Postoperative Red Cell Distribution Width to Platelet Ratio Is Related To Cardiac Surgery-Associated Acute Kidney Injury
Zhao-XiLi1
Chen-YiCui1
Xiao-LiangQian2
Jia-XinHuang2
Jun-LongHu1
Bao-CaiWang1
Jian-ZhaoLi2
Zhao-YunCheng1✉Phone+86-13903712068Email
1Department of Cardiac surgery, Heart Center of Henan Province People’s Hospital, Henan Provincial Clinical Research Center for Cardiovascular DiseasesCentral China Fuwai Hospital, Zhengzhou UniversityZhengzhouChina
2Department of Extracorporeal Circulation, Central China Fuwai Hospital, Heart Center of Henan Province People’s Hospital, Henan Provincial Clinical Research Center for Cardiovascular DiseasesZhengzhou UniversityZhengzhouChina
Author: Zhao-Xi Lia, #, Chen-Yi Cuia, Xiao-Liang Qianb, Jia-Xin Huangb, Jun-Long Hua, Bao-Cai Wanga, Jian-Zhao Lib, Zhao-Yun Chenga, *
aDepartment of Cardiac surgery, Central China Fuwai Hospital, Zhengzhou University, Heart Center of Henan Province People’s Hospital, Henan Provincial Clinical Research Center for Cardiovascular Diseases, Zhengzhou, China
bDepartment of Extracorporeal Circulation, Central China Fuwai Hospital, Zhengzhou University, Heart Center of Henan Province People’s Hospital, Henan Provincial Clinical Research Center for Cardiovascular Diseases, Zhengzhou, China
Corresponding author : Zhao-Yun Cheng
Tel.+86-13903712068; e-mail: Chengzy@zzu.edu.cn
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Abstract
Background
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).
Methods
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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 KDIGO criteria. 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.
Results
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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(OR = 1.433, 95% CI: 1.158–1.774). 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 study further elucidated RPR’s pathophysiological role, integrating inflammatory and thrombotic mechanisms, potentially exacerbating renal injury through microcirculatory dysfunction and oxidative stress. However, the study is limited by its single-center retrospective design, necessitating validation through large-scale prospective trials.
Conclusion
RPR may serve as a potential predictor for CSA-AKI, and its integration with conventional biomarkers could inform renal protection strategies.
Key words:
Red cell distribution width to platelet ratio
Cardiac-Surgery-Associated Acute Kidney Injury
Cardiopulmonary bypass
Inflammation
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1. Introduction
Cardiac surgery on cardiopulmonary bypass (CPB) is strongly correlated with postoperative renal dysfunction, which represents one of the most frequent complications in this surgical population. This spectrum encompasses acute kidney injury (AKI), acute kidney disease (AKD), and chronic kidney disease (CKD), with AKI serving as a critical precursor to subsequent renal morbidity.1,2 Before potential progression to AKD or CKD, AKI is independently associated with elevated short-term morbidity and mortality, as well as adverse long-term outcomes. Given its critical role in both early and late disease burden, cardiac surgery-associated acute kidney injury (CSA-AKI) has emerged as a priority research domain. CSA-AKI is operationally defined by the Kidney Disease: Improving Global Outcomes (KDIGO) consensus criteria as AKI occurring within seven days after cardiac surgery.3,4 Its pathogenesis involves multifactorial mechanisms, prominently including CPB-induced systemic inflammatory responses. Blood contact with the extracorporeal circuit during CPB activates innate immune pathways through bio-incompatibility reactions with artificial surfaces, triggering the release of pro-inflammatory mediators.5 Concurrently, ischemia-reperfusion injury and oxidative stress synergistically potentiate inflammatory cascades, resulting in endothelial dysfunction and immune cell activation, which drives the systemic release of inflammatory cytokines including Tumor Necrosis Factor-α (TNF-α), Interleukin-6 (IL-6) and Interleukin-8 (IL-8) and complement activation products.6 Notably, reactive oxygen species (ROS) also induce transcriptional activation of pro-inflammatory pathways via nuclear factor kappa B (NF-κB) upregulation.58 The resultant microvascular dysregulation compromises renal hemodynamic autoregulation, while chemokine-mediated leukocyte infiltration promotes direct tubular injury and interstitial inflammation, ultimately culminating in the pathogenesis of AKI. Persistent inflammation may also contribute to progressive renal fibrosis through maladaptive repair mechanisms.3
Red cell distribution width (RDW), a routine parameter in complete blood count (CBC), has evolved beyond its traditional role in anemia diagnosis and classification. Emerging evidence positions RDW as a biomarker of chronic inflammatory states, with elevated values reflecting underlying pathophysiological stress. Clinically, RDW demonstrates prognostic utility for cardiovascular morbidity, including hypertension progression, decompensated heart failure, atherosclerotic disease severity, and postoperative cerebrovascular events.9 Platelets (PLT), beyond their hemostatic functions, play a pivotal role in systemic inflammation, injury, and stress responses. Pro-inflammatory mediators such as IL-6 and IL-1β stimulate thrombopoiesis while enhancing platelet reactivity.10 The resultant thrombocytosis exacerbates microcirculatory dysfunction through multiple pathways: amplified platelet-leukocyte aggregate formation, increased vascular permeability, and perpetuation of pro-thrombotic inflammatory feedback loops. The red cell distribution width-to-platelet ratio (RPR), a novel composite biomarker integrating inflammatory and thrombotic dimensions, has demonstrated prognostic value in critical illnesses including septic shock and acute pancreatitis.11,12 However, its association with CSA-AKI pathogenesis remains insufficiently characterized. This study is aimed at identifying the value of RPR for diagnosing CSA-AKI and evaluating the value of RPR combined with other factors for diagnosing CSA-AKI.
2. Materials and Methods
2.1 Data Source
This retrospective study enrolled 251 consecutive patients who underwent cardiac surgery under CPB at Fuwai Central China Cardiovascular Hospital from January 2024 to December 2024. Preoperative and early postoperative data were collected through medical record reviews and laboratory test results. RDW and PLT values were collected on the first postoperative day. Peripheral blood samples were obtained from all patients preoperatively and within seven days postoperatively for CBC, C-reactive protein (CRP), liver function, renal function, high sensitivity-cardiac troponin I (Hs-cTnI), N-terminal pro-B-type natriuretic peptide (NT-proBNP), and procalcitonin (PCT) analyses. All laboratory tests were performed according to standardized protocols in the Department of Laboratory Medicine.
2.2 Participant Selection
In this study, initially 283 eligible participants were recruited. Following rigorous eligibility assessment, 15 patients were subsequently removed from the study due to incomplete postoperative follow-up data, eight patients exhibiting preoperative thrombocytopenia (defined as platelet count < 100 ×10⁹/L), two individuals receiving percutaneous coronary intervention (PCI) within seven days before CPB surgery and 6 non-survivors who died of multiorgan failure secondary to complications within the first postoperative week. Exclusion criteria included preoperative anemia (hemoglobin < 13 g/dL in males or < 12 g/dL in females), emergency surgery for acute myocardial infarction, thyroid disorders, autoimmune diseases, blood transfusion or donation within four months before surgery, history of surgical intervention within three months before surgery, preoperative thrombocytopenia (platelet count < 100×109/L), and incomplete data. The primary outcome was postoperative AKI, defined by the KDIGO criteria. Patients were categorized into AKI and non-AKI groups based on these criteria. The final analytical cohort included 252 patients where 136 patients were diagnosed with CSA-AKI meeting the KDIGO criteria while 116 maintained normal postoperative renal function.
2.3 Statistical Analysis
For continuous variables with non-normal distributions, the Wilcoxon Mann–Whitney U test was employed and as medians with interquartile ranges and the median along with interquartile ranges was utilized as the relevant statistical measures. Means and standard deviations were used to summarize normally distributed continuous variables, while differences between the two groups were analyzed with Student’s t-test. Categorical variables were compared between two groups using Pearson’s chi-square test or Fisher’s exact test as appropriate for expected cell frequencies. The significance threshold for each test was set at less than 0.05. The association between RPR and postoperative AKI was evaluated using multivariable logistic regression analysis. Diagnostic performance was assessed through receiver operating characteristic (ROC) curve analysis, with the Youden index determining optimal cut-off values for investigated parameters. Predictive accuracy was quantified using the area under the ROC curve (AUC), complemented by calculations of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RPR was obtained by calculating the following equation: RPR, RDW(%) / platelet count (×104/µL). All these analyses were performed with the Statistical Package for the Social Sciences (SPSS) version 25 (IBM, Armonk, NY, USA). Figures were generated using SPSS and GraphPad Prism version 9 (GraphPad Software, San Diego, CA, USA).
3. Results
3.1 Baseline Characteristics
The median age of the cohort was 70 years old (68,72). The median CPB duration and aortic cross-clamp time were 155.5 minutes (116, 204) and 98.5 minutes (65, 134.75) respectively. Significant differences were revealed between the AKI and non-AKI groups: the AKI group exhibited higher RDW [ 14 (14, 15)% vs 13 (13, 14)% ; p < 0.001], lower platelet count [ 95 (73, 135.5)×109/L vs. 159.5 (124, 201)×109/L ; p < 0.001] and elevated RPR (14.94 vs. 8.46, p < 0.001) (Table 1). Significant differences between groups were observed in multiple laboratory parameters, including CRP, total bilirubin (TBil), direct bilirubin (DBil), lactate dehydrogenase (LDH), blood urea nitrogen (BUN), uric acid (UA), anion gap (AG), creatinine (Cr), hemoglobin (Hb), and monocyte count (p < 0.001).Cardiovascular assessments demonstrated substantial disparities in key cardiac biomarkers. NT-proBNP, high-sensitivity cardiac troponin I (Hs-cTnI) and myoglobin (Mb) were all significantly higher in AKI group but creatine kinase-MB (CK-MB) became lower in this group (p < 0.001) (Fig. 1)
Table 1
Comparison of demographic, clinical and laboratory findings of patients classified by KDIGO criteria.
Characteristics
All patients
(n = 252)
AKI
(n = 136)
No AKI
(n = 116)
p-value
Age (years)
70 (68, 72)
70 (68, 72)
69 (68, 72)
0.289
Male (n,%)
Hypertension (n,%)
Diabetes (n,%)
Smoke (n,%)
Alcohol (n,%)
Hyperlipidemia (n,%)
Weigh (kg)
144 (57%)
121 (48%)
34 (13%)
65 (26%)
49 (19%)
121 (48%)
65.35 (59.70, 72.30)
81 (59%)
64 (47%)
15 (11%)
35 (26%)
28 (21%)
68 (50%)
65.00 (58.92, 72.45)
63 (54%)
57 (49%)
19 (16%)
30 (26%)
21 (18%)
53 (46%)
65.80 (61.00, 72.15)
0.401
0.742
0.216
0.982
0.620
0.496
0.718
Aortic Clamping Time (min)
CPB Time (min)
RDW (%)
Platelet (×109/L)
RPR
CRP (mg/L)
Hb (g/dL)
Lymphocyte (×109/L)
Monocyte (×109/L)
Neutrophil (×109/L)
ALT (U/L)
AST (U/L)
Total Protein (g/L)
Albumin (g/L)
Globulin (g/L)
TBil (umol/L)
DBil (umol/L)
IBil (umol/L)
LDH (U/L)
BUN (mmol/L)
Scr (umol/L)
UA (mmol/L)
NT-proBNP (pg/mL)
Hs-cTnI (ng/mL)
Mb (ng/mL)
CK-MB (ng/mL)
AG (mmol/L)
CT (ng/mL)
98.50 (65.00, 134.75)
155.50 (116.00, 204.00)
14 (13, 14)
124 (89.25, 164.75)
11.11 (8.14, 15.85)
95.26 (56.99, 205.00)
109 (98, 119)
0.63 (0.44, 0.92)
0.81 (0.62, 1.07)
12.11 (10.00, 15.36)
23 (16.10, 31.15)
63.50 (44.00, 86.75)
62.14 ± 6.48
42.33 ± 3.66
19.82 ± 5.04
26.15 (15.93, 45.50)
14.85 (8.53, 26.78)
10.10 (6.73, 15.10)
425.50 (365.00, 514.75)
9.40 (6.53, 12.88)
105.50 (77.00, 142.50)
368.50 (297.00, 433.75)
2265 (1272.50, 4152.50)
684.15 (441.78, 1281.75)
286.80 (176.73, 520.60)
17.23 (8.84, 33.31)
16 (14, 18)
2.34 (1.01, 7.00)
115.50 (68.25, 154.75)
183.00 (133.00, 228.75)
14 (14, 15)
95 (73.00, 133.50)
14.94 (11.13, 20.20)
181.16 (95.57, 253.76)
103 (95, 117)
0.63 (0.43, 0.93)
0.74 (0.55, 0.99)
11.99 (9.91, 15.70)
23 (17.10, 32.75)
66 (44.25, 92.75)
62.43 ± 7.33
42.59 ± 3.91
19.84 ± 5.15
30.70 (17.95, 54.55)
18.30 (10.88, 40.05)
10.60 (6.80, 16.28)
461.50 (383.00, 540.75)
12.25 (10.03, 15.48)
139 (119.25, 159.50)
410.50 (344.25, 511.50)
3129 (2114.50, 5329.00)
1009.50 (570.38, 1566.75)
446.70 (238.93, 804.98)
11.80 (5.40, 26.02)
17 (15, 19)
5.67 (2.19, 9.24)
89.00 (58.00, 111.75)
134.50 (110.00, 168.00)
13 (13, 14)
159.50 (124, 201.00)
8.46 (6.67, 10.72)
62.88 (43.52, 90.87)
112.50 (104.00, 120.00)
0.63 (0.44, 0.91)
0.92 (0.70, 1.12)
12.30 (10.02, 15.25)
22.50 (16.00, 30.00)
61.25 (43.25, 83.00)
61.80 ± 5.36
42.02 ± 3.33
19.79 ± 4.92
20.95 (14.30, 34.40)
11.10 (7.43, 19.35)
9.30 (6.63, 14.45)
393 (350.50, 469.50)
6.45 (5.30, 8.08)
77 (66.00, 85.00)
316.50 (257.25, 379.75)
1451 (745.75, 2447.25)
549.80 (345.03, 851.03)
207.05 (153.00, 303.68)
24.23 (14.87, 37.94)
15 (13, 17)
1.06 (0.60, 2.10)
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
0.868
< 0.001
0.626
0.373
0.391
0.433
0.220
0.860
< 0.001
< 0.001
0.430
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
Fig. 1
Differences in RPR, CRP, BUN, UA, Hs-cTnI and Mb levels between patients with CSA-AKI and control.
Click here to Correct
3.2 Nonlinear Relationship between RPR and CSA-AKI
All varieties exhibiting statistically significant associations in preliminary analyses were entered into a logistic regression model utilizing stepwise regression that identified 6 variables in the final results including RPR, CRP, BUN, UA, Hs-cTnI, and Mb. Multivariable logistic regression incorporating these variables revealed that elevated RPR was closely associated with AKI risk [Odd Ratio (OR) = 1.433, 95% Confidence Interval (95%CI) 1.158–1.774). The model further demonstrated that postoperative AKI was strongly related to CRP (OR = 1.037, 95% CI 1.020–1.054) and BUN (OR = 1.833, 95%CI 1.336–2.513). Additionally, UA, Hs-cTnI, and Mb exhibited statistically significant associations with renal injury outcomes. (Table 2)
Table 2
Diagnostic performance of the tested varieties individually.
Variables
OR (95% CI)
AUC (95% CI)
Youden index
Cut-off point
Sensitivity
Specificity
PPV
NPV
RPR
1.433 (1.158, 1.744)
0.855 (0.810, 0.901)
0.574
11.416
0.721
0.853
0.852
0.723
CRP
1.037 (1.020, 1.054)
0.831 (0.779, 0.883)
0.605
116.265
0.691
0.914
0.904
0.716
BUN
1.833 (1.336, 2.513)
0.926 (0.894, 0.957)
0.715
9.650
0.801
0.914
0.916
0.797
UA
1.164 (1.039, 1.303)
0.777 (0.721, 0.833)
0.409
37.050
0.676
0.733
0.748
0.659
Hs-cTnI
1.014 (1.005, 1.023)
0.708 (0.644, 0.772)
0.365
74.785
0.632
0.733
0.735
0.629
Mb
1.058 (1.012, 1.106)
0.763 (0.704, 0.823)
0.481
39.300
0.610
0.871
0.847
0.656
3.3 Predictive value of RPR and other variables
ROC curve analysis identified that BUN exhibited the highest AUC of 0.926 (95% CI 0.894–0.957) differentiating CSA-AKI with an acceptable sensitivity of 0.801 and specificity of 0.914, using the optimal cutoff of 9.65 g/L determined by the Youden index for this study. RPR displayed robust discriminative capacity for AKI identification ranking the second AUC of 0.855 (95% CI 0.810–0.901). At its optimal cutoff of 11.416, the sensitivity is 0.721 and the specificity is 0.853. While demonstrating marginally reduced discriminative capability compared to BUN, CRP maintained clinically significant effectiveness in AKI identification achieving the third highest AUC of 0.831 (95%CI 0.779–0.883) showing the sensitivity of 0.619 and specificity of 0.914 at its optimal threshold of 116.265 mg/L. UA, Hs-cTnI, and Mb showed moderate predictive value but were less effective than the aforementioned markers. The integration of RPR with complementary biomarkers significantly augmented predictive performance for AKI. (Table 3) A two-marker panel combining RPR and CRP demonstrated superior diagnostic accuracy when the AUC was 0.931 (95% CI 0.901–0.960), achieving balanced sensitivity (0.875) and specificity (0.845) at an optimized threshold of 0.399. The combination of RPR and BUN outperformed all other pairwise varieties, attaining exceptional discriminative capacity to AKI (AUC = 0.964, 95% CI 0.943–0.985) with high concordance between sensitivity (0.890) and specificity (0.940) at the optimal cutoff of 0.594. The combination of RPR, CRP and BUN demonstrated highest diagnostic efficiency, achieving the AUC of 0.978. At an optimal cutoff value of 0.457, this triad exhibited balanced sensitivity of 0.926 and specificity of 0.931. Notably, the integration of RPR with CRP and UA yielded the highest sensitivity of 0.934 whereas the combination of RPR, CRP, and Hs-cTnI achieved maximal specificity of 0.940. (Fig. 2)
Table 3
Diagnostic performance of the tested varieties in combination.
Combinations
AUC (95% CI)
Youden index
Cut-off point
Sensitivity
Specificity
PPV
NPV
Combination of two variables
       
RPR + CRP
0.931 (0.901, 0.960)
0.720
0.399
0.875
0.845
0.869
0.852
RPR + BUN
0.964 (0.943, 0.985)
0.830
0.594
0.890
0.940
0.946
0.879
RPR + UA
0.922 (0.889, 0.956)
0.741
0.519
0.853
0.888
0.899
0.837
RPR + Hs-cTnI
RPR + Mb
0.869 (0.826, 0.913)
0.893 (0.853, 0.934)
0.609
0.665
0.448
0.527
0.816
0.794
0.793
0.871
0.822
0.878
0.786
0.783
Combination of three variables
       
RPR + CRP + BUN
0.978 (0.965, 0.992)
0.857
0.457
0.926
0.931
0.940
0.915
RPR + CRP + UA
0.969 (0.951, 0.986)
0.831
0.432
0.934
0.897
0.914
0.921
RPR + CRP + Hs-cTnI
0.945 (0.919, 0.970)
0.749
0.679
0.809
0.940
0.941
0.808
RPR + CRP + Mb
0.953 (0.930, 0.977)
0.779
0.520
0.882
0.897
0.909
0.866
RPR + BUN + UA
0.969 (0.951, 0.987)
0.831
0.399
0.934
0.897
0.914
0.921
RPR + BUN + Hs-cTnI
0.967 (0.948, 0.986)
0.824
0.507
0.919
0.905
0.919
0.905
RPR + BUN + Mb
0.972 (0.956, 0.989)
0.834
0.533
0.912
0.922
0.932
0.899
RPR + UA + Hs-cTnI
0.927 (0.895, 0.960)
0.768
0.572
0.846
0.922
0.927
0.836
RPR + UA + Mb
0.938 (0.908, 0.967)
0.750
0.427
0.897
0.853
0.877
0.876
RPR + Hs-cTnI + Mb
0.897 (0.856, 0.937)
0.687
0.539
0.816
0.871
0.881
0.801
Fig. 2
Receiver operating characteristic curves of RPR, CRP, BUN, UA, His-cTnI and Mb for predicting the incidence of cardiac surgery-associated acute kidney injury.
Click here to Correct
4. Discussion
RPR has been demonstrated to correlate significantly with various infectious and cardiovascular disorders. This prospective cohort study investigated the association between preoperative RPR levels and postoperative AKI in 252 patients undergoing open-heart surgery with CPB. Our findings revealed a dose-dependent association between elevated RPR concentrations and increased AKI incidence. In predictive performance analyses, RPR exhibited superior diagnostic accuracy compared to CRP, UA, Hs-cTnI and Mb, though marginally lower than BUN. These findings collectively suggest RPR may serve as a promising predictor for CPB-associated AKI risk stratification. Notably, the integration of RPR with conventional varieties demonstrated augmented predictive efficacy, highlighting the clinical potential of RPR in perioperative renal protection strategies.
Cardiopulmonary bypass establishes a bloodless surgical field through cardiac arrest, enabling intricate intracardiac procedures. Distinct from conventional surgery, CPB induces multifaceted pathophysiological alterations encompassing extracorporeal oxygenation, systemic inflammation activation, and obligatory anticoagulation. Chenoweth et al. first demonstrated complement system activation during CPB, which was a critical early-phase response in inflammatory cascades triggered by diverse pathological stimuli including trauma, thermal injury, infection, and notably, CPB itself.13 Chemotactic factors are released from activated complement components that drive inflammatory cascades within local tissues. Notably, Factor XII participates in bradykinin generation through the contact system, establishing a coagulation-inflammation interplay. CPB circuit bioincompatibility predominantly activates alternative complement pathways through blood-material interactions, independent of Factor XII activation.1416 Mechanistic studies reveal IL-6 as a pivotal cytokine with dual-phase immunomodulatory properties in AKI pathogenesis. Experimental evidence from genetically engineered murine ischemia models demonstrates IL-6-mediated regulation of IL-10, which exerts compensatory anti-inflammatory effects. Clinically, CPB patients exhibit significant perioperative elevation of both IL-6 and IL-10 serum levels.17,18
RDW has been established as a novel inflammatory prognosticator across diverse pathologies, including functional bowel disorders, autoimmune conditions, malignancies, and chronic diseases requiring recurrent hospitalization.1921 Mechanistically, RDW was elevated because of AKI-induced oxidative stress, metabolic dysregulation, and hemodynamic instability which collectively enhance erythrocyte destruction rates, promoting immature reticulocyte release into circulation.22,23 Platelets exacerbate inflammatory progression through pro-inflammatory cytokine secretion and immunocyte interactions.24,25 Of clinical significance, thrombocytopenia in critically ill patients demonstrates strong correlation with adverse outcomes.26 RPR integrates these pathophysiological dynamics, emerging as a robust inflammatory indicator across multiple disease.27,28 Clinically validated as a novel prognostic biomarker, RPR demonstrates predictive capacity for disease trajectories in neonatal sepsis, acute pancreatitis, and advanced hepatic fibrosis and hepatic cirrhosis.11,2931 Its clinical utility is enhanced by its routine accessibility through standard complete blood count parameters, non-invasive acquisition with rapid analytical turnaround, and superior diagnostic accuracy compared to conventional inflammatory markers.28,32 These characteristics make RPR as a cost-effective prognostic tool with significant translational potential in resource-constrained settings.
This investigation has several methodological constraints requiring critical consideration. First, the retrospective single-center design relying on electronic health records inherently limits generalizability, compounded by an underpowered sample size that increases susceptibility to selection bias. Then, the observational nature of data introduces unavoidable risks of measurement inaccuracies and residual confounding, particularly regarding undocumented perioperative variables. These limitations collectively constrain causal inference and external validity of our predictive models. Crucially, the diagnostic performance of RPR requires external validation through prospective multicenter trials employing standardized data collection protocols.
5. Conclusions
Elevated RPR upon admission is substantially associated with high risk of CSA-AKI. Therefore, RPR may serve as a novel predictor and its combination with other biomarks may facilitate the clinical decision making in these patients undergoing CPB. However, the result are not conclusive because of lacking of further larger sizes of samples to validate it for the effectiveness. So in the future larger samples will be needed to verify the outcomes.
Abbreviations
RPR
Red cell distribution width-to-platelet ratio
CSA-AKI
Cardiac surgery-associated acute kidney injury
CPB
Cardiac surgery with cardiopulmonary bypass
ROC curve
Receiver operating characteristic curve
AUC
Area under the curve
BUN
Blood urea nitrogen
CRP
C-reactive protein
CPB
Cardiac surgery on cardiopulmonary bypass
AKI
Acute kidney injury
AKD
Acute kidney disease
CKD
Chronic kidney disease
KDIGO
Kidney Disease:Improving Global Outcomes
TNF-α
Tumor Necrosis Factor-α
IL-6
Interleukin-6
IL-8
Interleukin-8
ROS
Reactive oxygen species
NF-κB
Nuclear factor kappa B
RDW
Red cell distribution width
PLT
Platelets
Hs-cTnI
High sensitivity-cardiac troponin I
NT-proBNP
N-terminal pro-B-type natriuretic peptide
PCT
Procalcitonin
Mb
Myoglobin
PCI
Percutaneous coronary intervention
PPV
Positive predictive value
NPV
Negative predictive value
TBil
Total bilirubin
DBil
Direct bilirubin
LDH
Lactate dehydrogenase
BUN
Blood urea nitrogen
UA
Uric acid
AG
Anion gap
Cr
Creatinine
Hb
Hemoglobin
Ethics declarations
A
A
This study complied with the Declaration of Helsinki and was approved by Fuwai Central China Cardiovascular Hospital’s Institutional Review Board, which waived informed consent for this anonymized retrospective analysis of clinical data.
Consent for publication
Not Applicable.
A
Data Availability
The corresponding author will share the data underlying this article on reasonable request.
Competing Interests
The authors have no conflicts of interest to declare.
A
Funding
This study was supported by the Major Project of Provincial-Ministerial Co-construction under the Henan provincial Medical Science and Technology Research Program (SBGJ202101005).
A
Author Contribution
Conceptualization: Zhao-Xi Li, Jun-Long Hu, Zhao-Yun ChengData curation: Zhao-Xi Li, Xiao-Liang Qian, Jia-Xin Huang, Jian-Zhao LiMethodology: Zhao-Xi Li, Chen-Yi Cui, Xiao-Liang QianFormal analysis: Zhao-Xi Li, Bao-Cai WangWriting – original draft: Zhao-Xi Li, Chen-Yi CuiWriting – review and editing: Zhao-Yun Cheng
Data curation: Zhao-Xi Li, Xiao-Liang Qian, Jia-Xin Huang, Jian-Zhao Li
Methodology: Zhao-Xi Li, Chen-Yi Cui, Xiao-Liang Qian
Formal analysis: Zhao-Xi Li, Bao-Cai Wang
Writing – original draft: Zhao-Xi Li, Chen-Yi Cui
Writing – review and editing: Zhao-Yun Cheng
Acknowledgement
We sincerely thank the experts who contributed their time and insights to this research.
Reference
1.
Corredor C, Thomson R, Al-Subaie N. Long-Term Consequences of Acute Kidney Injury After Cardiac Surgery: A Systematic Review and Meta-Analysis. J Cardiothorac Vasc Anesth. 2016;30:69–75. https://doi.org:10.1053/j.jvca.2015.07.013.
2.
Lysak N, Bihorac A, Hobson C. Mortality and cost of acute and chronic kidney disease after cardiac surgery. Curr Opin Anaesthesiol. 2017;30:113–7. https://doi.org:10.1097/aco.0000000000000422.
3.
Milne B, Gilbey T, De Somer F, Kunst G. Adverse renal effects associated with cardiopulmonary bypass. Perfusion. 2024;39:452–68. https://doi.org:10.1177/02676591231157055.
4.
Wang Y, Bellomo R. Cardiac surgery-associated acute kidney injury: risk factors, pathophysiology and treatment. Nat Rev Nephrol. 2017;13:697–711. https://doi.org:10.1038/nrneph.2017.119.
5.
Kumar AB, Suneja M. Cardiopulmonary bypass-associated acute kidney injury. Anesthesiology. 2011;114:964–70. https://doi.org:10.1097/ALN.0b013e318210f86a.
6.
Chew STH, Hwang NC. Acute Kidney Injury After Cardiac Surgery: A Narrative Review of the Literature. J Cardiothorac Vasc Anesth. 2019;33:1122–38. https://doi.org:10.1053/j.jvca.2018.08.003.
7.
Wei C, Li L, Kim IK, Sun P, Gupta S. NF-κB mediated miR-21 regulation in cardiomyocytes apoptosis under oxidative stress. Free Radic Res. 2014;48:282–91. https://doi.org:10.3109/10715762.2013.865839.
8.
Ali F, Sultana S. Repeated short-term stress synergizes the ROS signalling through up regulation of NFkB and iNOS expression induced due to combined exposure of trichloroethylene and UVB rays. Mol Cell Biochem. 2012;360:133–45. https://doi.org:10.1007/s11010-011-1051-7.
9.
Arkew M, Gemechu K, Haile K, Asmerom H. Red Blood Cell Distribution Width as Novel Biomarker in Cardiovascular Diseases: A Literature Review. J Blood Med. 2022;13:413–24. https://doi.org:10.2147/jbm.S367660.
10.
Islam MM, Satici MO, Eroglu SE. Unraveling the clinical significance and prognostic value of the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, systemic immune-inflammation index, systemic inflammation response index, and delta neutrophil index: An extensive literature review. Turk J Emerg Med. 2024;24:8–19. https://doi.org:10.4103/tjem.tjem_198_23.
11.
Cetinkaya E, Senol K, Saylam B, Tez M. Red cell distribution width to platelet ratio: new and promising prognostic marker in acute pancreatitis. World J Gastroenterol. 2014;20:14450–4. https://doi.org:10.3748/wjg.v20.i39.14450.
12.
Wu J et al. Red Cell Distribution Width to Platelet Ratio Is Associated with Increasing In-Hospital Mortality in Critically Ill Patients with Acute Kidney Injury. Dis Markers 2022, 4802702 (2022). https://doi.org:10.1155/2022/4802702
13.
Yang X, Zhu L, Pan H, Yang Y. Cardiopulmonary bypass associated acute kidney injury: better understanding and better prevention. Ren Fail. 2024;46:2331062. https://doi.org:10.1080/0886022x.2024.2331062.
14.
Jongman RM, et al. Off-pump CABG surgery reduces systemic inflammation compared with on-pump surgery but does not change systemic endothelial responses: a prospective randomized study. Shock. 2014;42:121–8. https://doi.org:10.1097/shk.0000000000000190.
15.
Parolari A, et al. Systemic inflammation after on-pump and off-pump coronary bypass surgery: a one-month follow-up. Ann Thorac Surg. 2007;84:823–8. https://doi.org:10.1016/j.athoracsur.2007.04.048.
16.
Bronicki RA, Hall M. Cardiopulmonary Bypass-Induced Inflammatory Response: Pathophysiology and Treatment. Pediatr Crit Care Med. 2016;17:272–8. https://doi.org:10.1097/pcc.0000000000000759.
17.
Andres-Hernando A, et al. Circulating IL-6 upregulates IL-10 production in splenic CD4(+) T cells and limits acute kidney injury-induced lung inflammation. Kidney Int. 2017;91:1057–69. https://doi.org:10.1016/j.kint.2016.12.014.
18.
Zhang WR, et al. Plasma IL-6 and IL-10 Concentrations Predict AKI and Long-Term Mortality in Adults after Cardiac Surgery. J Am Soc Nephrol. 2015;26:3123–32. https://doi.org:10.1681/asn.2014080764.
19.
Aktas G, et al. Red cell distribution width and mean platelet volume in patients with irritable bowel syndrome. Prz Gastroenterol. 2014;9:160–3. https://doi.org:10.5114/pg.2014.43578.
20.
Aktas G, et al. Could red cell distribution width be a marker in Hashimoto's thyroiditis? Exp Clin Endocrinol Diabetes. 2014;122:572–4. https://doi.org:10.1055/s-0034-1383564.
21.
Aktas G, et al. Could Red Cell Distribution Width be a Marker of Thyroid Cancer? J Coll Physicians Surg Pak. 2017;27:556–8.
22.
Pavlakou P, Liakopoulos V, Eleftheriadis T, Mitsis M, Dounousi E. Oxidative Stress and Acute Kidney Injury in Critical Illness: Pathophysiologic Mechanisms-Biomarkers-Interventions, and Future Perspectives. Oxid Med Cell Longev 2017, 6193694 (2017). https://doi.org:10.1155/2017/6193694
23.
Förhécz Z, et al. Red cell distribution width in heart failure: prediction of clinical events and relationship with markers of ineffective erythropoiesis, inflammation, renal function, and nutritional state. Am Heart J. 2009;158:659–66. https://doi.org:10.1016/j.ahj.2009.07.024.
24.
Morrell CN, Aggrey AA, Chapman LM, Modjeski K. L. Emerging roles for platelets as immune and inflammatory cells. Blood. 2014;123:2759–67. https://doi.org:10.1182/blood-2013-11-462432.
25.
Shen Y, Huang X, Zhang W. Platelet-to-lymphocyte ratio as a prognostic predictor of mortality for sepsis: interaction effect with disease severity-a retrospective study. BMJ Open. 2019;9:e022896. https://doi.org:10.1136/bmjopen-2018-022896.
26.
Zarychanski R, Houston DS. Assessing thrombocytopenia in the intensive care unit: the past, present, and future. Hematol Am Soc Hematol Educ Program. 2017;2017:660–6. https://doi.org:10.1182/asheducation-2017.1.660.
27.
Li X, Xu H, Gao P. Red Blood Cell Distribution Width-to-Platelet Ratio and Other Laboratory Indices Associated with Severity of Histological Hepatic Fibrosis in Patients with Autoimmune Hepatitis: A Retrospective Study at a Single Center. Med Sci Monit. 2020;26:e927946. https://doi.org:10.12659/msm.927946.
28.
Chen B, Ye B, Zhang J, Ying L, Chen Y. RDW to platelet ratio: a novel noninvasive index for predicting hepatic fibrosis and cirrhosis in chronic hepatitis B. PLoS ONE. 2013;8:e68780. https://doi.org:10.1371/journal.pone.0068780.
29.
Cai Y, et al. Diagnostic accuracy of red blood cell distribution width to platelet ratio for predicting staging liver fibrosis in chronic liver disease patients: A systematic review and meta-analysis. Med (Baltim). 2019;98:e15096. https://doi.org:10.1097/md.0000000000015096.
30.
Karabulut B, Arcagok BC. New Diagnostic Possibilities for Early Onset Neonatal Sepsis: Red Cell Distribution Width to Platelet Ratio. Fetal Pediatr Pathol. 2020;39:297–306. https://doi.org:10.1080/15513815.2019.1661051.
31.
Takeuchi H, et al. Elevated red cell distribution width to platelet count ratio predicts poor prognosis in patients with breast cancer. Sci Rep. 2019;9:3033. https://doi.org:10.1038/s41598-019-40024-8.
32.
Nie S, et al. Risk factors of prognosis after acute kidney injury in hospitalized patients. Front Med. 2017;11:393–402. https://doi.org:10.1007/s11684-017-0532-9.
Abbreviations
RDW, red cell distribution width; RPR, red cell distribution width to platelet ratio; CRP, C-reactive protein; Hb, hemoglobin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; TBil, total bilirubin; DBil, direct bilirubin; IBil, indirect bilirubin; LDH, lactate dehydrogenase; BUN, blood urea nitrogen; Scr, serum creatinine; UA, uric acid; NT-proBNP, N-terminal pro-B-type natriuretic peptide; Hs-cTnI, high sensitivity-cardiac troponin I; Mb, myoglobin; CK-MB, creatine kinase-MB; AG, anion gap; PCT, procalcitonin.
Abbreviations
95% CI, 95% confidence interval; AUC, area under the curve; PPV, positive predictive value; NPV, negative predictive value; RPR, red cell distribution width to platelet ratio; CRP, C-reactive protein; BUN, blood urea nitrogen; UA, uric acid; Hs-cTnI, high sensitivity-cardiac troponin I; Mb, myoglobin.
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Table 1
Comparison of demographic, clinical and laboratory findings of patients classified by KDIGO criteria
Characteristics
All patients
(n = 252)
AKI
(n = 136)
No AKI
(n = 116)
p-value
Age (years)
70 (68, 72)
70 (68, 72)
69 (68, 72)
0.289
Male (n,%)
Hypertension (n,%)
Diabetes (n,%)
Smoke (n,%)
Alcohol (n,%)
Hyperlipidemia (n,%)
Weigh (kg)
144 (57%)
121 (48%)
34 (13%)
65 (26%)
49 (19%)
121 (48%)
65.35 (59.70, 72.30)
81 (59%)
64 (47%)
15 (11%)
35 (26%)
28 (21%)
68 (50%)
65.00 (58.92, 72.45)
63 (54%)
57 (49%)
19 (16%)
30 (26%)
21 (18%)
53 (46%)
65.80 (61.00, 72.15)
0.401
0.742
0.216
0.982
0.620
0.496
0.718
Aortic Clamping Time (min)
CPB Time (min)
RDW (%)
Platelet (×109/L)
RPR
CRP (mg/L)
Hb (g/dL)
Lymphocyte (×109/L)
Monocyte (×109/L)
Neutrophil (×109/L)
ALT (U/L)
AST (U/L)
Total Protein (g/L)
Albumin (g/L)
Globulin (g/L)
TBil (umol/L)
DBil (umol/L)
IBil (umol/L)
LDH (U/L)
BUN (mmol/L)
Scr (umol/L)
UA (mmol/L)
NT-proBNP (pg/mL)
Hs-cTnI (ng/mL)
Mb (ng/mL)
CK-MB (ng/mL)
AG (mmol/L)
CT (ng/mL)
98.50 (65.00, 134.75)
155.50 (116.00, 204.00)
14 (13, 14)
124 (89.25, 164.75)
11.11 (8.14, 15.85)
95.26 (56.99, 205.00)
109 (98, 119)
0.63 (0.44, 0.92)
0.81 (0.62, 1.07)
12.11 (10.00, 15.36)
23 (16.10, 31.15)
63.50 (44.00, 86.75)
62.14 ± 6.48
42.33 ± 3.66
19.82 ± 5.04
26.15 (15.93, 45.50)
14.85 (8.53, 26.78)
10.10 (6.73, 15.10)
425.50 (365.00, 514.75)
9.40 (6.53, 12.88)
105.50 (77.00, 142.50)
368.50 (297.00, 433.75)
2265 (1272.50, 4152.50)
684.15 (441.78, 1281.75)
286.80 (176.73, 520.60)
17.23 (8.84, 33.31)
16 (14, 18)
2.34 (1.01, 7.00)
115.50 (68.25, 154.75)
183.00 (133.00, 228.75)
14 (14, 15)
95 (73.00, 133.50)
14.94 (11.13, 20.20)
181.16 (95.57, 253.76)
103 (95, 117)
0.63 (0.43, 0.93)
0.74 (0.55, 0.99)
11.99 (9.91, 15.70)
23 (17.10, 32.75)
66 (44.25, 92.75)
62.43 ± 7.33
42.59 ± 3.91
19.84 ± 5.15
30.70 (17.95, 54.55)
18.30 (10.88, 40.05)
10.60 (6.80, 16.28)
461.50 (383.00, 540.75)
12.25 (10.03, 15.48)
139 (119.25, 159.50)
410.50 (344.25, 511.50)
3129 (2114.50, 5329.00)
1009.50 (570.38, 1566.75)
446.70 (238.93, 804.98)
11.80 (5.40, 26.02)
17 (15, 19)
5.67 (2.19, 9.24)
89.00 (58.00, 111.75)
134.50 (110.00, 168.00)
13 (13, 14)
159.50 (124, 201.00)
8.46 (6.67, 10.72)
62.88 (43.52, 90.87)
112.50 (104.00, 120.00)
0.63 (0.44, 0.91)
0.92 (0.70, 1.12)
12.30 (10.02, 15.25)
22.50 (16.00, 30.00)
61.25 (43.25, 83.00)
61.80 ± 5.36
42.02 ± 3.33
19.79 ± 4.92
20.95 (14.30, 34.40)
11.10 (7.43, 19.35)
9.30 (6.63, 14.45)
393 (350.50, 469.50)
6.45 (5.30, 8.08)
77 (66.00, 85.00)
316.50 (257.25, 379.75)
1451 (745.75, 2447.25)
549.80 (345.03, 851.03)
207.05 (153.00, 303.68)
24.23 (14.87, 37.94)
15 (13, 17)
1.06 (0.60, 2.10)
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
0.868
< 0.001
0.626
0.373
0.391
0.433
0.220
0.860
< 0.001
< 0.001
0.430
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
Abbreviations
RDW, red cell distribution width; RPR, red cell distribution width to platelet ratio; CRP, C-reactive protein; Hb, hemoglobin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; TBil, total bilirubin; DBil, direct bilirubin; IBil, indirect bilirubin; LDH, lactate dehydrogenase; BUN, blood urea nitrogen; Scr, serum creatinine; UA, uric acid; NT-proBNP, N-terminal pro-B-type natriuretic peptide; Hs-cTnI, high sensitivity-cardiac troponin I; Mb, myoglobin; CK-MB, creatine kinase-MB; AG, anion gap; PCT, procalcitonin.
Table 2
Diagnostic performance of the tested varieties individually.
Variables
OR (95% CI)
AUC (95% CI)
Youden index
Cut-off point
Sensitivity
Specificity
PPV
NPV
RPR
1.433 (1.158, 1.744)
0.855 (0.810, 0.901)
0.574
11.416
0.721
0.853
0.852
0.723
CRP
1.037 (1.020, 1.054)
0.831 (0.779, 0.883)
0.605
116.265
0.691
0.914
0.904
0.716
BUN
1.833 (1.336, 2.513)
0.926 (0.894, 0.957)
0.715
9.650
0.801
0.914
0.916
0.797
UA
1.164 (1.039, 1.303)
0.777 (0.721, 0.833)
0.409
37.050
0.676
0.733
0.748
0.659
Hs-cTnI
1.014 (1.005, 1.023)
0.708 (0.644, 0.772)
0.365
74.785
0.632
0.733
0.735
0.629
Mb
1.058 (1.012, 1.106)
0.763 (0.704, 0.823)
0.481
39.300
0.610
0.871
0.847
0.656
Table 3
Diagnostic performance of the tested varieties in combination.
Combinations
AUC (95% CI)
Youden index
Cut-off point
Sensitivity
Specificity
PPV
NPV
Combination of two variables
       
RPR + CRP
0.931 (0.901, 0.960)
0.720
0.399
0.875
0.845
0.869
0.852
RPR + BUN
0.964 (0.943, 0.985)
0.830
0.594
0.890
0.940
0.946
0.879
RPR + UA
0.922 (0.889, 0.956)
0.741
0.519
0.853
0.888
0.899
0.837
RPR + Hs-cTnI
RPR + Mb
0.869 (0.826, 0.913)
0.893 (0.853, 0.934)
0.609
0.665
0.448
0.527
0.816
0.794
0.793
0.871
0.822
0.878
0.786
0.783
Combination of three variables
       
RPR + CRP + BUN
0.978 (0.965, 0.992)
0.857
0.457
0.926
0.931
0.940
0.915
RPR + CRP + UA
0.969 (0.951, 0.986)
0.831
0.432
0.934
0.897
0.914
0.921
RPR + CRP + Hs-cTnI
0.945 (0.919, 0.970)
0.749
0.679
0.809
0.940
0.941
0.808
RPR + CRP + Mb
0.953 (0.930, 0.977)
0.779
0.520
0.882
0.897
0.909
0.866
RPR + BUN + UA
0.969 (0.951, 0.987)
0.831
0.399
0.934
0.897
0.914
0.921
RPR + BUN + Hs-cTnI
0.967 (0.948, 0.986)
0.824
0.507
0.919
0.905
0.919
0.905
RPR + BUN + Mb
0.972 (0.956, 0.989)
0.834
0.533
0.912
0.922
0.932
0.899
RPR + UA + Hs-cTnI
0.927 (0.895, 0.960)
0.768
0.572
0.846
0.922
0.927
0.836
RPR + UA + Mb
0.938 (0.908, 0.967)
0.750
0.427
0.897
0.853
0.877
0.876
RPR + Hs-cTnI + Mb
0.897 (0.856, 0.937)
0.687
0.539
0.816
0.871
0.881
0.801
Abbreviations
95% CI, 95% confidence interval; AUC, area under the curve; PPV, positive predictive value; NPV, negative predictive value; RPR, red cell distribution width to platelet ratio; CRP, C-reactive protein; BUN, blood urea nitrogen; UA, uric acid; Hs-cTnI, high sensitivity-cardiac troponin I; Mb, myoglobin.
Abstract
Background: 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). Methods: 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 KDIGO criteria. 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. Results: Clinical outcomes revealed significantly higher RPR levels in the AKI group compared to the non-AKI group (14.94 vs. 8.46, p0.001), with elevated RPR independently associated with AKI risk(OR = 1.433, 95% CI: 1.158–1.774). 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 study further elucidated RPR’s pathophysiological role, integrating inflammatory and thrombotic mechanisms, potentially exacerbating renal injury through microcirculatory dysfunction and oxidative stress. However, the study is limited by its single-center retrospective design, necessitating validation through large-scale prospective trials. Conclusion: RPR may serve as a potential predictor for CSA-AKI, and its integration with conventional biomarkers could inform renal protection strategies.
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