研究者情報

イノウエ　ソウイチロウ   INOUE SOICHIRO   井上莊一郎    所属   医学部医学科 麻酔学    職種   主任教授
論文種別	原著
言語種別	英語
査読の有無	査読あり
表題	Preoperative Risk Stratification of Acute Kidney Injury After Transcatheter Aortic Valve Implantation Using a Multimarker Model
掲載誌名	正式名：Cureus
掲載区分	国外
出版社名	Palo Alto, CA : Cureus, Inc.
巻・号・頁	18(7),e112756頁
著者・共著者	Obata Y, Shimmi S, Seino Y, Kamijo-Ikemori A, Inoue S.
担当区分	最終著者
発行年月	2026/07
概要	We retrospectively analyzed data from 186 patients undergoing TAVI under general anesthesia (AKI: 24 cases, 12.9%). Candidate predictors were selected based on previous studies and univariable analysis. Least absolute shrinkage and selection operator (LASSO) regression was used for variable selection. Model performance was assessed using area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). Internal validation was performed using 200 bootstrap resamples. The final model incorporating preoperative estimated glomerular filtration rate (eGFR), urinary liver-type fatty acid-binding protein (L-FABP), clusterin, and C-reactive protein (CRP) demonstrated good discrimination for post-TAVI AKI (AUC=0.85). The model also showed improved risk reclassification (C-index 0.84, NRI 0.132, IDI 0.143) and favorable net clinical benefit in decision curve analysis, particularly at low-risk thresholds (0.10-0.20). These variables reflect distinct biological domains, including renal functional reserve, tubular injury, and systemic inflammation. A preoperative risk stratification model integrating urinary biomarkers and inflammatory and renal functional markers may facilitate the identification of patients with latent renal vulnerability before TAVI. Post-TAVI AKI appears to arise from multiple interrelated pathophysiological processes rather than isolated renal dysfunction.
DOI	10.7759/cureus.112756