# Postoperative red cell distribution width to platelet ratio is related to cardiac surgery-associated acute kidney injury

## TL;DR

This study found that a blood test called RPR can help predict kidney injury after heart surgery, and combining it with other tests improves accuracy.

## Contribution

The study introduces RPR as a novel predictor for cardiac surgery-associated acute kidney injury and demonstrates its combined predictive power with conventional biomarkers.

## Key findings

- RPR levels were significantly higher in patients with acute kidney injury after heart surgery.
- Combining RPR with blood urea nitrogen and CRP improved prediction accuracy with an AUC of 0.978.
- RPR alone had a strong predictive ability (AUC = 0.855) for cardiac surgery-associated acute kidney injury.

## Abstract

This study aimed to investigate the predictive value of the red cell distribution width-to-platelet ratio (RPR) for cardiac surgery-associated acute kidney injury (CSA-AKI).

A retrospective analysis of clinical data from 252 patients undergoing cardiac surgery with cardiopulmonary bypass (CPB) was conducted. Patients were classified into AKI (n = 136) and non-AKI (n = 116) groups based on the Kidney Disease: Improving Global Outcomes (KDIGO) consensus criteria. Baseline creatinine was defined as the last measurement obtained before surgery. For the first seven days postoperatively, patients underwent sequential assessments of complete blood counts, hepatic function, and renal function. For the analysis, we used the laboratory values collected on the day immediately preceding the onset of AKI. Missing values were addressed using simple imputation. Continuous variables with an approximately normal distribution were imputed with their mean, whereas skewed variables were imputed using the median. Receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value, and the area under the curve (AUC) was applied to compare predictive ability among different indices.

Clinical outcomes revealed significantly higher RPR levels in the AKI group compared to the non-AKI group (14.94 vs. 8.46, p < 0.001), with elevated RPR independently associated with AKI risk(Odd Ratio = 1.433, 95% CI: 1.158–1.774). The model satisfied the linearity-in-the-logit assumption, indicating that its estimated effects are reliable. ROC curve analysis demonstrated that RPR ranked second in predictive efficacy for CSA-AKI after blood urea nitrogen (BUN) (AUC = 0.855 vs. 0.926), with an optimal cutoff value of 11.416. Varieties’ combination analysis showed that combining RPR with BUN or C-reactive protein (CRP) significantly enhanced predictive accuracy, achieving an AUC of 0.978 for the RPR + CRP + BUN triad. The combined model was pre-specified prior to data analysis. The reliability had been verified and there is no interaction between variables. However, the study’s single-center design, inconsistent RPR measurement thresholds, and lack of external validation limited the generalizability of its findings. Thus, the design did not support establishing a causal link between RPR and CSA-AKI, necessitating validation through large-scale prospective trials. All ORs were subjected to adjustment. The AUC values were internally derived, with no external validation conducted.

RPR may serve as a potential predictor for CSA-AKI, and its integration with conventional biomarkers could inform renal protection strategies.