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.

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

CITTANTI, Corrado;
2012

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.
2012
W., Acampa; M., Petretta; L., Evangelista; S., Daniele; E., Xhoxhi; M. L., De Rimini; Cittanti, Corrado; F., Marranzano; M., Spadafora; S., Baldari; L., Mansi; A., Cuocolo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/1641073
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