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. 2024 Feb;117(2):432-438.
doi: 10.1016/j.athoracsur.2023.07.011. Epub 2023 Jul 23.

Furosemide Responsiveness Predicts Acute Kidney Injury Progression After Cardiac Surgery

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Furosemide Responsiveness Predicts Acute Kidney Injury Progression After Cardiac Surgery

Ying Su et al. Ann Thorac Surg. 2024 Feb.

Abstract

Background: As patients with acute kidney injury (AKI) progress to a higher stage, the risk for poor outcomes dramatically rises. Early identification of patients at high risk for AKI progression remains a major challenge. This study aimed to evaluate the value of furosemide responsiveness (FR) for predicting AKI progression in patients with initial mild and moderate AKI after cardiac surgery.

Methods: We performed 2 separate exploratory analyses. The Zhongshan cohort was a single-center, prospective, observational cohort, whereas the Beth Israel Deaconess Medical Center cohort was a single-center, retrospective cohort. We calculated 2 FR parameters for each patient, namely the FR index and modified FR index, defined as 2-hour urine output divided by furosemide dose (FR index, mL/mg/2 h) and by furosemide dose and body weight (modified FR index, mL/[mg·kg]/2 h), respectively. The primary outcome was AKI progression within 7 days.

Results: AKI progression occurred in 80 (16.0%) and 359 (11.3%) patients in the Zhongshan and Beth Israel Deaconess Medical Center cohorts, respectively. All FR parameters (considered continuously or in quartiles) were inversely associated with risk of AKI progression in both cohorts (all adjusted P < .01). The addition of FR parameters significantly improved prediction for AKI progression based on baseline clinical models involving C-index, net reclassification improvement, and integrated discrimination improvement index in both cohorts (all P < .01).

Conclusions: FR parameters were inversely associated with risk of AKI progression in patients with mild and moderate AKI after cardiac surgery. The addition of FR parameters significantly improved prediction for AKI progression based on baseline clinical models.

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