RESEARCH ARTICLE
Myocardial Perfusion SPECT Utility in Predicting Cardiovascular Events Among Indonesian Diabetic Patients
Manoefris Kasim1, Geoffrey M. Currie2, 3, Markus Tjahjono1, Bambang B. Siswanto1, Ganesja M. Harimurti1, Hosen Kiat2, 3, 4, *
Article Information
Identifiers and Pagination:
Year: 2013Volume: 7
First Page: 82
Last Page: 89
Publisher ID: TOCMJ-7-82
DOI: 10.2174/1874192401307010082
Article History:
Received Date: 6/8/2012Acceptance Date: 1/9/2012
Electronic publication date: 20 /9/2013
Collection year: 2013
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
Abstract
Background:
Indonesia has the fourth largest number of diabetes patients after India, China and the USA. Coronary artery disease (CAD) is the most common cause of death in diabetic patients. Early detection and risk stratification is important for optimal management. Abnormal myocardial perfusion imaging (MPI) is an early manifestation in the ischemic cascade. Previous studies have demonstrated the use of MPI to accurately diagnose obstructive CAD and predict adverse cardiac events. This study evaluated whether MPI predicts adverse cardiac event in an Indonesian diabetic population.
Method:
The study was undertaken in a consecutive cohort of patients with suspected or known CAD fulfilling entry criteria. All had adenosine stress MPI. The end point was a major adverse cardiac event (MACE) defined as cardiac death or nonfatal myocardial infarction (MI).
Results:
Inclusion and exclusion criteria were satisfied by 300 patients with a mean follow-up of 26.7 ± 8.8 months. The incidence of MACEs was 18.3% among diabetic patients, versus 9% in the non-diabetic population (p < 0.001). A multivariable Cox proportional hazard model demonstratedin dependent predictors for a MACE as abnormal MPI [HR: 9.30 (3.01 – 28.72), p < 0.001], post stress left ventricular ejection fraction (LVEF) ≤30% [HR:2.72 (1.21 – 6.15), p = 0.016] and the patients diabetic status [HR:2.28 (1.04 – 5.01), p = 0.04]. The Kaplan Meier event free survival curve constructed for the different subgroups based on the patients’ diabetic status and MPI findings demonstrated that diabetic patients with an abnormal MPI had the worst event free survival (log rank p value < 0.001).
Conclusions:
In an Indonesian population with suspected or known CAD abnormal adenosine stress MPI is an independent and potent predictor for adverse cardiovascular events and provides incremental prognostic value in cardiovascular risk stratification of patients with diabetes.
