Iron metabolism indexes as predictors of the incidence of cardiac surgery-associated acute kidney surgery

Iron metabolism indexes as predictors of the incidence of cardiac surgery-associated acute kidney surgery | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Iron metabolism indexes as predictors of the incidence of cardiac surgery-associated acute kidney surgery Wenxiu Chen, Hao Zhang, Xiao Shen, Liang Hong, Hong Tao, Jilai Xiao, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4549588/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Sep, 2024 Read the published version in Journal of Cardiothoracic Surgery → Version 1 posted 11 You are reading this latest preprint version Abstract Background: Acute kidney injury (AKI) is a major complication following cardiac surgery. We explored the clinical utility of iron metabolism indexes for identification of patients at risk for AKI after cardiac surgery. Methods: This prospective observational study included patients who underwent cardiac surgery between March 2023 and June 2023. Iron metabolism indexes were measured upon admission to the intensive care unit. Multivariable logistic regression analyses were performed to explore the relationship between iron metabolism indexes and cardiac surgery-associated AKI (CSA-AKI). Receiver operating characteristic (ROC) curve was used to assess the predictive ability of iron, APACHE II score and the combination of the two indicators. Restricted cubic splines (RCS) was used to further confirm the linear relationship between iron and CSA-AKI. Results: Among the 112 recruited patients, 38 (33.9%) were diagnosed with AKI. Multivariable logistic regression analysis indicated that APACHE II score (odds ratio [OR], 1.208; 95% confidence interval [CI], 1.003-1.455, P = 0.036) and iron (OR 1.069; 95% CI 1.009-1.133, P = 0.036) could be used as independent risk factors to predict CSA-AKI. ROC curve analysis showed that iron (area under curve[AUC] = 0.669, 95% CI 0.572-0.757), APACHE II score (AUC = 0.655, 95% CI 0.557-0.744) and iron and APACHE II score combination (AUC = 0.726, 95% CI 0.632-0.807) were predictive indicators for CSA-AKI. RCS further confirmed the linear relationship between iron and CSA-AKI. Conclusions: Elevated iron levels were independently associated with higher risk of CSA-AKI, and there was a linear relationship between iron and CSA-AKI. iron metabolism indexes cardiac surgery-associated acute kidney injury receiver operating characteristic restricted cubic splines Figures Figure 1 Figure 2 Figure 3 Introduction More than two million heart surgeries are performed worldwide each year[1]. Acute kidney injury (AKI) is a common and serious complication after cardiac surgery with cardiopulmonary bypass (CPB). The incidence of cardiac surgery-associated AKI (CSA-AKI) ranges from 20–40%, which is the second leading cause of AKI in intensive care unit (ICU)[2]. Patients with severe CSA-AKI are confronted with a 3- 8‐fold higher perioperative mortality, prolonged length of ICU and hospital stay, and an increased expenses[3]. Approximately 25% of CSA-AKI patients develop chronic kidney disease (CKD) after three years[4]. The exact pathophysiology of CSA-AKI is multifactorial, complex, and incompletely understood. However, animal and human studies have shown that these factors interact before, during, and after cardiac surgery, including genetic susceptibility, nephrotoxicity, CPB-induced haemolysis, ischemia-reperfusion injury, complexity of cardiac surgery, oxidative stress, and inflammation[5–7]. Although the specific mechanism linked to the occurrence and development of CSA-AKI has yet to be identified, release of free heme and iron during CPB is likely to play an important role[8]. During CPB, red blood cells are exposed to nonphysiologic surfaces, shear forces may injure red blood cells, leading to the release of free hemoglobin and catalytic iron[9]. During ischemia-reperfusion period, a decrease in pH or superoxide-induced reduction of Fe 3+ may cause dissociation of protein-bound intracellular iron, thereby increasing the levels of catalytic iron, which could contribute to oxidative stress and cellular damage, resulting in tubular necrosis[10]. Some studies have shown that iron metabolism-related indicators (serum ferritin, transferrin, urine catalytic iron) can be used as predictors of the incidence of CSA-AKI[11–13]. There are no effective treatment measures for CSA-AKI, so it is necessary to identify risk factors associated with CSA-AKI to allow that preventive and early diagnosis measures to be taken, thereby reducing the incidence of CSA-AKI. However, at present, serum creatinine and urine have a lag in the diagnosis of CSA-AKI. Many other well-known biomarkers for CSA-AKI such as neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1) have been identified based on their known biology[14]. Nevertheless, the ideal biomarker for predicting CSA-AKI remains controversial. Therefore, we conducted a prospective, single center cohort study in patients undergoing cardiac surgery to evaluate the predictive performance of iron metabolism indexes for CSA-AKI. Methods and Materials Study design and participants This is a single-center, prospective, and observational study. This study included adult patients (≥ 18 years old) who underwent cardiac surgery at a university-affiliated hospital between March 2023 and June 2023. Patients were excluded if they met any of the following criteria: (1) pre-existing AKI, end-stage renal disease, or dialysis; (2) emergency cardiac surgery; (3) kidney or heart transplantation; (4) recent urinary tract infection or obstruction; (5) pregnant women. This study was approved by the Ethics Committee of our hospital (KY20220518-KS-01). All procedures performed in studies involving human participants were in accordance with the ethical standards of our hospital and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Participants or their legal representatives provided written informed consent. Data collection The study data were collected from electronic medical record databases of our hospital. Demographic information was recorded for further analyses (e.g., age, gender, EuroSCORE, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, body mass index (BMI), and co-morbidities as well as iron metabolism indexes). In addition, types of operation, surgery time, CPB time and Cross-clamp time were collected. Moreover, outcome variables (e.g., AKI, hospital mortality, length of hospital stay) were recorded for further comparison. Measurement of iron metabolism indexes To conduct iron metabolism indexes analysis, serum concentration of iron (S-Fe), serum unsaturated iron binding capacity (UIBC), serum total iron binding capacity (TIBC), transferrin (TRF), ferritin, soluble transferrin receptor (sTfR), transferrin saturation (TS) were determined from blood samples of the patients when they were transferred to the Cardiovascular Intensive Care Unit postoperatively. Iron metabolism indexes was detected with immunonephelometry method (C16000, Abbott, America). In addition, the reference values of the hospital of the authors included: S-Fe: 9.00-27.00umol/L, UIBC: 31.00-51.00umol/L, TIBC: 45.00-75.00umol/L, TRF: 2.00-4.00g/L, ferritin: 4.63-204.00ng/ml, sTfR: 0.76-1.76mg/L, TS: 20–55%. Main outcome The main outcome was the development of AKI. According to the newest consensus-based KDIGO criteria, postoperative AKI was defined as (1) small changes in serum creatinine (≥ 0.3 mg/dl or 26.5 mmol/l) when they occurred within 48h; (2) a maximal change in serum creatinine ≥ 1.5 times the baseline value until postoperative day 7 compared with preoperative baseline values; (3) urine volume < 0.5 ml/kg/h for 6 h. In our study, we did not take urine volume into consideration owing to its inaccuracy, as done in the previous study[15]. The serum creatinine levels measured before surgery were used as the baseline value[16]. Statistical analysis Descriptive statistics were reported as numbers and percentages for categorical variables and means (SD) or medians (IQR) for continuous variables. Statistical differences between categorical or continuous variables were carried out using Student’s t test, Mann Whitney U test, chi-square test, or Fisher’s exact probability method as appropriate. We performed the multivariable binary logistic regression models to evaluate the association of S-Fe and AKI with a statistically signifcant association at P < 0.05 in univariate regression analysis. For a long time, the APACHE II score was reported to be good indicator of prognosis in critically ill patients. Some studies have shown that iron metabolism indexes can be used as predictors of the incidence of CSA-AKI, so we combined S-Fe and APACHE II score to predict CSA-AKI. The receiver operating characteristics curve (ROC) analysis was used to assess the overall discriminative ability of S-Fe, APACHE II score, the combination of the two indicators to predict CSA-AKI. Restricted cubic splines (RCS) was used to explore the relationship between S-Fe and CSA-AKI. All statistical tests were conducted with SPSS version 25.0 (SPSS Inc, Chicago, IL, USA) and R statistical software version 4.0.0 (R Foundation, Vienna, Austria). In all analyses, differences were considered statistically significant with a P value < 0.05. Results On the whole, 134 patients admitting to the center of the authors and having undergone cardiac surgery were screened, and 22 patients were excluded (Fig. 1 ), leaving 112 patients included and analyzed in this study. Patients fell to the AKI group (n = 38) and no AKI group (n = 74). The baseline characteristics exhibited by patients and patients’s poor outcomes were listed in Table 1 . Significant differences between the two groups were described as follows: age ( P = 0.028), CPB time ( P = 0.037), cross-clamp time ( P = 0.048), APACHE II score ( P = 0.003), S-Fe ( P = 0.011). Table 1 Baseline characteristics of patients undergoing cardiac surgery according to acute kidney injury status. Demographics Total(n = 112) No AKI(n = 74) AKI(n = 38) P value Female, n (%) 46(41.1) 33(44.6) 13(34.2) 0.290 Age, yrs 61.6 ± 11.7 59.9 ± 12.3 65.0 ± 9.7 0.028 BMI, kg/m2 23.7 ± 3.3 23.7 ± 3.1 23.7 ± 3.6 0.999 Smoke, n (%) 20(17.9) 11(14.9) 9(23.9) 0.249 Hypertension, n (%) 58(51.8) 35(47.3) 23(60.5) 0.185 Diabetes, n (%) 17(15.2) 11(14.9) 6(15.8) 0.897 COPD, n (%) 5(4.5) 4(5.4) 1(2.6) 0.501 Previous myocardial infarction, n (%) 2(1.8) 1(1.4) 1(2.6) 0.999 Preoperative hemoglobin, g/dL 130.5 ± 16.1 131.0 ± 17.2 129.6 ± 13.8 0.660 Operative variables Surgery type, (n%) 0.502 CABG alone 26(23.2) 19(25.7) 7(18.4) Valve alone 69(61.6) 42(56.8) 27(71.1) CABG and valve surgery 7(6.3) 5(6.8) 2(5.3) Others 10(8.9) 8(10.8) 2(5.3) Surgery time, h 4.1(3.7–4.7) 4.1(3.7–4.7) 4.1(3.7–4.7) 0.868 CPB time, min 110.5 (85.0-135.0) 103.5 (81.3-129.3) 126 (100.0-140.5) 0.037 Cross-clamp time, min 78.7 ± 28.0 75.0 ± 27.7 86.0 ± 27.4 0.048 Clinical scores EuroScore 6(5–6) 5(4–6) 6(5–6) 0.054 APACHE-II Score 10(8–13) 9(8–12) 12(9–13) 0.003 Iron metabolism indexes Iron, umol/L 19.8(12.9–28.3) 18.2(12.2–26.7) 25.5(15.8–30.9) 0.011 UIBC, umol/L 14.8(6.1–28.3) 15.8(7.5–23.4) 12.2(4.2–24.8) 0.363 TIBC, umol/L 36.3 ± 8.1 35.1 ± 6.9 38.5 ± 9.7 0.065 TRF, g/l 1.4 ± 0.3 1.3 ± 0.3 1.4 ± 0.4 0.158 Ferritin, ng/ml 209.9 (110.6-318.8) 201.8 (107.6-292.7) 234.1 (123.0-431.0) 0.150 sTfR, mg/L 0.6(0.5–0.8) 0.6(0.5–0.8) 0.6(0.5–0.8) 0.951 TS(%) 58.9(38.8–80.7) 56.5(38.2–78.5) 64.8(42.5–87.2) 0.139 Poor outcomes Hospital stay, d 17(15–20) 17(14 − 2) 19(16–21) 0.126 In-hospital death, n (%) 2(1.8) 1(1.4) 1(2.6) 0.999 Abbreviation: AKI, acute kidney injury; BMI, body mass index; COPD, chronic obstructive pulmonary disease; CABG, coronary artery bypass grafting; CPB, cardiopulmonary bypass; EuroSCORE, european system for cardiac operative risk evaluation; APACHE II, acute physiology and chronic health evaluation II; UIBC, unsaturated iron binding capacity; TIBC, total iron binding capacity; TRF, transferrin; sTfR, soluble transferrin receptor; TS, transferrin saturation. Table 2 illustrated the results of the logistic regression for CSA-AKI. Age, CPB time, cross-clamp time, APACHE II score and S-Fe were enrolled in the multivariable logistic regression analysis. The results showed that APACHE II score (OR 1.208; 95% CI 1.003–1.455, P = 0.047) and S-Fe (OR 1.069; 95% CI 1.009–1.133, P = 0.024) might be able to predict CSA-AKI. Table 2 Multivariate regression analysis for cardiac surgery associated acute kidney injury. Variables Regression Coefficient SE OR(95%CI) P Value Age, yrs 0.018 0.026 1.018(0.968–1.070) 0.490 CPB time, min 0.005 0.014 1.005(0.977–1.033) 0.750 Cross-clamp time, min −0.004 0.018 0.996(0.962–1.031) 0.816 APACHE-II Score 0.189 0.095 1.208(1.003–1.455) 0.047 Iron, umol/L 0.067 0.030 1.069(1.009–1.133) 0.024 Abbreviation: CPB, cardiopulmonary bypass; APACHE II, acute physiology and chronic health evaluation II. ROC curve analysis showed that S-Fe (area under curve[AUC] = 0.669, 95% CI 0.572–0.757), APACHE II score (AUC = 0.655, 95% CI 0.557–0.744) and S-Fe and APACHE II score combination (AUC = 0.726, 95% CI 0.632–0.807) might be predictive indicators for CSA-AKI (Fig. 2 ). As shown in Fig. 3 , a linear association was observed between the S-Fe and CSA-AKI (Fig. 3 ). The OR increased evidently when the S-Fe reached approximately 19.92umol/L. Discussion In this prospective study we reported the comprehensive analysis of iron metabolism indexes after cardiac surgery. We found that levels of S-Fe, unlike other iron related metabolism indicators, rose in AKI group. we also examined the ability of S-Fe to predict postoperative AKI by ROC curve. Furthermore, we combined S-Fe and APACHE II score to plot ROC curve to predict CSA-AKI. These findings support the notion that S-Fe levels may be an indicator of poor outcomes after cardiac surgery. Iron is an essential microelement for the human body, which plays an active role in maintaining physiological functions. During CPB, red blood cells are exposed to nonphysiologic surfaces, shear forces may injure red blood cells, leading to the release of free hemoglobin and catalytic iron[9]. During aortic cross-clamp and reperfusion period, the levels of catalytic iron increase, which could contribute to oxidative stress and cellular damage, resulting in tubular necrosis[10]. After cardiac surgery, a large amount of catalytic iron is released, which may trigger AKI and mediate death[8]. Some studies found that elevated plasma catalytic iron levels are independently associated with increased prevalence of AKI after cardiac surgery[17,18]. We found that patients who developed CSA-AKI had longer CPB time, longer aortic cross-clamp time. Adding to these findings, in this study we reported that patients who developed AKI had elevated serum catalytic iron levels upon admission to the ICU. Ferritin is the primary tissue iron-storage protein, and may release iron in the presence of superoxide produced under conditions of inflammation[19]. Ferritin consists of 24 subunits of heavy (FtH) and light (FtL) chains. FtH can convert ferrous iron into ferric iron, which is stored within the ferritin shell subsequently[20]. The level of serum ferritin can indirectly reflect the level of FtH in the body. Nora Choi et al. found that patients with higher baseline serum ferritin levels have a low probability of developing CSA-AKI[21]. It is further suggested that high serum ferritin levels can reflect the body’s immunoregulatory capacity and ability to process catalytic iron release during CPB. We found no significant difference in the serum ferritin levels between the AKI group and non-AKI group. Because we only included data at the time of ICU admission, we did not collect the data of preoperative serum ferritin levels. TRF is produced in the liver and binds one or two Fe 3+ atoms in the circulation, and therefrom endocytosed by a variety of cells through specific plasma membrane receptors (TfR1 and TfR2). TRF is known to be filtered through the glomerular filtration barrier, and reabsorbed in the proximal tubule[22]. Increased urinary excretion of TRF results from decreased tubular uptake. Some studies show that urinary TRF can be further explored as a wider biomarker of renal damage induced by insults causing subclinical tubular alterations[22]. Iron binding capacity is the capacity of TRF to bind with iron. There are two types of iron binding capacity, TIBC and UIBC. As only one-third of TRF is saturated with iron, so the TRF present in serum has an extra binding capacity (67%), which is called UIBC. TIBC was calculated as the sum of the serum iron plus the UIBC[23]. TS was calculated as the percentage of iron to total iron binding capacity. Choi N et al. found that TS elevated at 1h after CPB, which can be used as an independent predictor of the incidence of AKI after cardiac surgery[24]. sTfR is derived from proteolysis of the membrane transferrin receptor, it can be used to assess iron status. sTfR has been widely used in cardiopulmonary disease research[25]. Frise et al. found that elevated sTfR in non-anemic cardiac surgery patients can be used as a treatable trait that might improve postoperative recovery and shorten hospital stay[26]. We found no statistically significant difference in TRF, UIBC, TIBC, TS and sTfR between the two groups. There are several limitations in our study. First, the sample size of our study is relatively small, so the level of evidence provided by our study is not high enough. Second, we did not take urine volume into consideration as done in the previous study, because some patients were treated with diuretics and the urine volume is inaccurate, which may result in bias. Third, we did not record intraoperative packed red blood cell transfusions, which are associated with plasma catalytic iron. Fourth, we did not study the relationship between the patient's preoperative iron metabolism indexes and CSA-AKI. However, we found no difference in preoperative haemoglobin between the two groups. We plan to study the role of preoperative and postoperative iron metabolism indexes in the more crowds and explore the mechanism between ferroptosis and CSA-AKI in the future. Conclusions In summary, our study showed that S-Fe levels upon admission to the ICU were associated with CSA-AKI. Meanwahile, S-Fe and APACHE II score combination was predictive indicator for CSA-AKI. What is more, the relationship between the S-Fe and CSA-AKI is a linear relationship. Further investigations will be required to verify the results of iron metabolism indexes for CSA-AKI. Declarations Ethics approval and consent to participate In accordance with the Declaration of Helsinki, this study was approved by the Ethics Committee of Nanjing First Hospital(KY20220518-KS-01). Informed consent was obtained from participants or their legal representatives. All the methods were carried out in accordance with relevant guidelines and regulations in the declaration. Consent for publication All the authors agree to publish. Competing interests All the authors declare that there is no competing interests. Funding None. Author Contribution Wenxiu Chen wrote the main manuscript text and prepared the figures and tables. Hao Zhang, Xiao Shen, Liang Hong, Hong Tao, Jilai Xiao, Shuai Nie, Meng Wei and Ming Chen collected the data. Cui Zhang and Wenkui Yu were involved in study design, data interpretation, and manuscript preparation. Acknowledgement We express our gratitude to all the researchers and patients who participated in this study. Availability of data and materials The datasets analysed during the current study available from the corresponding author on reasonable request. References 1. Peng K, McIlroy DR, Bollen BA, et al. Society of cardiovascular anesthesiologists clinical practice update for management of acute kidney injury associated with cardiac surgery. Anesth Analg. 2022;135(4):744–756. 2. Tomoya Oshita, Arudo Hiraoka, Kosuke Nakajima, et al. A Better Predictor of Acute Kidney Injury After Cardiac Surgery: The Largest Area Under the Curve Below the Oxygen Delivery Threshold During Cardiopulmonary Bypass. J Am Heart Assoc. 2020 Aug 4;9(15):e015566. 3. Amer Harky, Mihika Joshi, Shubhi Gupta, et al. Acute Kidney Injury Associated with Cardiac Surgery: a Comprehensive Literature Review. Braz J Cardiovasc Surg. 2020 Apr 1;35(2):211–224. 4. Horne KL, Packington R, Monaghan J, et al. Three-year outcomes after acute kidney injury: results of a prospective parallel group cohort study. 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A M F Martines, R Masereeuw, H Tjalsma, et al. Iron metabolism in the pathogenesis of iron-induced kidney injury. Nat Rev Nephrol. 2013 Jul;9(7):385 − 98. 11. Albert C, Haase M, Albert A, et al. Urinary biomarkers may complement the cleveland score for prediction of adverse kidney events after cardiac surgery: a pilot study. Ann Lab Med. 2020;40(2):131–141. 12. Choi N, Whitlock R, Klassen J, et al. Early intraoperative iron-binding proteins are associated with acute kidney injury after cardiac surgery. J Thorac Cardiovasc Surg. 2019;157(1):287–297. 13. Limei Zhao, Xiaoyu Yang, Shengchao Zhang, et al. Iron metabolism-related indicators as predictors of the incidence of acute kidney injury after cardiac surgery: a meta-analysis.Renal Failure.2023 Dec;45(1):2201362. 14. J.A. Neyra, M.C. Hu, A. Minhajuddin, et al. Kidney tubular damage and functional biomarkers in acute kidney injury following cardiac surgery. Kidney Int.2019; 4 (8):1131–1142. 15. Liu J, Xue Y, Jiang W, Zhang H, Zhao Y. Thyroid Hormone Is Related to Postoperative AKI in Acute Type A Aortic Dissection. Front Endocrinol (Lausanne). 2020; 11:588149. 16. Kellum JA, Lameire N. Diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary (part 1). Crit Care. 2013;17(1):204. 17. van Swelm, R. P. L., Wetzels, et al. The multifaceted role of iron in renal health and disease. Nat. Rev. Nephrol. 2020; 16 (2), 77–98. 18. Akrawinthawong K, Shaw MK, Kachner J, et al. Urine catalytic iron and neutrophil gelatinaseassociated lipocalin as companion early markers of acute kidney injury after cardiac surgery: a prospective pilot study. Cardiorenal Med. 2013; 3:7–16. 19. Paul Angulo, Jacob George, Christopher P. Day, et al. Serum Ferritin Levels Lack Diagnostic Accuracy for Liver Fibrosis in Patients with Nonalcoholic Fatty Liver Disease. Clin Gastroenterol Hepatol. 2014 Jul;12(7):1163–1169. 20. Bradley JM, Le Brun NE, Moore GR. Ferritins: furnishing proteins with iron. J Biol Inorg Chem. 2016;21(1):13–28. 21. Choi N, Whitlock R, Klassen J, et al. Early intraoperative iron-binding proteins are associated with acute kidney injury after cardiac surgery. J Thorac Cardiovasc Surg. 2019;157(1):287–297. 22. Alfredo G Casanova, Laura Vicente-Vicente, M Teresa Hernández-Sánchez, et al. Urinary transferrin pre-emptively identifies the risk of renal damage posed by subclinical tubular alterations. Biomed Pharmacother. 2020 Jan:121:109684. 23. Faruqi A, Mukkamalla SKR. Iron Binding Capacity. In: StatPearls [Internet].2023 Jan 2. 24. Choi N, Whitlock R, Klassen J, et al. Early intraoperative iron-binding proteins are associated with acute kidney injury after cardiac surgery. J Thorac Cardiovasc Surg. 2019;157(1):287–297. 25. von Haehling S, Jankowska EA, van Veldhuisen DJ, et al. Iron deficiency and cardiovascular disease. Nat Rev Cardiol. 2015;12(11):659 − 69. 26. Matthew C Frise, David A Holdsworth, Manraj S Sandhu, et al. Non-anemic iron deficiency predicts prolonged hospitalisation following surgical aortic valve replacement: a single-centre retrospective study. J Cardiothorac Surg. 2022 Jun 16;17(1):157. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Sep, 2024 Read the published version in Journal of Cardiothoracic Surgery → Version 1 posted Editorial decision: Revision requested 29 Jul, 2024 Reviews received at journal 16 Jul, 2024 Reviewers agreed at journal 02 Jul, 2024 Reviews received at journal 30 Jun, 2024 Reviews received at journal 28 Jun, 2024 Reviewers agreed at journal 21 Jun, 2024 Reviewers agreed at journal 20 Jun, 2024 Reviewers invited by journal 20 Jun, 2024 Editor assigned by journal 14 Jun, 2024 Submission checks completed at journal 14 Jun, 2024 First submitted to journal 08 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4549588","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":320854198,"identity":"ff1d9bd3-c042-466a-a6c3-4e1986cee93d","order_by":0,"name":"Wenxiu Chen","email":"","orcid":"","institution":"Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenxiu","middleName":"","lastName":"Chen","suffix":""},{"id":320854199,"identity":"34846562-33f9-45a3-a837-2023840c0af3","order_by":1,"name":"Hao Zhang","email":"","orcid":"","institution":"Nanjing Medical 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Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYHACAwYGHhs5Nvb2AyRpSTPm4zmTQIoWhsOJ8yQcDIhUfyN52+cCGeb0NgmGBIYfFduI0ZJWPHsGD1tum3TjAcaeM7eJ0ZJjzMzDw5PbJnMggZmxjXgtEulsEgkGJGkxSCBei+SZZ8VALQmGbcBAPkiUX/iOJ29m5u35Ly/f3n7wwY8KIrQoHAASjD0QzgHC6oFAvgFE/iBK7SgYBaNgFIxUAABINjeQq8x8OgAAAABJRU5ErkJggg==","orcid":"","institution":"Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wenkui","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2024-06-08 08:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4549588/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4549588/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13019-024-03080-4","type":"published","date":"2024-09-19T15:57:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60201065,"identity":"3ea2cd91-a66f-4bac-87cf-efdca33f6a28","added_by":"auto","created_at":"2024-07-13 02:41:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27104,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of participants selection.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4549588/v1/f5d498d1676a6324fd90aac6.png"},{"id":60201064,"identity":"47eed605-71f8-41ee-ad56-67f4b5c2c265","added_by":"auto","created_at":"2024-07-13 02:41:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61623,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristics curve of serum concentration of iron, acute physiology and chronic health evaluation II score and the combination of the two indicators for predicting cardiac surgery-associated acute kidney surgery.\u003c/p\u003e","description":"","filename":"fig2ROC.png","url":"https://assets-eu.researchsquare.com/files/rs-4549588/v1/ceeb21b376967efa1e886d46.png"},{"id":60201646,"identity":"f5475f4b-21b3-40cd-bf6e-e17689bfc581","added_by":"auto","created_at":"2024-07-13 02:49:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":12784,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted spline curves for the relationship between serum concentration of iron and the incidence of cardiac surgery-associated acute kidney surgery. The red bold line denotes the odds ratio, while the shaded area represents the 95% confidence intervals.\u003c/p\u003e","description":"","filename":"fig3RCS.png","url":"https://assets-eu.researchsquare.com/files/rs-4549588/v1/43f2f0737dd9ef6d80937c6a.png"},{"id":65103946,"identity":"68c0e218-a80b-4e6f-86fc-17e31cdcaa2a","added_by":"auto","created_at":"2024-09-23 16:09:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":584319,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4549588/v1/2f71fe66-dbbf-4731-b70e-3c949713e47d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Iron metabolism indexes as predictors of the incidence of cardiac surgery-associated acute kidney surgery","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMore than two million heart surgeries are performed worldwide each year[1]. Acute kidney injury (AKI) is a common and serious complication after cardiac surgery with cardiopulmonary bypass (CPB). The incidence of cardiac surgery-associated AKI (CSA-AKI) ranges from 20\u0026ndash;40%, which is the second leading cause of AKI in intensive care unit (ICU)[2]. Patients with severe CSA-AKI are confronted with a 3- 8‐fold higher perioperative mortality, prolonged length of ICU and hospital stay, and an increased expenses[3]. Approximately 25% of CSA-AKI patients develop chronic kidney disease (CKD) after three years[4].\u003c/p\u003e \u003cp\u003eThe exact pathophysiology of CSA-AKI is multifactorial, complex, and incompletely understood. However, animal and human studies have shown that these factors interact before, during, and after cardiac surgery, including genetic susceptibility, nephrotoxicity, CPB-induced haemolysis, ischemia-reperfusion injury, complexity of cardiac surgery, oxidative stress, and inflammation[5\u0026ndash;7].\u003c/p\u003e \u003cp\u003eAlthough the specific mechanism linked to the occurrence and development of CSA-AKI has yet to be identified, release of free heme and iron during CPB is likely to play an important role[8]. During CPB, red blood cells are exposed to nonphysiologic surfaces, shear forces may injure red blood cells, leading to the release of free hemoglobin and catalytic iron[9]. During ischemia-reperfusion period, a decrease in pH or superoxide-induced reduction of Fe\u003csup\u003e3+\u003c/sup\u003e may cause dissociation of protein-bound intracellular iron, thereby increasing the levels of catalytic iron, which could contribute to oxidative stress and cellular damage, resulting in tubular necrosis[10]. Some studies have shown that iron metabolism-related indicators (serum ferritin, transferrin, urine catalytic iron) can be used as predictors of the incidence of CSA-AKI[11\u0026ndash;13].\u003c/p\u003e \u003cp\u003eThere are no effective treatment measures for CSA-AKI, so it is necessary to identify risk factors associated with CSA-AKI to allow that preventive and early diagnosis measures to be taken, thereby reducing the incidence of CSA-AKI. However, at present, serum creatinine and urine have a lag in the diagnosis of CSA-AKI. Many other well-known biomarkers for CSA-AKI such as neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1) have been identified based on their known biology[14]. Nevertheless, the ideal biomarker for predicting CSA-AKI remains controversial. Therefore, we conducted a prospective, single center cohort study in patients undergoing cardiac surgery to evaluate the predictive performance of iron metabolism indexes for CSA-AKI.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis is a single-center, prospective, and observational study. This study included adult patients (\u0026ge;\u0026thinsp;18 years old) who underwent cardiac surgery at a university-affiliated hospital between March 2023 and June 2023. Patients were excluded if they met any of the following criteria: (1) pre-existing AKI, end-stage renal disease, or dialysis; (2) emergency cardiac surgery; (3) kidney or heart transplantation; (4) recent urinary tract infection or obstruction; (5) pregnant women. This study was approved by the Ethics Committee of our hospital (KY20220518-KS-01). All procedures performed in studies involving human participants were in accordance with the ethical standards of our hospital and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Participants or their legal representatives provided written informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe study data were collected from electronic medical record databases of our hospital. Demographic information was recorded for further analyses (e.g., age, gender, EuroSCORE, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, body mass index (BMI), and co-morbidities as well as iron metabolism indexes). In addition, types of operation, surgery time, CPB time and Cross-clamp time were collected. Moreover, outcome variables (e.g., AKI, hospital mortality, length of hospital stay) were recorded for further comparison.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of iron metabolism indexes\u003c/h2\u003e \u003cp\u003eTo conduct iron metabolism indexes analysis, serum concentration of iron (S-Fe), serum unsaturated iron binding capacity (UIBC), serum total iron binding capacity (TIBC), transferrin (TRF), ferritin, soluble transferrin receptor (sTfR), transferrin saturation (TS) were determined from blood samples of the patients when they were transferred to the Cardiovascular Intensive Care Unit postoperatively. Iron metabolism indexes was detected with immunonephelometry method (C16000, Abbott, America). In addition, the reference values of the hospital of the authors included: S-Fe: 9.00-27.00umol/L, UIBC: 31.00-51.00umol/L, TIBC: 45.00-75.00umol/L, TRF: 2.00-4.00g/L, ferritin: 4.63-204.00ng/ml, sTfR: 0.76-1.76mg/L, TS: 20\u0026ndash;55%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMain outcome\u003c/h2\u003e \u003cp\u003eThe main outcome was the development of AKI. According to the newest consensus-based KDIGO criteria, postoperative AKI was defined as (1) small changes in serum creatinine (\u0026ge;\u0026thinsp;0.3 mg/dl or 26.5 mmol/l) when they occurred within 48h; (2) a maximal change in serum creatinine\u0026thinsp;\u0026ge;\u0026thinsp;1.5 times the baseline value until postoperative day 7 compared with preoperative baseline values; (3) urine volume\u0026thinsp;\u0026lt;\u0026thinsp;0.5 ml/kg/h for 6 h. In our study, we did not take urine volume into consideration owing to its inaccuracy, as done in the previous study[15]. The serum creatinine levels measured before surgery were used as the baseline value[16].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were reported as numbers and percentages for categorical variables and means (SD) or medians (IQR) for continuous variables. Statistical differences between categorical or continuous variables were carried out using Student\u0026rsquo;s t test, Mann Whitney U test, chi-square test, or Fisher\u0026rsquo;s exact probability method as appropriate. We performed the multivariable binary logistic regression models to evaluate the association of S-Fe and AKI with a statistically signifcant association at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate regression analysis. For a long time, the APACHE II score was reported to be good indicator of prognosis in critically ill patients. Some studies have shown that iron metabolism indexes can be used as predictors of the incidence of CSA-AKI, so we combined S-Fe and APACHE II score to predict CSA-AKI. The receiver operating characteristics curve (ROC) analysis was used to assess the overall discriminative ability of S-Fe, APACHE II score, the combination of the two indicators to predict CSA-AKI. Restricted cubic splines (RCS) was used to explore the relationship between S-Fe and CSA-AKI. All statistical tests were conducted with SPSS version 25.0 (SPSS Inc, Chicago, IL, USA) and R statistical software version 4.0.0 (R Foundation, Vienna, Austria). In all analyses, differences were considered statistically significant with a \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOn the whole, 134 patients admitting to the center of the authors and having undergone cardiac surgery were screened, and 22 patients were excluded (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), leaving 112 patients included and analyzed in this study. Patients fell to the AKI group (n\u0026thinsp;=\u0026thinsp;38) and no AKI group (n\u0026thinsp;=\u0026thinsp;74).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe baseline characteristics exhibited by patients and patients\u0026rsquo;s poor outcomes were listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Significant differences between the two groups were described as follows: age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), CPB time (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037), cross-clamp time (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048), APACHE II score (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), S-Fe (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of patients undergoing cardiac surgery according to acute kidney injury status.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;112)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo AKI(n\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAKI(n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, yrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58(51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious myocardial infarction, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative hemoglobin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperative variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery type, (n%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCABG alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValve alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69(61.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42(56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCABG and valve surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery time, h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1(3.7\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1(3.7\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.1(3.7\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPB time, min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110.5\u003c/p\u003e \u003cp\u003e(85.0-135.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103.5\u003c/p\u003e \u003cp\u003e(81.3-129.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126\u003c/p\u003e \u003cp\u003e(100.0-140.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCross-clamp time, min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.7\u0026thinsp;\u0026plusmn;\u0026thinsp;28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.0\u0026thinsp;\u0026plusmn;\u0026thinsp;27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.0\u0026thinsp;\u0026plusmn;\u0026thinsp;27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuroScore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(5\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(4\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(5\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE-II Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(8\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(8\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(9\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron metabolism indexes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron, umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.8(12.9\u0026ndash;28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.2(12.2\u0026ndash;26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.5(15.8\u0026ndash;30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUIBC, umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.8(6.1\u0026ndash;28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.8(7.5\u0026ndash;23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.2(4.2\u0026ndash;24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIBC, umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRF, g/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFerritin, ng/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e209.9\u003c/p\u003e \u003cp\u003e(110.6-318.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201.8\u003c/p\u003e \u003cp\u003e(107.6-292.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e234.1\u003c/p\u003e \u003cp\u003e(123.0-431.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esTfR, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6(0.5\u0026ndash;0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6(0.5\u0026ndash;0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6(0.5\u0026ndash;0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTS(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.9(38.8\u0026ndash;80.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.5(38.2\u0026ndash;78.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.8(42.5\u0026ndash;87.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor outcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital stay, d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(15\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(14\u0026thinsp;\u0026minus;\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(16\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-hospital death, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation: AKI, acute kidney injury; BMI, body mass index; COPD, chronic obstructive pulmonary disease; CABG, coronary artery bypass grafting; CPB, cardiopulmonary bypass; EuroSCORE, european system for cardiac operative risk evaluation; APACHE II, acute physiology and chronic health evaluation II; UIBC, unsaturated iron binding capacity; TIBC, total iron binding capacity; TRF, transferrin; sTfR, soluble transferrin receptor; TS, transferrin saturation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrated the results of the logistic regression for CSA-AKI. Age, CPB time, cross-clamp time, APACHE II score and S-Fe were enrolled in the multivariable logistic regression analysis. The results showed that APACHE II score (OR 1.208; 95% CI 1.003\u0026ndash;1.455, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047) and S-Fe (OR 1.069; 95% CI 1.009\u0026ndash;1.133, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) might be able to predict CSA-AKI.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate regression analysis for cardiac surgery associated acute kidney injury.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, yrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.018(0.968\u0026ndash;1.070)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPB time, min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.005(0.977\u0026ndash;1.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCross-clamp time, min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.996(0.962\u0026ndash;1.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE-II Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.208(1.003\u0026ndash;1.455)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron, umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.069(1.009\u0026ndash;1.133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation: CPB, cardiopulmonary bypass; APACHE II, acute physiology and chronic health evaluation II.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eROC curve analysis showed that S-Fe (area under curve[AUC]\u0026thinsp;=\u0026thinsp;0.669, 95% CI 0.572\u0026ndash;0.757), APACHE II score (AUC\u0026thinsp;=\u0026thinsp;0.655, 95% CI 0.557\u0026ndash;0.744) and S-Fe and APACHE II score combination (AUC\u0026thinsp;=\u0026thinsp;0.726, 95% CI 0.632\u0026ndash;0.807) might be predictive indicators for CSA-AKI (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, a linear association was observed between the S-Fe and CSA-AKI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The OR increased evidently when the S-Fe reached approximately 19.92umol/L.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this prospective study we reported the comprehensive analysis of iron metabolism indexes after cardiac surgery. We found that levels of S-Fe, unlike other iron related metabolism indicators, rose in AKI group. we also examined the ability of S-Fe to predict postoperative AKI by ROC curve. Furthermore, we combined S-Fe and APACHE II score to plot ROC curve to predict CSA-AKI. These findings support the notion that S-Fe levels may be an indicator of poor outcomes after cardiac surgery.\u003c/p\u003e \u003cp\u003eIron is an essential microelement for the human body, which plays an active role in maintaining physiological functions. During CPB, red blood cells are exposed to nonphysiologic surfaces, shear forces may injure red blood cells, leading to the release of free hemoglobin and catalytic iron[9]. During aortic cross-clamp and reperfusion period, the levels of catalytic iron increase, which could contribute to oxidative stress and cellular damage, resulting in tubular necrosis[10]. After cardiac surgery, a large amount of catalytic iron is released, which may trigger AKI and mediate death[8]. Some studies found that elevated plasma catalytic iron levels are independently associated with increased prevalence of AKI after cardiac surgery[17,18]. We found that patients who developed CSA-AKI had longer CPB time, longer aortic cross-clamp time. Adding to these findings, in this study we reported that patients who developed AKI had elevated serum catalytic iron levels upon admission to the ICU.\u003c/p\u003e \u003cp\u003eFerritin is the primary tissue iron-storage protein, and may release iron in the presence of superoxide produced under conditions of inflammation[19]. Ferritin consists of 24 subunits of heavy (FtH) and light (FtL) chains. FtH can convert ferrous iron into ferric iron, which is stored within the ferritin shell subsequently[20]. The level of serum ferritin can indirectly reflect the level of FtH in the body. Nora Choi et al. found that patients with higher baseline serum ferritin levels have a low probability of developing CSA-AKI[21]. It is further suggested that high serum ferritin levels can reflect the body\u0026rsquo;s immunoregulatory capacity and ability to process catalytic iron release during CPB. We found no significant difference in the serum ferritin levels between the AKI group and non-AKI group. Because we only included data at the time of ICU admission, we did not collect the data of preoperative serum ferritin levels.\u003c/p\u003e \u003cp\u003eTRF is produced in the liver and binds one or two Fe\u003csup\u003e3+\u003c/sup\u003e atoms in the circulation, and therefrom endocytosed by a variety of cells through specific plasma membrane receptors (TfR1 and TfR2). TRF is known to be filtered through the glomerular filtration barrier, and reabsorbed in the proximal tubule[22]. Increased urinary excretion of TRF results from decreased tubular uptake. Some studies show that urinary TRF can be further explored as a wider biomarker of renal damage induced by insults causing subclinical tubular alterations[22]. Iron binding capacity is the capacity of TRF to bind with iron. There are two types of iron binding capacity, TIBC and UIBC. As only one-third of TRF is saturated with iron, so the TRF present in serum has an extra binding capacity (67%), which is called UIBC. TIBC was calculated as the sum of the serum iron plus the UIBC[23]. TS was calculated as the percentage of iron to total iron binding capacity. Choi N et al. found that TS elevated at 1h after CPB, which can be used as an independent predictor of the incidence of AKI after cardiac surgery[24]. sTfR is derived from proteolysis of the membrane transferrin receptor, it can be used to assess iron status. sTfR has been widely used in cardiopulmonary disease research[25]. Frise et al. found that elevated sTfR in non-anemic cardiac surgery patients can be used as a treatable trait that might improve postoperative recovery and shorten hospital stay[26]. We found no statistically significant difference in TRF, UIBC, TIBC, TS and sTfR between the two groups.\u003c/p\u003e \u003cp\u003eThere are several limitations in our study. First, the sample size of our study is relatively small, so the level of evidence provided by our study is not high enough. Second, we did not take urine volume into consideration as done in the previous study, because some patients were treated with diuretics and the urine volume is inaccurate, which may result in bias. Third, we did not record intraoperative packed red blood cell transfusions, which are associated with plasma catalytic iron. Fourth, we did not study the relationship between the patient's preoperative iron metabolism indexes and CSA-AKI. However, we found no difference in preoperative haemoglobin between the two groups. We plan to study the role of preoperative and postoperative iron metabolism indexes in the more crowds and explore the mechanism between ferroptosis and CSA-AKI in the future.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our study showed that S-Fe levels upon admission to the ICU were associated with CSA-AKI. Meanwahile, S-Fe and APACHE II score combination was predictive indicator for CSA-AKI. What is more, the relationship between the S-Fe and CSA-AKI is a linear relationship. Further investigations will be required to verify the results of iron metabolism indexes for CSA-AKI.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eIn accordance with the Declaration of Helsinki, this study was approved by the Ethics Committee of Nanjing First Hospital(KY20220518-KS-01). Informed consent was obtained from participants or their legal representatives. All the methods were carried out in accordance with relevant guidelines and regulations in the declaration.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eAll the authors agree to publish.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eAll the authors declare that there is no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eWenxiu Chen wrote the main manuscript text and prepared the figures and tables. Hao Zhang, Xiao Shen, Liang Hong, Hong Tao, Jilai Xiao, Shuai Nie, Meng Wei and Ming Chen collected the data. Cui Zhang and Wenkui Yu were involved in study design, data interpretation, and manuscript preparation.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe express our gratitude to all the researchers and patients who participated in this study.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e1. Peng K, McIlroy DR, Bollen BA, et al. Society of cardiovascular anesthesiologists clinical practice update for management of acute kidney injury associated with cardiac surgery. Anesth Analg. 2022;135(4):744\u0026ndash;756.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e2. Tomoya Oshita, Arudo Hiraoka, Kosuke Nakajima, et al. A Better Predictor of Acute Kidney Injury After Cardiac Surgery: The Largest Area Under the Curve Below the Oxygen Delivery Threshold During Cardiopulmonary Bypass. J Am Heart Assoc. 2020 Aug 4;9(15):e015566.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e3. Amer Harky, Mihika Joshi, Shubhi Gupta, et al. Acute Kidney Injury Associated with Cardiac Surgery: a Comprehensive Literature Review. Braz J Cardiovasc Surg. 2020 Apr 1;35(2):211\u0026ndash;224.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e4. Horne KL, Packington R, Monaghan J, et al. Three-year outcomes after acute kidney injury: results of a prospective parallel group cohort study. BMJ Open. 2017;7(3):e15316.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e5. J.H. Ix, M.G. Shlipak. The Promise of Tubule Biomarkers in Kidney Disease: A Review. Am. J. Kidney Dis. 2021;78 (5):719\u0026ndash;727.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e6. O'Neal JB, Shaw AD, Billings FT. Acute kidney injury following cardiac surgery: current understanding and future directions. Critical Care. 2016;20(1):187.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e7. Amer Harky, Mihika Joshi, Shubhi Gupta, et al. Acute Kidney Injury Associated with Cardiac Surgery: a Comprehensive Literature Review. Braz J Cardiovasc Surg. 2020 Apr 1;35(2):211\u0026ndash;224.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e8. David E Leaf, Mohan Rajapurkar, Suhas S Lele, et al. Increased plasma catalytic iron in patients may mediate acute kidney injury and death following cardiac surgery. Kidney Int. 2015 May;87(5):1046-54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e9. Akrawinthawong K, Shaw MK, Kachner J, et al. Urine catalytic iron and neutrophil gelatinaseassociated lipocalin as companion early markers of acute kidney injury after cardiac surgery: a prospective pilot study. Cardiorenal Med. 2013;3:7\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e10. A M F Martines, R Masereeuw, H Tjalsma, et al. Iron metabolism in the pathogenesis of iron-induced kidney injury. Nat Rev Nephrol. 2013 Jul;9(7):385\u0026thinsp;\u0026minus;\u0026thinsp;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e11. Albert C, Haase M, Albert A, et al. Urinary biomarkers may complement the cleveland score for prediction of adverse kidney events after cardiac surgery: a pilot study. Ann Lab Med. 2020;40(2):131\u0026ndash;141.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e12. Choi N, Whitlock R, Klassen J, et al. Early intraoperative iron-binding proteins are associated with acute kidney injury after cardiac surgery. J Thorac Cardiovasc Surg. 2019;157(1):287\u0026ndash;297.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e13. Limei Zhao, Xiaoyu Yang, Shengchao Zhang, et al. Iron metabolism-related indicators as predictors of the incidence of acute kidney injury after cardiac surgery: a meta-analysis.Renal Failure.2023 Dec;45(1):2201362.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e14. J.A. Neyra, M.C. Hu, A. Minhajuddin, et al. Kidney tubular damage and functional biomarkers in acute kidney injury following cardiac surgery. Kidney Int.2019; 4 (8):1131\u0026ndash;1142.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e15. Liu J, Xue Y, Jiang W, Zhang H, Zhao Y. Thyroid Hormone Is Related to Postoperative AKI in Acute Type A Aortic Dissection. Front Endocrinol (Lausanne). 2020; 11:588149.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e16. Kellum JA, Lameire N. Diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary (part 1). Crit Care. 2013;17(1):204.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e17. van Swelm, R. P. L., Wetzels, et al. The multifaceted role of iron in renal health and disease. Nat. Rev. Nephrol. 2020; 16 (2), 77\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e18. Akrawinthawong K, Shaw MK, Kachner J, et al. Urine catalytic iron and neutrophil gelatinaseassociated lipocalin as companion early markers of acute kidney injury after cardiac surgery: a prospective pilot study. Cardiorenal Med. 2013; 3:7\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e19. Paul Angulo, Jacob George, Christopher P. Day, et al. Serum Ferritin Levels Lack Diagnostic Accuracy for Liver Fibrosis in Patients with Nonalcoholic Fatty Liver Disease. Clin Gastroenterol Hepatol. 2014 Jul;12(7):1163\u0026ndash;1169.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e20. Bradley JM, Le Brun NE, Moore GR. Ferritins: furnishing proteins with iron. J Biol Inorg Chem. 2016;21(1):13\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e21. Choi N, Whitlock R, Klassen J, et al. Early intraoperative iron-binding proteins are associated with acute kidney injury after cardiac surgery. J Thorac Cardiovasc Surg. 2019;157(1):287\u0026ndash;297.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e22. Alfredo G Casanova, Laura Vicente-Vicente, M Teresa Hern\u0026aacute;ndez-S\u0026aacute;nchez, et al. Urinary transferrin pre-emptively identifies the risk of renal damage posed by subclinical tubular alterations. Biomed Pharmacother. 2020 Jan:121:109684.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e23. Faruqi A, Mukkamalla SKR. Iron Binding Capacity. In: StatPearls [Internet].2023 Jan 2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e24. Choi N, Whitlock R, Klassen J, et al. Early intraoperative iron-binding proteins are associated with acute kidney injury after cardiac surgery. J Thorac Cardiovasc Surg. 2019;157(1):287\u0026ndash;297.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e25. von Haehling S, Jankowska EA, van Veldhuisen DJ, et al. Iron deficiency and cardiovascular disease. Nat Rev Cardiol. 2015;12(11):659\u0026thinsp;\u0026minus;\u0026thinsp;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e26. Matthew C Frise, David A Holdsworth, Manraj S Sandhu, et al. Non-anemic iron deficiency predicts prolonged hospitalisation following surgical aortic valve replacement: a single-centre retrospective study. J Cardiothorac Surg. 2022 Jun 16;17(1):157.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cardiothoracic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcts","sideBox":"Learn more about [Journal of Cardiothoracic Surgery](http://cardiothoracicsurgery.biomedcentral.com)","snPcode":"13019","submissionUrl":"https://submission.nature.com/new-submission/13019/3","title":"Journal of Cardiothoracic Surgery","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"iron metabolism indexes, cardiac surgery-associated acute kidney injury, receiver operating characteristic, restricted cubic splines","lastPublishedDoi":"10.21203/rs.3.rs-4549588/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4549588/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eAcute kidney injury (AKI) is a major complication following cardiac surgery. We explored the clinical utility of iron metabolism indexes for identification of patients at risk for AKI after cardiac surgery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis prospective observational study included patients who underwent cardiac surgery between March 2023 and June 2023. Iron metabolism indexes were measured upon admission to the intensive care unit. Multivariable logistic regression analyses were performed to explore the relationship between iron metabolism indexes and cardiac surgery-associated AKI (CSA-AKI). Receiver operating characteristic (ROC) curve was used to assess the predictive ability of iron, APACHE II score and the combination of the two indicators. Restricted cubic splines (RCS) was used to further confirm the linear relationship between iron and CSA-AKI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAmong the 112 recruited patients, 38 (33.9%) were diagnosed with AKI. Multivariable logistic regression analysis indicated that APACHE II score (odds ratio [OR], 1.208; 95% confidence interval [CI], 1.003-1.455, \u003cem\u003eP\u003c/em\u003e = 0.036) and iron (OR 1.069; 95% CI 1.009-1.133, \u003cem\u003eP\u003c/em\u003e = 0.036) could be used as independent risk factors to predict CSA-AKI. ROC curve analysis showed that iron (area under curve[AUC] = 0.669, 95% CI 0.572-0.757), APACHE II score (AUC = 0.655, 95% CI 0.557-0.744) and iron and APACHE II score combination (AUC = 0.726, 95% CI 0.632-0.807) were predictive indicators for CSA-AKI. RCS further confirmed the linear relationship between iron and CSA-AKI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003eElevated iron levels were independently associated with higher risk of CSA-AKI, and there was a linear relationship between iron and CSA-AKI.\u003c/p\u003e","manuscriptTitle":"Iron metabolism indexes as predictors of the incidence of cardiac surgery-associated acute kidney surgery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-13 02:41:44","doi":"10.21203/rs.3.rs-4549588/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-29T18:25:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-16T08:40:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28621123388414193621102060953171346813","date":"2024-07-03T01:23:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-30T18:01:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-28T20:03:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279235322423582189429361386798742367718","date":"2024-06-21T06:21:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157652001087047876169031735192678126882","date":"2024-06-20T20:57:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-20T12:21:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-14T07:35:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-14T07:35:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cardiothoracic Surgery","date":"2024-06-08T08:14:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cardiothoracic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcts","sideBox":"Learn more about [Journal of Cardiothoracic Surgery](http://cardiothoracicsurgery.biomedcentral.com)","snPcode":"13019","submissionUrl":"https://submission.nature.com/new-submission/13019/3","title":"Journal of Cardiothoracic Surgery","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"33870337-02da-4f19-8982-1f63ae414f9b","owner":[],"postedDate":"July 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-23T16:00:04+00:00","versionOfRecord":{"articleIdentity":"rs-4549588","link":"https://doi.org/10.1186/s13019-024-03080-4","journal":{"identity":"journal-of-cardiothoracic-surgery","isVorOnly":false,"title":"Journal of Cardiothoracic Surgery"},"publishedOn":"2024-09-19 15:57:09","publishedOnDateReadable":"September 19th, 2024"},"versionCreatedAt":"2024-07-13 02:41:44","video":"","vorDoi":"10.1186/s13019-024-03080-4","vorDoiUrl":"https://doi.org/10.1186/s13019-024-03080-4","workflowStages":[]},"version":"v1","identity":"rs-4549588","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4549588","identity":"rs-4549588","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}