Radial Pulse Spectrum Analysis as Risk Markers to Improve the Risk Stratification of Silent Myocardial Ischemia in Type 2 Diabetic Patients.

Abstract

Diabetic patients with silent myocardial ischemia (SMI) have elevated rates of morbidity and mortality and need intensive care and monitoring. An early predictor of SMI may lead to early diagnosis and medical treatment to prevent progression and adverse clinical events. Therefore, this paper was aimed to evaluate the radial pulse spectrum as risk markers to improve the risk stratification of SMI in type-2 diabetic patients; 195 diabetic patients at high-risk of SMI were enrolled. All patients underwent myocardial perfusion imaging and radial pressure wave measurement. The spectrum analysis of the radial pressure wave was calculated and transformed into Fourier series coefficients Cns and Pns. The risk of SMI (odds ratio: 4.46, 95%, C.I. 1.61-12.4, [Formula: see text]) was raised in diabetic patients classified high-risk group by C2. Multivariable regression analysis showed that C2 ([Formula: see text]) and ankle-brachial index [(ABI) [Formula: see text])] were related to SMI ([Formula: see text] and [Formula: see text]). The myocardial ischemic score (MIS), combining C2, C3, and P5, the albumin-to-creatinine ratio (ACR), and ABI, presented an excellent risk stratification performance in enrolled patients (odds ratio: 5.78, 95%, C.I. 2.29-14.6, [Formula: see text]). The area under receiver operating characteristic curves for C2, C3, P5, ABI, ACR, and MIS were 0.66, 0.60, 0.68, 0.51, 0.56, and 0.74, respectively, in identifying SMI. This paper demonstrated that C2 was independently associated with the extent of SMI in multivariable regression analysis. Odds ratio and chi-square tests reflected that C2 could be an important marker for the risk stratification of SMI. Furthermore, MIS, adding radial pulse spectrum analysis to ACR and ABI, could significantly improve the risk stratification of SMI in type-2 diabetic patients compared to any single risk factor.

• Authors: Chi-Wei Chang, Kuo-Meng Liao, Ying-Chun Chen, Sheng-Hung Wang, Ming-Yie Jan, Gin-Chung Wang
• Journal: IEEE journal of translational engineering in health and medicine (IEEE J Transl Eng Health Med) Vol. 6 Pg. 1900509 ( 2018) ISSN: 2168-2372 [Print] United States
• PMID: 30245944 (Publication Type: Journal Article)