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01.03.2012 | Original Article | Ausgabe 3/2012

European Journal of Nuclear Medicine and Molecular Imaging 3/2012

Myocardial perfusion imaging and risk classification for coronary heart disease in diabetic patients. The IDIS study: a prospective, multicentre trial

Zeitschrift:
European Journal of Nuclear Medicine and Molecular Imaging > Ausgabe 3/2012
Autoren:
Wanda Acampa, Mario Petretta, Laura Evangelista, Stefania Daniele, Evgjeni Xhoxhi, Maria Luisa De Rimini, Corrado Cittanti, Filippo Marranzano, Marco Spadafora, Sergio Baldari, Luigi Mansi, Alberto Cuocolo
Wichtige Hinweise
Wanda Acampa and Mario Petretta contributed equally to this work

Abstract

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.

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