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Nomograms for estimating coronary artery disease prognosis with gated stress myocardial perfusion SPECT
Leslee J Shaw1, James K Min, Rory Hachamovitch
1Emory Clinical Cardiovascular Research Institute, Emory University School of Medicine, 1462 Clifton Rd NE, Room 530, Atlanta, GA 30306, USA. leslee.shaw@emory.edu
Insights
New nomograms predict coronary artery disease (CAD) risk using myocardial perfusion SPECT. These tools estimate 2-year survival, aiding targeted interventions for high-risk patients.
Area of Science:
- Cardiology
- Nuclear Medicine
- Medical Imaging
Background:
- Coronary artery disease (CAD) risk stratification is crucial for patient management.
- Nomograms are established tools for estimating disease risk.
- Stress myocardial perfusion SPECT provides valuable diagnostic information.
Purpose of the Study:
- To develop risk-based nomograms for stress myocardial perfusion SPECT.
- To incorporate measures of ischemic myocardium and left ventricular function into risk prediction.
- To improve the clinical utility of SPECT in assessing CAD risk.
Main Methods:
- Prospective enrollment of 4,575 patients in the Myoview Prognosis Registry.
- Multivariable Cox proportional hazards models to predict CAD death or myocardial infarction (MI).
- Nomogram development based on Cox model results, including % ischemic myocardium and left ventricular ejection fraction (LVEF).
Main Results:
- CAD death or MI rates increased with higher % ischemic myocardium.
- Patients with rest and/or post-stress LVEF ≤45% were at higher risk.
- Developed nomograms accurately predicted 2-year CAD death or MI-free survival, with moderate-strong correlation (r=0.71) in a validation cohort.
- Pharmacologic stress with depressed LVEF and high ischemic burden indicated the lowest survival rates.
Conclusions:
- Risk-based nomograms utilizing SPECT data can effectively estimate significant CAD outcomes.
- These nomograms serve as a clinically useful tool for identifying high-risk patient subsets.
- Targeted therapeutic interventions can be guided by these risk-based nomograms.
Background:
Nomograms can be useful tools for estimating coronary artery disease (CAD) risk. We sought to devise risk-based nomograms for stress myocardial perfusion SPECT to include measures of % ischemic myocardium and left ventricular function.
Methods:
A total of 4,575 patients were consecutively and prospectively enrolled in the Myoview Prognosis Registry. Multivariable Cox proportional hazards model were employed to estimate CAD death or MI. Nomograms were devised from the results of the Cox models.
Results:
CAD death or MI rates worsened in a gradient manner by the % ischemic myocardium. Higher risk patients were those with a rest and/or post-stress LVEF ≤ 45%. A nomogram was developed to estimate 2-year CAD death or MI-free survival for exercising and pharmacologic stress patients by their post-stress LVEF and % ischemic myocardium MPS results. Patients undergoing pharmacologic stress with a rest and/or post-stress LVEF ≤ 45% with high risk ischemic findings had the lowest CAD death or MI event-free survival. For exercising patients with a preserved resting LVEF > 45%, 2-year CAD death or MI event-free survival ranged from 99.4% to 89% for 0% to ≥20% ischemic myocardium. Those at highest risk included patients undergoing pharmacologic stress with depressed LVEF. For pharmacologic stress patients with a resting LVEF ≤45%, 2-year CAD death or MI event-free survival ranged from 89% to 48% for 0% to ≥20% ischemic myocardium. For pharmacologic stress patients with a post-stress LVEF ≤ 45%, 2-year CAD death or MI event-free survival ranged from 88% to 46% for 0% to ≥20% ischemic myocardium. A validation cohort revealed moderate-strong correlation between observed and predicted survival (r = 0.71). Average discordance between observed and predicted survival was ≤2% but was greater for higher risk patients with lower predicted survival estimates.
Conclusions:
Risk-based nomograms estimating important CAD outcomes may serve as a clinically useful tool to target therapeutic intervention for high risk patient subsets.
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