TY - JOUR
T1 - Performance and Validation of the U.S. NCDR Acute Kidney Injury Prediction Model in Japan
AU - Inohara, Taku
AU - Kohsaka, Shun
AU - Miyata, Hiroaki
AU - Ueda, Ikuko
AU - Maekawa, Yuichiro
AU - Fukuda, Keiichi
AU - Cohen, David J.
AU - Kennedy, Kevin F.
AU - Rumsfeld, John S.
AU - Spertus, John A.
N1 - Funding Information:
The present study was funded by the Grants-in-Aid for Scientific Research from Japan Society for the Promotion of Science (Grant Nos. 25460630, 80571398) and Pfizer Health Research Foundation. The funders had no role in the conduct of the study; in the collection, management, analysis, and interpretation of the data; or in the preparation or approval of the manuscript. Dr. Kohsaka has received unrestricted research grants for the Department of Cardiology, Keio University School of Medicine, from Pfizer Japan, Inc. and Bayer Pharmaceutical Co., Ltd. Dr. Rumsfeld is the Chief Science Officer of the National Cardiovascular Data Registry. Dr. Spertus is the principal investigator of a contract from the American College of Cardiology Foundation to analyze the National Cardiovascular Data Registry data; and has an equity interest in Health Outcomes Sciences. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Peter A. McCullough, MD, served as Guest Editor for this paper.
Publisher Copyright:
© 2016 American College of Cardiology Foundation.
PY - 2016/4/12
Y1 - 2016/4/12
N2 - Background Stratifying patient risk for acute kidney injury (AKI) prior to percutaneous coronary intervention (PCI) can enable clinicians to tailor their approach to minimize AKI. The National Cardiovascular Data Registry (NCDR) CathPCI Registry recently developed 2 prediction models: for AKI and AKI requiring dialysis (AKI-D). Objectives This study sought to externally validate the NCDR AKI and AKI-D models in a Japanese population. Determining the generalizability of the U.S. model could support quality improvement efforts in Japan. Methods The NCDR prediction models were applied to 11,041 consecutive patients in the Japanese multicenter PCI registry. AKI was defined as an absolute increase ≥0.3 mg/dl or a relative increase of 50% in serum creatinine, in accordance with the definition of AKI Network criteria; AKI-D was defined as initiation of dialysis after PCI. Discrimination and calibration of the NCDR models were tested in the Japanese cohort. If the model was perfectly calibrated, the slope and intercept would equal 1.0 and 0.0, respectively. Results In the Japanese PCI cohort, AKI and AKI-D occurred in 10.5% and 1.5% of patients, respectively. The NCDR AKI prediction model showed good discrimination (c-statistic = 0.76) and calibration (slope = 0.93 and intercept = -0.10) in both acute and nonacute PCI. The AKI-D prediction model had good discrimination (c-statistic = 0.92), but while the calibration slope was good (1.04), the intercept was significantly underestimated (0.96). However, this was corrected with recalibration (slope = 1.04 and intercept = -0.087). Conclusions In a Japanese population, the NCDR AKI models validly predict post-procedural AKI and, with recalibration, AKI-D. Prospective use of these models to inform clinical decision making should be tested as a means of reducing AKI after PCI in Japan. (Japan Cardiovascular Database, Percutaneous Coronary Intervention Registry; UMIN R000004736).
AB - Background Stratifying patient risk for acute kidney injury (AKI) prior to percutaneous coronary intervention (PCI) can enable clinicians to tailor their approach to minimize AKI. The National Cardiovascular Data Registry (NCDR) CathPCI Registry recently developed 2 prediction models: for AKI and AKI requiring dialysis (AKI-D). Objectives This study sought to externally validate the NCDR AKI and AKI-D models in a Japanese population. Determining the generalizability of the U.S. model could support quality improvement efforts in Japan. Methods The NCDR prediction models were applied to 11,041 consecutive patients in the Japanese multicenter PCI registry. AKI was defined as an absolute increase ≥0.3 mg/dl or a relative increase of 50% in serum creatinine, in accordance with the definition of AKI Network criteria; AKI-D was defined as initiation of dialysis after PCI. Discrimination and calibration of the NCDR models were tested in the Japanese cohort. If the model was perfectly calibrated, the slope and intercept would equal 1.0 and 0.0, respectively. Results In the Japanese PCI cohort, AKI and AKI-D occurred in 10.5% and 1.5% of patients, respectively. The NCDR AKI prediction model showed good discrimination (c-statistic = 0.76) and calibration (slope = 0.93 and intercept = -0.10) in both acute and nonacute PCI. The AKI-D prediction model had good discrimination (c-statistic = 0.92), but while the calibration slope was good (1.04), the intercept was significantly underestimated (0.96). However, this was corrected with recalibration (slope = 1.04 and intercept = -0.087). Conclusions In a Japanese population, the NCDR AKI models validly predict post-procedural AKI and, with recalibration, AKI-D. Prospective use of these models to inform clinical decision making should be tested as a means of reducing AKI after PCI in Japan. (Japan Cardiovascular Database, Percutaneous Coronary Intervention Registry; UMIN R000004736).
KW - external validation
KW - percutaneous coronary intervention
KW - risk model
KW - serum creatinine
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U2 - 10.1016/j.jacc.2016.01.049
DO - 10.1016/j.jacc.2016.01.049
M3 - Article
C2 - 27056778
AN - SCOPUS:84962148641
SN - 0735-1097
VL - 67
SP - 1715
EP - 1722
JO - Journal of the American College of Cardiology
JF - Journal of the American College of Cardiology
IS - 14
ER -