Vol.48 - Número 2, Abril/Junio 2019 Imprimir sólo la columna central

Ergoscore: proposed diagnostic algorithm for ischemic heart disease in the chest pain unit
JUAN M. SALVADOR-CASABÓN, PEDRO J. SERRANO-AÍSA,
DANIEL CANTERO-LOZANO, ARTURO ANDRÉS-SÁNCHEZ
Servicio de Cardiología, Hospital Clínico Universitario “Lozano Blesa”.
(50008) Zaragoza, España.
E-mail
Recibido 21-DIC-2018 – ACEPTADO después de revisión 17-ENERO-2019.
There are no conflicts of interest to disclose.

 

ABSTRACT

Assessment of chest pain in the emergency room can be complex. We propose a diagnostic algorithm that allows classifying patients in groups of high and low risk of coronary etiology for chest pain units.
Methods: an observational retrospective cohort study with prospective follow-up of patients who came to the emergency room for chest pain suspected of ischemic etiology with normal electrocardiogram and biomarkers that underwent conventional stress test was analyzed. The mean follow-up was 15 months. Coronary catheterization showing significant lesions was considered as the gold standard for coronary etiology; while catheterization without lesions and/or absence of recurrence of pain during follow-up was considered a negative datum. Logistic regression was used to obtain predictors of coronary origin and therefore a diagnostic algorithm was elaborated, based on the Ergoscore (score made with the selected factors) and on stress test. The discriminant power of the algorithm was evaluated by Area Under the ROC curve (AUC).
Results: 100 patients were analyzed. 17% had significant coronary lesions. The diagnostic accuracy of the ergometer test was 89%. Cardiovascular risk score and pretest probability were the selected factors. Ergoscore results less than or equal to 3 and greater than or equal to 7 presented the best behavior with respect to the validity parameters. The results of the algorithm were: sensitivity = 82.4%, specificity 89.2%, accuracy 88%. No false negatives were observed. The AUC was 0.915.
Conclusions: This algorithm presents an adequate diagnostic accuracy detecting cases of coronary origin. Its application would avoid performing a stress test in 32% of patients.
Key words: Ischemic heart disease. Chest pain unit. Stress test. Algorithm.

 

INTRODUCTION
Chest pain is a reason to go the ER and it amounts to 5-15% of all visits to this service [1,2]. Occasionally, it is difficult to differentiate the mild cases from those with potentially lethal causes. Acute coronary syndrome may amount to 15.25% of cases of visits to the ER due to chest pain [3]. Up to 10% [4,5] of cases that go to the ER with acute coronary syndrome are discharged when deemed not coronary, which leads to significant consequences for the health of these patients [6].

The characteristics of chest pain in ischemic patients may present a limited value, mainly in female, elderly and diabetic patients where pain may present atypically as angina equivalent [7]. Up to 50% of cases with acute coronary syndrome do not present typical characteristics [8]. Occasionally, ECG and cardiac markers could be insufficient at the time of ruling out coronary origin [9,10].

For these reasons, prediction methods have been tried for the selection and prioritization of really urgent cases through scores [11], preventing inappropriate discharges and unnecessary admissions. Between the first posted scores we found the one by Geleijnse [12], based on a questionnaire about the chest pain characteristics that led to the ER visit. Other scores as TIMI [13] (Thrombolysis in Myocardial Infarction); GRACE [14] (Global Registry of Acute Coronary Events); HEART [15] (that evaluates clinical history, ECG, age, risk factors and biomarkers); or the Banach score [16], are also used as methods to stratify risk in patients, but their usefulness is prognostic, and not so much to determine the source of pain as coronary or not.

In this study, the validity of conventional stress test in the chest pain unit was evaluated, as well as the factors that better predict the coronary origin of chest pain. Once the results were obtained, the aim was developing an algorithm based on the chosen factors that would enable predicting the coronary origin, classifying the patients into low or high risk groups, with the goal of discharging or admitting with certainty, without the requirement of an ischemia detection test if possible, conducting the stress test in patients in whom the algorithm would not allow discriminating them into either of these two groups.

 

METHODS
Observational retrospective cohort study, with a prospective follow-up, the sample of which consisted of consecutive patients with suspicion of chest pain of the ischemic type, referred from the ER to perform a conventional stress test according to the chest pain protocol of a tertiary hospital, since April 2015 until May 2016, including both months.

The standard Bruce protocol on a treadmill was applied, with continuous electrocardiographic monitoring, and manual monitoring of blood pressure.


Inclusion criteria

  • Patients able to walk, who came to the ER because of chest pain of possible coronary origin.
  • Asymptomatic at the time of performing the test, after remaining at least 5 hours in the ER.
  • ER physician having previously checked:
    • Serial ECGs not indicating ischemia, the first within 10 min of arriving at the ER.
    • Absence of increase in 2 conventional, serial TnI measurements, at least 4 hours apart.

In all of them age, gender, medical history of cardiovascular risk (previous ischemic heart disease, hypertension, dyslipidemia, diabetes, smoking) and other pathologies were determined according to the internationally established standards [17,18], as well as their usual medication at the time of the stress test.

The basic parameters of the stress test were collected. Pretest probabilities and cardiovascular risk score (taking as data, blood pressure at the time of the ergometer test and last measurement of cholesterol in blood sample) were estimated. To simplify, we decided to appoint numbers from 1 to 4 for each category, that in each score are acknowledged by the clinical practice guidelines of the European Society of Cardiology [19,20], as observed in Tables 1 and 2.

 

Table 1. Classification of pretest probability into 4 categories, according to age,
gender and clinical characteristics of pain according to the ESC guidelines[19].
  Typical angina Atypical angina Nonanginal pain
Age (years) Males Females Males Females Males Females
30-39
40-49
50-59
60-69
70-79
>80
59
69
77
84
89
93
28
37
47
58
68
76
29
38
49
59
69
78
10
14
20
28
37
47
18
25
34
44
54
65
5
8
12
17
24
32

Pretest probability
Category Value
Low
Intermediate
Intermediate-high
High
1
2
3
4

 

Table 2. Classification of the risk score into 4 categories according to the ESC guidelines[18].
 
  Very high risk Some of the following:
• Clinical CVD: AMI, coronary or artery revascularization, stroke, aortic aneurysm, peripheral artery disease.
• CVD by imaging: plaque in coronary angiography or carotid artery echocardiography.
• Diabetic with target organ lesion
• Severe CKD (GFR<30 ml/min/1.73m2)
• SCORE ≥ 10%
  High risk • Cholesterol >310 mg/dl,
• Blood pressure≥ 180/110 mmHg
• Most diabetic patients
• Moderate CKD (GFR 30-59 ml/min/1.73m2)
• SCORE ≥5% and < 10%
  Moderate risk SCORE ≥1 y <5%
  Low risk SCORE <1%
CVD: Cardiovascular disease; CKD: Chronic kidney disease; GFR: Glomerular filtration rate

SCORE risk
Category Value
Low
Moderate
High
Very high
1
2
3
4

 

As positivity data in stress test, we considered the development of chest angina during stress test, as well as the electrocardiographic alterations in repolarization according to international standards. Patients were monitored at least for 5 minutes after the end of the stress test. Three degrees of positivity were established: positive clinical and electrical stress test when meeting clinical and electrical positivity criteria; positive clinical and/or electrical stress test when meeting at least one criterion; doubtful positive clinical and/or electrical stress test when the clinical symptoms were atypical and/or ST depression >0.5 mm but <1 mm.

We considered coronary angiography during admission or during follow-up, showing significant coronary lesions, as the diagnostic gold standard; considering as negativity the absence of pain recurrence during follow-up. Therefore, the patients were assigned to 2 groups: positive ischemic heart disease when coronary source was confirmed, and negative ischemic heart disease when no coronary source for the pain was proven. Follow-up was made through the Electronic Clinical History.

  • Authorization was requested to the local Ethics Committee to use retrospective data and the prospective follow-up of electronic clinical records.

 

STATISTICAL ANALYSIS
A comparative study of patients was made, grouped according to the existence or not of proof of coronary source for the pain in regard to all the variables considered in the study. For this reason, the Chi-square independence test was used for qualitative variables and student’s t test for comparing means was used for quantitative variables. Subsequently, a study was made on the discrimination power of the analyzed variables in regard to the two previous groups by logistic regression, applying a variable selection procedure (stepwise-forward) with input p value of 5% and output p value of 10%, using likelihood ratios (LR) contrast as selection criterion. From the selected variables, a score would be assigned to factors selected with the aim of performing a diagnostic algorithm, using ergometer test in the cases in which the model was not able to discriminate completely. Diagnostic validity was verified by the estimation of sensitivity, specificity, positive predictive value, negative predictive value and overall value of the test. A ROC curve was estimated, and compared to other existing models. All estimations were made using the SPSS 22.0 statistics package.

 

RESULTS
A total of 100 patients were included after ruling out the cases with abnormal ECG and/or elevated troponin. The average age was 58.23±2.48 years, with 68% being males.

From the 100 stress tests, there were 20 with some degree of positivity in it; 7 clinically and electrically positive (3 of which were categorized as of high risk); 6 clinically or electrically positive but not both; and 7 of them borderline or doubtful clinically or electrically positive.

The mean follow-up was 15 months. 17 % of patients presented coronary lesions, while 83% did not show ischemic heart disease. The patients were assigned to 2 groups: positive ischemic heart disease and negative ischemic heart disease. The diagnostic accuracy of stress test was 89% (Table 3). The characteristics of each group are observed in Table 4.

Table 3. Validity parameters for conventional stress test
for the diagnosis of ischemic heart disease.
    Catheterization and/or clinical follow-up
Ergometer test   Positive Negative Total
Test + 13
7 20
Test - 4 76 80
TOTAL 17 83 100

Ergometer test
Sensitivity
Specificity
Positive predictive value
Negative predictive value
Accuracy
77%
92%
65%
95%
89%

 

Table 4. Comparison of the factors/variables studied for each group
Variable Ischemic heart disease Negative % (n=83) Ischemic heart disease Positive % (n=17) p value
  Age (years)
Age >70 years
Males
Risk score
Previous ischemic heart disease
Hypertension
Dyslipidemia
Diabetes
Smoker
Obesity
COPD
Atrial fibrillation
Pretest probability
Aspirin
Beta blockers
ACEI / ARBs
Calcium antagonists
AV nodal blocking drugs
Statins
Oral hypoglycemic drugs
Insulin
PPI
%MTHR>85
%MTHR>95
BRUCE stage
METS
% MTHR
Double product
57.14±1.41
21.7 (18)
65.1 (54)
2.25±0.11
7.2 (6)
49.4 (41)
45.8 (38)
18.1 (15)
19.3 (16)
10.8 (9)
9.6 (8)
6.0 (5)
1.95±0.05
14.5 (12)
16.9 (14)
36.1 (30)
10.8 (9)
18.1 (15)
33.7 (28)
15.7 (13)
6.0 (5)
22.9 (19)
88.0 (73)
68.7 (57)
3.42±0.12
11.67±0.40
96.76±1.25
26586.63±702.94
63.53±2.06
23.5 (4)
82.4 (14)
3.24± 0.18
35.3 (6)
47.1 (8)
70.6 (12)
35.3 (6)
64.7 (11)
17.6 (3)
17.6 (3)
5.9 (1)
2.59±0.17
29.4 (5)
11.8 (2)
41.2 (7)
11.8 (2)
29.4 (5)
64.7 (11)
29.4 (5)
5.9 (1)
47.1 (8)
8.2.4 (14)
47.1 (8)
3.18±0.21
10.76±0.71
94.24±2.51
24209.35±1107.12
0.015
1.000
0.254
<0.001
0.005

1.000
0.108
0.187
<0.001
0.424
0.392
1.000
0.002
0.159
1.000
0.785
1.000
0.322
0.027
0.183
1.000
0.068
0.691
0.101
0.397
0.339
0.401
0.149
COPD: Chronic obstructive pulmonary disease. ARBs: Angiotensin II receptor blockers. PPI: Proton-pump inhibitors. ACEI: Angiotensin-converting-enzyme inhibitors. METS: Measurement unit of metabolic rate. % MTHR: % Maximum theoretical heart rate.

 

When performing the logistic regression of the observed factors in Table 4, the cardiovascular risk score and the pretest probability were selected as the best independent predictors of a diagnosis of chest pain due to coronary origin. Since the classification of both factors was distributed in ascending order from 1 to 4, we decided to define the addition of both factors as ERGOSCORE, with values from 2 to 8.

After examining the different scores of the Ergoscore, the values of less than 3 and greater than 7 were optimal at the time of differentiating a coronary source for the pain or not, leaving patients with values from 4 to 6 undefined. These patients should be referred to stress test. The algorithm is observed in Figure 1. The results of the validity of the algorithm with these cutoff scores when applied on our sample were: sensitivity 82%, specificity 90%. The overall value of the test was 88% (Table 5). The area under the curve (AUC) of the resulting model was 0.915 as observed in Figure 2.

Figure 1. Algorithm proposed for the management of chest pain in the ER, based on the Ergoscore.

 

Table 5. Results of the sample when applying the Algorithm and validity parameters
   

Catheterization/
Positive follow-up

Catheterization/
Negative follow-up

Total
  Ergoscore ≤3

Predicted +
Predicted –
TOTAL

0
0
0
0
25
25
0
25
25
  Ergoscore 4-6 Ergometer (+)
Ergometer (–)
TOTAL
9
3
12
7
49
56
16
52
68
  Ergoscore ≥7 Predicted +
Predicted –
TOTAL
5
0
5
2
0
2
7
0
7
  OVERALL Ergometer (+) + Ergoscore≥7

Ergometer (-) + Ergoscore≤3
14


3
9


74
23


77
  TOTAL 17 83 100

Algorithm
Sensitivity
Specificity
Positive predictive value
Negative predictive value
Accuracy
82,4 %
89,2%
60.9%
96,1%
88%

 

Figure 2. Area under the ROC curve of the proposed algorithm for the management of patients with chest pain in the ER.

 

 

To solve the difference in magnitude between the group with coronary origin for the pain (17%) against the group without ischemic heart disease (83%), we decided to assess the sample at 50%, so that both groups would have the same weight. Thus, we observed that the sensitivity, specificity and AUC of the algorithm remained constant. The complete results of the sample with and without assessment are available as Supplementary Material in the Online version of Revista FAC (www.revistafac.org.ar)

 

DISCUSSION
This study proposes an algorithm as a diagnostic tool within the ER chest pain unit, for patients with acute chest pain and normal additional tests after a stay in the observation unit. It was carried out from factors chosen in logistic regression, which were the pretest probability and cardiovascular risk score, assigning a score of 1 to 4 to each of the 4 categories for each factor and adding them, thus obtaining the Ergoscore with values from 2 to 8 points.

The proposed algorithm presents values ≤3 and ≥7 for Ergoscore (which represents 32% of all patients); the best performance and according to whose values we may refer patients without requiring more procedures, either to discharge or admission for coronary angiography with certainty. For the scores from 4 to 6 of the Ergoscore, it is necessary to enhance the study with ergometer test to differentiate a coronary source or not for the chest pain, as the score per se does not discriminate well enough.

Jointly, the algorithm obtains results of validity in terms of sensitivity 82.4%, specificity 89.2% and an overall accuracy of the test of 88%. The area under the ROC curve was greater than other published diagnostic scores [12,15,16,21,22].

When comparing the results of the algorithm with those of the stress test, similar results are observed (test accuracy of 88% before 89% respectively) with mild improvement in sensitivity (82.4% before 77%).

There are other scores that usually are used in acute coronary syndrome when already diagnosed, and validated by risk stratification, prognosis and therapeutic decisions in these patients [16,23]. However, at the time of helping to determine the coronary origin of pain, its usefulness is not so clear [24,25,26,27,28]: the GRACE score [14] overestimates the age parameter and its estimation is complex, requiring a computer or calculator. The TIMI [13] and Banach [16] scores only collect binary variables. None of them take into account the symptoms of the patient either.

The HEART score was defined from the usual elements an ER physician uses when assessing a patient with chest pain, with each parameter being assigned values from 0 to 2 points, with the factors used not being selected in a logistic regression. It presents a score of values similar to our Ergoscore estimation. Moreover, the disease variable is biased as it is subjective. In a multicenter study that evaluated the usefulness of the HEART score in patients that went to the ER due to chest pain, a score less than or equal to 3 implied 0.99% of adverse events (acute coronary syndrome, revascularization or death) during the follow-up in 6 weeks, while in those patients with a HEART score greater than or equal to 7, the percentage observed was 65.2% [26].

In this study, performed at the Hospital Clínico Universitario “Lozano Blesa”, with a 15-month follow-up, by applying the Ergoscore on the sample, no patient with a score of less than 3 presented events; while those with an Ergoscore greater than 7 observed coronary lesions in 71.4% of patients. In the intermediate results of the HEART score (from 4 to 6 points), Backus observed 11.6% of adverse events during 6 weeks [26]. In this study, patients with intermediate Ergoscore (4-6), through the algorithm are referred to ergometer test, and we observed significant coronary lesions in 17.6%.

The Geleijnse score [12] uses exclusively chest pain characteristics, regardless of the other characteristics of the individual, which limits its diagnostic capacity. This score was included by Sanchis et al, in their prognostic assessment studies on coronary risk in chest pain [29,30,31]; although its use was prognostic and not diagnostic. In the Boubaker et al study, a model was prepared for the diagnosis of chest pain based on the combination of the chest pain characteristics of the patient and cardiovascular risk factors. This was made by estimating the Geleijnse score and the TIMI score [21]. The AUC of both combined scores was 0.85 (0.60-0.90 CI 95%); while the areas of the original scores were 0.74 (0.67-0.81 CI 95%) and 0.79 (0.74-0.84 CI 95%) respectively. However, the diagnosis of acute coronary syndrome was made with clinical criteria and cardiovascular events were collected over a month of follow-up. For the estimation of the TIMI score [13], two of the variables (biomarkers and ST anomalies) were always negative because of the inclusion criteria of the study.

A composite score based on the pain and the risk factors of the patient seems to make sense. Montero et al, recently observed a substantial improvement of the Geleijnse score values by adding the history of ischemic heart disease and dyslipidemia [22]. In their study, they evaluated a population sample similar to that in our study, and found that the best predictive factors for ischemic heart disease were the Geleijnse score [12], history of ischemic heart disease and dyslipidemia. Nevertheless, the diagnosis of acute coronary syndrome was defined according to the criteria by the clinician and the events during follow-up were not taken into account. They made a score combining these three factors with sensitivity results of 76.7%, specificity of 91.8% for the optimal cutoff point, and AUC of 0.89.

Similarly, the study made at the Hospital Clínico Universitario “Lozano Blesa”, uses the chest pain characteristics (by the pretest probability, a score usually used in stable ischemic heart disease [10]; it shows its usefulness by being chosen within logistic regression) and the risk factors (by the risk score) to generate the algorithm. The validity results of this model are similar to ours, where a discrete better sensitivity and AUC were observed, although the proposed algorithm includes the performance of a stress test for the individuals with intermediate values.

The chest pain unit presents a low prevalence of ischemic heart disease [32,33]; however, it will also depend on the criterion used to define ischemic heart disease: Montero et al [22] found 36% of cases of ACS during admission with normal ECG and troponin levels, based on clinical criteria. In other studies, the coronary cases were diagnosed by evidence of ischemia in stress test imaging [12], or coronary angiography with lesions during admission in the cases with positive ergometer test [34]. We consider as diagnostic of coronary origin, a coronary angiography during the visit to the ER or during mean 15-month follow-up showing coronary lesions, as this is an objective proof and the gold standard for the diagnosis of CAD. When observing in our sample a lower prevalence of disease (17%), predictive values are in agreement with a high negative predictive value and a lower positive predictive value. When assessing the sample with a prevalence of ischemic heart disease in 50% of patients, to prevent individuals without ischemic heart disease having weight, we verified that the algorithm is still predicting properly with sensitivity, specificity and AUC remaining constant.

It is true that the studied population presents a low prevalence of disease and most patients will be categorized by the diagnostic algorithm as healthy, so a high sensitivity of the test is important to not discharge patients with acute coronary syndrome. In this sense, the proposed algorithm presents a good behavior.


Limitations
It is a single-center study, with a not very wide sample size and with retrospective inclusion of patients, although there was a prospective collection of events. The prevalence of proven ischemic heart disease was low, in spite of considering true positives as those with coronary angiography with coronary lesions and also those who had events during follow-up. Nevertheless, in most of the published series, prevalences are also low. The assignment of patients to the group of ischemic heart disease due to finding coronary lesions also during follow-up, could overestimate the positives in some case. The use of conventional troponin I instead of high-sensitivity techniques could have underestimated the initial detection.

These results would require an out-of-sample validation, and if possible, a multicenter one, to corroborate its true usefulness.

 

CONCLUSIONS
The Ergoscore, based on pretest probability and cardiovascular risk score, is useful to decide on the management of patients in the chest pain unit and presents a remarkable diagnostic validity to detect cases of coronary origin. In the extreme values (≤3 and ≥7), it presents a better performance and where its discriminating capacity is most useful and safe at the time of discharging or admitting a patient, preventing the performance of 32% of ergometer tests. We think that in the most doubtful cases with intermediate Ergoscore values, the indication of ergometer turns the overall value of the algorithm very useful, with an area under the curve of 0.915.

 

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Publication: June 2019



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