Article, Cardiology

Identifying low-risk chest pain in the emergency department: Obstructive coronary artery disease and major adverse cardiac events

a b s t r a c t

Background: Accurate risk stratification for obstructive coronary artery disease (CAD) and major cardiac adverse events (MACE) is important in emergency departments. We compared six established chest pain risk scores (the HEART score, CAD basic model, CAD clinical model, TIMI, GRACE, uDF) for prediction of obstructive CAD and MACE.

Methods: Patients who presented to the emergency department with chest pain or symptoms of suspected CAD and underwent coronary computed tomographic angiography were analyzed. The primary endpoint was adverse outcomes including the presence of obstructive CAD (>=50% stenosis) and the occurrence of MACE within 6 weeks. We compared the risk scores by the area under the receiver-operating characteristic curve (AUC) and calculated

their respective net reclassification index (NRI).

Results: Adverse outcomes occurred in 285 (28.4%) out of the 1002 patients included. For the prediction of ad- verse outcomes, the AUC of the HEART score (0.792) was superior to those of the CAD clinical model (0.760), CAD basic model (0.749), TIMI (0.749), uDF (0.703), and GRACE (0.653). In terms of the NRI, the HEART score sig- nificantly improved the reclassification abilities of the uDF (0.39), GRACE score (0.27), CAD basic model (0.11), TIMI (0.10), and CAD clinical model (0.08) (all P b 0.05). The HEART score also had the highest negative predictive value as well (0.893).

Conclusions: The HEART score was superior to other cardiac risk scores in predicting both obstructive CAD and MACE. However, due to the high false-negative rate (11%) of the HEART score, its use for identifying low-risk pa- tients should be considered with caution.

(C) 2020

Introduction

Chest pain in patients presenting to the emergency department (ED) is a worrisome symptom that may indicate the presence of coronary ar- tery disease (CAD), which is one of the most common causes of morbid- ity and mortality worldwide [1]. Patients with chest pains are heterogeneous in terms of clinical presentation and outcomes including the risk of death or non-fatal Ischemic events. Invasive coronary angiog- raphy (CAG) remains as the gold standard for diagnosis of CAD; how- ever, its diagnostic yield was reported to be low, indicating the need for a better risk stratification tool for these patients [2].

ED physicians need to identify low-risk patients who may be safely

be discharged without further evaluation. To aid such decision-making process, several scores have been developed to stratify patients with

* Corresponding author at: Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea.

E-mail address: [email protected] (S. Ahn).

chest pain into high- and low-risk, and the scores could be grouped into two categories depending on the outcome of interest–obstructive CAD or major adverse cardiac event . For predicting obstructive CAD, the CAD consortium models (basic and clinical) and the updated Diamond-Forrester (uDF) method are respectively recommended by the European Society of Cardiology and the American College of Cardiol- ogy/American Heart Association guidelines [3,4]. For predicting the risk of MACE, the Thrombolysis in Myocardial Infarction , the GRACE (Global Registry of Acute coronary events) score, and the HEART (his- tory, electrocardiography, age, risk factors, and troponin) score have been developed for use in patients with acute coronary syndrome, and have also been widely used for patients with Undifferentiated chest pains and identifying low-risk patients for early discharge without fur- ther cardiac testing [5-7].

Both the accurate prediction of obstructive CAD and evaluation of the possibility of MACE are pivotal in establishing prognosis and guiding treatment; therefore, estimating both risks simultaneously at the pre- sentation to the ED could be a more practical approach than identifying the risk of each outcome separately. Several studies have been carried

https://doi.org/10.1016/j.ajem.2020.06.019

0735-6757/(C) 2020

out on identifying the relationship between the severity of atherosclero- sis and the GRACE or TIMI risk scores, [8-10] but none have focused on determining whICH Score has a better performance for the prediction of both obstructive CAD and MACE. In this study, we tried to determine which of the six established risk scores could best predict both obstruc- tive CAD and MACE, irrespective of their original goal in development, among patients presenting to the ED with chest pain suspected of CAD.

Methods

We carried out a retrospective observational study by collecting data from January 2017 to December 2018 at Asan Medical Center (Seoul, Korea), a tertiary referral center with an annual ED census of approxi- mately 120,000 patients. All consecutive adult patients with chest pain or symptoms of suspected CAD who underwent coronary computed to- mography angiography (CTA) at the ED were included. Patients were excluded if they had a history of acute myocardial infarction (AMI) or underwent coronary revascularizations including percutaneous coro- nary intervention (PCI) or Coronary artery bypass graft surgery (CABG). We also excluded those without troponin assay results. The de- cision for referral to CTA was made based on the discretion of the treating physicians. The study protocol was approved by the institu- tional review board of Asan Medical Center, which waived the require- ment for informed consent due to the retrospective nature of this study. We collected the following data from electronic medical records: age, sex, symptoms, and Cardiovascular risk factors. Multidetector coro- nary CTA was performed using a dual-source scanner (Somatom Defini- tion, Siemens, Munich, Germany). The presence of obstructive CAD was defined as stenosis of >=50% in N1 major epicardial coronary artery [11].

For those who showed obstructive CAD on CTA, invasive CAG was per-

formed to ensure the presence of stenosis when clinically indicated; when the results from CTA and CAG had discrepancies, the result from CAG was considered as true findings. The MACE was defined as the oc- currence of AMI, PCI, CABG, or All-cause death within 6 weeks following presentation to the ED [12]. The diagnosis of AMI was made according to the current guidelines [13]. Type I MI was defined as elevated troponin level with obstructive coronary artery stenosis, and type II MI was de- fined as elevated troponin level elevated without obstructive coronary artery stenosis. The cardiac troponin I (cTnI-Ultra) assay was performed using Siemens ADVIA Centaur (Siemens, Munich, Germany) with 99th percentile of 0.04 g/L as a cutoff for Myocardial necrosis [14].

The pretest probability of obstructive CAD was calculated using three scores: the CAD consortium basic, CAD consortium clinical model, and uDF method [15,16]. We used an updated version of the DF score, which allows the inclusion of patients ?70 years of age and in- corporates age as a continuous variable. We compared these scores ac-

cording to the coefficients provided by Genders et al. [17]. Cardiac risk scores for MACE including the HEART, TIMI, and GRACE were calculated using electrocardiography, initial troponin, and other laboratory results when indicated [6,18,19]. Each score was dichotomized into low- and high-risk groups based on previously published binary low-risk catego- ries. Low-risk was defined as b15% risk for obstructive CAD for CAD basic, CAD clinical model [4], and uDF method [16]. For risk scores in MACE, low-risk categories for each scoring system were as follows: 0-3 for the HEART [19], 0-1 for TIMI [6], and <= 108 for GRACE score [20].

The primary endpoint was adverse outcomes defined as the sum of

two outcomes: the presence of obstructive CAD, and the occurrence of MACE within 6 weeks following presentation to the ED. At first, theses scores were analyzed according to their original outcomes. Then, the scores were compared by exchanging their outcomes–the CAD basic model, CAD clinical model, and uDF method were used for the predic- tion of MACE, and the HEART, TIMI, and GRACE score for the probability of obstructive CAD. Finally, the performances of these scores for the pri- mary endpoint were analyzed.

Statistical analysis

Continuous variables with normal distributions were compared by the Student’s t-test and are expressed as mean with standard deviation; variables with non-normal distributions were analyzed by the Mann- Whitney U test and expressed as median with interquartile range (IQR). Categorical variables were compared using the Chi-squared test and expressed as proportions. The area under the receiver-operating characteristic (ROC) curves (AUC) and the corresponding 95% confi- dence intervals (CI) were examined to compare the accuracy of the scores. Comparison between AUCs was carried out using the non- parametric method described by De Long et al. [21]. Sensitivity, specific- ity, positive predictive value (PPV), and negative predictive value (NPV) for adverse outcome were calculated for each score and compared all to- gether. Net reclassification index (NRI), which is the sum of the score’s ability to increase both the true proportions of high-risk and low-risk patients, was computed for each pairwise comparison by setting one score with the highest AUC as the reference [22, 23]. Statistical analyses were carried out using the IBM SPSS Statistics V21.0 (SPSS Inc., Chicago, IL, USA) and the R package (Version 3.5; The R Foundation for Statistical Computing, Vienna, Austria) designed for the evaluation of risk predic- tion models, PredictABEL. P values b0.05 were considered statistically significant.

Results

Out of the total of 1247 patients with chest pain or symptoms of suspected CAD, we excluded 110 patients with histories of MI, 133 pa- tients who had coronary revascularizations procedures before presenta- tion to the ED, and 2 patients without troponin data. We finally analyzed 1002 patients, of whom 259 (25.8%) and 186 (18.6%) had obstructive CAD and MACE, respectively. A total of 285 patients (28.4%) had adverse outcomes; among them, 160 patients (56.1%) had both obstructive CAD and MACE, 99 patients (34.7%) had obstructive CAD alone, to whom medical treatment rather than coronary revascularizations were de- cided, and 26 patients (9.1%) had MACE alone without evidence of ob- structive CAD. These 26 patients were diagnosed with type II MI and no one received coronary revascularization (Fig. 1). No patients died within 6 weeks following presentation to the ED.

Table 1 shows the patient demographics, vital signs, cardiac risk fac- tors, and laboratory values according to the presence of adverse out- come. Age (P b 0.001), systolic blood pressure (P b 0.001), proportion of male sex (P b 0.001), and prevalence of diabetes mellitus (P b 0.001), hypertension (P b 0.001), smoking (P = 0.003), and aspirin use within the last 7 days (P b 0.001) were higher among patients

Fig. 1. Distribution of adverse outcomes. CAD coronary artery disease; MACE major adverse cardiac event.

Table 1

Baseline characteristics of patients.

All patients (n = 1002)

Patients with adverse outcomes (n = 285)

Patients without adverse outcomes (n = 717)

P value

Demographics

Age (SD)

60.75 (12.98)

64.98 (11.66)

59.07 (13.11)

b0.001

Male gender, n (%)

579 (57.8)

197 (69.1)

382 (53.3)

b0.001

Vital signs at presentation (SD) SBP (mm Hg)

143.83 (24.60)

149.09 (27.90)

141.74 (22.85)

b0.001

DBP (mm Hg)

85.95 (15.04)

87.01 (17.39)

85.53 (13.98)

0.202

Heart rate per minute

80.57 (17.83)

79.12 (17.88)

81.14 (17.79)

0.106

cardiac risk factors, n (%)

Diabetes mellitus

173 (17.3)

81 (28.4)

92 (12.8)

b0.001

Hypertension

411 (41.0)

148 (51.9)

263 (36.7)

b0.001

Hyperlipidemia

158 (15.8)

41 (44.9)

117 (16.3)

0.449

Current smoking

91 (9.1)

38 (13.3)

53 (7.4)

0.003

History of CVA/TIA

33 (3.3)

11 (3.9)

22 (3.1)

0.527

Family history of CAD

28 (2.8)

10 (3.5)

18 (2.5)

0.387

Other

Aspirin use within the last 7 days, n (%)

108 (10.8)

52 (18.2)

56 (7.8)

b0.001

Creatinine (mg/dL) (IQR)

0.83 (0.71-0.98)

0.88 (0.77-1.04)

0.82 (0.69-0.95)

b0.001

Initial troponin (ng/mL) (IQR) 0.006

0.018

0.006

b0.001

(0.006-0.019)

(0.006-0.111)

(0.006-0.006)

SD standard deviation; SBP systolic blood pressure; DBP diastolic blood pressure; mm Hg millimeters of mercury; CVA cerebrovascular attack; TIA transient ischemic attack; CAD coronary artery disease; IQR interquartile range.

with adverse outcomes. Moreover, median creatinine (P b 0.001) and initial troponin level (P b 0.001) were also significantly higher in pa- tients with adverse outcomes. However, the prevalence of other cardiac risk factors such as hyperlipidemia, history of cerebrovascular attack, and family history of coronary artery disease did not show significant differences between the two groups.

Based on the original goals of each risk score, we first calculated the ROC curves for the CAD basic model, CAD clinical model, and uDF method for prediction of obstructive CAD and those for the HEART, TIMI, and GRACE scores for prediction of MACE (Supplementary Fig. 1). We then exchanged the goals of each risk score and calculated the ROC curves for the CAD basic model, CAD clinical model, and uDF method for prediction of MACE, and those for the HEART, TIMI, and GRACE scores for prediction of obstructive CAD (Fig. 2). For the predic- tion of MACE, the CAD clinical model had the highest AUC (0.721; 95% CI, 0.682-0.762), and for prediction of obstructive CAD, the HEART score had the highest AUC (0.775; 95% CI, 0.744-0.806). In terms of the prediction for adverse outcomes, the HEART score had the highest AUC (0.792; 95% CI, 0.762-0.822), followed by the CAD clinical model (0.760; 95% CI, 0.729-0.792), TIMI score (0.749; 95% CI, 0.716-0.782),

CAD basic model (0.749; 95% CI, 0.715-0.782), uDF method (0.703;

95% CI, 0.666-0.740), and GRACE score (0.653; 95% CI 0.616-0.689)

(Fig. 3). The AUC of the HEART score was significantly higher compared with those of all other scores (all P b 0.05).

The performances of each score in predicting adverse outcomes, re- garding sensitivity, specificity, PPV, and NPV, were compared by divid- ing the patients into low- and high-risk groups by a predefined low- risk cutoff (Table 2). The uDF method had the highest sensitivity (0.968; 95% CI, 0.941-0.986), and the TIMI score had the highest speci- ficity (0.829; 95% CI, 0.799-0.855) and PPV (0.550; 95% CI,

0.501-0.597). The HEART score had the highest NPV (0.893; 95% CI, 0.868-0.914).

Considering that the HEART score had the highest AUC, we con- structed a reclassification table for pairwise comparison to assess the improvement in reclassification of each score by comparing the HEART score with other scores (Table 3). By using the HEART score, re- classification of the CAD clinical model improved by 0.077 (95% CI, 0.011-0.143). Likewise, the HEART score improved reclassification of the CAD basic model by 0.106 (0.036-0.178), the uDF method by

0.391 (0.331-0.453), TIMI by 0.101 (0.032-0.171), and GRACE by

0.271 (0.197-0.345). Compared with the HEART score, the TIMI and GRACE scores tended to assign more patients with adverse outcomes into the low-risk group. On the contrary, the CAD basic model and

uDF method assigned more patients without adverse outcomes into the high-risk group than did the HEART score. Overall, the HEART score showed the best reclassification ability regarding adverse out- comes by the preset categories. (all P b 0.05).

Discussion

Of the 1002 patients in our study who presented with chest pain or symptoms of suspected CAD, 285 (28.4%) had adverse outcomes. Of them, 160 patients (56.1%) had both obstructive CAD and MACE; how- ever, considering that the remaining 125 patients only had either ob- structive CAD or MACE, we reasoned that a risk score for predicting either the presence of obstructive CAD or the probability of MACE may not accurately identify low-risk patients to allow them to be safely discharged. We found that the HEART score had the best performance in predicting adverse outcomes and risk-stratifying ability as evidenced by the highest NPV and NRI values. However, the HEART score had a 11% false-negative rate in stratifying patients into the low-risk group, sug- gesting that a considerable portion of patients regarded as low-risk may still develop obstructive CAD or MACE.

Our study is the first to compare chest pain scores in terms of their performances on predicting both CAD and MACE in patients visiting the ED. In order to reduce the occurrence of serious complications in the ED, it is important to rule out the presence of obstructive CAD as well as to anticipate MACE; we therefore decided to compare the well-known scores for broader endpoints. Previous studies showed that the HEART score outperformed TIMI and GRACE in predicting pa- tients at high-risk of developing MACE [24,25]. These results are consis- tent with our findings, in which the AUC of the HEART score regarding the prediction of MACE was highest among the 3 risk-stratification scores (Supplementary Fig. 1). Thus, the HEART score has a better per- formance in predicting MACE even in varioUS settings of ED among pa- tients suspected of CAD; such desirable performance of the HEART score may be due to the fact that because unlike TIMI and GRACE score, the HEART score was developed for use in a wide range of patients with un- differentiated chest pain upon presentation to the ED. In our study, the HEART score also showed the highest AUC in predicting adverse out- comes, which was followed by TIMI and GRACE. This finding could be explained by the recent studies that demonstrated the usefulness of tro- ponin assays for identifying patients in the ED who may be discharged early [26,27]; in these papers, the authors concluded that troponin as- says can aptly rule out AMI and allow low-risk patients to be discharged without further cardiac testing. From these findings, we realized the

Fig. 2. Receiver operating characteristic curves for the CAD basic, clinical model, and uDF method for prediction of MACE (a) and for the HEART, TIMI, and GRACE score for prediction of obstructive CAD (b). CAD coronary artery disease; MACE major adverse cardiac event.

Fig. 3. Receiver operating characteristic curves for prediction of adverse outcomes (both obstructive CAD and MACE). CAD coronary artery disease; MACE major adverse cardiac event.

importance of adding the troponin assay for risk-stratification. The su- perior performance of the HEART score over TIMI or GRACE may thus be explained by the fact that the HEART score has the largest proportion of elevated cardiac markers when calculating the total points.

When comparing the test characteristics for previously published low-risk categories, the uDF method had the highest sensitivity. Previ- ous studies have shown that the traditional DF model leads to a signifi- cant overestimation of the likelihood of obstructive CAD, resulting in many low-risk patients undergoing unnecessary noninvasive cardiac testing in the ED [28,29]. Our result was similar to those of previous re- ports even though we applied the uDF method that was calibrated to a more contemporary cohort of patients and extended to ages beyond 30 to 69 years [29]. Considering the particularly low specificity of the uDF method as found in our study, using the uDF method may also result

in many patients receiving further testing in the ED, which is not suit- able for an effective Risk-stratification tool.

The NPV denotes how well the scores correctly assign low-risk pa-

tients into the low-risk category; thus, high NPV would directly contrib- ute to accurate identification of low-risk patients, which is helpful in reducing the length of ED stay and relevant costs. Importantly, the HEART score also had superior performance in the NPV over other scores; even so, approximately 11% of patients who were assigned into the low-risk group by the HEART score appeared to have adverse outcomes, which is a higher than the suggestions from previous studies [7,12,24]. This might be due to the relatively wide range of primary end- points used in our study, in which 99 patients with obstructive CAD without MACE were included as those with adverse outcomes. The false-negative rate may be reduced if the cutoff for low-risk criteria is

Table 2

Test characteristics for previously published low-risk categories regarding adverse outcomes.

Sensitivity (95% CI)

Specificity (95% CI)

Positive predictive value (95% CI)

Negative predictive value (95% CI)

CAD basic model

0.821

0.529

0.409

0.881

(0.772-0.864)

(0.491-0.566)

(0.386-0.432)

(0.852-0.906)

CAD clinical model

0.747

0.632

0.447

0.863

(0.693-0.797)

(0.595-0.667)

(0.418-0.476)

(0.836-0.886)

uDF method

0.968

0.096

0.299

0.885

(0.941-0.986)

(0.076-0.120)

(0.292-0.305)

(0.795-0.938)

HEART score

0.804

0.653

0.479

0.893

(0.753-0.848)

(0.617-0.688)

(0.450-0.508)

(0.868-0.914)

TIMI score

0.526

0.829

0.550

0.815

(0.467-0.586)

(0.799-0.855)

(0.501-0.597)

(0.795-0.833)

GRACE score

0.516

0.670

0.383

0.777

(0.456-0.575)

(0.634-0.704)

(0.348-0.420)

(0.753-0.799)

CAD coronary artery disease; uDF updated Diamond-Forrester method; HEART, history, ECG, age, risk factors, and troponin; GRACE, Global Registry of acute coronary events; MACE, major adverse cardiac event; TIMI, thrombolysis in myocardial infarction.

Table 3

Net classification improvement (NRI) and area under the receiver-operating characteristic curves (AUC) difference of the HEART score in comparison with other scores.

Comparing model

NRIea

NRIneb

NRI (95% CI)

P value

CAD basic model

-0.018

0.124

0.106

(0.036-0.178)

0.003

CAD clinical model

0.056

0.021

0.077

0.021

(0.011-0.143)

HEART score vs. uDF method

-0.165

0.556

0.391

b0.001

(0.331-0.453)

TIMI score

0.277

-0.176

0.101

0.004

(0.032-0.171)

GRACE score

0.288

-0.017

0.271

b0.001

(0.197-0.345)

CAD coronary artery disease; uDF updated Diamond-Forrester method; HEART, history, ECG, age, risk factors, and troponin; GRACE, Global Registry of Acute Cor- onary Events; MACE, major adverse cardiac event; TIMI, thrombolysis in myocardial infarction.

a Reclassification of true positive primary outcome with the HEART score.

b Reclassification of true negative primary outcome with the HEART score.

changed from 3 points to a lower value. However, the goal of our study was not to determine an optimal cutoff for risk scores; also, it should be considered that fewer patients being included in the low-risk group as a candidate for early discharge without further evaluation would impose burdens on the ED workload.

clinical risk scores are used for stratifying the risk of patients being evaluated for suspected CAD. An ideal risk score in the ED setting should reliably identify all patients at low-risk of both CAD and MACE, thus allowing quick and safe discharge. Our study is important because it is the first in the literature to assess both categories of the well-known risk scores for CAD and MACE together for their ability in predicting adverse outcomes. In our study, reclassification by the HEART score significantly improved the identification of patients with adverse outcomes. It is not surprising to find an association be- tween risk scores for CAD and MACE, considering that there are con- siderable overlaps in the predictors for the two outcomes. However, as a significant proportion of patients in our study only had either ob- structive CAD or MACE, a new risk stratification tool for identifying true low-risk patients is warranted.

There are several limitations to our study. First, as the study was car- ried out based on patient database from a single center, its results may not be readily generalizable. Second, considering the retrospective na- ture of the study, caution is required when interpreting and applying the current results because not all patients are able to provide precise information required for calculating scores, and certain symptoms and the presence of risk factors may not be fully described in the medical re- cords, which could lead to data abstraction errors. Third, risks should only be considered within the boundaries of the outcome and follow- up periods used in our study. There are some differences between our study and others regarding the components and inclusion period of

MACEs. Several studies included life-threatening dysrhythmias or heart failure in their MACE [30,31]; others also included unstable angina [12,24], which could alter the performance of the risk scores. Moreover, previous studies used different definitions of follow-up periods (e.g., 30 days, 6 weeks, 60 days, or 3 months following presentation to the ED) [12,19,32]. Definition regarding obstructive CAD was also different:

whereas we defined obstructive lesion as stenosis >=50%, others have used >=75% [33]. Moreover, the threshold for low-risk category also dif-

fered, ranging between b5%, b15%, and b30% [21,28,34]. Considering such diversity in the definitions and measurements of outcomes, inter- pretation of our results should be done with caution. Lastly, as changes in electrocardiography and troponin levels may be subtle in early courses, we might have overlooked such changes and resulted in an in- crease in the number of patients with false-negative results.

Conclusions

We found that the HEART score was significantly superior to the TIMI, GRACE, CAD basic model, CAD clinical model, and uDF method in predicting obstructive CAD and MACE in patients presenting to the ED with symptoms of suspected CAD. However, considering its high false- negative rate (11%), the HEART score alone might not be a safe tool for identifying low-risk patients for early discharge. In order to further aid Clinical decisions in the ED, future studies should focus on develop- ing better models that aptly predict the risk of both obstructive CAD and MACE.

Supplementary data to this article can be found online at https://doi. org/10.1016/j.ajem.2020.06.019.

Funding

This research did not receive any specific grant from funding agen- cies in the public, commercial, or not-for-profit sectors.

CRediT authorship contribution statement

Yo Sep Shin: Conceptualization, Formal analysis, Writing – original draft, Investigation. Shin Ahn: Conceptualization, Formal analysis, Writ- ing – review & editing, Investigation. Youn-Jung Kim: Supervision, Writing – review & editing. Seung Mok Ryoo: Supervision, Writing – re- view & editing. Chang Hwan Sohn: Supervision, Writing – review & editing.Dong Woo Seo: Supervision, Data curation. Won Young Kim: Supervision, Methodology.

Acknowledgement

None to declare.

References

  1. Yusuf S, Rangarajan S, Teo K, Islam S, Li W, Liu L, et al. Cardiovascular risk and events in 17 low-, middle-, and high-income countries. N Engl J Med. 2014;371:818-27. https://doi.org/10.1056/NEJMoa1311890.
  2. Patel MR, Peterson ED, Dai D, Brennan JM, Redberg RF, Anderson HV, et al. Low di- agnostic yield of elective coronary angiography. N Engl J Med. 2010;362:886-95. https://doi.org/10.1056/NEJMoa0907272.
  3. Fihn SD, Gardin JM, Abrams J, Berra K, Blankenship JC, Dallas AP, et al. 2012 ACCF/ AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of pa- tients with stable ischemic heart disease: a report of the American College of Cardi- ology Foundation/American Heart Association task force on practice guidelines, and the American College of Physicians, American Association for Thoracic Surgery, Pre- ventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of thoracic surgeons. Circulation. 2012;126: e354-471. https://doi.org/10.1161/CIR.0b013e318277d6a0.
  4. Montalescot G, Sechtem U, Achenbach S, Andreotti F, Arden C, Budaj A, et al. 2013 ESC guidelines on the management of stable coronary artery disease: the Task Force on the management of stable coronary artery disease of the European Society of Cardiology. Eur Heart J. 2013;34:2949-3003. https://doi.org/10.1093/eurheartj/ eht296.
  5. Pollack Jr CV, Sites FD, Shofer FS, Sease KL, Hollander JE. Application of the TIMI risk score for unstable angina and non-ST elevation acute coronary syndrome to an un- selected emergency department chest pain population. Acad Emerg Med. 2006;13: 13-8. https://doi.org/10.1197/j.aem.2005.06.031.
  6. Antman EM, Cohen M, Bernink PJ, McCabe CH, Horacek T, Papuchis G, et al. The TIMI risk score for unstable angina/non-ST elevation MI: a method for prognostication and therapeutic decision making. JAMA. 2000;284:835-42. https://doi.org/10. 1001/jama.284.7.835.
  7. Backus BE, Six AJ, Kelder JC, Mast TP, van den Akker F, Mast EG, et al. Chest pain in the emergency room: a multicenter validation of the HEART Score. Crit Pathw Cardiol. 2010;9:164-9. https://doi.org/10.1097/HPC.0b013e3181ec36d8.
  8. Cakar MA, Sahinkus S, Aydin E, Vatan MB, Keser N, Akdemir R, et al. Relation be- tween the GRACE score and severity of atherosclerosis in acute coronary syndrome. J Cardiol. 2014;63:24-8. https://doi.org/10.1016/j.jjcc.2013.06.017.
  9. Hammami R, Jdidi J, Mroua F, Kallel R, Hentati M, Abid L, et al. Accuracy of the TIMI and GRACE scores in predicting coronary disease in patients with non-ST-elevation acute coronary syndrome. Rev Port Cardiol. 2018;37:41-9. https://doi.org/10.1016/j. repc.2017.05.012.
  10. Garcia S, Canoniero M, Peter A, de Marchena E, Ferreira A. Correlation of TIMI risk score with angiographic severity and extent of coronary artery disease in patients with non-ST-elevation acute coronary syndromes. Am J Cardiol. 2004;93:813-6. https://doi.org/10.1016/j.amjcard.2003.12.015.
  11. Harris PJ, Behar VS, Conley MJ, Harrell Jr FE, Lee KL, Peter RH, et al. The prognostic significance of 50% coronary stenosis in medically treated patients with coronary ar- tery disease. Circulation. 1980;62:240-8. https://doi.org/10.1161/01.cir.62.2.240.
  12. Poldervaart JM, Reitsma JB, Backus BE, Koffijberg H, Veldkamp RF, Ten Haaf ME, et al. Effect of using the HEART score in patients with chest pain in the emergency depart- ment: a stepped-wedge, cluster randomized trial. Ann Intern Med. 2017;166: 689-97. https://doi.org/10.7326/M16-1600.
  13. Thygesen K, Alpert JS, Jaffe AS, Simoons ML, Chaitman BR, White HD, et al. Third uni- versal definition of myocardial infarction. Circulation. 2012;126:2020-35. https:// doi.org/10.1161/CIR.0b013e31826e1058.
  14. Apple FS, Smith SW, Pearce LA, Ler R, Murakami MM. Use of the Centaur TnI-Ultra assay for detection of myocardial infarction and adverse events in patients present- ing with symptoms suggestive of acute coronary syndrome. Clin Chem. 2008;54: 723-8. https://doi.org/10.1373/clinchem.2007.097162.
  15. Ferreira AM, Marques H, Tralhao A, Santos MB, Santos AR, Cardoso G, et al. Pre-test probability of obstructive coronary stenosis in patients undergoing coronary CT an- giography: comparative performance of the modified diamond-Forrester algorithm versus methods incorporating cardiovascular risk factors. Int J Cardiol. 2016;222: 346-51. https://doi.org/10.1016/j.ijcard.2016.07.180.
  16. Sorgaard M, Linde JJ, Kofoed KF, Kuhl JT, Kelbaek H, Nielsen WB, et al. Diagnostic value of the updated Diamond and Forrester score to predict coronary artery disease in patients with acute-onset chest pain. Cardiology. 2016;133:10-7. https://doi.org/ 10.1159/000438980.
  17. Genders TS, Steyerberg EW, Hunink MG, Nieman K, Galema TW, Mollet NR, et al. Prediction model to estimate presence of coronary artery disease: retrospective Pooled analysis of existing cohorts. BMJ. 2012;344:e3485. https://doi.org/10.1136/ bmj.e3485.
  18. Abu-Assi E, Ferreira-Gonzalez I, Ribera A, Marsal JR, Cascant P, Heras M, et al. Do GRACE (Global Registry of Acute Coronary events) risk scores still maintain their performance for predicting mortality in the era of contemporary management of acute coronary syndromes. Am Heart J. 2010;160:826-34 e1-3 https://doi. org/10.1016/j.ahj.2010.06.053.
  19. Six AJ, Backus BE, Kelder JC. Chest pain in the emergency room: value of the HEART score. Neth Heart J. 2008;16:191-6. https://doi.org/10.1007/bf03086144.
  20. Hamm CW, Bassand JP, Agewall S, Bax J, Boersma E, Bueno H, et al. ESC guidelines for the management of acute coronary syndromes in patients presenting without per- sistent ST-segment elevation: the Task Force for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation of the European Society of Cardiology (ESC). Eur Heart J. 2011;32:2999-3054. https:// doi.org/10.1093/eurheartj/ehr236.
  21. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Bio- metrics. 1988;44:837-45.
  22. Kerr KF, Wang Z, Janes H, McClelland RL, Psaty BM, Pepe MS. Net reclassification in- dices for evaluating risk prediction instruments: a critical review. Epidemiology. 2014;25:114-21. https://doi.org/10.1097/ede.0000000000000018.
  23. Pencina MJ, D’Agostino Sr RB, D’Agostino Jr RB, Vasan RS. Evaluating the added pre- dictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27:157-72 discussion 207-12 https://doi.org/10.1002/ sim.2929.
  24. Poldervaart JM, Langedijk M, Backus BE, Dekker IMC, Six AJ, Doevendans PA, et al. Comparison of the GRACE, HEART and TIMI score to predict Major adverse cardiac events in chest pain patients at the emergency department. Int J Cardiol. 2017; 227:656-61. https://doi.org/10.1016/j.ijcard.2016.10.080.
  25. Sakamoto JT, Liu N, Koh ZX, Fung NX, Heldeweg ML, Ng JC, et al. Comparing HEART, TIMI, and GRACE scores for prediction of 30-day major adverse cardiac events in high acuity chest pain patients in the emergency department. Int J Cardiol. 2016; 221:759-64. https://doi.org/10.1016/j.ijcard.2016.07.147.
  26. Andruchow JE, Kavsak PA, McRae AD. Contemporary emergency department man- agement of patients with chest pain: a concise review and guide for the high- sensitivity Troponin era. Can J Cardiol. 2018;34:98-108. https://doi.org/10.1016/j. cjca.2017.11.012.
  27. Westwood M, van Asselt T, Ramaekers B, Whiting P, Thokala P, Joore M, et al. High- sensitivity troponin assays for the early rule-out or diagnosis of acute myocardial in- farction in people with acute chest pain: a systematic review and cost-effectiveness analysis. Health Technol Assess. 2015;19:1-234. https://doi.org/10.3310/hta19440.
  28. Bittencourt MS, Hulten E, Polonsky TS, Hoffman U, Nasir K, Abbara S, et al. European Society of Cardiology-recommended coronary artery disease consortium pretest probability scores more accurately predict obstructive coronary disease and cardio- vascular events than the Diamond and Forrester score: the Partners Registry. Circu- lation. 2016;134:201-11. https://doi.org/10.1161/CIRCULATIONAHA.116.023396.
  29. Genders TS, Steyerberg EW, Alkadhi H, Leschka S, Desbiolles L, Nieman K, et al. A clinical prediction rule for the diagnosis of coronary artery disease: validation, updating, and extension. Eur Heart J. 2011;32:1316-30. https://doi.org/10.1093/ eurheartj/ehr014.
  30. Reaney PDW, Elliott HI, Noman A, Cooper JG. risk stratifying chest pain patients in the emergency department using HEART, GRACE and TIMI scores, with a single con- temporary troponin result, to predict major adverse cardiac events. Emerg Med J. 2018;35:420-7. https://doi.org/10.1136/emermed-2017-207172.
  31. Than M, Flaws D, Sanders S, Doust J, Glasziou P, Kline J, et al. Development and val- idation of the emergency department assessment of chest pain score and 2 h accel- erated Diagnostic protocol. Emerg Med Australas. 2014;26:34-44. https://doi.org/10. 1111/1742-6723.12164.
  32. Mark DG, Huang J, Chettipally U, Kene MV, Anderson ML, Hess EP, et al. Performance of Coronary risk scores among patients with chest pain in the emergency depart- ment. J Am Coll Cardiol. 2018;71:606-16. https://doi.org/10.1016/j.jacc.2017.11.064.
  33. Jensen JM, Voss M, Hansen VB, Andersen LK, Johansen PB, Munkholm H, et al. Risk stratification of patients suspected of coronary artery disease: comparison of five dif- ferent models. Atherosclerosis. 2012;220:557-62. https://doi.org/10.1016/j.athero- sclerosis.2011.11.027.
  34. Wasfy MM, Brady TJ, Abbara S, Nasir K, Ghoshhajra BB, Truong QA, et al. Comparison of the Diamond-Forrester method and Duke Clinical score to predict obstructive cor- onary artery disease by computed tomographic angiography. Am J Cardiol. 2012; 109:998-1004. https://doi.org/10.1016/j.amjcard.2011.11.028.

Leave a Reply

Your email address will not be published. Required fields are marked *