Comparison of usual care and the HEART score for effectively and safely discharging patients with low‐risk chest pain in the emergency department: would the score always help?

Wang, Guangmei; Zheng, Wen; Wu, Shuo; Ma, Jingjing; Zhang, He; Zheng, Jiaqi; Wang, Jiali; Xu, Feng; Chen, Yuguo

doi:10.1002/clc.23325

Cited by 14 publications

(13 citation statements)

References 44 publications

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“…The sensitivity of HEART ≤2 was higher at 98.8%(97.9, 99.7%) at the cost of a lower proportion (11%) of patients identified as low risk. In our previous report, the HEART score would not appear to provide additionally helpful risk stratification to the usual care for discharging low-risk patients [36]. Regarding the high-risk category, HEART ≥7 did not perform as well (specificity 87.0%; PPV 53.4%) as in Reaney's study [16].…”

Section: Discussionmentioning

confidence: 63%

Evaluation and comparison of six GRACE models for the stratification of undifferentiated chest pain in the emergency department

Zheng

Wang

et al. 2020

BMC Cardiovasc Disord

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Background: The Global Registry of Acute Coronary Events (GRACE) score is recommended for stratifying chest pain. However, there are six formulas used to calculate the GRACE score for different outcomes of acute coronary syndrome (ACS), including death (Dth) or composite of death and myocardial infarction (MI), while in hospital (IH), within 6 months after discharge (OH6m) or from admission to 6 months later (IH6m). We aimed to perform the first comprehensive evaluation and comparison of six GRACE models to predict 30-day major adverse cardiac events (MACEs) in patients with acute chest pain in the emergency department (ED). Methods: Patients with acute chest pain were consecutively recruited from August 24, 2015 to September 30, 2017 from the EDs of two public hospitals in China. The 30-day MACEs included death, acute myocardial infarction (AMI), emergency revascularization, cardiac arrest and cardiogenic shock. The correlation, calibration, discrimination, reclassification and diagnostic accuracy at certain cutoff values of six GRACE models were evaluated. Comparisons with the History, ECG, Age, Risk Factors, and Troponin (HEART) and Thrombolysis in Myocardial Infarction (TIMI) scores were conducted.

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Section: Discussionmentioning

confidence: 63%

Evaluation and comparison of six GRACE models for the stratification of undifferentiated chest pain in the emergency department

Zheng

Wang

et al. 2020

BMC Cardiovasc Disord

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“…More recent studies that included comparisons have instead compared ML to current risk stratification tools such as the TIMI and HEART score. Though routinely used in clinical practice, there is emerging evidence that the HEART score may not perform better than clinical gestalt in certain clinical scenarios [ 54 ]. As ML tools become integrated into practice it will continue to be important to compare ML tools to physicians.…”

Section: Discussionmentioning

confidence: 99%

Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review

et al. 2021

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Background Chest pain is amongst the most common reason for presentation to the emergency department (ED). There are many causes of chest pain, and it is important for the emergency physician to quickly and accurately diagnose life threatening causes such as acute myocardial infarction (AMI). Multiple clinical decision tools have been developed to assist clinicians in risk stratifying patients with chest. There is growing recognition that machine learning (ML) will have a significant impact on the practice of medicine in the near future and may assist with diagnosis and risk stratification. This systematic review aims to evaluate how ML has been applied to adults presenting to the ED with undifferentiated chest pain and assess if ML models show improved performance when compared to physicians or current risk stratification techniques. Methods and findings We conducted a systematic review of journal articles that applied a ML technique to an adult patient presenting to an emergency department with undifferentiated chest pain. Multiple databases were searched from inception through to November 2020. In total, 3361 articles were screened, and 23 articles were included. We did not conduct a metanalysis due to a high level of heterogeneity between studies in both their methods, and reporting. The most common primary outcomes assessed were diagnosis of acute myocardial infarction (AMI) (12 studies), and prognosis of major adverse cardiovascular event (MACE) (6 studies). There were 14 retrospective studies and 5 prospective studies. Four studies reported the development of a machine learning model retrospectively then tested it prospectively. The most common machine learning methods used were artificial neural networks (14 studies), random forest (6 studies), support vector machine (5 studies), and gradient boosting (2 studies). Multiple studies achieved high accuracy in both the diagnosis of AMI in the ED setting, and in predicting mortality and composite outcomes over various timeframes. ML outperformed existing risk stratification scores in all cases, and physicians in three out of four cases. The majority of studies were single centre, retrospective, and without prospective or external validation. There were only 3 studies that were considered low risk of bias and had low applicability concerns. Two studies reported integrating the ML model into clinical practice. Conclusions Research on applications of ML for undifferentiated chest pain in the ED has been ongoing for decades. ML has been reported to outperform emergency physicians and current risk stratification tools to diagnose AMI and predict MACE but has rarely been integrated into practice. Many studies assessing the use of ML in undifferentiated chest pain in the ED have a high risk of bias. It is important that future studies make use of recently developed standardised ML reporting guidelines, register their protocols, and share their datasets and code. Future work is required to assess the impact of ML model implementation on clinical decision making, patient orientated outcomes, and patient and physician acceptability. Trial registration International Prospective Register of Systematic Reviews registration number: CRD42020184977.

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“…Current guidelines recommend using structured risk stratification tools like HEART score to evaluate patients with suspected ACS presenting to the ED [16]. Nevertheless, some researchers argue that there is a limit to the performance of the HEART score to discharge low-risk patients and require attention to its application because patients with ACS-related symptoms can be undertriage or overtriage by HEART strategies [17,18]. Therefore, it is necessary to perform complementary measures irrespective of the HEART score, or it is reasonable for low-risk patients to be discharged with close follow-up.…”

Section: Discussionmentioning

confidence: 99%

Determining whether A Low HEART Score Is Associated with Low Risk in Coronary Angiography Results: Validation of HEART Score Using Coronary Angiography Results

Park¹,

Yoon²,

Jun³

et al. 2020

Signa Vitae

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Purpose: The history, electrocardiogram, age, risk factor, and troponin (HEART) score have not yet been adopted in the emergency department (ED) in South Korea. We aimed to investigate whether patients with low HEART scores have a low risk of coronary angiography (CAG) results. Methods: Patients with chest pain with a possible cardiac etiology in the ED were included. Patients were divided into low-risk (0 – 3), intermediate-risk (4 – 6), and high-risk (7 – 9) groups according to the HEART score. We analysed the CAG results of the included patients. CAG results were divided into four grades: Grade I, > 70%; Grade II, > 50%; Grade III, 10% – 50%; and Grade IV, < 10%of stenosis. The occurrence of a major adverse cardiac event (MACE) within 28 days was also investigated. Results: The study included 787 patients, of whom 458, 262, and 67 were included in the low-risk, intermediate-risk, and high-risk groups, respectively. A total of 118 cases of MACE occurred (average: 0.15 MACE/patient). MACE incidence was lower in the low-risk HEART score group than in the intermediate-risk and high-risk groups (0.4% vs. 22.1% and 86.6%, p < 0.001). The CAG results of the admitted patients with a low-risk HEART score were as follows: Grade I, 6.1%; Grade II, 3.0%; Grade III, 27.3%; and Grade IV, 63.6%. Conclusions: Patients with a low HEART score visiting the ED in Korea had a low risk in CAG results and a low probability of developing MACE. The successful utilization of the HEART score appears to be a rational approach that may avoid unnecessary testing in chest pain patients presenting to the ED.

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Comparison of usual care and the HEART score for effectively and safely discharging patients with low‐risk chest pain in the emergency department: would the score always help?

Cited by 14 publications

References 44 publications

Evaluation and comparison of six GRACE models for the stratification of undifferentiated chest pain in the emergency department

Evaluation and comparison of six GRACE models for the stratification of undifferentiated chest pain in the emergency department

Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review

Determining whether A Low HEART Score Is Associated with Low Risk in Coronary Angiography Results: Validation of HEART Score Using Coronary Angiography Results

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