ArticleCureus2026
Preoperative Risk Stratification of Acute Kidney Injury After Transcatheter Aortic Valve Implantation Using a Multimarker Model.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
Background Acute kidney injury (AKI) following transcatheter aortic valve implantation (TAVI) is a major predictor of poor postoperative outcomes. Post-TAVI AKI is considered a multifactorial syndrome involving renal functional reserve, tubular injury, and systemic inflammation; however, reliable preoperative stratification methods remain limited. This study aimed to identify clinically relevant preoperative predictors reflecting these complementary pathophysiological domains and to evaluate their ability to stratify AKI risk before TAVI. Methods 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. Results 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. Conclusion 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.
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