European journal of nuclear medicine and molecular imaging · 2011
Myocardial perfusion imaging and risk classification for coronary heart disease in diabetic patients. The IDIS study: a prospective, multicentre trial.
Клиническое исследованиеСтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Acampa Wanda, Petretta Mario, Evangelista Laura, Daniele Stefania, Xhoxhi Evgjeni, De Rimini Maria Luisa, Cittanti Corrado, Marranzano Filippo, Spadafora Marco, Baldari Sergio, Mansi Luigi, Cuocolo Alberto
Аннотация
PURPOSE: To determine whether stress-rest myocardial perfusion single-photon emission (MPS) computed tomography improves coronary heart disease (CHD) risk classification in diabetic patients.
METHODS: In 822 consecutive diabetic patients, risk estimates for a CHD event were categorized as 0% to <3%, 3% to <5%, and ≥5% per year using Cox proportional hazards models. Model 1 used traditional CHD risk factors and electrocardiography (ECG) stress test data and model 2 used these variables plus MPS imaging data. We calculated the net reclassification improvement (NRI) and compared the distribution of risk using model 2 vs. model 1. CHD death, myocardial infarction and unstable angina requiring coronary revascularization were the outcome measures.
RESULTS: During follow-up (58 ± 11 months), 148 events occurred. Model 2 improved risk prediction compared to model 1 (NRI 0.25, 95% confidence interval, CI, 0.15-0.34; p < 0.001). Overall, 301 patients were reclassified to a higher risk category, with an event rate of 28%, and 26 to a lower risk category, with an event rate of 15%. Among patients at 3% to <5% risk, 53% were reclassified at higher risk and 25% at lower risk (NRI 0.42, 95% CI 0.07-0.76; p < 0.05). The cost per NRI was $880.80 for MPS imaging as compared to an outpatient visit with an ECG stress test.
CONCLUSION: The addition of MPS imaging data to a prediction model based on traditional risk factors and ECG stress test data significantly improved CHD risk classification in patients with diabetes.
METHODS: In 822 consecutive diabetic patients, risk estimates for a CHD event were categorized as 0% to <3%, 3% to <5%, and ≥5% per year using Cox proportional hazards models. Model 1 used traditional CHD risk factors and electrocardiography (ECG) stress test data and model 2 used these variables plus MPS imaging data. We calculated the net reclassification improvement (NRI) and compared the distribution of risk using model 2 vs. model 1. CHD death, myocardial infarction and unstable angina requiring coronary revascularization were the outcome measures.
RESULTS: During follow-up (58 ± 11 months), 148 events occurred. Model 2 improved risk prediction compared to model 1 (NRI 0.25, 95% confidence interval, CI, 0.15-0.34; p < 0.001). Overall, 301 patients were reclassified to a higher risk category, with an event rate of 28%, and 26 to a lower risk category, with an event rate of 15%. Among patients at 3% to <5% risk, 53% were reclassified at higher risk and 25% at lower risk (NRI 0.42, 95% CI 0.07-0.76; p < 0.05). The cost per NRI was $880.80 for MPS imaging as compared to an outpatient visit with an ECG stress test.
CONCLUSION: The addition of MPS imaging data to a prediction model based on traditional risk factors and ECG stress test data significantly improved CHD risk classification in patients with diabetes.