Presenting Symptoms in Patients Undergoing Coronary Artery Disease Evaluation: Association With Noninvasive Test Results and Clinical Outcomes in the PROMISE Trial

Increasing frequency of dyspnea among patients referred for cardiac stress testing

OBJECTIVE

To assess the frequency, change in prevalence, and prognostic significance of dyspnea among contemporary patients referred for cardiac stress testing.

PATIENTS AND METHODS

We evaluated the prevalence of dyspnea and its relationship to all-cause mortality among 33,564 patients undergoing stress/rest SPECT-MPI between January 1, 2002 and December 31, 2017. Dyspnea was assessed as a single-item question. Patients were divided into three temporal groups.

RESULTS

The overall prevalence of dyspnea in our cohort was 30.2%. However, there was a stepwise increase in the temporal prevalence of dyspnea, which was present in 25.6% of patients studied between 2002 and 2006, 30.5% of patients studied between 2007 and 2011, and 38.7% of patients studied between 2012 and 2017. There was a temporal increase in the prevalence of dyspnea in each age, symptom, and risk factor subgroup. The adjusted hazard ratio for mortality was higher among patients with dyspnea vs those without dyspnea both among all patients, and within each chest pain subgroup.

CONCLUSIONS

Dyspnea has become increasingly prevalent among patients referred for cardiac stress testing and is now present among nearly two-fifths of contemporary cohorts referred for stress-rest SPECT-MPI. Prospective study is needed to standardize the assessment of dyspnea and evaluate the reasons for its increasing prevalence.

Abnormal Resting Myocardial Contrast Echocardiographic Uptake: Clue of an Ongoing Acute Coronary Artery Event

Acute coronary syndromes (ACSs) present most frequently with chest pain, but angina equivalents such as dyspnea, diaphoresis, and fatigue are not uncommon. Atypical presentations are especially seen in women, the elderly, and diabetics. Cardiac evaluation using a transthoracic echocardiogram is almost always done before or immediately after someone undergoes left heart catheterization for ACS. It provides information valuable information regarding wall motion, left ventricular systolic function, diastolic function, right ventricular involvement, pulmonary pressures, incidental valvular disease, pericardial fluid, or any other unsuspected abnormality. We describe a novel case where an atypical presentation of ACS was suspected based on the lack of intravenous contrast administered, to enhance endocardial border resolution. The use of contrast during echocardiography has been used during stress protocols to assess microcirculation during perfusion assessment studies. However, we described a reduced uptake during the acquisition of resting myocardial echocardiogram images and it was very useful to direct therapy.

Predicting left main stenosis in stable ischemic heart disease using logistic regression and boosted trees

BACKGROUND

The ISCHEMIA trial showed similar cardiovascular outcomes of an initial conservative strategy as compared with invasive management in patients with stable ischemic heart disease without left main stenosis. We aim to assess the feasibility of predicting significant left main stenosis using extensive clinical, laboratory and non-invasive tests data.

METHODS

All adult patients who had stress testing prior to undergoing an elective coronary angiography for stable ischemic heart disease in Ontario, Canada, between April 2010 and March 2019, were included. Candidate predictors included comprehensive demographics, comorbidities, laboratory tests, and cardiac stress test data. The outcome was stenosis of 50% or greater in the left main coronary artery. A traditional model (logistic regression) and a machine learning algorithm (boosted trees) were used to build prediction models.

RESULTS

Among 150,423 patients included (mean age: 64.2 ± 10.6 years; 64.1% males), there were 9,225 (6.1%) with left main stenosis. The final logistic regression model included 24 predictors and 3 interactions, had an optimism-adjusted c-statistic of 0.72 and adequate calibration (optimism-adjusted Integrated Calibration Index 0.0044). These results were consistent in subgroups of males and females, diabetes and non-diabetes, and extent of ischemia. The boosted tree algorithm had similar accuracy, also resulting in a c-statistic of 0.72 and adequate calibration (Integrated Calibration Index 0.0054).

CONCLUSIONS

In this large population-based study of patients with stable ischemic heart disease using extensive clinical data, only modest prediction of left main coronary artery disease was possible with traditional and machine learning modelling techniques.