Prediction of Acute Kidney Injury for Acute Type A Aortic Dissection Patients Who Underwent Sun’s Procedure by a Perioperative Nomogram

Abstract

Introduction: Postoperative acute kidney injury (AKI) occurs in 20–40% of acute type A aortic dissection (ATAAD) patients undergoing cardiac surgery. A predictive model could be developed to assess the probability of AKI in patients with ATAAD before and after cardiac surgery in a timely manner. Methods: This retrospective study enrolled a total of 224 patients with ATAAD. Patients were subjected to total arch replacement using a tetrafurcate graft with stented elephant trunk implantation according to Sun’s procedure. Statistical comparison for the collected data was done with Student’s t test or Mann-Whitney U test (continuous variables) and χ2 test (categorical variables). The independent predictors were screened by multivariate logistic regression analysis and then incorporated into a nomogram. The reliability of cardiac surgery-associated AKI (CSA-AKI) models was evaluated using the area under the receiver operating characteristic curve (AUC). Results: This study enrolled 224 ATAAD patients, including 53 patients with AKI and 171 patients without AKI. The incidence of ATAAD-induced AKI in the cohort was 23.66%. The screened predictors for AKI include iliac artery involvement, creatinine, D-dimer, autotransfusion, platelet-rich plasma reinfusion, nasal temperature, red blood cells, fresh frozen plasma, drainage, and mechanical ventilation. The calculated AUC values for model 1, model 2, model 3, and model 4 were 0.710, 0.777, 0.827, and 0.848, respectively. Model 4 was optimum for AKI risk scoring compared with model 1, model 2, and model 3. Conclusions: AKI prediction models were established for ATAAD patients using preoperative, intraoperative, and postoperative information. Particularly, model 4 shows superiority in risk prediction for CSA-AKI.

© 2022 The Author(s). Published by S. Karger AG, Basel

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Abstract of Research Article

Received: September 28, 2021
Accepted: April 22, 2022
Published online: June 22, 2022

Number of Print Pages: 14
Number of Figures: 6
Number of Tables: 6

ISSN: 1664-3828 (Print)
eISSN: 1664-5502 (Online)

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