. Chinese Journal of Internal Medicine, 61, 177-184.
[26] Ramesh, A.N., Kambhampati, C., Monson, J.R., et al. (2004) Artificial Intelligence in Medicine. Annals of the Royal College of Surgeons of England, 86, 334-338. [Google Scholar] [CrossRef] [PubMed]
[27] Hashimoto, D.A., Witkowski, E., Gao, L., et al. (2020) Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations. Anesthesiology, 132, 379-394. [Google Scholar] [CrossRef
[28] Gupta, R., Srivastava, D., Sahu, M., et al. (2021) Artificial Intelligence to Deep Learning: Machine Intelligence Approach for Drug Discovery. Molecular Diversity, 25, 1315-1360. [Google Scholar] [CrossRef] [PubMed]
[29] Ellahham, S. (2020) Artificial Intelligence: The Future for Diabetes Care. The American Journal of Medicine, 133, 895-900. [Google Scholar] [CrossRef] [PubMed]
[30] 巫嘉陵, 韩建达. 医疗人工智能: 知识引导与数据挖掘联合驱动[J]. 中国现代神经疾病杂志, 2023, 23(1): 1-4.
[31] Wang, G., Liu, X., Shen, J., et al. (2021) A Deep-Learning Pipeline for the Diagnosis and Discrimination of Viral, Non-Viral and COVID-19 Pneumonia from Chest X-Ray Images. Nature Biomedical Engineering, 5, 509-521. [Google Scholar] [CrossRef] [PubMed]
[32] Lu, M., Zhao, Q., Poston, K.L, et al. (2021) Quantifying Parkinson’s Disease Motor Severity under Uncertainty Using MDS-UPDRS Videos. Medical Image Analysis, 73, Article ID: 102179. [Google Scholar] [CrossRef] [PubMed]
[33] Zhang, S., Lu, J., Huo, W., et al. (2022) Estimation of Knee Joint Movement Using Single-Channel SEMG Signals with a Feature-Guided Convolutional Neural Network. Frontiers in Neurorobotics, 16, Article 978014. [Google Scholar] [CrossRef] [PubMed]
[34] Yasmin, F., Shah, S.M.I., Naeem, A., et al. (2021) Artificial Intelligence in the Diagnosis and Detection of Heart Failure: The Past, Present, and Future. Reviews in Cardiovascular Medicine, 22, 1095-1113. [Google Scholar] [CrossRef] [PubMed]
[35] Nagarajan, V.D., Lee, S.L., Robertus, J.L., et al. (2021) Artificial Intelligence in the Diagnosis and Management of Arrhythmias. European Heart Journal, 42, 3904-3916. [Google Scholar] [CrossRef] [PubMed]
[36] Molenaar, M.A., Selder, J.L., Nicolas, J., et al. (2022) Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease. Current Cardiology Reports, 24, 365-376. [Google Scholar] [CrossRef] [PubMed]
[37] Attia, Z.I., Harmon, D.M., Behr, E.R., et al. (2021) Application of Artificial Intelligence to the Electrocardiogram. European Heart Journal, 42, 4717-4730. [Google Scholar] [CrossRef] [PubMed]
[38] Kobayashi, M., Huttin, O., Magnusson, M., et al. (2022) Machine Learning-Derived Echocardiographic Phenotypes Predict Heart Failure Incidence in Asymptomatic Individuals. JACC: Cardiovascular Imaging, 15, 193-208. [Google Scholar] [CrossRef] [PubMed]
[39] Deo, R.C. (2015) Machine Learning in Medicine. Circulation, 132, 1920-1930. [Google Scholar] [CrossRef
[40] Myszczynska, M.A., Ojamies, P.N., Lacoste, A.M.B., et al. (2020) Applications of Machine Learning to Diagnosis and Treatment of Neurodegenerative Diseases. Nature Reviews Neurology, 16, 440-456. [Google Scholar] [CrossRef] [PubMed]
[41] Zeron, R.M.C. and Serrano Junior, C.V. (2019) Artificial Intelligence in the Diagnosis of Cardiovascular Disease. Revista da Associação Médica Brasileira, 65, 1438-1441. [Google Scholar] [CrossRef] [PubMed]
[42] Ngiam, K.Y. and Khor, I.W. (2019) Big Data and Machine Learning Algorithms for Health-Care Delivery. The Lancet Oncology, 20, E262-E273. [Google Scholar] [CrossRef
[43] Yang, J., Zhang, B., Jiang, X., et al. (2024) Application of Artificial Intelligence to Advance Individualized Diagnosis and Treatment in Emergency and Critical Care Medicine. Diagnostics, 14, Article 687. [Google Scholar] [CrossRef] [PubMed]
[44] Attia, Z.I., Kapa, S., Lopez-Jimenez, F., et al. (2019) Screening for Cardiac Contractile Dysfunction Using an Artificial Intelligence-Enabled Electrocardiogram. Nature Medicine, 25, 70-74. [Google Scholar] [CrossRef] [PubMed]
[45] Sengupta, P.P., Kulkarni, H. and Narula, J. (2018) Prediction of Abnormal Myocardial Relaxation from Signal Processed Surface ECG. Journal of the American College of Cardiology, 71, 1650-1660. [Google Scholar] [CrossRef] [PubMed]
[46] Kagiyama, N., Piccirilli, M., Yanamala, N., et al. (2020) Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features. Journal of the American College of Cardiology, 76, 930-941. [Google Scholar] [CrossRef] [PubMed]
[47] Wijnberge, M., Geerts, B.F., Hol, L., et al. (2020) Effect of a Machine Learning-Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension during Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial. JAMA, 323, 1052-1060. [Google Scholar] [CrossRef] [PubMed]
[48] Pavel, A.M., Rennie, J.M., De Vries, L.S., et al. (2020) A Machine-Learning Algorithm for Neonatal Seizure Recognition: A Multicentre, Randomised, Controlled Trial. The Lancet Child & Adolescent Health, 4, 740-749. [Google Scholar] [CrossRef
[49] Manz, C.R., Zhang, Y., Chen, K., et al. (2023) Long-Term Effect of Machine Learning-Triggered Behavioral Nudges on Serious Illness Conversations and End-Of-Life Outcomes among Patients with Cancer: A Randomized Clinical Trial. JAMA Oncology, 9, 414-418. [Google Scholar] [CrossRef] [PubMed]
[50] Goecks, J., Jalili, V., Heiser, L.M. and Gray, J.W. (2020) How Machine Learning Will Transform Biomedicine. Cell, 181, 92-101. [Google Scholar] [CrossRef] [PubMed]
[51] Costantino, G., Falavigna, G., Solbiati, M., et al. (2017) Neural Networks as a Tool to Predict Syncope Risk in the Emergency Department. EP Europace, 19, 1891-1895. [Google Scholar] [CrossRef] [PubMed]
[52] 耿世佳, 周杨, 徐伟伦, 等. 人工智能心电分析技术在临床诊疗中的应用进展[J]. 实用心电学杂志, 2023, 32(1): 15-21.
[53] Jang, D.H., Kim, J., Jo, Y.H., et al. (2020) Developing Neural Network Models for Early Detection of Cardiac Arrest in Emergency Department. The American Journal of Emergency Medicine, 38, 43-49. [Google Scholar] [CrossRef] [PubMed]
[54] Levin, S., Toerper, M., Hamrock, E., et al. (2018) Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients with Respect to Clinical Outcomes Compared with the Emergency Severity Index. Annals of Emergency Medicine, 71, 565-574.E2. [Google Scholar] [CrossRef] [PubMed]
[55] Björkelund, A., Ohlsson, M., Lundager, Forberg, J., et al. (2021) Machine Learning Compared with Rule-In/Rule-Out Algorithms and Logistic Regression to Predict Acute Myocardial Infarction Based on Troponin T Concentrations. Journal of the American College of Emergency Physicians Open, 2, e12363. [Google Scholar] [CrossRef] [PubMed]
[56] Khera, R., Haimovich, J., Hurley, N.C., et al. (2021) Use of Machine Learning Models to Predict Death after Acute Myocardial Infarction. JAMA Cardiology, 6, 633-641. [Google Scholar] [CrossRef] [PubMed]
[57] Kolossváry, M., Raghu, V.K., Nagurney, J.T., et al. (2023) Deep Learning Analysis of Chest Radiographs to Triage Patients with Acute Chest Pain Syndrome. Radiology, 306, e221926. [Google Scholar] [CrossRef] [PubMed]
[58] Than, M.P., Pickering, J.W., Sandoval, Y., et al. (2019) Machine Learning to Predict the Likelihood of Acute Myocardial Infarction. Circulation, 140, 899-909. [Google Scholar] [CrossRef
[59] Shturman, A., Bickel, A. and Atar, S. (2012) The Predictive Value of P-Wave Duration by Signal-Averaged Electrocardiogram in Acute ST Elevation Myocardial Infarction. The Israel Medical Association Journal, 14, 493-497.
[60] Rosiak, M., Ruta, J. and Bolińska, H. (2003) Usefulness of Prolonged P-Wave Duration on Signal Averaged ECG in Predicting Atrial Fibrillation in Acute Myocardial Infarction Patients. Medical Science Monitor, 9, MT85-88.
[61] Lecun, Y., Bengio, Y. and Hinton, G. (2015) Deep Learning. Nature, 521, 436-444. [Google Scholar] [CrossRef] [PubMed]
[62] Hayıroğlu, M., Lakhani, I., Tse, G., et al. (2020) In-Hospital Prognostic Value of Electrocardiographic Parameters Other than ST-Segment Changes in Acute Myocardial Infarction: Literature Review and Future Perspectives. Heart, Lung and Circulation, 29, 1603-1612. [Google Scholar] [CrossRef] [PubMed]