Literature DB >> 25891989

Development and validation of a pre-percutaneous coronary intervention risk model of contrast-induced acute kidney injury with an integer scoring system.

Taku Inohara1, Shun Kohsaka2, Takayuki Abe3, Hiroaki Miyata4, Yohei Numasawa5, Ikuko Ueda1, Yutaro Nishi6, Kotaro Naito7, Masaru Shibata8, Kentaro Hayashida1, Yuichiro Maekawa1, Akio Kawamura1, Yuji Sato9, Keiichi Fukuda1.   

Abstract

Previous models for contrast-induced acute kidney injury (CI-AKI) after percutaneous coronary intervention (PCI) include procedure-related variables in addition to pre-procedural variables. We sought to develop a risk model for CI-AKI based on pre-procedural variables and compare its predictability with a conventional risk model and also to develop an integer score system based on selected variables. A total of 5,936 consecutive PCIs registered in the Japanese Cardiovascular Database were analyzed (derivation cohort, n = 3,957; validation cohort, n = 1,979). CI-AKI was defined as an increase in serum creatinine of 50% or 0.3 mg/dl compared with baseline. From the derivation cohort, 2 different CI-AKI risk models were generated using logistic regression analyses: a pre-procedural model and a conventional model including both pre-procedural and procedure-related variables. The predictabilities of the models were compared by c-statistics. An integer score was assigned to each variable in proportion to each estimated regression coefficient for the final model. In our derivation cohort, the proportion of CI-AKI was 9.0% (n = 358). Predictors for CI-AKI included older age, heart failure, diabetes, previous PCI, hypertension, higher baseline creatinine level, and acute coronary syndrome. Presence of procedure-related complications and insertion of intra-aortic balloon pumping were included as procedure-related variables in the conventional model. Both the conventional model (c-statistics 0.789) and the pre-procedural model (c-statistics 0.799) demonstrated reasonable discrimination. The integer risk-scoring method demonstrated good agreement between the expected and observed risks of CI-AKI in the validation cohort. In conclusion, the pre-procedural risk model for CI-AKI had acceptable discrimination compared with the conventional model and may aid in risk stratification of CI-AKI before PCI.
Copyright © 2015 Elsevier Inc. All rights reserved.

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Year:  2015        PMID: 25891989     DOI: 10.1016/j.amjcard.2015.03.004

Source DB:  PubMed          Journal:  Am J Cardiol        ISSN: 0002-9149            Impact factor:   2.778


  13 in total

1.  Baseline atrial fibrillation is associated with contrast-induced nephropathy after cardiac catheterization in coronary artery disease: Systemic review and meta-analysis.

Authors:  Narut Prasitlumkum; Chanavuth Kanitsoraphan; Veraprapas Kittipibul; Pattara Rattanawong; Pakawat Chongsathidkiet; Wisit Cheungpasitporn
Journal:  Clin Cardiol       Date:  2018-11-26       Impact factor: 2.882

2.  Development and validation of AKI prediction model in postoperative critically ill patients: a multicenter cohort study.

Authors:  Yu Zhang; Xiaochong Zhang; Xiuming Xi; Wei Dong; Zongmao Zhao; Shubo Chen
Journal:  Am J Transl Res       Date:  2022-08-15       Impact factor: 3.940

3.  A simple proteinuria-based risk score predicts contrast-associated acute kidney injury after percutaneous coronary intervention.

Authors:  Wakaya Fujiwara; Hideki Ishii; Yoshihiro Sobue; Shinya Shimizu; Tomoya Ishiguro; Ryo Yamada; Sayano Ueda; Hideto Nishimura; Yudai Niwa; Akane Miyazaki; Wataru Miyagi; Shuhei Takahara; Hiroyuki Naruse; Junichi Ishii; Ken Kiyono; Eiichi Watanabe; Hideo Izawa
Journal:  Sci Rep       Date:  2022-07-19       Impact factor: 4.996

4.  Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records.

Authors:  Kang Liu; Xiangzhou Zhang; Weiqi Chen; Alan S L Yu; John A Kellum; Michael E Matheny; Steven Q Simpson; Yong Hu; Mei Liu
Journal:  JAMA Netw Open       Date:  2022-07-01

Review 5.  Application of appropriate use criteria for percutaneous coronary intervention in Japan.

Authors:  Taku Inohara; Shun Kohsaka; Ikuko Ueda; Takashi Yagi; Yohei Numasawa; Masahiro Suzuki; Yuichiro Maekawa; Keiichi Fukuda
Journal:  World J Cardiol       Date:  2016-08-26

6.  Development of a preprocedure nomogram for predicting contrast-induced acute kidney injury after coronary angiography or percutaneous coronary intervention.

Authors:  Bao-Liang Guo; Fu-Sheng Ouyang; Shao-Ming Yang; Zi-Wei Liu; Shao-Jia Lin; Wei Meng; Xi-Yi Huang; Li-Zhu Ouyang; Hai-Xiong Chen; Qiu-Gen Hu
Journal:  Oncotarget       Date:  2017-08-24

7.  Preprocedural Prediction Model for Contrast-Induced Nephropathy Patients.

Authors:  Wen-Jun Yin; Yi-Hu Yi; Xiao-Feng Guan; Ling-Yun Zhou; Jiang-Lin Wang; Dai-Yang Li; Xiao-Cong Zuo
Journal:  J Am Heart Assoc       Date:  2017-02-03       Impact factor: 5.501

8.  Acute Kidney Injury Risk Assessment: Differences and Similarities Between Resource-Limited and Resource-Rich Countries.

Authors:  Kianoush Kashani; Etienne Macedo; Emmanuel A Burdmann; Lai Seong Hooi; Dinesh Khullar; Arvind Bagga; Rajasekara Chakravarthi; Ravindra Mehta
Journal:  Kidney Int Rep       Date:  2017-04-25

9.  Proteinuria as an independent risk factor for contrast-induced acute kidney injury and mortality in patients with stroke undergoing cerebral angiography.

Authors:  Yiming Tao; Wei Dong; Zhilian Li; Yuanhan Chen; Huaban Liang; Ruizhao Li; Liyi Mo; Lixia Xu; Shuangxin Liu; Wei Shi; Li Zhang; Xinling Liang
Journal:  J Neurointerv Surg       Date:  2016-04-22       Impact factor: 5.836

10.  Prognostic Impact of Subsequent Acute Coronary Syndrome and Unplanned Revascularization on Long-Term Mortality After an Index Percutaneous Coronary Intervention: A Report From a Japanese Multicenter Registry.

Authors:  Taku Inohara; Shun Kohsaka; Hiroaki Miyata; Mitsuaki Sawano; Ikuko Ueda; Yuichiro Maekawa; Keiichi Fukuda; Philip G Jones; David J Cohen; Zhenxiang Zhao; John A Spertus; Kim G Smolderen
Journal:  J Am Heart Assoc       Date:  2017-10-27       Impact factor: 5.501

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