Abstract
Background
Preoperative occult renal dysfunction (PORD), defined as a normal serum creatinine (Scr) level with glomerular filtration rate (eGFR) < 60 mL/min/1.73 m², may worsen outcomes after cardiac surgery. This study aimed to evaluate whether PORD is associated with adverse short- and long-term outcomes in elderly patients undergoing coronary artery bypass grafting (CABG).
Methods
A retrospective analysis was conducted in 1,157 patients aged ≥ 70 years with normal Scr who underwent CABG at four centers. Patients were classified into PORD and control groups according to eGFR. Propensity score matching was used to balance preoperative characteristics. Logistic regression was performed to identify risk factors for postoperative acute kidney injury (AKI) and in-hospital mortality. Long-term survival was assessed using Kaplan–Meier analysis and Cox proportional hazards models.
Results
PORD was present in 29.3% of elderly patients with normal Scr. Compared with controls, the PORD group had higher preoperative Scr, higher rates of postoperative AKI (33.33% vs. 14.30%), and higher in-hospital mortality (6.19% vs. 3.55%). PORD independently predicted postoperative AKI and in-hospital mortality, and was associated with increased long-term mortality after CABG.
Conclusions
Nearly one-third of elderly CABG patients with normal Scr have PORD, which is associated with a higher risk of postoperative AKI, in-hospital death, and long-term mortality. Routine eGFR assessment is recommended to improve preoperative renal risk stratification in this population.
Clinical trial number
Not applicable.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12882-026-04761-z.
Keywords: Preoperative occult renal dysfunction, Coronary artery bypass grafting, Elderly, Acute kidney injury, Mortality
Introduction
Poor preoperative renal dysfunction is a clear risk factor for death after cardiac operations. The gold standard for assessing renal function is glomerular filtration rate (GFR), but due to the expensive and time-consuming measurement of GFR, renal function is widely assessed using the serum creatinine (Scr) value in clinic. However, the Scr value is affected by many factors such as age, race, muscle mass and dietary intake [1]. The renal function declines with ageing, and renal function insufficiency is common in the elderly [2]. In initial stage of renal insufficiency, the filtration rate decreases without a significant increase in Scr. Therefore, patients with normal Scr value may be in a state of impaired renal function, which is a possible clinical problem. The patient who has a normal range of Scr value but abnormal GFR (< 60 ml/min/1.73m2) is considered as having preoperative occult renal dysfunction (PORD) [3].
Coronary artery disease (CAD) is also a common concomitant in elderly patients. Coronary artery bypass grafting (CABG) is one of the most common treatments for severe CAD. Compared with other cardiac surgeries, patients undergoing CABG usually have more underlying diseases, including poor preoperative renal function [4]. Relevant research mostly focuses on the effect of renal function based on Scr assessment on CABG postoperative outcomes. However, PORD accounts for a high proportion of elderly patient undergoing CABG, which has been rarely studied. As a result, the role of CABG in PORD patients is overlooked constantly, especially in the elderly population, which may result in adverse consequences, including more postoperative acute kidney injury (AKI), early death and poor long-term prognosis. To test this hypothesis, we conducted a multicenter retrospective study including patients aged ≥ 70 years undergoing CABG at four medical centers. Patients aged ≥ 70 years were considered an elderly, high-risk population according to previous CABG literature and local clinical practice, and we therefore focused on this group to evaluate the incidence of PORD and its impact on short-term and long-term outcomes [5, 6].
Patients and methods
Study population
This retrospective observational cohort study included four medical centers—Jiangsu People’s Hospital (JSPH), Shanghai Chest Hospital (SHCH), Qilu Hospital (QLH), and General Hospital of Ningxia Medical University (GHN)—from January 2005 to January 2019. Elderly patients were defined as those aged ≥ 70 years; among 1,781 such patients, 1,157 were enrolled according to the exclusion criteria described below. Exclusion criteria were: (1) preoperative Scr above the upper limit of normal range; (2) concomitant cardiac procedures other than CABG; and (3) absence of key medical records, especially Scr values. All patients underwent preoperative evaluation, surgery and postoperative management according to routine institutional protocols. Clinical data were retrieved retrospectively from hospital information systems. The criteria and process of patient selection are summarized in Fig. 1.
Fig. 1.
Patients’ enrollment flowchart. CABG, coronary artery bypass grafting; JSPH, Jiangsu Province Hospital; SHCH, Shanghai Chest Hospital; QLH, Qilu Hospital of Shandong University; GHN, General Hospital of Ningxia Medical University; Scr, serum creatinine; PORD, preoperative occult renal dysfunction
Preoperative Scr values (within 48 h before CABG) and postoperative Scr values (within 7 days after surgery) were obtained from hospital information systems. Scr was measured in the local clinical laboratories of each participating center, and “normal Scr” was defined according to the sex-specific reference ranges used at each laboratory (Supplement file, Sp_Table 1). Patients were eligible for inclusion only if their baseline Scr fell within the normal range for their sex at the center where they underwent surgery. Preoperative renal function in this study was assessed by estimated GFR (eGFR) calculated with the CKD-EPI serum creatinine equation (CKD-EPI_Scr). According to the definition of PORD, patients were divided into a PORD group and a control group.
This study was approved by the Ethics Committees of all participating hospitals (JSPH, No. 2022-SR-209; SHCH, No. IS22026; QLH, No. KYLL-202204-016; GHN, No. KYLL20240262). In view of the retrospective design and use of de-identified data, the requirement for informed consent was waived by the ethics committees.
Study endpoints
The primary endpoint was the occurrence of mortality and postoperative AKI. The secondary endpoint was long-term all-cause death.
AKI was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) serum creatinine criteria [7] and the use of renal replacement therapy during the index hospitalisation. In brief, AKI was diagnosed if there was an increase in serum creatinine of ≥ 26.5 µmol/L within 48 h, an increase in serum creatinine to ≥ 1.5 times the baseline value within 7 days, or the initiation of renal replacement therapy. Because of the retrospective and multicenter nature of the study, accurate hourly urine output was not consistently or reliably recorded across the participating centers. Therefore, KDIGO urine output criteria could not be applied in this analysis.
Because of the retrospective, multicenter design, perioperative hemodynamic and pharmacologic data were not recorded in a fully standardized manner across the four hospitals. Continuous intra- and postoperative hemodynamic profiles (e.g., detailed mean arterial pressure targets and cardiac output), cumulative fluid balance, transfusion volumes, and the exact duration, dose, and type of vasoactive and inotropic agents were incompletely or heterogeneously documented in the source records. These variables were therefore not included in the predefined dataset and could not be entered into the multivariable models.
Follow-Up
According to the regulations on follow-up after CABG, patients who survived and were discharged from hospitals were followed up by outpatient clinic services or telephone in the first and third months after operation. Subsequent follow-up was every six months, with patient survival status and cause of death being the main follow-up goals. The latest follow-up date was 9 July 2023.
Statistical analysis
Continuous variables were tested for normality using the Shapiro–Wilk test. Normally distributed variables are presented as mean ± standard deviation and were compared between groups using the Student’s t-test. Skewed variables are presented as median (interquartile range) and were compared using the Mann–Whitney U test. Categorical variables are expressed as counts and percentages and were compared using the chi-square test or Fisher’s exact test, as appropriate. Missing values for key exposure, outcome, or adjustment variables were not imputed; patients with missing data for these variables were excluded from the corresponding analyses. Multicollinearity among candidate covariates was assessed using variance inflation factors (VIF) and correlation matrices (Supplement file, Sp_Table 2).
Univariable logistic regression analyses were performed to identify potential predictors of postoperative AKI and early (in-hospital) mortality. Variables considered clinically relevant a priori and/or showing an association with the outcome at a significance level of p < 0.20 in univariable analyses were then entered as candidate covariates into multivariable models. Occult renal dysfunction (PORD) was forced into all multivariable models irrespective of its univariable p value. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were calculated for postoperative AKI and early mortality. The association between PORD and long-term all-cause mortality was assessed using Cox proportional hazards regression, and results are reported as hazard ratios (HRs) with 95% CIs. The proportional hazards assumption was evaluated and found to be acceptable.
To further reduce confounding by baseline differences, a propensity score for having PORD was estimated using logistic regression including clinically relevant preoperative variables (age, height, and cerebrovascular disease). Patients with and without PORD were then matched 1:1 using nearest-neighbor matching within a specified caliper of the propensity score. Covariate balance before and after matching was assessed using standardized mean differences (SMDs), with values < 0.10 indicating adequate balance; detailed covariate balance is shown in supplementary file (Supplement file, Sp_Table 3). In the propensity score–matched cohort, paired tests were used for outcome comparisons (McNemar’s test for categorical variables and the paired t test or Wilcoxon signed-rank test for continuous variables), and stratified Cox models were applied for time-to-event analyses. All statistical tests were two-sided, and p < 0.05 was considered statistically significant. Statistical analyses were performed using SPSS version 22.0 (IBM, USA).
Results
Baseline demographic and clinical characteristics
Of the 1157 included patients, 339 (29.3%) had PORD. Compared with the control group, patients in the PORD group had a higher prevalence of cerebrovascular disease (21.53% vs. 16.50%, p = 0.033) and higher preoperative Scr (92.00[78.10-103.20] vs. 70.40[60.98–80.85] µmol/L, p < 0.001). The incidence rates of postoperative AKI (33.33% vs. 14.30%, p < 0.001) and in-hospital mortality (6.19% vs. 3.55%, p = 0.044) were also higher in the PORD group.
After 1:1 propensity score matching (PSM), 311 patients were included in each group. In the matched cohort, the PORD group continued to show higher preoperative Scr (92.80[80.00-104.00] vs. 72.80[66.15–80.77] µmol/L, p < 0.001), higher rates of postoperative AKI (32.80% vs. 17.75%, p < 0.001), and higher in-hospital mortality (6.11% vs. 1.24%, p = 0.001). Detailed perioperative characteristics in the overall and matched cohorts are summarized in Table 1.
Table 1.
The baseline clinical characteristic of PORD and control group before and after propensity score matching
| Before PSM | After PSM | |||||
|---|---|---|---|---|---|---|
| PORD (n = 339) | Control group (n = 818) | P value | PORD (n = 311) | Control group (n = 311) | P value | |
| Age(y) | 75.24 ± 3.90 | 73.20 ± 3.07 | < 0.001 | 74.71 ± 3.38 | 74.72 ± 3.39 | 0.852 |
| Male sex (n, %) | 241(71.09) | 603(73.72) | 0.360 | 223(71.70) | 226(72.67) | 0.484 |
| Weight (kg) | 60.00(55.00–69.00) | 68.89(62.00–75.00) | < 0.001 | 61.00(56.00–69.00) | 70.00(64.00–75.00) | < 0.001 |
| Height (cm) | 165.00(158.00-170.00) | 168.00(160.00-172.00) | < 0.001 | 165.00(158.00-170.00) | 165.00(160.00-170.00) | 0.062 |
| BMI (kg/m2) | 22.77(21.09–24.62) | 24.68(22.86–26.64) | < 0.001 | 22.84(21.23–24.68) | 23.30(21.43–25.39) | 0.051 |
| BSA (m2) | 1.74(1.67–1.86) | 1.86(1.77–1.96) | < 0.001 | 1.75(1.68–1.87) | 1.75(1.69–1.90) | 0.479 |
| BMI classification | < 0.001 | 0.078 | ||||
| Normal weight | 203(59.88) | 318(38.88) | 183(58.84) | 105(33.76) | ||
| Lean | 16(4.72) | 5(0.611) | 13(4.18) | 2(0.64) | ||
| Overweight | 105(30.93) | 389(47.56) | 100(32.15) | 154(49.52) | ||
| Obesity | 13(3.83) | 64(7.82) | 13(4.18) | 26(8.36) | ||
| Severe obesity | 2(0.59) | 42(5.13) | 2(0.64) | 24(7.72) | ||
| CAD classification | 0.555 | 0.055 | ||||
| Stable angina pectoris (n, %) | 78(23.01) | 194(23.72) | 71(22.84) | 113(36.33) | ||
| Unstable angina pectoris (n, %) | 229(67.56) | 558(67.97) | 210(67.52) | 172(55.30) | ||
| Acute myocardial infarction (n, %) | 32(9.44) | 66(8.07) | 30(9.65) | 26(8.36) | ||
| Hypertension (n, %) | 239(70.50) | 552(67.48) | 0.315 | 216(69.45) | 207(66.56) | 0.732 |
| Diabetes mellitus (n, %) | 99(29.20) | 283(34.60) | 0.076 | 91(29.26) | 101(32.48) | 0.203 |
| Cerebrovascular disease | 0.033 | 0.198 | ||||
| Stroke (n, %) | 35(10.32) | 71(8.68) | 32(10.29) | 59(18.97) | ||
| Lacunar infarction (n, %) | 38(11.21) | 64(7.82) | 29(9.32) | 21(6.75) | ||
| Peripheral vascular disease (n, %) | 42(10.82) | 82(9.15) | 0.237 | 37(11.90) | 17(5.47) | 0.001 |
| NYHA classification | 0.033 | 0.901 | ||||
| I (n, %) | 29(8.55) | 121(14.79) | 28(9.00) | 14(4.50) | ||
| II (n, %) | 163(48.08) | 370(45.23) | 151(48.55) | 180(57.88) | ||
| III (n, %) | 134(39.53) | 300(36.67) | 121(38.90) | 102(32.80) | ||
| IV (n, %) | 13(3.83) | 27(3.30) | 11(3.54) | 15(4.82) | ||
| Preoperative LVEF (%) | 61.20(55.00–65.00) | 61.00(55.36-65.00) | 0.803 | 61.20(55.00–65.00) | 62.70(58.80–65.40) | 0.186 |
| Preoperative Scr (umol) | 92.00(78.10-103.20) | 70.40(60.98–80.85) | < 0.001 | 92.80(80.00-104.00) | 72.80(66.15–80.77) | < 0.001 |
| Preoperative eGFR(mL/min/1.73m2) | 53.25(48.62–56.76) | 75.17(67.58–85.21) | < 0.001 | 53.65(48.77–56.98) | 72.07(66.15–80.77) | < 0.001 |
| hs-cTnT (ng/L) | 7.46(2.31–17.50)- | 6.35 (1.74–13.73) | 0.004 | 7.17(2.21–15.51) | 7.45(2.07–14.11) | 0.045 |
| Valvular disease (n, %) | 35(10.32) | 95(11.61) | 0.527 | 31(9.97) | 45(14.472) | 0.031 |
| COPD (n, %) | 22(6.49) | 40(4.89) | 0.271 | 21(6.75) | 17(5.47) | 0.236 |
| Preoperative AF (n, %) | 15(4.42) | 27(3.30) | 0.352 | 12(3.86) | 6(1.93) | 0.288 |
| Pulmonary hypertension (n, %) | 118(27.54) | 247(28.07) | 0.124 | 106(34.08) | 81(26.45) | 0.368 |
| Previous PCI (n, %) | 31(9.14) | 67(8.19) | 0.596 | 28(9.00) | 22(7.07) | 0.148 |
| Diseased Coronary arteries (n) | 3(3–3) | 3(3–3) | 0.701 | 3(3–3) | 2.82 ± 0.46 | 0.383 |
| EuroSCORE II | 2.37(1.69–3.45) | 1.66(1.25–2.24) | < 0.001 | 2.33(1.63–3.43) | 2.31 ± 1.35 | < 0.001 |
| Emergency operation | 0.629 | 0.314 | ||||
| Emergency operation (n, %) | 4(1.18) | 20(2.44) | 4(1.29) | 11(3.54) | ||
| Salvage (n, %) | 2(0.59) | 2(0.24) | 1(0.32) | 2(0.64) | ||
| Coronary artery bypass grafts (n) | 3(3–3) | 3(3–3) | 0.277 | 3(3–3) | 3.41 ± 0.92 | 0.303 |
| Cardiopulmonary bypass (n, %) | 26(7.67) | 67(8.19) | 0.767 | 23(7.40) | 22(7.07) | 0.679 |
| Postoperative Scr (umol) | ||||||
| 1 day | 100.61 ± 33.31 | 77.49 ± 29.21 | < 0.001 | 101.54 ± 33.77 | 85.41 ± 32.13 | < 0.001 |
| 2 days | 105.46 ± 45.65 | 79.78 ± 35.35 | < 0.001 | 105.99 ± 46.04 | 88.24 ± 36.66 | < 0.001 |
| 3 days | 108.31 ± 49.31 | 80.89 ± 39.33 | < 0.001 | 107.45 ± 49.24 | 85.42 ± 31.89 | < 0.001 |
| 4 days | 106.93 ± 51.69 | 82.04 ± 45.70 | < 0.001 | 107.83 ± 50.78 | 84.83 ± 46.39 | < 0.001 |
| 5 days | 106.86 ± 67.40 | 79.38 ± 42.38 | < 0.001 | 107.59 ± 68.84 | 83.23 ± 26.99 | < 0.001 |
| 6 days | 107.72 ± 51.34 | 81.47 ± 36.28 | < 0.001 | 109.55 ± 52.23 | 82.87 ± 24.28 | < 0.001 |
| 7 days | 105.33 ± 57.04 | 80.13 ± 54.04 | < 0.001 | 106.00 ± 75.90 | 79.62 ± 66.75 | < 0.001 |
| Postoperative AKI (n, %) | 113(33.33) | 117(14.30) | < 0.001 | 102(32.80) | 39(17.75) | < 0.001 |
| IABP implantation (n, %) | 6(1.77) | 15(1.83) | 0.941 | 6(1.93) | 4(1.25) | 0.524 |
| Postoperative mortality (n, %) | 21(6.19) | 29(3.55) | 0.044 | 19(6.11) | 4(1.24) | 0.001 |
PORD, preoperative occult renal dysfunction; PSM, propensity score matching; BMI, body mass index; BSA, body surface area; CAD, coronary artery disease; NYHA, New York heart association; LVEF, left ventricular ejection fraction; Scr, serum creatinine; COPD, chronic obstructive pulmonary disease; AF, atrial fibrillation; PCI, percutaneous coronary intervention; EuroSCORE II, European system for cardiac operative risk evaluation II; AKI, acute renal failure; IABP, Intra-aortic ballon pump
Perioperative trends in serum creatinine
Scr was assessed at eight perioperative time points. Overall, Scr increased during the first 3 postoperative days and then gradually declined. At all time points, Scr values were significantly higher in the PORD group than in the control group (Fig. 2A).
Fig. 2.
The average values of serum creatinine (Scr) in total cohort, PORD group and control group. (A) before PSM (B) after PSM
After PSM, Scr remained significantly higher in the PORD group at each time point. In the PORD group, Scr rose over the first 2 postoperative days and then decreased, whereas in the control group Scr increased for 4 postoperative days before gradually declining (Fig. 2B).
Risk factors for in-hospital mortality
In multivariable analysis of the overall cohort, age (OR = 1.148, p = 0.007), gender (OR = 0.321, p = 0.002), NYHA classification (OR = 1.763, p = 0.015), and IABP implantation (OR = 3.285, p < 0.001) were identified as independent predictors of in-hospital mortality (Table 2).
Table 2.
Risk factors of postoperative mortality by univariate and multivariate logistic regression analysis before and after propensity score matching
| Before PSM | After PSM | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Univariate logistic regression | Multivariate logistic regression | Univariate logistic regression | Multivariate logistic regression | |||||||||
| OR | 95% CI | P value | OR | 95% CI | P value | OR | 95% CI | P value | OR | 95% CI | P value | |
| Age | 1.066 | 0.989–1.149 | 0.095 | 1.148 | 1.038–1.270 | 0.007 | 1.042 | 0.926–1.173 | 0.497 | 1.089 | 1.016–1.168 | 0.016 |
| Gender | 0.495 | 0.278–0.881 | 0.017 | 0.321 | 0.156–0.662 | 0.002 | 1.543 | 0.663–3.592 | 0.314 | 0.301 | 0.129–0.701 | 0.005 |
| BMI | 0.936 | 0.848–1.033 | 0.189 | 0.897 | 0.796–1.010 | 0.074 | 0.861 | 0.744-996 | 0.043 | 0.964 | 0.799–1.162 | 0.700 |
| CAD classification | 1.590 | 0.940–2.689 | 0.084 | 0.554 | 0.184–1.664 | 0.434 | 1.856 | 0.807–4.268 | 0.146 | 1.097 | 0.327–3.688 | 0.492 |
| Number of diseased vessels | 0.485 | 0.311–0.757 | 0.001 | 0.598 | 0.333–1.074 | 0.383 | 0.449 | 0.239–0.844 | 0.013 | 0.814 | 0.324–2.045 | 0.661 |
| Hypertension | 1.332 | 0.699–2.538 | 0.383 | 0.888 | 0.374–2.110 | 0.778 | ||||||
| Diabetes mellitus | 1.496 | 0.842–2.661 | 0.170 | 0.496 | 0.245–1.004 | 0.051 | 1.085 | 0.456–2.577 | 0.854 | 0.613 | 0.291–1.290 | 0.197 |
| Cerebrovascular disease | 1.034 | 0.656–1.630 | 0.886 | 0.777 | 0.372–1.624 | 0.503 | ||||||
| Preoperative LVEF | 0.984 | 0.962–1.008 | 0.189 | 0.969 | 0.935–1.004 | 0.078 | 0.985 | 0.946–1.025 | 0.448 | 0.973 | 0.938–1.010 | 0.149 |
| hs-cTnT | 1.047 | 0.919–2.121 | 0.021 | 1.441 | 1.101–2.763 | 0.057 | 1.531 | 0.917–2.761 | < 0.001 | 1.663 | 1.071–3.178 | 0.033 |
| NYHA classification | 1.908 | 1.278–2.849 | 0.002 | 1.763 | 1.119–2.778 | 0.015 | 2.009 | 1.180–3.734 | 0.012 | 2.381 | 1.157–4.897 | 0.018 |
| Peripheral vascular disease | 0.715 | 0.253–2.022 | 0.527 | 2.132 | 0.703–6.468 | 0.181 | ||||||
| Valvular disease | 0.873 | 0.340–2.240 | 0.777 | 1.623 | 0.468–5.629 | 0.445 | ||||||
| COPD | 0.727 | 0.173–3.063 | 0.664 | 0.703 | 0.092–5.353 | 0.734 | ||||||
| Preoperative AF | 2.446 | 0.838–7.144 | 0.711 | 6.305 | 1.964–20.347 | 0.002 | 5.969 | 1.631–21.851 | 0.007 | |||
| Pulmonary hypertension | 1.340 | 0.813–2.209 | 0.102 | 0.080 | 0.002–3.746 | 0.197 | 0.914 | 0.477–1.753 | 0.787 | 0.340 | 0.041–0.801 | 0.144 |
| PORD | 1.797 | 1.009–3.189 | 0.046 | 0.876 | 0.400-1.916 | 0.279 | 5.266 | 1.779–15.584 | 0.003 | 2.503 | 0.385–16.274 | 0.037 |
| Previous PCI | 0.937 | 0.330–2.660 | 0.903 | 1.033 | 0.236–4.519 | 0.966 | ||||||
| Emergency operation | 4.254 | 1.964–9.215 | < 0.001 | 1.358 | 0.032–5.928 | 0.639 | 7.163 | 2.503–20.502 | < 0.001 | 2.616 | 1.553–4.405 | < 0.001 |
| IABP implantation | 3.391 | 2.556–4.766 | < 0.001 | 3.285 | 2.315–4.660 | <0.001 | 2.612 | 1.692–4.031 | < 0.001 | 5.298 | 1.124–24.977 | 0.035 |
| Cardiopulmonary bypass | 2.096 | 1.064–4.130 | 0.032 | 2.255 | 1.107–4.608 | 0.002 | 4.482 | 1.777–11.301 | 0.001 | 1.460 | 0.603–3.532 | 0.401 |
PSM, propensity score matching; BMI, body mass index; BSA, body surface area; CAD, coronary artery disease; NYHA, New York heart association; COPD, chronic obstructive pulmonary disease; AF, atrial fibrillation; PORD, preoperative occult renal dysfunction; PCI, percutaneous coronary intervention; IABP, Intra-aortic ballon pump; LVEF, left ventricular ejection fraction
In the matched cohort, age (OR = 1.089, p = 0.016), gender (OR = 0.301, p = 0.005), preoperative hs-cTnT (OR = 1.663, p = 0.033), NYHA class (OR = 2.503, p = 0.037), preoperative AF (OR = 5.969, p = 0.007), PORD (OR = 2.503, p = 0.037), emergency surgery (OR = 2.616, p < 0.001), and IABP implantation (OR = 5.298, p = 0.035) were independently associated with in-hospital mortality (Table 2).
Risk factors for postoperative AKI
In multivariable logistic regression of the overall cohort, NYHA class (OR = 1.292, p = 0.023), pulmonary hypertension (OR = 1.946, p = 0.020), PORD (OR = 2.751, p < 0.001), IABP implantation (OR = 1.946, p < 0.001), and emergency surgery (OR = 2.167, p < 0.001) were independent risk factors for postoperative AKI (Table 3).
Table 3.
Risk factors of postoperative acute kidney injury by univariate and multivariate logistic regression analysis before and after propensity score matching
| Before PSM | After PSM | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Univariate logistic regression | Multivariate logistic regression | Univariate logistic regression | Multivariate logistic regression | |||||||||
| OR | 95% CI | P value | OR | 95% CI | P value | OR | 95% CI | P value | OR | 95% CI | P value | |
| Age | 1.063 | 1.022–1.107 | 0.003 | 1.031 | 0.969–1.060 | 0.221 | 1.026 | 0.972–1.083 | 0.350 | 1.033 | 0.975–1.095 | 0.271 |
| Gender | 1.050 | 0.760–1.450 | 0.768 | 0.878 | 0.623–2.010 | 0.459 | 0.860 | 0.567–1.306 | 0.479 | 1.015 | 0.655–1.571 | 0.498 |
| BMI | 0.989 | 0.942–1.038 | 0.651 | 0.995 | 0.937–1.057 | 0.879 | ||||||
| CAD classification | 1.068 | 0.818–1.392 | 0.628 | 1.114 | 0.768–1.616 | 0.569 | ||||||
| Number of diseased vessels | 0.726 | 0.543–0.696 | 0.030 | 0.793 | 0.623–1.238 | 0.212 | 0.827 | 0.561–1.219 | 0.337 | |||
| Hypertension | 1.189 | 0.866–1.633 | 0.285 | 1.097 | 0.698–1.725 | 0.688 | ||||||
| Diabetes mellitus | 1.158 | 0.855–1.568 | 0.343 | 1.001 | 0.671–1.493 | 0.995 | ||||||
| Cerebrovascular disease | 1.200 | 0.961–1.499 | 0.108 | 0.599 | 0.282–1.273 | 0.183 | 1.147 | 0.873–1.507 | 0.325 | |||
| Preoperative LVEF | 0.979 | 0.965–0.994 | 0.005 | 0.994 | 0.976–1.012 | 0.491 | 0.992 | 0.974–1.011 | 0.425 | |||
| hs-cTnT | 1.001 | 0.511–2.101 | 0.437 | 1.441 | 0.875–2.910 | 0.179 | 2.141 | 0.965–2.675 | 0.030 | 1.104 | 0.719–2.114 | 0.015 |
| NYHA classification | 1.481 | 1.211–1.810 | < 0.001 | 1.292 | 0.533–2.792 | 0.023 | 1.425 | 1.113–1.824 | 0.005 | 1.507 | 1.130–2.009 | 0.005 |
| Peripheral vascular disease | 1.690 | 1.109–2.575 | 0.015 | 1.385 | 0.870–2.203 | 0.169 | 2.760 | 1.573–4.843 | < 0.001 | 1.925 | 1.047–3.539 | 0.035 |
| Valvular disease | 1.009 | 0.639–1.592 | 0.971 | 0.994 | 0.508–1.945 | 0.986 | ||||||
| COPD | 1.704 | 0.965–3.008 | 0.066 | 0.837 | 0.422–1.659 | 0.610 | 1.227 | 0.603–2.705 | 0.523 | |||
| Preoperative AF | 2.326 | 1.216–4.448 | 0.011 | 1.713 | 0.837-3.504 | 0.135 | 2.262 | 0.958–5.337 | 0.062 | 1.701 | 0.120–4.945 | 0.093 |
| Pulmonary hypertension | 1.560 | 1.253–1.941 | < 0.001 | 1.338 | 1.017–1.745 | 0.020 | 1.179 | 0.894–1.555 | 0.242 | |||
| PORD | 2.996 | 2.222–3.039 | < 0.001 | 2.751 | 1.986–3.810 | < 0.001 | 3.615 | 2.406–5.434 | < 0.001 | 3.401 | 2.213–5.226 | < 0.001 |
| Previous PCI | 1.109 | 0.669–1.839 | 0.688 | 1.145 | 0.594–2.209 | 0.685 | ||||||
| Emergency operation | 1.271 | 0.619–2.610 | 0.513 | 1.426 | 0.534–3.804 | 0.479 | ||||||
| IABP implantation | 2.051 | 1.556–2.703 | < 0.001 | 1.946 | 1.443–2.626 | < 0.001 | 1.970 | 1.378–2.815 | < 0.001 | 2.011 | 1.366–2.961 | < 0.001 |
| Cardiopulmonary bypass | 2.294 | 1.456–3.616 | < 0.001 | 2.167 | 1.745–3.111 | < 0.001 | 1.860 | 1.048–3.302 | 0.034 | 3.692 | 2.410–5.655 | < 0.001 |
PSM, propensity score matching; BMI, body mass index; BSA, body surface area; CAD, coronary artery disease; LVEF, left ventricular ejection fraction; NYHA, New York heart association; COPD, chronic obstructive pulmonary disease; AF, atrial fibrillation; PORD, preoperative occult renal dysfunction; PCI, percutaneous coronary intervention; IABP, Intra-aortic ballon pump
In the matched cohort, preoperative hs-cTnT (OR = 1.104, p = 0.015), NYHA class (OR = 1.507, p = 0.005), peripheral vascular disease (OR = 1.925, p = 0.035), PORD (OR = 3.401, p < 0.001), IABP implantation (OR = 2.011, p < 0.001), and emergency surgery (OR = 3.692, p < 0.001) remained independent risk factors for postoperative AKI (Table 3).
Risk factors for long-term mortality
Kaplan–Meier survival analysis showed that the 1-, 3-, 5- and 10-year survival rates in the PORD group were 96.7%, 93.8%, 89.7% and 63.1% respectively, which were significantly lower than those in the control group (97.9%, 94.5%, 90.8% and 78.7% respectively). The median duration of follow-up was 145.8 months (range 1.0–193.4 months).
Kaplan–Meier curves demonstrated worse long-term survival in the PORD group in both the overall cohort (Fig. 3A) and the matched cohort (Fig. 3B). In cox regression analysis of the overall cohort, age (HR = 1.054, p = 0.040), preoperative LVEF (HR = 0.977, p = 0.032), PORD (HR = 2.670, p = 0.021), and IABP implantation (OR = 4.147, p = 0.010) were independently associated with long-term mortality. After PSM, age (HR = 1.177, p = 0.039), hs-cTNT (HR = 1.73, p = 0.021), NYHA class (HR = 1.950, p = 0.008) and PORD (HR = 2.565, p = 0.001) remained independent predictors of long-term mortality (Fig. 4).
Fig. 3.
Impact of PORD on long term survival in CABG patients aged 70 years and older. (A) before PSM (B) after PSM
Fig. 4.
Forest plots of independent risk factors of long-term mortality before and after propensity score matching
Causes of postoperative and late mortality
In the entire cohort of 1,157 elderly patients undergoing CABG, 180 patients (15.6%) died during follow-up. Among them, 50 patients (4.3%) died within 30 days after surgery and were classified as postoperative mortality, whereas 130 patients (11.2%) died beyond 30 days and were classified as late mortality.
Among postoperative deaths (≤ 30 days), the leading causes were cardiac causes, mainly postoperative myocardial infarction with low cardiac output (41/50, 82.0%), followed by renal failure (7/50, 14.0%), major bleeding (1/50, 2.0%) and severe infection (1/50, 2.0%) (Supplement file, Sp_Table 4). Among late deaths (> 30 days), cardiac causes remained predominant (55/130, 42.3%), while non-cardiac causes were also frequent, including malignancy (18/130, 13.8%), infectious causes including COVID-19 (16/130, 12.3%), cerebrovascular events (12/130, 9.2%), pulmonary or respiratory causes (7/130, 5.4%), systemic or multi-organ failure or metabolic disease (5/130, 3.8%), gastrointestinal causes (4/130, 3.1%), renal failure (3/130, 2.3%), trauma or accidental causes (2/130, 1.5%), sudden unexplained death (1/130, 0.8%), and other or unclassified causes (7/130, 5.4%) (Supplement file, Sp_Table 5).
In sensitivity analyses restricted to patients undergoing off-pump, non-emergency, non-salvage CABG without IABP support, PORD remained significantly associated with worse long-term survival, and the hazard ratios were similar to those in the primary analysis (Supplement file, Sp_Figure 1).
Discussion
PORD is very common in older Chinese patients undergoing CABG. In this multicenter retrospective cohort, almost one-third (29.3%) of patients with normal Scr met the definition of PORD. PORD was independently associated with postoperative AKI and in-hospital mortality, and these associations remained significant after propensity score matching. Long-term survival was also significantly worse in patients with PORD, both before and after matching. In addition, impaired cardiac function and PORD emerged as important risk factors for long-term mortality in elderly patients undergoing CABG.
Preoperative renal function is a key determinant of outcomes after cardiac surgery and is closely linked to postoperative AKI, early mortality, and long-term survival [8]. Although direct GFR measurement using exogenous markers is considered the reference standard [9], it is impractical in routine practice. Scr therefore remains the most commonly used surrogate of renal function, and elevated Scr is well known to predict adverse outcomes after CABG [10, 11]. However, Scr is strongly influenced by age, sex, muscle mass, and nutritional status, and may remain “normal” despite marked reductions in GFR [12]. Up to two-thirds of patients with normal Scr have a creatinine clearance < 60 mL/min [13]. The present data confirm that PORD, defined by reduced eGFR despite normal Scr, is highly prevalent in elderly CABG candidates and identifies a subgroup with substantially increased risks of cardiac surgery-associated acute kidney injury (CSA-AKI) and long-term mortality.
The high burden and prognostic impact of PORD in this cohort are consistent with recent CABG studies showing that even mild preoperative reductions in eGFR are among the strongest predictors of long-term mortality [14]. CSA-AKI itself is associated with increased short- and long-term mortality and with accelerated progression to chronic kidney disease and end-stage kidney disease [15, 16]. In the current analysis, PORD was a strong and independent predictor of CSA-AKI and conferred an approximately 2.5-fold higher risk of long-term death, underscoring that occult renal dysfunction is not a benign finding in elderly surgical candidates. In sensitivity analyses restricted to patients undergoing off-pump, non-emergent CABG without IABP support, the associations between PORD and both CSA-AKI and mortality remained materially unchanged, further supporting the robustness of these findings.
In this elderly CABG cohort, the observed 30-day mortality in patients with PORD was almost twice that predicted by EuroSCORE II. Similar underestimation of risk has been reported in Asian cardiac surgery populations, where EuroSCORE II shows good discrimination but suboptimal calibration, particularly in high-risk patients and certain CABG subgroups [17, 18]. Because EuroSCORE II represents renal risk only through serum creatinine and dialysis dependence, it does not capture occult dysfunction defined by reduced creatinine clearance or eGFR in patients with “normal” creatinine, a situation common in elderly individuals with low muscle mass. In addition, important prognostic features such as frailty, sarcopenia and the broader cardiovascular–kidney–metabolic syndrome, which are prevalent in older adults with chronic kidney disease and strongly associated with mortality, are not included in the score [19–21]. These limitations likely contribute to systematic underestimation of operative risk in elderly Chinese patients with PORD and highlight the need for risk models that incorporate more sensitive markers of renal function and geriatric vulnerability.
The interaction between cardiac dysfunction and PORD appears to be particularly important in this setting. Patients with PORD more often had advanced NYHA class, reduced LVEF, and preoperative AF than those without PORD, and these markers of impaired cardiac function remained associated with adverse outcomes after matching [22–24]. Heart failure with reduced ejection fraction is known to decrease effective renal plasma flow and GFR [25, 26], and AF may further worsen renal perfusion and neurohormonal activation. Improvement in both eGFR and LVEF after rhythm control of AF has been reported [27], supporting the notion that PORD in elderly CABG patients reflects not only intrinsic kidney disease but also advanced cardiac and systemic pathology. From a broader perspective, PORD in this population may represent a clinical manifestation of the cardiovascular–kidney–metabolic syndrome, which is associated with stepwise increases in cardiovascular and all-cause mortality [16, 28, 29].
Perioperative biomarkers and management factors may further modulate risk in patients with PORD. In this cohort, higher postoperative hs-cTnT was independently associated with 30-day and long-term mortality in matched analyses, but showed a weaker and non-independent association with CSA-AKI. These findings are in line with previous studies in cardiac surgery populations linking elevated hs-cTnT to myocardial injury, prolonged ICU stay, and serious complications, including AKI and death [30, 31], and suggest that hs-cTnT primarily captures global cardiovascular risk, while PORD remains the principal renal determinant of CSA-AKI. Perioperative red blood cell transfusion, hypotension, and reduced perfusion pressure have also been associated with CSA-AKI and mortality after cardiac surgery [32–36], and inappropriate use of nephrotoxic agents may further increase risk [37]. Although detailed hemodynamic and transfusion data were not available in this study, these factors likely interact with PORD to precipitate CSA-AKI and influence outcomes.
Taken together, these observations have several implications for clinical practice and nephrology care. First, reliance on Scr alone is inadequate for risk assessment in elderly CABG candidates; preoperative renal function should be routinely evaluated using eGFR, and PORD should be actively identified. Second, recognition of PORD may help refine perioperative risk stratification and guide more intensive hemodynamic monitoring, careful avoidance of nephrotoxins and excess contrast exposure, and closer collaboration between cardiac surgeons, cardiologists, nephrologists, and anesthesiologists. Finally, the strong association between PORD and long-term mortality suggests that occult renal dysfunction in this setting is a marker of high global cardiovascular and renal risk, warranting long-term follow-up and optimization of secondary prevention.
Limitations
This study has several limitations. First, its retrospective observational design is subject to selection bias and residual confounding, despite multivariable adjustment and propensity score matching. Second, AKI was defined exclusively by KDIGO Scr criteria and the need for renal replacement therapy, because hourly urine output was not reliably documented. The true incidence and severity of AKI were therefore likely underestimated, and any misclassification would tend to bias associations toward the null, implying that the impact of PORD on CSA-AKI may be even greater than observed. Third, detailed perioperative hemodynamic parameters, fluid balance, transfusion exposure, and vasoactive or inotropic support were not available in a standardized format and could not be incorporated into the analyses; these unmeasured factors may influence both CSA-AKI and mortality. Finally, the cohort was drawn from four centers in China, which may limit the generalizability of the findings to other healthcare systems and ethnic populations. Prospective multicenter studies with standardized collection of perioperative hemodynamic, transfusion, and nephrotoxic exposure data are needed to confirm and extend these results.
Conclusions
Nearly one in three elderly patients with normal Scr had occult renal dysfunction before CABG. PORD was frequently overlooked when renal function was assessed by Scr alone, yet it was strongly associated with postoperative AKI, in-hospital mortality, and long-term all-cause mortality. In elderly patients undergoing CABG, preoperative renal function should therefore be evaluated using eGFR rather than Scr alone, and PORD should be incorporated into perioperative risk stratification and management.
Supplementary Information
Below is the link to the electronic supplementary material.
Author contributions
Z.L., K.X. and C.Z. contributed equally to this work and share first authorship. X.L., Z.Li and Y.Z. are co-corresponding authors. Z.L., K.X., C.Z., X.L., Z.Li and Y.Z. conceived and designed the study. Z.L., K.X., C.Z., M.L., Y.S., Y.P., W.D., L.H., Z.H.L., W.L., X.Z., X.L., Z.Li and Y.Z. were responsible for patient enrolment, data acquisition and verification across the participating centres. Z.L., K.X., C.Z., M.L., W.D. and L.H. performed the statistical analyses and contributed to data interpretation, with critical input from X.L., Z.Li and Y.Z. Z.L., K.X. and C.Z. drafted the main manuscript text. M.L., Y.S., Y.P., W.D., L.H., Z.H.L., W.L., X.Z., X.L., Z.Li and Y.Z. critically revised the manuscript for important intellectual content. X.L., Z.Li and Y.Z. provided overall supervision of the study and interpretation of the findings. All authors discussed the results, reviewed and approved the final manuscript, and agree to be accountable for all aspects of the work.
Funding
This research received no grant from any funding agency in the public, commercial, or not-for-profit sectors.
Data availability
We are pleased to share data. The data involved in our research are available from the corresponding author. We will respond in 7 days on reasonable request.
Declarations
Ethical approval
This study was approved by the Ethics Committees of Jiangsu Province Hospital (No. 2022-SR-209), the Ethics Committees of Shanghai Chest Hospital (No. IS22026), the Ethics Committees of Qilu Hospital (No. KYLL-202204-016) and, the Ethics Committees of General Hospital of Ningxia Medical University (No. KYLL20240262). Considering the retrospective and observational nature of this study, the Ethics Committees approved and granted an exemption from the requirement of informed consent for research participation. All procedures were performed in accordance with the Declaration of Helsinki.
Consent for publication
Not applicable. The requirement for individual informed consent for publication was waived by the institutional Ethics Committees because of the retrospective nature of the study and the use of anonymized data.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Zhonghe Liu, Kai Xu and Chong Zhang contributed equally to this work and share first authorship.
Contributor Information
Xudong Liu, Email: xudong669@163.com.
Zhi Li, Email: zhili_cths@163.com.
Yangyang Zhang, Email: zhangyangyang_wy@vip.sina.com.
References
- 1.Pottel H, Bjork J, Courbebaisse M, Couzi L, Ebert N, Eriksen BO, Dalton RN, Dubourg L, Gaillard F, Garrouste C, et al. Development and validation of a modified full age spectrum Creatinine-Based equation to estimate glomerular filtration rate: a cross-sectional analysis of pooled data. Ann Intern Med. 2021;174(2):183–91. [DOI] [PubMed] [Google Scholar]
- 2.Balderas-Vargas NA, Legorreta-Soberanis J, Paredes-Solis S, Flores-Moreno M, Santos F, Andersson N. Occult renal failure and associated factors in patients with chronic conditions. Gac Med Mex. 2020;156(1):11–6. [DOI] [PubMed] [Google Scholar]
- 3.Fernandez-Fresnedo G, de Francisco AL, Rodrigo E, Pinera C, Herraez I, Ruiz JC, Arias M. ["Occult" renal insufficiency due to evaluating renal function using only serum creatinine]. Nefrologia. 2002;22(2):144–51. [PubMed] [Google Scholar]
- 4.Huang YC, Li SJ, Chen M, Lee TS, Chien YN. Machine-learning techniques for feature selection and prediction of mortality in elderly CABG patients. Healthc (Basel) 2021;9(5). [DOI] [PMC free article] [PubMed]
- 5.Houlind K, Kjeldsen BJ, Madsen SN, Rasmussen BS, Holme SJ, Nielsen PH, Mortensen PE, Group DS. On-pump versus off-pump coronary artery bypass surgery in elderly patients: results from the Danish on-pump versus off-pump randomization study. Circulation. 2012;125(20):2431–9. [DOI] [PubMed] [Google Scholar]
- 6.Ng CY, Ramli MF, Awang Y. Coronary bypass surgery in patients aged 70 years and over: mortality, morbidity, length of stay and hospital cost. Asian Cardiovasc Thorac Ann. 2004;12(3):218–23. [DOI] [PubMed] [Google Scholar]
- 7.Tomson CRV, Cheung AK, Mann JFE, Chang TI, Cushman WC, Furth SL, Hou FF, Knoll GA, Muntner P, Pecoits-Filho R, et al. Management of blood pressure in patients with chronic kidney disease not receiving dialysis: synopsis of the 2021 KDIGO clinical practice guideline. Ann Intern Med. 2021;174(9):1270–81. [DOI] [PubMed] [Google Scholar]
- 8.Wang W, Wang Y, Xu R, Chai J, Zhou W, Chen H, Wang K, Kong X. Outcomes following coronary artery bypass graft surgery in patients with mild preoperative renal insufficiency. Braz J Cardiovasc Surg. 2018;33(2):155–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Speeckaert MM, Seegmiller J, Glorieux G, Lameire N, Van Biesen W, Vanholder R, Delanghe JR. Measured glomerular filtration rate: the query for a workable golden standard technique. J Pers Med 2021, 11(10). [DOI] [PMC free article] [PubMed]
- 10.Sajja LR, Singh S, Mannam G, Guttikonda J, Pusapati VRR, Saikiran K. Impact of occult renal disease on the outcomes of off-pump and on-pump coronary artery bypass grafting. Indian J Thorac Cardiovasc Surg. 2019;35(2):150–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Salmasi MY, Ravishankar R, Abdullahi Y, Hartley P, Kyriazis PG, Athanasiou T, Punjabi P. Predictors of outcome after CABG in the South-Asian community: a propensity matched analysis. Perfusion. 2023;38(1):75–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Cai F, Ren L, Liu H, Fang Y, Jiang S, Xu X, Ding X. The relationship between the level of serum creatinine, modified diet and renal disease formula, Cockcroft-Gault formula, and renal tubulointerstitial lesion. Ren Fail. 2012;34(3):334–7. [DOI] [PubMed] [Google Scholar]
- 13.Rashid ST, Salman M, Agarwal S, Hamilton G. Occult renal impairment is common in patients with peripheral vascular disease and normal serum creatinine. Eur J Vasc Endovasc Surg. 2006;32(3):294–9. [DOI] [PubMed] [Google Scholar]
- 14.Chua TKT, Gao F, Chia SY, Sin KYK, Naik MJ, Tan TE, Tham YC. Long-term mortality after isolated coronary artery bypass grafting and risk factors for mortality. J Cardiothorac Surg. 2024;19(1):429. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Kamla CE, Meersch-Dini M, Palma LMP. Kidney injury following cardiac surgery: A review of our current Understanding. Am J Cardiovasc Drugs. 2025;25(3):337–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Scurt FG, Bose K, Mertens PR, Chatzikyrkou C, Herzog C. Cardiac surgery-associated acute kidney injury. Kidney360. 2024;5(6):909–926. [DOI] [PMC free article] [PubMed]
- 17.Fang SY, Chen JW, Chou HW, Chan CY, Wu IH, Chou NK, Wang CH, Chi NH, Huang SC, Yu HY, et al. Validation of the European system for cardiac operative risk evaluation II in a large Taiwan cardiac surgical centre. J Formos Med Assoc. 2023;122(12):1265–73. [DOI] [PubMed] [Google Scholar]
- 18.Widyastuti Y, Boom CE, IM AP, Kurniawaty J, Jufan AY, Hanafy DA, Videm V. Validation in Indonesia of two published scores for mortality prediction after cardiac surgery. Ann Card Anaesth. 2023;26(1):23–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Ng JY, Tan EF, Kemberi M, Urgesi E, Jubouri M, Bailey DM, Bashir M, Awad WI. EuroSCORE II: current limitations and physiological gaps in risk stratification. Exp Physiol. 2025. [DOI] [PMC free article] [PubMed]
- 20.Chao CT, Kovesdy CP, Merchant RA. Sarcopenia, sarcopenic obesity, and frailty in individuals with chronic kidney disease: a comprehensive review. Kidney Res Clin Pract. 2025. [DOI] [PMC free article] [PubMed]
- 21.Liu T, Wu Y, Cao X, Yang K, Tong Y, Zhang F, Wang C, Cui R, Ren J, Li Q, et al. Association between sarcopenia and new-onset chronic kidney disease among middle-aged and elder adults: findings from the China health and retirement longitudinal study. BMC Geriatr. 2024;24(1):134. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Bjork JB, Alton KK, Georgiopoulou VV, Butler J, Kalogeropoulos AP. Defining advanced heart failure: A systematic review of criteria used in clinical trials. J Card Fail. 2016;22(7):569–77. [DOI] [PubMed] [Google Scholar]
- 23.Lubas A, Ryczek R, Maliborski A, Dyrla P, Niemczyk L, Niemczyk S. Left ventricular strain and relaxation are independently associated with renal cortical perfusion in hypertensive patients. Adv Exp Med Biol. 2019;1133:1–8. [DOI] [PubMed] [Google Scholar]
- 24.Shi X, Wu Y, Li H, Ma S, Li D, Gao D, Cui H, Yu C, Yang S, Tang Z, et al. Outcome of bystander cardiopulmonary resuscitation after out-of-hospital cardiac arrest in Beijing. Emerg Crit Care Med. 2021;1(2):64–9. [Google Scholar]
- 25.Schroten NF, Damman K, Valente MA, Smilde TD, van Veldhuisen DJ, Navis G, Gaillard CA, Voors AA, Hillege HL. Long-term changes in renal function and perfusion in heart failure patients with reduced ejection fraction. Clin Res Cardiol. 2016;105(1):10–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Zhou B, Ao Q, Zhao H, Ye P. Rosuvastatin alleviates renal injury in cardiorenal syndrome model rats through anti-inflammatory and antioxidant pathways. Emerg Crit Care Med. 2022;2(4):203–8. [Google Scholar]
- 27.Diaz CL, Kaplan RM, Peigh G, Bavishi A, Baman JR, Trivedi A, Shen MJ, Sattayaprasert P, Wasserlauf J, Arora R, et al. Improvement in renal function following cryoballoon ablation for atrial fibrillation. J Interv Card Electrophysiol. 2021;60(3):513–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Tsai MK, Kao JT, Wong CS, Liao CT, Lo WC, Chien KL, Wen CP, Wu MS, Wu MY. Cardiovascular-kidney-metabolic syndrome and all-cause and cardiovascular mortality: A retrospective cohort study. PLoS Med. 2025;22(6):e1004629. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Ndumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, Coresh J, Mathew RO, Baker-Smith CM, Carnethon MR, et al. Cardiovascular-Kidney-Metabolic health: A presidential advisory from the American heart association. Circulation. 2023;148(20):1606–35. [DOI] [PubMed] [Google Scholar]
- 30.Duchnowski P, Smigielski W, Kolsut P. High-sensitivity troponin T as a predictor of prolonged intensive care unit stay and worse treatment outcomes in patients undergoing heart valve surgery. J Clin Med. 2025;14(14). [DOI] [PMC free article] [PubMed]
- 31.Omar AS, Mahmoud K, Hanoura S, Osman H, Sivadasan P, Sudarsanan S, Shouman Y, Singh R, AlKhulaifi A. Acute kidney injury induces high-sensitivity troponin measurement changes after cardiac surgery. BMC Anesthesiol. 2017;17(1):15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Boyko Y, Rasmussen SB, Bartoszko J, van Klei WA, McCluskey S, Karkouti K, Ravn HB. Blood transfusion and acute kidney injury after cardiac surgery: a retrospective observational study. Can J Anaesth. 2025. [DOI] [PMC free article] [PubMed]
- 33.Koch CG, Li L, Duncan AI, Mihaljevic T, Cosgrove DM, Loop FD, Starr NJ, Blackstone EH. Morbidity and mortality risk associated with red blood cell and blood-component transfusion in isolated coronary artery bypass grafting. Crit Care Med. 2006;34(6):1608–16. [DOI] [PubMed] [Google Scholar]
- 34.Kuduvalli M, Oo AY, Newall N, Grayson AD, Jackson M, Desmond MJ, Fabri BM, Rashid A. Effect of peri-operative red blood cell transfusion on 30-day and 1-year mortality following coronary artery bypass surgery. Eur J Cardiothorac Surg. 2005;27(4):592–8. [DOI] [PubMed] [Google Scholar]
- 35.Miles TJ, Guinn MT, Tan X, Qi H, Orozco-Sevilla V, Moon MR, Coselli JS, Rosengart TK, Li M, Chatterjee S, et al. Tissue perfusion pressure: a novel hemodynamic measure to assess risk of acute kidney injury after cardiac surgery. J Thorac Cardiovasc Surg. 2025. [DOI] [PubMed]
- 36.He L, Liang S, Liang Y, Fang M, Li J, Deng J, Fang H, Li Y, Jiang X, Chen C. Defining a postoperative mean arterial pressure threshold in association with acute kidney injury after cardiac surgery: a prospective observational study. Intern Emerg Med. 2023;18(2):439–48. [DOI] [PubMed] [Google Scholar]
- 37.Beaver TM, Dass B, Patel AM, Ejaz AA. Acute kidney injury after cardiac surgery. Cardiorenal Med. 2024;14(1):437–42. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
We are pleased to share data. The data involved in our research are available from the corresponding author. We will respond in 7 days on reasonable request.