Modifying the Renal Angina Index for Predicting AKI and Related Adverse Outcomes in Pediatric Heart Surgery

Autor: Katja M Gist, Megan SooHoo, Emily Mack, Zaccaria Ricci, David M Kwiatkowski, David S Cooper, Catherine D Krawczeski, Jeffrey A Alten, Stuart L Goldstein, Rajit K Basu
Rok vydání: 2022
Předmět:
Zdroj: World Journal for Pediatric and Congenital Heart Surgery. 13:196-202
ISSN: 2150-136X
2150-1351
DOI: 10.1177/21501351211073615
Popis: Background:Reliable prediction of severe acute kidney injury (AKI) and related poor outcomes has the potential to optimize treatment. The purpose of this study was to modify the renal angina index in pediatric cardiac surgery to predict severe AKI and related poor outcomes. Methods: We performed a multicenter retrospective study with the population divided into a derivation and validation cohort to assess the performance of a modified renal angina index assessed at 8 h after cardiac intensive care unit (CICU) admission to predict a complex outcome of severe day 3 AKI or related poor outcomes (ventilation duration >7 days, CICU length of stay >14 days, and mortality). The derivation sample was used to determine the optimal cut-off value. Results: There were 298 and 299 patients in the derivation and validation cohorts, respectively. The incidence of severe day 3 AKI and the complex outcome was 1.7% and 28% in the derivation and validation cohort. The sensitivity analysis for fulfillment of renal angina was a score >8 with a sensitivity of 63%, specificity of 73%, and negative predictive value of 83%. The cardiac renal angina index predicted the composite outcome with an area under the curve of 0.7 (95% confidence interval: 0.62-0.78). Renal angina patients had a significantly higher probability of the complex outcome when compared to individual risk and injury categories. Conclusions: We operationalized the renal angina index for use after cardiac surgery. Further revision and modification of the construct with integration of biomarkers in a prospective cohort are necessary to refine the prediction model.
Databáze: OpenAIRE
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