Myocardial perfusion imaging for predicting cardiac events in Japanese patients with advanced chronic kidney disease: 1-year interim report of the ... View Full Text


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Article Info

DATE

2014-05-15

AUTHORS

Nobuhiko Joki, Hiroki Hase, Yuhei Kawano, Satoko Nakamura, Kenichi Nakajima, Tsuguru Hatta, Shigeyuki Nishimura, Masao Moroi, Susumu Nakagawa, Tokuo Kasai, Hideo Kusuoka, Yasuchika Takeishi, Mitsuru Momose, Kazuya Takehana, Mamoru Nanasato, Shunichi Yoda, Hidetaka Nishina, Naoya Matsumoto, Tsunehiko Nishimura

ABSTRACT

PurposeWhether myocardial perfusion imaging (MPI) can predict cardiac events in patients with advanced conservative chronic kidney disease (CKD) remains unclear.MethodsThe present multicenter prospective cohort study aimed to clarify the ability of MPI to predict cardiac events in 529 patients with CKD and estimated glomerular filtration rates (eGFR) < 50 ml/min per 1.732 without a definitive diagnosis of coronary artery disease. All patients were assessed by stress-rest MPI with 99mTc-tetrofosmin and analyzed using summed defect scores and QGS software. Cardiac events were analyzed 1 year after registration.ResultsMyocardial perfusion abnormalities defined as summed stress score (SSS) ≥4 and ≥8 were identified in 19 and 7 % of patients, respectively. At the end of the 1-year follow-up, 33 (6.2 %) cardiac events had occurred that included cardiac death, sudden death, nonfatal myocardial infarction, and hospitalization due to heart failure. The event-free rates at that time were 0.95, 0.90, and 0.81 for groups with SSS 0–3, 4–7, and ≥8, respectively (p = 0.0009). Thus, patients with abnormal SSS had a higher incidence of cardiac events. Multivariate Cox regression analysis showed that SSS significantly impacts the prediction of cardiac events independently of eGFR and left ventricular ejection fraction.ConclusionMPI would be useful to stratify patients with advanced conservative CKD who are at high risk of cardiac events without adversely affecting damaged kidneys. More... »

PAGES

1701-1709

References to SciGraph publications

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    http://scigraph.springernature.com/pub.10.1007/s00259-014-2781-z

    DOI

    http://dx.doi.org/10.1007/s00259-014-2781-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1050277279

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/24827603


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