Abstract
This study aimed to determine whether coronary artery calcium score (CACS) can be a prognostic indicator for the development of major adverse cardiac events (MACEs) and compare the value of CACS with that of the 123I-betamethyl-p-iodophenyl-pentadecanoic acid (123I-BMIPP) defect score (BDS) in patients with non-ischemic heart failure with preserved ejection fraction (NIHFpEF). Among 643 consecutive patients hospitalized due to acute heart failure, 108 (74 ± 13y) were identified to have NIHFpEF on non-contrast regular chest computed tomography and 123I-BMIPP single-photon emission computed tomography (SPECT). We evaluated whether CACS and BDS were associated with MACEs using multivariate Cox models. Thirty-two MACEs developed at a mean follow-up period of 2.4years. CACS > 0 (hazard ratio [HR] 2.38, 95% confidence interval [CI] 1.02-5.54) and higher BDS (HR 16.00, 95% CI 5.88-43.49) were significantly associated with the development of MACEs. The proportion of patients who experienced MACEs was significantly higher in the CACS > 0 and high BDS group than in the CACS = 0 and low BDS group (3% vs. 75%, p < 0.001). CACS, as well as BDS, could serve as potential prognostic indicators in patients with NIHFpEF.
Highlights
One in nine people die of heart failure (HF), and half the patients diagnosed as having HF die within 5 years [1]
Higher calcium score (CACS) and higher BMIPP defect score (BDS) (HR 22.14, 95% CI 7.95–61.60) were significantly associated with the development of major cardiac events (MACEs)
We aimed to investigate whether CACS is associated with the occurrence of major cardiac events (MACEs) and to compare the prognostic value of CACS with that of BDS in patients with non-ischemic HFpEF (NIHFpEF)
Summary
Among 643 consecutive patients hospitalized due to acute heart failure, 108 (74 ± 13y) were identified to have NIHFpEF on non-contrast regular chest computed tomography and 123I-BMIPP single-photon emission computed tomography (SPECT). We evaluated whether CACS and BDS were associated with MACEs using multivariate Cox models
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