scholarly journals Assessment of Agreement between Pretest Probability Score and Summed Stress Score of Myocardial Perfusion Imaging in Coronary Artery Disease

2019 ◽  
Vol 21 (2) ◽  
pp. 73-76
Author(s):  
Sharmin Rahman ◽  
Mohammad Simoon Salekin ◽  
Pupree Mutsuddy ◽  
Rezoana Ahmed ◽  
Tasnia Kawser Kanika ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Nuclear Medicine ◽  
Coronary Artery ◽  
Myocardial Perfusion Imaging ◽  
Myocardial Perfusion ◽  
Risk Stratification ◽  
Perfusion Imaging ◽  
Pretest Probability ◽  
Stress Score ◽  
Artery Disease

Objectives: Cardiovascular diseases are considered an important cause of mortality & morbidity in many developing countries including Bangladesh. The first step in evaluating a patient with Coronary Artery Disease (CAD) is the clinical assessment of pretest probability. American Heart Association/ American College of Cardiology (AHA/ACC) guidelines recommend the use of Diamond and Forrester Method (DFM) or Duke Clinical Score (DCS) for calculating Pretest Probability Score (PPS). Myocardial Perfusion study (MPI) can calculate the Summed Stress Score (SSS), an index obtained by adding the individual scores derived from the 17 segments. This study was performed to assess the agreement between the established PPS with SSS so that it can help in risk stratification. Patients and Methods: This cross-sectional observational study was carried out in National Institute of Nuclear Medicine & Allied Science (NINMAS), BSMMU from July 2016 to June 2017. A total of 89 suspected or known CAD patients were included in this study. PPS was calculated by Duke clinical scoring from brief clinical history. SSS was calculated by nuclear medicine software while performing Myocardial Perfusion Imaging (MPI). Statistical analyses were carried out by using the IBM Statistical Package for Social Sciences (SPSS) version 20.0.0 (IBM Corporation Software Group Somers, NY). Pearson correlation and Bland & Altman analyses were applied for assessing correlation and agreement between PPS and SSS. Degree of relation between variables is expressed by ‘r’ (Pearson’s correlation coefficient). Results: The mean of PPS was found 14.73 ± 3.35 and that of SSS was found 16 ± 14.01.  A positive correlation (r=0.108; p=0.312) between PPS and SSS. With Bland and Altman analysis, it was observed that mean difference of PPS and SSS was -1.27 ± 14.045. The limit of agreement ranged from -28.798 to 26.259. There was a positive correlation between PPS and SSS. Mean difference between the two scores was small. The bias between the scores was not significant. The differences within mean ± 1.96 SD were not statistically significant. Conclusion: This study shows PPS and SSS can be used interchangeably. This analysis of agreement between PPS and SSS can further enhance prediction of CAD and upgrade the utilization of SSS for risk stratification in CAD patients, which will influence therapeutic management of the patients and play a major role to reduce cardiovascular mortality and morbidity. Bangladesh J. Nuclear Med. 21(2): 73-76, July 2018

Abstract 40: Temporal Trends of SPECT Myocardial Perfusion Imaging in Patients without Coronary Artery Disease: A 17-Year Experience from a Tertiary Academic Medical Center

2013 ◽  
Vol 6 (suppl_1) ◽  
Author(s):  
Hayan Jouni ◽  
J. W Askew ◽  
Daniel J Crusan ◽  
Todd D Miller ◽  
Raymond J Gibbons
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
High Risk ◽  
Myocardial Perfusion Imaging ◽  
Myocardial Perfusion ◽  
Perfusion Imaging ◽  
Mayo Clinic ◽  
Stress Score ◽  
Artery Disease ◽  
Over Time

Background: Between 1990-2006, there was a tremendous increase in utilization of single photon emission computed tomography myocardial perfusion imaging (SPECT). Characteristics of the observed increase of SPECT utilization have never been studied. Methods: Using the Mayo Clinic nuclear cardiology database, we examined all SPECT tests performed between 1/1/1991-12/31/2007 in patients without prior coronary artery disease (CAD). Patients with history of CAD, left bundle branch block, paced rhythm, bicycle/ rest-only tests, cardiomyopathy, valvular heart disease, and technically unsatisfactory studies were excluded. The study cohort was divided into 4 time periods: 1991-5, 1996-2000, 2001-5, and 2006-7. Results: There were 29255 eligible SPECT tests (mean age 62.4 ± 12y, 54% men). Annual utilization of SPECT studies increased from 682 in 1992 to a peak of 2738 in 2002, and then decreased to 2134 in 2007, Fig 1. There were very modest changes in CAD risk factors over time. Testing of asymptomatic patients nearly doubled (21.9% in 1991-5 to 42.7% in 2006-7). Tests on patients with typical angina decreased dramatically (18.3% in 1991-5 to 5.3% in 2006-7), Fig 2. Summed stress score, summed difference score, and high risk SPECT tests decreased over time, Fig 3. Normal SPECT studies and negative study ECG increased for both pharmacologic and exercise tests. Conclusions: In Mayo Clinic Rochester, annual SPECT utilization in patients without prior CAD increased between 1992 and 2002, but then decreased in advance of national trends. Normal tests increased, while summed stress score, summed difference score, and high risk tests all decreased. These data suggest that SPECT was increasingly utilized in patients with a lower prevalence of CAD.

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