Chest Pain Categorization to Predict Coronary Artery Disease
Alternative title: Limitations of Chest Pain Categorization Models to Predict Coronary artery Disease
Daniele Rovai, MD,a Danilo Neglia, MD,ab Valentina Lorenzoni, MSc,c Chiara Caselli, MSc,a Juhani Knuuti, MD,d S Richard Underwood, MDe on behalf of the EVINCI Study Investigators
aCNR, Institute of Clinical Physiology, Pisa, Italy; bFondazione CNR - Regione Toscana Gabriele Monasterio, Pisa, Italy; cScuola Superiore Sant’Anna, Pisa, Italy; dUniversity of Turku and Turku University Hospital, Turku, Finland; eImperial College London, United Kingdom
Sources of Funding
This work was supported by a grant from the European Union FP7-CP-FP506 2007 (grant agreement no. 222915, EVINCI [Evaluation of Integrated Cardiac Imaging for the Detection and Characterization
of Ischemic Heart Disease]). It was also supported in part by the Centre of Excellence in Molecular Imaging in Cardiovascular and Metabolic Research, the Academy of Finland, the Cardiovascular Biomedical Research Unit of the Royal Brompton & Harefield NHS Foundation Trust, the NIHR Cardiovascular Biomedical Research Unit at St Bartholomew’s Hospital, the Ministry of Science and Higher Education, Poland, and unrestricted grant and products from General Electric Healthcare.
Corresponding author: Daniele Rovai, MD
CNR, Institute of Clinical Physiology
Via Moruzzi 1
56124 - Pisa, Italy
Phone 0039 050 315 2216; Fax 0039 050 315 2166 ; Mobile 335 6188312
Email:
Running head: Chest pain categorization and coronary disease
Abstract
We aimed to evaluate how chest pain categorization, currently used to assess the pre-test probability of coronary artery disease (CAD), predicts the actual presence of CAD in a population of patients with stable symptoms. We studied 475 consecutive patients enrolled in the EVINCI study based on possible symptoms of CAD. Chest pain or discomfort was categorized as typical angina, atypical angina or as non-anginal according to the guidelines. Exertional dyspnea and fatigue suspected to be angina equivalents were classified as atypical angina. Patients with a probability of CAD < 20 or > 90% based on age, sex and symptoms were excluded. The endpoints of this sub-study were significant CAD (defined by invasive coronary angiography as > 50% reduction in lumen diameter in the left main stem or > 70% stenosis in a major coronary vessel, or 30-70% stenosis with fractional flow reserve ≤ 0.8), inducible myocardial ischemia at non-invasive stress imaging, and their association. Patients’ symptoms had limited ability to predict the presence of significant CAD, global c2 being 5.0. The inclusion of age increased global c2 to 18.7, and gender increased it further to 51.1. Using inducible myocardial ischemia or the association of CAD with inducible ischemia as endpoints, the ability to predict these endpoints was again better for patient demographics than for patient symptoms. Thus, the ability of current models based on symptoms, age and gender to predict the presence of CAD is mainly based on patient demographics as opposed to symptoms.
Keyword
Coronary artery disease, pre-test probability, chest pain, patient demographics
Chest pain accounts for up to 8% of all visits to the emergency departments and up to 10% of acute complaints evaluated in outpatient care (1, 2). After the first systematic analysis of chest pain characteristics by Rose et al. (3), Diamond and Forrester (4) combined symptoms with patient age and sex to assess the pre-test likelihood of coronary artery disease (CAD) at angiography. These results were confirmed by the Coronary Artery Surgery Study (5). Additional studies were conducted with data from the Duke Databank for Cardiovascular Disease, which also incorporated ECG findings and information about risk factors (6). More recently, the predictive model used by Diamond and Forrester was updated by Genders in a contemporary European population of patients with chest pain of uncertain origin (7). These risk scores are widely accepted in both European and American guidelines on stable angina (8, 9) to estimate the pre-test probability of CAD. The aim of this study was to evaluate how chest pain categorization predicts the actual presence of CAD in a contemporary European population of patients with stable symptoms.
Methods
We studied the patients enrolled in the Evaluation of Integrated Cardiac Imaging for the Detection and Characterization of Ischemic Heart Disease (EVINCI) study (10). Consecutive patients were enrolled from 14 European centers in 8 countries based on possible symptoms of stable CAD.
Chest pain or discomfort was defined as typical angina if substernal, provoked by exertion or emotional stress and relieved by rest or nitrates, as atypical angina if satisfying two of the criteria, and as non-anginal if satisfying one or none (4, 7-9). Exertional dyspnea and fatigue suspected to be angina equivalents were classified as atypical angina. Patients with an intermediate probability of CAD (20-90%) based on symptoms, age, and gender according to the Diamond and Forrester model (4) were invited to participate. Patients with acute coronary syndrome, known CAD, left ventricular ejection fraction < 35%, more than moderate valve disease or cardiomyopathy were excluded.
Each of the 475 patients who completed the protocol underwent a study of coronary anatomy by computed X-ray tomographic angiography (CCTA) and at least one coronary functional imaging study, including stress single photon emission computed tomography (293 patients) or positron emission tomography (96 patients), and/or stress echocardiography (261 patients) or cardiac magnetic resonance (85 patients). CCTA was defined as abnormal if ≥1 major coronary artery had a diameter stenosis >50%. Inducible myocardial perfusion abnormality was defined as a summed segmental difference score between stress and rest images ≥2. Inducible wall motion abnormality was defined as an increase in segmental wall motion score ≥1 from rest to stress in ≥2 contiguous segments. If at least one non-invasive study was abnormal, patients underwent invasive coronary angiography (307 patients). Significant CAD was defined by invasive angiography as >50% stenosis in the left main stem or >70% stenosis in a major coronary vessel, or 30-70% stenosis with fractional flow reserve ≤0.8. The endpoints of the sub-study on patients symptoms were significant CAD at invasive angiography, inducible myocardial ischemia at non invasive stress imaging, and the association of significant CAD with inducible ischemia. The incremental value of patients symptoms and demographics was assessed using global c2 from logistic regression models (11). Likelihood ratio test was used to evaluate the significance of the addition of age and gender to model including only symptoms. P ≤0.05 was considered significant. Calculations were made using STATA v10.
Results
A total of 475 patients with chest pain or equivalent symptoms completed the protocol (Table 1). Patients' symptoms were categorized as atypical angina in 61% of cases, typical angina in 25%, and as non-anginal chest pain in 14% of patients. The estimated pretest probability of CAD predicted by the Diamond and Forrester model was 59%. The actual prevalence of the study endpoints was significantly lower: 140 patients (29%, p < 0.001). Coronary stenoses involved a single coronary vessel in 21% of patients, two vessels in 5%, three vessels in 2% and the left main stem in 1% of patients. A total of 172 patients (36%) had inducible myocardial ischemia, and 100 (21%) had both significant coronary stenoses and inducible ischemia (P < 0.001).
Patients with significant CAD were older and more frequently male, but had chest pain characteristics similar to those of patients without significant CAD (Table 1). Diabetes mellitus was more frequent in patients with significant CAD than in those without. The pre-test probability of CAD estimated according to the Diamond & Forrester (4) and to the Genders (7) models was significantly higher in patients with significant CAD than in those without.
In our patients, symptoms had limited ability to predict the presence of significant CAD, global c2 being 5.0. The inclusion of age increased global c2 to 18.7, and sex increased it further to 51.1. Thus, the ability of a model based on symptoms, age and sex to predict the presence of CAD was mainly based on patient demographics as opposed to symptoms. Using inducible myocardial ischemia or the association of CAD with inducible ischemia as endpoints, the ability to predict these endpoints was again better for patient demographics than for patient symptoms (Fig. 1).
Discussion
This study shows that patient symptoms, categorized according to the classical definition of typical angina, atypical angina e non-anginal chest pain (4, 7-9), have a limited ability to predict significant CAD and inducible myocardial ischemia. Such a limited predictive power derives from several factors, starting from the EVINCI study design. According to the protocol, we excluded patients with <20% probability of CAD, such as young or female patients with atypical symptoms or non-anginal chest pain. We also excluded patients with >90% probability of CAD, such as patients aged over 50 years with angina. The patients excluded from the study according to the Diamond and Forrester score and to the Gender’s score are illustrated in Figs. 2 and 3. From the clinical viewpoint the patients with intermediate probability of CAD are certainly more challenging, but are those in whom prediction models are more disadvantaged. Other possible explanations can be related to accuracy in obtaining medical history, which was not monitored. Furthermore, exertional dyspnea was classified as atypical chest pain; however, if the dyspnea has some mild aspect of chest tightness it could orient the physician toward typical chest pain.
An additional explanation lies in the type of questions asked in the Diamond and Forrester and Genders models. Firstly, these questions implicitly refer to a form of angina caused by increased myocardial oxygen consumption in the presence of obstructive coronary stenoses. For this reason, chest pain is considered typical only when provoked by exertion or emotional stress, not when it occurs at rest. Secondly, only substernal chest pain is considered typical, while other common chest pain locations, such as the epigastrium, the lower jaw or teeth, the shoulders or the wrist, are considered atypical. Chest pain is also considered typical when it disappears after administration of sublingual nitrates which, however, are only taken by informed patients. Finally, symptoms associated with chest pain such as shortness of breath, fatigue or sense of impending doom, are not considered. Thus, a patient with epigastric pain, fatigue and sweating that appear at rest may erroneously be considered as being non-cardiac and at low risk according to current symptom categorization.
Another factor that could have contributed to the limited ability of patient symptoms to predict significant CAD is the frequent dissociation between coronary atherosclerosis and ischemic heart disease (16, 17). It is well known that angina can occur in the absence of obstructive lesions, and two-thirds of patients with stable angina but no coronary stenoses have abnormal vasomotion (18). It is also known that coronary obstruction does not always lead to inducible ischemia and symptoms. Autopsy studies of young victims of accidents, homicides, and suicides showed that 20% of men and 8% of women had advanced coronary lesions without clinically evident coronary disease (19). Finally, in the BARI 2D trial women were more likely than men to have angina or anginal equivalents, but had less obstructive CAD, suggesting that factors other than CAD influence symptom presentation (20).
The higher prevalence of CAD in patients with non-anginal chest pain than in those with atypical angina is in agreement with previous studies (21,22).
Whatever the reason underlying the discrepancies between predicted and observed prevalence across all ages, genders and symptoms (23), the overestimation of likelihood of CAD by common algorithms will lead to overuse of functional testing. In the current cost-conscious climate, a reassessment and recalibration of clinical algorithms is required.
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