Background: Adverse hemodynamic responses on exercise stress testing have prognostic impact, but the cumulative effect of multiple abnormalities is not known. These markers include chronotropic incompetence, reduced metabolic equivalents achieved, abnormal hemodynamic gain index, impaired heart rate recovery, and abnormal blood pressure response. Research Questions: In a contemporary population undergoing exercise stress testing, what is the cumulative prevalence and prognostic impact of the number of abnormal stress hemodynamic markers (asHD)? Aims: We measured the prevalence of increasing values of asHD and the associated burden of all-cause mortality and non-fatal myocardial infarction (death/NFMI). Methods: We performed a retrospective, single-center analysis of patients referred for exercise stress myocardial perfusion imaging from 2015-2017. Multivariable Cox proportional hazards modeling was used to explore the relationship between cumulative burden of asHD markers and death/NFMI. Data: The study population included 1,595 patients (mean age 62.1, 36.9% female) with mean follow-up 4.8 years stratified by number of asHD markers (0, 1, 2, or 3+). 60.2% of patients tested had at least one asHD marker. The cumulative burden was 1 asHD in 32%, 2 asHD in 19%, and 3+ asHD in 9.3%. Patients with any asHD were at greater risk for death/NFMI compared to patients with 0 asHD (HR 1.98 [95% CI, 1.39 - 2.83], p<0.001) with a stepwise increase in risk seen with a greater burden of asHD (compared to 0 asHD): 1 asHD: HR 1.70 [95% CI,1.14 - 2.52], p=0.009; 2 asHD: HR 2.02 [95% CI, 1.32-3.10], p=0.001; and 3+ asHD: HR 2.88 [95% CI 1.82-4.56], p<0.001. Conclusion: Contemporary patients referred to exercise stress testing have a substantial burden of abnormal hemodynamic markers. There is an increasing risk of death/NFMI with a greater number of abnormal markers. This data may illuminate cause of symptoms and inform prognosis, supporting the value of functional testing in this group.
Introduction: Patients with diabetes mellitus (DM) and angina have high morbidity and mortality after negative stress cardiac imaging, and myocardial perfusion reserve (MPR) measures provide additional prognostic information. We aimed to determine the association of abnormal MPR with the extent of coronary artery disease (CAD) in symptomatic diabetics. Method: Symptomatic diabetics age 18-85 without known CAD referred for stress imaging were included. Quantitative stress CMR and coronary CT angiography (CTA) were performed. Plaque distribution was characterized by “segment score” (max score of 16). Plaque severity was graded as 0=none; 1≤30%; 2=30-50%; 3=50-75%; 4≥75%. Plaque scores were summed across all segments into a “total plaque score” (max= 64). Quantitative CMR perfusion analysis was performed using Fermi deconvolution. Patients were stratified by MPR <2 and ≥2. Data were compared using Kruskal-Wallis and Fisher’s Exact testing. Result: Complete data were available in 39 patients. Median age was 5...
Abstract Objectives Early detection of subacute potentially catastrophic illnesses using available data is a clinical imperative, and scores that report risk of imminent events in real time abound. Patients deteriorate for a variety of reasons, and it is unlikely that a single predictor such as an abnormal National Early Warning Score (NEWS) will detect all of them equally well. The objective of this study was to test the idea that the diversity of reasons for clinical deterioration leading to ICU transfer mandates multiple targeted predictive models. Design Individual chart review to determine the clinical reason for ICU transfer; determination of relative risks of individual vital signs, lab tests and cardiorespiratory monitoring measures for prediction of each clinical reason for ICU transfer; logistic regression modeling for the outcome of ICU transfer for a specific clinical reason. Setting Cardiac medical-surgical ward; tertiary care academic hospital. Patients 8111 adult patients, 457 of whom were transferred to an ICU for clinical deterioration. Interventions None. Measurements and main results We calculated the contributing relative risks of individual vital signs, lab tests and cardiorespiratory monitoring measures for prediction of each clinical reason for ICU transfer, and used logistic regression modeling to calculate ROC areas and relative risks for the outcome of ICU transfer for a specific clinical reason. The reasons for clinical deterioration leading to ICU transfer were varied, as were their predictors. For example, the three most common reasons – respiratory instability, infection and suspected sepsis, and heart failure requiring escalated therapy – had distinct signatures of illness. Statistical models trained to target specific reasons for ICU transfer performed better than one model targeting combined events, and both performed better than the untrained NEWS score. Conclusions and relevance A single predictive model for clinical deterioration does not perform as well as having multiple models trained for the individual specific clinical events leading to ICU transfer.
Background Coronary artery disease (CAD) is a source of significant mortality and morbidity in diabetes mellitus (DM). Abnormal myocardial perfusion reserve (MPR) has been shown to predict worse outcome in patients with diabetes, but limited data is available using CMR. The correlation of MPR derived from Cardiac Magnetic resonance imaging (CMR) with plaque burden on cardiac computed tomography angiography (CTA) has not been previously evaluated. We aim to look at this correlation in patients undergoing vasodilator stress CMR.
Background: The utilization of myocardial perfusion imaging (MPI) for ischemia identification leads to increased expense and radiation exposure but is driven by the limited diagnostic accuracy of e...
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