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Association between inflammatory biomarkers and postoperative acute kidney injury after cardiac surgery in patients with preoperative renal dysfunction: a retrospective pilot analysis

Journal of Cardiothoracic Surgery volume 19, Article number: 583 (2024) Cite this article

Abstract

Background

Acute kidney injury (AKI) represents a significant post-cardiac surgery complication, particularly prevalent among individuals with pre-existing renal dysfunction. Chronic kidney disease (CKD) is frequently accompanied by persistent, low-grade inflammation, which is known to exacerbate systemic stress responses during surgical procedures. This study hypothesizes that these inflammatory responses might influence the incidence and severity of postoperative acute kidney injury (AKI), potentially serving as a protective mechanism by preconditioning the kidney to stress.

Methods

This retrospective study enrolled patients with preoperative renal dysfunction (eGFR between 15 and 60 ml/min/1.73 m²) who underwent cardiac surgery between January 2020 and December 2022. Preoperative inflammatory biomarkers were evaluated. The primary outcome was the incidence of postoperative AKI, as defined by the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Multivariate regression models and sensitivity analyses were conducted to ascertain the relationship between inflammatory biomarkers and AKI. Restricted cubic spines (RCS) was conducted to explore nonlinear associations between inflammatory biomarkers and AKI.

Results

AKI occurred in 53.4% (392/734) of patients, accompanied by significant mortality and length of hospital stay increases in cases of AKI (P < 0.005). After full adjustment of confounders, neutrophil percentage-to-albumin ratio (OR = 0.28), systemic inflammation response index (OR = 0.70), systemic immune inflammation index (OR = 0.69), neutrophil-to-lymphocyte ratio (OR = 0.70), monocyte/high-density lipoprotein cholesterol ratio (OR = 0.53), neutrophil/high-density lipoprotein cholesterol ratio (OR = 0.43) demonstrated an inverse association with AKI. Sensitivity analyses revealed that patients in the highest quartile of these biomarkers exhibited a significantly lower prevalence of AKI compared to those in the lowest quartile (p for trend < 0.05). The RCS analysis suggested an “Inverted U-shaped” association of both LnNPAR and LnSIRI with AKI.

Conclusions

This study identified an inverse association between preoperative inflammatory biomarkers and postoperative AKI in patients with preoperative renal dysfunction. The findings implied that preoperative inflammation may play a protective role against postoperative AKI in this patient population undergoing cardiac surgery.

Peer Review reports

Introduction

Cardiac surgery-associated acute kidney injury (AKI) is a serious and common complication following cardiac surgery [1]. AKI is associated with increased mortality and progression to end-stage renal disease, often necessitating dialysis and kidney replacement therapy [2]. With the rapid advancements in cardiac surgery technology, the number of procedures in developing countries like China has significantly increased, making it possible for more patients with renal insufficiency to undergo cardiac surgery. However, preoperative renal dysfunction remains a significant risk factor for adverse outcomes. The molecular mechanisms underlying AKI are not well understood, and no effective therapeutic strategies currently exist [3]. Therefore, efforts towards the prevention, early diagnosis, and treatment of AKI have garnered significant attention, particularly for patients with preoperative renal dysfunction.

Although numerous biomarkers, such as neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), and tissue inhibitor of metalloproteinases-2× insulin-like growth factor-binding protein 7 (TIMP-2×IGFBP7), have been developed for early AKI screening [4,5,6], their diagnostic value in AKI superimposed on chronic kidney disease (CKD) has not been extensively validated. Moreover, their high costs limit their utility in economically underdeveloped regions. Recent studies have demonstrated that preoperative inflammatory biomarkers, which reflect the systemic inflammatory state, can predict outcomes in CKD and AKI [7,8,9,10,11]. These biomarkers, such as neutrophil-to-lymphocyte ratio (NLR) and monocyte-to-lymphocyte ratio (MLR), are easily obtained from routine blood tests and provide insights into the patient’s baseline inflammatory status, which may influence their resilience to perioperative stress. CKD is associated with low-grade, persistent inflammation [12], and factors during cardiac surgery, such as cardiopulmonary bypass [13, 14] and blood transfusions [15, 16], exacerbate systemic inflammation and oxidative stress. Thus, understanding how preoperative inflammation in patients with preoperative renal dysfunction influences the risk of AKI during surgery is essential. Therefore, we focused on these systemic inflammatory biomarkers due to their established associations with inflammation and adverse outcomes in patients with cardiovascular and renal diseases. This study aims to evaluate the relationship between a series of preoperative inflammatory biomarkers and postoperative AKI in patients with preoperative renal dysfunction undergoing cardiac surgery.

Methods

Study population

We enrolled adult patients with preoperative renal dysfunction (eGFR between 15 and 60 ml/min/1.73 m²) who underwent valve or coronary artery bypass surgery, or combined procedures, at our hospital between January 2020 and December 2022. Exclusion criteria included prior renal replacement therapy or transplant, preoperative AKI (KDIGO criteria [17]), incomplete medical records, death within 48 h of ICU admission, transferred from within the hospital, and emergent surgeries. The study was approved by the Zhongshan Hospital’s Ethics Committee (Approval Number B2021–873R), and informed consent was obtained from all participants.

Study design

This retrospective study utilized clinical data extracted from electronic medical records, including demographics, comorbidities, laboratory results, surgical parameters, cardiopulmonary bypass (CPB) duration, postoperative medications, urine output, length of hospital stay, and mortality rates. Preoperative eGFR was calculated using the CKD-EPI equation [18] based on the most recent serum creatinine (SCr) measurement before surgery. Postoperative SCr was monitored daily in the ICU, with renal function tests conducted daily for the first three days post-ICU and every other day until discharge.

The primary outcome was the incidence of postoperative AKI, classified according to KDIGO guidelines. Participants were divided into two groups based on AKI development, allowing for a detailed investigation of various risk factors.

The secondary outcomes included the incidence of AKI stage 2–3, classified according to KDIGO guidelines and the length of hospital stay.

Definition of inflammatory biomarkers

Full blood counts were obtained from BD EDTA-K2 samples and analyzed using a Sysmex XN9000 electronic counter. Lymphocytes, neutrophils, and other blood cells were calculated based on their proportions from the total leukocyte count. We assessed inflammatory biomarkers, including the neutrophil percentage-to-albumin ratio (NPAR), systemic inflammation response index (SIRI), systemic immune inflammation index (SII), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), monocyte/high-density lipoprotein cholesterol ratio (MHR), neutrophil/high-density lipoprotein cholesterol ratio (NHR), platelet/high-density lipoprotein cholesterol ratio (PHR), and lymphocyte/high-density lipoprotein cholesterol ratio (LHR).

NPAR = neutrophil percentage/serum albumin level (g/L), SIRI = neutrophil count × monocyte count/lymphocyte count, SII = platelet counts × neutrophil counts/lymphocyte counts, PLR = platelet counts/lymphocyte counts, MLR = monocyte counts/lymphocyte counts, NLR = neutrophil counts/lymphocyte counts, NHR = neutrophil counts/high-density lipoprotein cholesterol (HDL-C) (mmol/L), MHR = monocyte counts/HDL-C, LHR = lymphocyte counts/HDL-C, and PHR = platelet counts/HDL-C are the formulas for these inflammatory indicators. To measure HDL-C levels, Chemistry Analyzer Roche Cobas c702 and Roche modular P (Roche Diagnostics, Basel, Switzerland) were utilized.

Statistical analysis

Data analysis was conducted using R version 4.3.0. Normally distributed data were presented as mean ± standard deviation, non-normally distributed data as medians with interquartile ranges, and categorical variables as counts and percentages. We assessed normality and variance homogeneity using the Kolmogorov-Smirnov test. Differences in continuous data were determined using the Student t-test or nonparametric tests, while categorical variables were analyzed using Fisher’s exact or chi-square tests.

We employed multivariable logistic regression models (Model 1 to Model 3) to examine associations between inflammatory biomarkers and AKI incidence, adjusting for confounders such as age, sex, surgery type, cardiopulmonary bypass duration, body mass index, history of hypertension, diabetes mellitus, recent myocardial infarction within one month, baseline eGFR, albumin, and hemoglobin. Considering that inflammatory biomarkers are skewed distribution and some of their values are relatively small, which might lead to less intuitive effect sizes, we performed a logarithmic transformation using the natural logarithm. We used restricted cubic splines (RCS) to address nonlinear relationships between potentially AKI-associated biomarkers, selecting models with the lowest Akaike information criterion value. When interpreting the results of an RCS analysis, the inflection point represented an inflection point or boundary between different patterns of association between the predictor variable and the outcome. In cases where the RCS analysis revealed a U-shaped, inverted U-shaped, or L-shaped curve, with a clearly identifiable inflection point, the data were divided into two distinct segments based on this inflection point. This segmented logistic regression allowed for a more nuanced understanding of the relationship between the predictor variable and the outcome in each segment, as it accounted for the distinct patterns of association in different parts of the curve. Predictive efficacy of inflammatory biomarkers was assessed using area under the curve (AUC) values and receiver operating characteristic (ROC) curves. Missing values for categorical variables were resolved using mode imputation, and median imputation was applied to continuous variables. Statistical significance was determined with a two-tailed p-value < 0.05.

Results

Basic characteristics

We enrolled 734 patients (Fig. 1) with preoperative eGFR between 15 and 60 ml/min/1.73 m²; 53.40% (n = 392) developed AKI post-surgery. AKI stage 1: 258 patients (35.1%), stage 2: 81 patients (11%), stage 3: 53 patients (7.2%), 33 patients (4.5%) underwent RRT, and 48 patients (6.5%) died. Patients with AKI had higher mortality rates (9.44 vs. 3.22%, p < 0.001), longer ICU stays [64.00 (23.00–115.00) vs. 30.00 (21.00–69.00) hours, p < 0.001], and longer hospital stays [15 (12–22) vs. 15 (11–19) days, p = 0.004]. The AKI group was older, predominantly male, with higher incidences of hypertension. Laboratory evaluations indicated more severe renal dysfunction and lower preoperative hemoglobin and white blood cell in AKI patients. Combined surgeries and longer CPB durations were more common in the AKI group (Table 1).

Fig. 1
figure 1

Flowchart of the study design

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Table 1 Perioperative characteristics of the study population
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Associations between inflammatory biomarkers and AKI

Our study revealed a negative association between NPAR, SIRI, SII, NLR, MHR, and NHR with AKI in the full adjusted Model 3 (LnNPAR: OR = 0.28; 95% CI: 0.09, 0.85; LnSIRI: OR = 0.70; 95% CI: 0.53, 0.91; LnSII: OR = 0.69; 95% CI: 0.51, 0.93; LnNLR: OR = 0.70; 95% CI: 0.49, 0.98; LnMHR: OR = 0.53; 95% CI: 0.36, 0.78; LnNHR: OR = 0.43; 95% CI: 0.25, 0.71). Patients in the highest quartile of these biomarkers had significantly lower AKI prevalence compared to the lowest quartile (p for trend < 0.05) (Table 2).

Table 2 Association between inflammatory biomarkers and AKI (logistic regression)
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The RCS analysis, adjusting for the effects of age, preoperative eGFR, sex, surgical type, CPB duration, body mass index (BMI), hypertension, diabetes mellitus, preoperative serum albumin, and preoperative hemoglobin suggested an “Inverted U-shaped” association of LnNPAR and LnSIRI with AKI. The inflection point of the RCS curve was identified at LnNPAR = 0.32 and LnSIRI=-0.22, representing an inflection point in their relationship between the LnNPAR (Fig. 2A) and LnSIRI (Fig. 2B) with the AKI. Using the inflection point, the data were stratified into two groups for each biomarker: (LnNPAR < 0.325 and LnNPAR ≥ 0.325) and (LnSIRI < -0.21 and LnSIRI ≥ -0.21). Segmented regression was then performed on each group separately, with the results presented in Supplementary Table S1. Over the respective inflection point, negative association was found between LnNPAR (OR = 0.82, 95%CI 0.69, 0.97) and LnSIRI (OR = 0.74, 95%CI 0.61, 0.89) with AKI. The relationships between other biomarkers with AKI were revealed linear (Fig. 2C-F).

Fig. 2
figure 2

Potential nonlinear relationship between the natural log-transformed inflammatory biomarkers and AKI. (A) Neutrophil percentage-to-Albumin Ratio (lnNPAR); (B) Systemic Inflammation Response Index (lnSIRI); (C) Systemic Immune-Inflammation Index (lnSII); (D) Monocyte-to-High-Density Lipoprotein Cholesterol Ratio (lnMHR); (E) Neutrophil-to-High-Density Lipoprotein Cholesterol Ratio (lnNHR); (F) Neutrophil-to-Lymphocyte Ratio (lnNLR)

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ROC analysis

Our results demonstrated that the AUC values for the biomarkers predicting AKI ranged from 0.514 to 0.565 (Supplementary Table S2), indicating varying degrees of predictive power. Notably, NHR had the highest AUC value of 0.565 (95% CI: 0.516–0.614), suggesting it was the most reliable predictor among the biomarkers studied. This was followed by MHR with an AUC of 0.551 (95% CI: 0.502–0.600) and SIRI with an AUC of 0.548 (95% CI: 0.506–0.590). The differences in AUC values highlighted the varying degrees of association between these inflammatory markers and the risk of developing AKI postoperatively.

Associations Between Inflammatory Biomarkers and AKI Stage 2–3.

Our study revealed a negative association between SIRI, SII, and PLR with AKI stage 2–3 after full adjustment (Model 3) (LnSIRI: OR = 0.68; 95% CI: 0.49, 0.97; LnSII: OR = 0.63; 95% CI: 0.42, 0.93; LnPLR: OR = 0.55; 95% CI: 0.31, 0.97). In addition, while continuous variable NLR was not significantly associated with AKI stage 2–3, the highest quartile of NLR presented significantly lower AKI prevalence compared to the lowest quartile (p = 0.048). Patients in the highest quartile of the above biomarkers had significantly lower AKI prevalence compared to the lowest quartile (p for trend < 0.05) (Table 3).

Table 3 Association between inflammatory biomarkers and AKI stage 2–3 (logistic regression)
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Associations Between Inflammatory Biomarkers and Length of Hospital Stay.

Positive linear association were found between NPAR, SIRI, SII, MLR, NLR, MHR, and NHR with length of hospital stay (LnNPAR: β = 10.70; 95% CI: 5.44, 15.97; LnSIRI: β = 2.38; 95% CI: 1.15, 3.62; LnSII: β = 1.73; 95% CI: 0.32, 3.14; LnMLR: β = 2.55; 95% CI: 0.66, 4.44; LnNLR: β = 2.42; 95% CI: 0.76, 4.08; LnMHR: β = 3.58; 95% CI: 1.30, 5.86; LnNHR: β = 4.18; 95% CI: 1.82, 6.53). Apart from MLR, patients in the highest quartile of these biomarkers had significantly lower AKI prevalence compared to the lowest quartile (p for trend < 0.05) (Table 4).

Table 4 Association between inflammatory biomarkers and length of hospital stay (linear regression)
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Discussion

This pilot study is the first to focus exclusively on patients with preoperative renal insufficiency. The selection of biomarkers to investigate was based on their proven relevance in reflecting systemic inflammation and predicting adverse outcomes in cardiovascular and renal disease populations, making them practical and valuable for clinical assessments. We found that elevated preoperative inflammatory biomarkers (NPAR, SIRI, SII, NLR, MHR, and NHR) were associated with a reduced risk of postoperative AKI. Trending analysis indicating that higher levels of these biomarkers are linked to lower AKI rates. Similar association was found between SIRI, SII, and PLR with AKI stage 2–3. On the other hand, positive linear association were found between NPAR, SIRI, SII, MLR, NLR, MHR, and NHR with length of hospital stay. Through RCS analysis, an “Inverted U-shaped” association of LnNPAR and LnSIRI with AKI was identified. Over their respective inflection point, negative association was found between LnNPAR and LnSIRI with AKI.

Low-grade, persistent inflammation in CKD patients can result from various factors, including uremia [19], oxidative stress [20], and comorbid conditions like diabetes and cardiovascular disease [21]. Inflammation in CKD is marked by elevated proinflammatory cytokines, leading to endothelial dysfunction [22, 23] and increased cardiovascular risk. Poor nutritional status and protein-energy malnutrition, common in CKD, are closely linked to inflammation [24]. For instance, low serum albumin levels reflect both poor nutrition and an inflammatory state. De Mutsert et al. found that low serum albumin in dialysis patients is associated with increased mortality due to inflammation rather than malnutrition [25], suggesting that targeting inflammation may improve outcomes more effectively than focusing solely on nutrition.

Previous studies have established a correlation between inflammatory biomarkers and AKI post-cardiac surgery. Wang et al. found that a higher preoperative monocyte-to-lymphocyte ratio significantly predicts AKI after cardiac valve surgery [26], suggesting MLR as a cost-effective AKI risk marker. Their study excluded patients with NYHA grades 3–4 and renal insufficiency, while our study focused on patients with preoperative renal dysfunction. A systematic review and meta-analysis confirmed that elevated neutrophil-lymphocyte ratios predict AKI post-cardiac surgery [27], reinforcing its clinical utility for risk stratification. Another study showed that higher neutrophil-to-lymphocyte ratios are associated with significant inflammatory responses and increased AKI risk in transcatheter aortic valve implantation patients [28]. Two studies with fewer than 400 patients identified that perioperative neutrophil-to-lymphocyte ratio changes predict acute renal failure post-coronary bypass with cardiopulmonary bypass [29, 30].

Regarding NPAR and AKI, a retrospective study with 5083 non-CKD patients found a higher neutrophil percentage-to-albumin ratio associated with increased contrast-associated AKI risk post-percutaneous coronary intervention [31]. NPAR was also an independent predictor of long-term mortality. Wang et al. found that higher NPAR levels significantly correlate with increased all-cause mortality in critically ill AKI patients [32].

Simultaneously, studies have shown that HDL possess anti-inflammatory and antioxidant properties [33]. Smith LE et al. conducted a randomized clinical trial with 391 subjects to analyze the relationship between HDL cholesterol concentration and AKI post-cardiac surgery [34]. They found that higher preoperative HDL levels are associated with a reduced incidence of postoperative AKI, a correlation that is enhanced with statin treatment. Additionally, Huang et al. conducted a retrospective study on 1505 patients undergoing cardiopulmonary bypass surgery and found that an elevated MHR is a significant predictor of AKI [35]. Moreover, one recent study [36] demonstrated that recombinant CER-001 HDL infusion significantly increased survival rates, reduced systemic inflammation, and improved renal and hepatic function in a swine model of lipopolysaccharide-induced AKI and in a Phase 2a clinical trial involving septic patients. CER-001 treatment showed promising results by enhancing lipopolysaccharide scavenging, modulating the cytokine storm, reducing endothelial dysfunction, and preventing the progression to severe AKI in septic patients.

Our findings, however, indicated that higher preoperative NPAR, SIRI, SII, NLR, MHR, and NHR levels are inversely correlated with AKI risk, suggesting that preoperative inflammation may enhance renal resilience and protective mechanisms, reducing AKI susceptibility post-surgery. This phenomenon’s mechanisms remain unclear but may involve adaptive responses triggered by low preoperative inflammation induced by pre-existing renal dysfunction. Prietl et al. investigated the immune response in hemodialysis (HD) patients with COVID-19 [37], discovering that chronic inflammation in these individuals might provide a protective effect against severe COVID-19 outcomes. In their study, HD patients showed stable levels of Th1 and Th17 cytokines and an increased presence of CD38 + CD8 + effector memory and TEMRA T cells, which remained less altered during COVID-19 compared to non-HD patients. This study suggests that the chronic inflammatory state in HD patients may modulate their immune response, potentially mitigating the severity of COVID-19. These findings indicate that chronic inflammation could have protective effects against severe acute inflammation in this vulnerable population, offering valuable insights into the role of chronic inflammation in preventing acute inflammatory responses.

Our study identified an inverted U-shaped nonlinear relationship between the LnNPAR and LnSIRI with the incidence of AKI. This finding suggested that moderate levels of these inflammatory markers may not have a clear impact on the risk of AKI whereas at higher levels of inflammation, a robust anti-inflammatory or adaptive immune response that mitigates the potential damage caused by inflammation. This phenomenon underscores the complexity of the inflammatory response in the context of AKI and highlights the need for a nuanced understanding of how different levels of inflammation affect renal outcomes.

Given that the severity of kidney dysfunction and the presence of diabetes could influence preoperative inflammation levels, we conducted relevant analyses (see Supplementary Table S3/S4). Our results showed that, except for LHR, all inflammatory markers were higher in the eGFR < 30 (ml/min/1.73m2) group compared to the eGFR ≥ 30 (ml/min/1.73m2) group. In the analysis based on diabetes status, we found that only PHR was significantly higher in the diabetes group compared to the non-diabetes group, while the other markers showed no significant differences between the two groups. Our multivariable regression analysis adjusted for baseline eGFR and diabetes as covariates to account for these factors.

Limitations

The limitation of the present study is: (1). The study’s retrospective nature means it relies on existing medical records, which may not capture all relevant data, leading to potential biases in data collection and analysis. (2). The study did not include measurements of cytokines such as interleukins and tumor necrosis factor, which could provide a more comprehensive picture of the inflammatory state and its impact on AKI. (3). Incomplete data on preoperative C-reactive protein (CRP) levels limited the ability to fully assess the inflammatory status of patients. (4). While the study suggests a negative correlation between preoperative inflammatory biomarkers and AKI, it does not establish a causal relationship due to its observational design. (5). The study was conducted at a single hospital, which may limit the generalizability of the findings to other populations or healthcare settings. (6). The study population may not be diverse enough, thus necessitating further research, particularly prospective studies with broader populations, to confirm and expand upon these results.

Conclusion

Our findings indicate that higher preoperative NPAR, SIRI, SII, NLR, MHR, and NHR levels are inversely correlated with AKI risk. Interestingly, our findings suggest that higher preoperative levels of certain inflammatory biomarkers are associated with a reduced risk of postoperative AKI. Moreover, an “inverted U-shaped” association was observed between both LnNPAR and LnSIRI and AKI, with a negative association appearing beyond their respective inflection points.

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Abbreviations

AKI:

acute kidney injury

AUC:

area under the curve

CKD:

chronic kidney disease

CPB:

cardiopulmonary bypass

CRP:

C-reactive protein

eGFR:

estimated glomerular filtration rate

HDL-C:

high-density lipoprotein-cholesterol

ICU:

intensive care unit

KDIGO:

Kidney Disease: Improving Global Outcomes

KIM-1:

kidney injury molecule-1

LHR:

lymphocyte-to-high-density lipoprotein cholesterol ratio

MHR:

monocyte-to-high-density lipoprotein cholesterol ratio

MLR:

monocyte-to-lymphocyte ratio

NGAL:

neutrophil gelatinase-associated lipocalin

NHR:

neutrophil-to-high-density lipoprotein cholesterol ratio

NLR:

neutrophil-to-lymphocyte ratio

NPAR:

neutrophil percentage-to-albumin ratio

PLR:

platelet-to-lymphocyte ratio

RCS:

restricted cubic splines

ROC:

receiver operating characteristic

SCr:

serum creatinine

SII:

systemic immune inflammation index

SIRI:

systemic inflammation response index

TIMP-2×IGFBP7:

tissue inhibitor of metalloproteinases-2× insulin-like growth factor-binding protein 7

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Acknowledgements

We are grateful for the contribution of the study personnel from the department of nephrology, cardiac surgery and critical care for persistent contribution to the maintenance of the cardiac surgery database.

Funding

This work was supported by National Nature Science Foundation of China, No. 82102289; Natural Science Foundation of Xiamen, 3502Z20227112; Shanghai Federation of Nephrology Project supported by Shanghai ShenKang Hospital Development Center, No. SHDC2202230; Shanghai “science and technology innovation plan " Yangtze River Delta scientific and technological Innovation Community project, No. 21002411500.

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Authors and Affiliations

  1. Department of Nephrology, Zhongshan Hospital, Fudan University, No 180 Fenglin Rd, Shanghai, China

    Wuhua Jiang, Yi Fang, Xiaoqiang Ding, Dong Zhang, Xialian Xu & Jiarui Xu

  2. Shanghai Institute of Kidney and Dialysis, Shanghai, China

    Yi Fang, Xiaoqiang Ding, Xialian Xu & Jiarui Xu

  3. Department of Cardiac Surgery Intensive Care Unit, Zhongshan Hospital, Fudan University, Shanghai, China

    Zhe Luo

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  1. Wuhua Jiang
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  2. Yi Fang
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  5. Dong Zhang
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  6. Xialian Xu
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  7. Jiarui Xu
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Contributions

WJ, XD and JX designed and directed the study, WJ, DZ and XX participated in data collection and maintenance, DZ, YF and JX analyzed the data, WJ, YF and ZL interpreted the results and writing. JX, XX, XD and ZL participated in reviewing the manuscript, the maintenance of dataset and facilitating the acquisition of data. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xiaoqiang Ding, Xialian Xu or Jiarui Xu.

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Ethics approval and consent to participate

This study was approved by the ethical board from Zhongshan Hospital, Fudan University (Approval Number B2021–873R). The requirement for informed consent was informed consent was obtained from all participants. The study was conducted in accordance with the Helsinki Declaration (WMA Declaration of Helsinki, 2013).

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Not applicable.

Competing interests

The authors declare no competing interests.

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Jiang, W., Fang, Y., Ding, X. et al. Association between inflammatory biomarkers and postoperative acute kidney injury after cardiac surgery in patients with preoperative renal dysfunction: a retrospective pilot analysis. J Cardiothorac Surg 19, 583 (2024). https://doi.org/10.1186/s13019-024-03067-1

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Keywords

Journal of Cardiothoracic Surgery

ISSN: 1749-8090

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