Intestinal Damage Marker as a Potential Predictor of Early Mortality after Cardiac Surgery

Intestinal Damage Marker as a Potential Predictor of Early Mortality after Cardiac 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 Article Intestinal Damage Marker as a Potential Predictor of Early Mortality after Cardiac Surgery Zulfugar T. Taghiyev, Carina Leweling, Lili-Marie Beier, Kevin M. Sadowski, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5382002/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives : Known associations between enterocyte injury and early mortality in adult patients following cardiac surgery highlight the critical role of timely detection and intervention. Elevated postoperative levels of intestinal fatty acid-binding protein (I-FABP) have been identified as a promising indicator for early identification of intestinal damage, potentially enhancing the risk prediction and treatment of critically ill patients after surgery. This study investigates the correlation between postoperative I-FABP levels and early mortality in patients at higher risk for early mortality undergoing cardiac surgery. Methods : 500 consecutive patients undergoing cardiac surgery with extracorporeal circulation were enrolled. Blood samples were collected at five time points perioperatively. The target population included 101 patients at high risk for systemic inflammation identified by lactic acidosis >4 mmol/L and IL-6 >600 pg/mL; these were categorized as survivors and non-survivors. Results : The mean age of patients in the target group was 66.5±12.3 y. Notably, 42% of patients developed septic shock within 12 hours of intensive care unit (ICU) admission, and the in-hospital mortality rate was 17%. Elevated serum I-FABP levels were significantly associated with non-survivors (MD 6945 pg/ml, 95%CI [2990.3 to 10899.8]; p=0.001), where the optimal threshold value for the I-FABP with >2527.3 pg/ml measured 12 h post-ICU admission predicted mortality with an AUC of 0.698 (95%CI [0.493-0.830], p=0.019). Univariate and multivariable logistic regression identified re-thoracotomy as a significant predictor of mortality, whereas lower age and body mass index indicated a survival advantage. Conclusion: Serum I-FABP level at 12 h after ICU admission was able to identify patients with a high risk of mortality, with >2527.3 pg/ml as the optimal cut-off value. Even if lactate and IL-6 levels are high, they cannot discriminate between patients with/without early death risk. Biological sciences/Chemical biology/Proteins/Blood proteins Health sciences/Biomarkers intestinal fatty acid-binding protein (I-FABP) mortality cardiac surgery enterocyte injury mesenteric ischemia Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Cardiac surgery carries a significant risk of postoperative complications that may result in early death. Reduced blood flow to the intestines and latent enterocyte injury during heart surgery can lead to sepsis, multiple organ dysfunction syndrome, and poor outcomes. It is known that mesenteric ischemia triggers a gut-derived, overwhelming systemic inflammatory response syndrome (SIRS) and multiple organ dysfunction 1 , 2 , and Hensel et al. (1998) found that the occurrence of SIRS was about 40% after interventions such as cardiac surgery with cardiopulmonary bypass 3 . Diagnosing intestinal ischemia following heart surgery can be challenging due to the unclear clinical symptoms and unconvincing blood laboratory findings in critically ill patients. Intestinal fatty acid-binding protein (I-FABP) has recently been recognized as a promising biomarker for the early diagnosis of intestinal damage 4 . I-FABP is a compact protein present in the enterocytes of the small intestine. It is discharged into the circulation when the intestinal epithelial cells are harmed, for example, during ischemic events. Increased concentrations of I-FABP in the blood have been associated with several disorders related to reduced blood supply to the intestines, suggesting its potential use in promptly detecting intestinal injury 5 . Multiple studies have shown the correlation between elevated I-FABP levels and the incidence of gastrointestinal problems after heart surgery. Although rare, these consequences considerably raise the chances of illness and premature death. Venkateswaren et al. (2002) found that 11% of deaths after cardiopulmonary bypass were due to or associated with mesenteric ischemia, with 96% of them being due to nonocclusive mesenteric ischemia (NOMI) 6 . Zou et al. (2018) conducted a recent study which revealed that individuals with elevated postoperative levels of I-FABP were at a greater risk of experiencing gastrointestinal issues and had a higher likelihood of early postoperative death 7 . In perioperative care, the clinical outcome is based on the assessment of a patient's health after surgery. It mainly focuses on early mortality, which is defined as hospital death after the surgical intervention. The present study seeks to establish whether increased levels of intestinal fatty acid-binding protein (I-FABP) in the blood during the time surrounding a surgical procedure might be used as a biomarker to predict which individuals are more likely to die early. The hypothesis is that increased perioperative blood I-FABP levels are associated with increased risk of gastrointestinal complications and adverse clinical outcomes. This suggests that patients with high I-FABP levels may need closer monitoring and more aggressive treatment to reduce the risk of early postoperative mortality. Accordingly, the purpose of this study is to investigate the correlation between postoperative I-FABP levels and early mortality in patients undergoing cardiac surgery. This research aims to validate the prognostic significance of postoperative I-FABP levels for early mortality. The findings might improve patient monitoring and intervention techniques in clinical practice. PATIENTS AND METHODS Screening and patient cohort In this prospective observational study, 500 of 929 consecutive patients undergoing cardiac surgery at a single institution were enrolled from Mar. 2022 to Dec. 2023. Patients with chronic organ dysfunction (e.g. hepatic or renal dysfunction), confirmed or strongly suspected infection before operation (e.g. endocarditis), immunodeficiency, and cases in which informed consent to participate in the study could not be obtained were all excluded. All patients received standard surgery and medical therapy, including intensive monitoring postoperatively. Moreover, all patients were followed until discharge or hospital death. All methods were performed in accordance with the relevant guidelines and regulations. Sepsis diagnosis adhered to the criteria established by the S3 Sepsis Guideline of the German Sepsis Society and the German Interdisciplinary Society for Intensive and Emergency Medicine 8 . Organ dysfunction was assessed using the Sequential Organ Failure Assessment (SOFA) score 9 . The APACHE II (Acute Physiology and Chronic Health Evaluation II) score is used to predict mortality and morbidity in intensive care units (ICU) 10 . The Acute Gastrointestinal Injury (AGI) grading system is used in medicine to categorize the extent of gastrointestinal dysfunction in severely unwell individuals. The European Society of Intensive Care Medicine (ESICM) working committee on abdominal disorders established the approach. The use of the AGI grading system is critical for evaluating individuals who are at heightened risk or already facing gastrointestinal issues caused by diverse illnesses such as sepsis, shock, and organ failure 11 . In the present study, the predictive models described comply fully with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines 20 ( Supplementary TRIPOD + AI Statement ). Sample collection and biochemical measurements After the inclusion of patients in the study, blood samples for analysis of I-FABP, interleukin (IL)-6, and lactate were taken pre-, peri-, and postoperatively according to the following sampling plan: 1. T 0 - preoperative baseline samples; 2. T 1 - intraoperatively, after the end of extracorporeal circulation (ECC) and protamine administration; 3. T 2 - postoperatively at admission to the ICU; 4. T 3 − 12 h after ICU admission; 5. T 4 − 36 h after ICU admission (T4). Figure 1 gives an overview of the sampling plan. Each blood sample (500-µl aliquot) was taken under the standard conditions alongside other routine pre- and perioperative laboratory assessments. All plasma samples for the I-FABP measurement were centrifuged within 30 min after collection (at 3000 rpm for 10 min at 4˚C) and stored in aliquots at − 80˚C until they were analyzed at the Clinical Research Center laboratory of the University of Marburg. I-FABP levels were measured from plasma samples by ELISA in relative units per mL (RU/mL). High-sensitivity commercial immunoassay kits (HK406 Hycult®Biotech) were utilized to measure plasma I-FABP levels in the blood following the manufacturer's protocols. Lactate and IL-6 levels were quantified using certified and standardized protocols within an accredited in-house laboratory. Target population Out of 500 consecutive patients who agreed to participate in the research and had heart surgery with ECC at the University Hospital, the study included 101 individuals at high risk for systemic inflammation. This was determined by their lactic acidosis levels being more than 4 mmol/L and their IL-6 levels over 600 pg/mL. The target population was then divided into two distinct groups: survivors and non-survivors (Fig. 1 ). Statistical analysis The patients’ characteristics were obtained from their digital files. Study data were collected and managed using REDCap v13.6.0 (PHP v7.4.3 Linux/Unix OS, MarianDB v10.3.38) electronic data capture tools hosted at the University Hospital Giessen. The statistical analyses were conducted using Statistical Package for the Social Sciences (SPSS®) version 26.0 for Mac OS (IBM® Corporation released 2019, Armonk, New York, United States), GraphPad Prism version 8.0.0 for Mac OS (GraphPad Software released 2018, San Diego, California USA) for graphical illustration, and NCCS Statistical Analysis and Graphics software version 23.0.2 (released 2023, NCSS, LLC., Kaysville, Utah, USA) for analysis of receiver operating characteristic following appropriate coding procedures. Continuous variables are expressed as mean ± standard deviation (SD) or ± standard error of the mean (SEM), while categorical variables are presented as frequencies and percentages. Inter-group disparities across various time points were assessed by one-way variance analysis (ANOVA), with Tukey's post hoc test applied in instances of observed differences. The normality of data distribution within each group was evaluated using the Shapiro-Wilk test. Student's t-test (unpaired) was used to compare normally distributed variables. In contrast, non-normally distributed variables were analyzed using the Mann-Whitney U-test or the Wilcoxon-signed-rank test. Furthermore, comparisons between groups were made using Pearson's chi-squared or Fisher's exact test to ascertain measurement independence. A standard confidence level of 95% was set, and statistical significance was determined at a p-value less than 0.05 (two-tailed). In multiple comparisons, adjustments were made using the Bonferroni correction method. The main research question was to determine whether elevated blood I-FABP levels during the perioperative period may be used to distinguish individuals who are at risk of early death. To tackle this issue, we conducted experiments to examine the following hypotheses: The null hypothesis (H0) states that the area under the receiver operating characteristics curve (AUC) is exactly 0.5, which suggests that serum I-FABP levels do not have any power to discriminate early death. Alternative hypothesis (Ha): The AUC is not equal to 0.5, indicating that serum I-FABP levels are not strong enough to distinguish early death. This approach assesses the predictive capability of I-FABP levels by calculating the AUC and comparing it to the value of the null hypothesis. In addition, a univariate and multivariable logistic regression analysis was performed to create a model predicting mortality in the analyzed cohort. RESULTS Demographic data and preoperative morbidity Table 1 provides detailed information on the total population in this study group, including the demographic parameters, clinical data, severity scores, and outcomes compared with the target population. The patients in the target group consisted of 29 adult females and 72 adult males with a mean age of 66.5 ± 12.3 y, a mean STS-Prom score of 5.93 ± 8.93%, and a EuroSCORE II of 6.32 ± 7.02%. Table 1 Characteristics of the study population. Variable, n (%) or mean ± SD Total Population (n = 500) Target Population (n = 101) p Value Female, n (%) 132 (26) 29 (28) 0.714 BMI, kg/m 2 28.11 ± 5.05 28.28 ± 4.82 0.774 Age, y 53.5 ± 26.72 66.5 ± 12.3 0.000 COPD Gold III-IV, n (%) 22 (4.4) 9 (8.7) 0.070 NYHA class III- IV, n (%) 152 (30) 58 (56) 0.000 CCS class III-IV, n (%) 66 (13.2) 18 (17) 0.254 Diabetes on insulin, n (%) 46 (9.2) 14 (13.6) 0.175 HbA1c, % 5.98 ± 0.95 6.42 ± 3.56 0.234 Ejection fraction, % 53.3 ± 11.1 50.4 ± 13.1 0.067 Serum creatinine, mg/dl 1.0 ± 0.6 1.2 ± 1.0 0.009 eGFR, ml/min/1.73 m 2 * 84.8 ± 26.0 74.0 ± 27.6 0.000 STS-Prom score, predicted mortality, % 5.29 ± 7.22 5.93 ± 8.93 0.006 EuroSCORE II 4.06 ± 3.6 6.32 ± 7.02 0.000 * Cockcroft-Gault Eq. 1 2 Values are mean ± SD or n (%). Abbreviations: BMI= body mass-index; COPD= chronic obstructive pulmonary disease; NYHA= New York Heart Association; CCS= Canadian Cardiovascular Society; eGFR= estimated glomerular filtration rate; STS= Society of Thoracic Surgeons Table 2 presents the characteristics of the study group divided into non-survivors and survivors based on in-hospital mortality. The mean APACHE II score of the target population on admission to the ICU was 20.4 ± 5.4 (95%CI [19.3 to 21.5]), and the mean SOFA score was 7.8 ± 2.7 (95%CI [7.2 to 8.3]). The Acute Gastrointestinal Injury (AGI) score was used to assess gastrointestinal dysfunction in patients in the ICU. The occurrence rate of AGI grade ≤II during the ICU period was 80%. A portion (30%) of these patients exhibited AGI grade ≥III the following days. The prevalence of AGI grade IV was 12%. The AGI score grad IV distribution showed significant differences between survivors and non-survivors (p=0.000) ( Table 2 ). Table 2. Characteristics of the target population. Variable, n (%) or mean ±SD Survivors (n=84) Non-survivors (n=17) p Value Preoperative characteristics Female, n (%) 22 (26) 7 (41) 0.213 BMI, kg/m 2 28.9 ± 4.9 25.6 ± 3.6 0.012 Age, y 66.0 ± 13.0 68.4 ± 8.4 0.475 Ejection fraction, % 51.2 ± 12.1 47.5 ± 17.6 0.374 STS-Prom score, predicted mortality, % 4.7 ± 8.8 7.2 ± 9.1 0.290 EuroSCORE II 5.3 ± 5.2 12.0 ± 11.7 0.000 Intraoperative characteristics Combined surgery, n (%) 33 (39) 9 (53) 0.668 eGFR, mL/min/1.73 m 2 * 76.1 ± 27.2 62.9 ± 27.9 0.078 CPB time, mean ± SD, hh: mm 2:24 ± 1:45 2:37 ± 1:36 0.672 Cross-clamp time, mean ± SD, hh: mm 1:25 ± 0:33 1:33 ± 1:08 0.489 Postoperative characteristics Need for assist devices (n, %) 10 (12) 7 (41) 0.003 ICU stay, days 7.6 ± 10.4 12.5 ± 22.4 0.168 Invasive ventilation, h 85.7 ± 234.0 99.4 ± 132.1 0.844 Dialysis, n (%) 15 (18) 9 (53) 0.002 Re-thoracotomy, n (%) 2 (2) 10 (59) 0.000 APACHE II score 20.2 ± 5.4 20.5 ± 5.4 0.855 SOFA score 7.6 ± 2.7 8.4 ± 2.6 0.302 AGI Grade IV, n (%) 5 (6) 7 (41) 0.000 Norepinephrine support, µg/kg 371.8 ± 694.1 1038.3 ± 1674.9 0.010 * Cockcroft-Gault Equation 12 Values are mean ± SD or n (%). Abbreviations: BMI= body mass-index; STS= Society of Thoracic Surgeons; eGFR= estimated glomerular filtration rate; CPB=cardiopulmonary bypass; ICU=intensive care unit; APACHE II= Acute Physiology and Chronic Health Evaluation II; SOFA = Sequential Organ Failure Assessment The median length of ICU stay was 8.6 ± 13.0 days (95% CI [6.0 to 11.1]), and the in-hospital mortality rate was 17%. In the ICU, dialysis was applied to 24 patients, accounting for 25% of the target group. A mechanical assist device was implanted in 17% of the target population. Based on the Sepsis-3 criteria 8 , 40% of patients were diagnosed with sepsis upon admission to the ICU, and 42% of patients developed septic shock within the first 12 hours after being admitted to the ICU. Prediction of mortality Univariate and multivariable logistic regression analyses were performed to create a model predicting in-hospital mortality in the target cohort. Only the re-thoracotomy was a significant predictor of death. In the target group, re-thoracotomy was performed due to bleeding in 50% (n=5), hemopericardium with tamponade in 40% (n=4), and open thorax with assist device implantation in 10% (n=1). In contrast, lower patient age, lower BMI values, and absence of diabetes mellitus showed a significant survival advantage. The results of the univariate and multivariable logistic regression analyses are presented in Table 3 . Table 3. Logistic regression analyses of the variables associated with mortality after cardiac surgery. Variables Simple logistic regression Multiple logistic regression OR [95% CI] p OR [95% CI] p Body-Mass-Index 0.84 [0.726 to 0.965] 0.014 0.67 [0.449 to 1.000] 0.050 Multiple surgeries 35.21 [1.180 to 1050.251] 0.040 Need for Asisst Device (Impella, ECMO, IABP) 6.58 [2.064 to 20.960] 0.001 Rethoracomtomy 118.75 [13.195 to 1065.455] 0.000 58.57 [10.666 to 321.618] 0.000 Left Ventricular Ejection Fraction 0.97 [0.928 to 1.003] 0.068 Prolonged intubation 1.00 [0.988 to 1.009] 0.738 Age 0.77 [0.593 to 0.997] 0.046 0.77 [0.600 to 0.971] 0.028 Diabetes mellitus II 0.01 [0.200 to 0.935] 0.047 0.01 [0.200 to 0.619] 0.029 Need for Vasopressors 1.00 [1.000 to 1.001] 0.057 EuroScore II 1.11 [1.035 to 1.186] 0.003 Hemodialysis after surgery 5.18 [1.716 to 15.609] 0.004 Estimated Glomerular Filtration Rate 0.98 [0.960 to 1.002] 0.081 STS-Score, risk of mortality 1.17 [1.018 to 1.349] 0.027 Cardiopulmonary Bypass -Time 0.99 [0.959 to 1.026] 0.646 Cross-Clamp-Time 1.00 [0.990 to 1.017] 0.618 Values are mean ± SD or n (%). Abbreviations: ECMO=Extracorporeal membrane oxygenation; IABP=intra-aortic balloon pump; STS= Society of Thoracic Surgeons Variations in I-FABP values between survivors and non-survivors Blood samples taken at defined points in time were used to analyze changes in I-FABP levels. As shown in Figure 2 , I-FABP levels were higher in non-survivors with statistically significant differences at ICU admission (T 2 ), 12 h after ICU admission (T 3 ), and 36 h after ICU admission (T 4 ): 3156.48 ± 5129.63 vs. 11757.62 ± 19275.97 (p=0.001); 1134.89 ± 1071.35 vs. 7887.64 ± 17317.52 (p=0.000); and 506.42 ± 419.27 vs. 28391.34 ± 67226.36 (p=0.000), respectively ( Figure 2c ). We found a positive correlation between IL-6 and lactate immediately after protamine administration (after ECC) (Spearman's Rho= 0.519, p=0.000). Also, a weak correlation of lactate and I-FABP was detected at ICU admission (Spearman's Rho= 0.207) and 36 h after ICU administration (Spearman's Rho= 0.291), respectively, p=0.043 and p=0.040. However, I-FABP did not correlate with IL-6 at any time point measured. I-FABP levels and prediction of mortality To study the feasibility of using I-FABP as a mortality biomarker, receiver operating characteristics (ROC) analyses were performed. Furthermore, the AUC between IL-6 and lactate at different time points was compared ( Figure 3 ). In the ROC curve analysis, the I-FABP value measured 12 h after ICU admission showed an ability to predict in-hospital mortality with an AUC of 0.6984 (95%CI [0.4933-0.8300], p=0.019). The optimal threshold value for the baseline I-FABP was >2527.3 pg/ml, exhibiting a specificity of 91.3%, a sensitivity of 50%, and an accuracy of 84.4%. The accuracies of the predictors with the optimal cut-off values derived from the ROC curves are shown in Table 4 in detail. Table 4. Diagnostic accuracy of I-FABP, IL-6, and lactate for predicting mortality after cardiac surgery. Time points Predictor AUC Cut-off 95% CI p Sensivity Specificity LR + LR- DOR After protamine administration I-FABP 0.504 ≥ 4140.70 0.255 to 0.690 0.972 30 82.4 1.7 0.85 2 IL-6 0.437 ≥ 76.30 0.240 to 0.600 0.499 80 25.5 10.7 0.8 13.684 Lactate 0.629 ≥ 1.60 0.430 to 0.770 0.134 70 51.0 1.4 0.6 24.267 ICU admission I-FABP 0.675 ≥ 4335.92 0.458 to 0.816 0.053 50 84.2 31.7 0.6 53.333 IL-6 0.561 ≥ 831.30 0.379 to 0.702 0.459 57.1 65.8 16.7 0.7 25.641 Lactate 0.505 ≥ 6.10 0.321 to 0.652 0.956 28.6 84.2 1,8 0.8 21.333 12 h after ICU admission I-FABP 0.698 ≥ 2527.3 0.493 to 0.830 0.019 50 91.3 57.1 0.5 104.286 IL-6 0.475 ≥ 5192.80 0.298 to 0.620 0.758 12. 1 98.8 10.0 0.9 112.857 Lactate 0.527 ≥ 19.0 0.356 to 0.776 0.732 12. 1 97.5 5.0 0.9 55.714 36 h after ICU admission I-FABP 0.627 ≥ 1413.39 0.388 to 0.787 0.210 30.8 96.0 76.9 0.7 106.666 IL-6 0.520 ≥ 1946.50 0.317 to 0.678 0.829 15.4 98.7 115.4 0.9 134.545 Lactate 0.455 ≥ 23 0.279 to 0.601 0.584 7.7 98.7 57.7 0.9 61.667 Abbreviations: I-FABP=intestinal fatty acid-binding protein; IL-6=interleukin 6; AUC= an area under the curve; CI=confidence interval, L.R.=likelihood ratio; DOR=diagnostic odds ratio. Univariate analysis In the univariate regression analysis, a significant negative association between postoperative mortality on the basis of I-FABP for values greater than the threshold was found for estimated glomerular filtration rate at ICU admission (OR = 0.98 [0.955 to 0.996]) and 12 h after ICU admission (OR= 0.97 [0.951 to 0.996]) ( Supplementary Table 1 ). Assuming that biomarkers eliminated renally may vary in blood concentration depending on renal clearance, a regression analysis was conducted with eGFR as a confounder. The results indicated that eGFR did not significantly impact mortality, respectively p=0.677 at ICU admission and p=0.211 at 12h after ICU Admission. We also found that multiple surgeries (OR =2.73 [1.000 to 7.474]) and cross-clamp time (OR= 1.02 [1.000 to 1.026]) at ICU admission were significantly associated with an increased I-FABP value (>threshold). Thirty-six hours after ICU admission, re-do surgeries (OR=7.08 [1.364 to 36.775]) and cardiac assist device implantation after surgery (OR= 4.18 [1.150 to 15.164]) were significantly associated with increased I-FABP ( Supplementary Table 1 ). DISCUSSION The primary aim of this study was to assess the utility of I-FABP as a biomarker for the prediction of in-hospital mortality. Our investigation demonstrated a strong association between elevated levels of I-FABP and an increased probability of mortality following cardiosurgical procedures. Non-survivors had significantly elevated levels of I-FABP at three critical time points: upon admission to the ICU, 12 hours after admission, and 36 hours after admission. The ROC curve analysis further confirmed the effectiveness of I-FABP as a predictor of mortality. The highest level of stratification was recorded 12 hours after admission to the ICU, with an AUC of 0.6984. In our study, I-FABP demonstrated high specificity but moderate sensitivity. In scenarios where there are no highly sensitive or specific alternatives available for predicting mortality, I-FABP, despite its moderate sensitivity, could prove valuable. As part of a multimodal approach, this biomarker can be used in conjunction with other tests, such as lactate or interleukin-6, as well as the patient's clinical status to compensate for its low sensitivity. Logistic regression analysis demonstrated that patients with a lower BMI, lower age, and no diabetes mellitus had a reduced mortality risk. On the other hand, the need for re-thoracotomy was identified as a significant predictor of mortality. Correlation analysis revealed a significant association between I-FABP and lactate levels upon ICU admission and 36 hours later. This suggests that I-FABP might be a valuable biomarker alongside other clinical markers. We used several clinical factors, such as severity scores (STS score, EUROscore II, APACHE II, and SOFA) 9,10,13,14 , to assess patients' illness severity. However, these scoring systems have limitations and may not provide a comprehensive picture of a patient's condition; additionally, they may not accurately predict outcomes for all patients. However, Groesdonk et al. (2013) have identified several risk factors for the development of mesenteric ischemia: case complexity, duration of cardiopulmonary bypass time (>100 min), and use of vasopressors have showed the highest (between 18- and 153-fold) odds ratios for postoperative mesenteric ischemia 15 . Other important risk factors include higher age, prolonged postoperative ventilation time, renal failure requiring intermittent dialysis, and postoperative atrial fibrillation 16,17 . Upon comparing our data with other recent research, we noted a constant correlation between I-FABP and mortality, indicating that I-FABP can serve as a reliable marker for predicting mortality. An investigation conducted by Lei Zou et al. (2018) revealed that elevated levels of I-FABP were linked to higher mortality rates in critically ill patients 7 . The study also found that the AUC values were comparable, suggesting a modest degree of predictive capability. The study by Sekino et al. examined the relationship between I-FABP levels and 28-day mortality in patients with septic shock. The findings revealed that higher levels of I-FABP at the time of admission to the ICU were linked to an increased probability of dying within 28 days. The study highlighted the significance of I-FABP as a marker of intestinal injury and its ability to predict death in septic shock patients, regardless of mesenteric ischemia 18 . Furthermore, Okada et al. (2018) discovered that I-FABP could predict acute kidney damage, as I-FABP negatively correlated with the estimated glomerular filtration rate and consequent death in critically sick patients, providing additional evidence for its usefulness as a biomarker in many clinical scenarios 19 . Our study also found a negative correlation between high levels of I-FABP and the estimated glomerular filtration rate at ICU admission and 12 hours after ICU admission. We interpret this as being consistent with a potential protection against postoperative mortality. Our research provides novel perspectives by concentrating on a distinct group of individuals who underwent cardiac surgery in whom the occurrence of postoperative complications, such as intestinal damage, is elevated. The unique circumstances of cardiac surgery, which involve an increased risk of mesenteric malperfusion damage, might clarify the notable correlation between I-FABP and mortality that we found in our cohort. Study limitations The study is limited because it is a non-randomized analysis of prospectively collected registry data from two relatively small cohorts of patients at a single center. The two treatment groups differed significantly in preoperative parameters. Summary and conclusions This study highlights the capacity of I-FABP to serve as a predictive biomarker in patients undergoing cardiac surgery, specifically in identifying those who are more likely to experience death in the immediate postoperative period. I-FABP has the potential to enable accurate determination of the need for diagnostic measurements (contrast CT, angiography) and for medical therapies such as arterial infusion therapy. Focusing on avoiding enterocyte damage during surgery and promptly diagnosing intestinal ischemia may be necessary to improve clinical results. Timely identification of individuals at risk for such consequences is essential for prompt management and enhanced survival rates. Future research should focus on confirming these findings in more extensive, multicenter trials and investigating the possibility of using I-FABP as a target for treatments aimed at decreasing postoperative mortality. Abbreviations ANOVA Analysis of variance BMI Body mass index CI Confidence interval CPB Cardiopulmonary bypass ECC Extracorporeal circulation eGFR Estimated glomerular filtration rate ICU Intensive care unit I-FABP Intestinal fatty acid-binding protein IL Interleukin LVEF Left ventricular ejection fraction OR Odds ratio SD Standard deviation SEM Standard error of mean Declarations Ethics approval and consent to participate This study obtained approval from the Institutional Review Board and Ethics Committee of Justus-Liebig-University Giessen (Registration Number: GI AZ 293/20, Registration Date: March 11, 2021), Clinical trial registration: www.clinicaltrials.gov. Unique identifier: NCT06365827, Registration Date: April 9, 2024. All patients provided written informed consent to participate in this prospective register study. Consent for publication Not applicable Acknowledgements We want to acknowledge the editorial assistance and language editing services of Elizabeth Martinson, Ph.D. We would also like to acknowledge Katharina Parzefal and Merle J. Horrelt for conducting all laboratory measurements at the Clinical Research Center laboratory of the University of Marburg. Funding This work was supported by the Clinician Scientist Program of Justus-Liebig University Giessen (JLU-CAREER), funded by the German Research Council (DFG, GEPRIS project: 413584448) Author contributions Zulfugar T. Taghiyev: Conceptualization; data collection and curation; investigation; methodology; supervision; validation; visualization; formal analysis; writing—original draft. Carina Leweling: Data–collecting, validation and curation. Lili-Marie Beier: Data–collecting, validation and curation. Mike Sadowski: Data–collecting, validation and curation. Sophia Gunkel: Data–collecting, validation and curation. Borros M. Arneth: Administration of laboratory analysis, review and editing. Chrysanthi Skevaki: Administration of laboratory analysis, review and editing. Johannes Kalder: Conceptualization, methodology, supervision, review and editing. Paula Keschenau: Conceptualization, methodology, supervision, review and editing. Andreas Boening: Administration, supervision, data curation, validation, review and editing. Conflict of interest None declared. Data availability statement The data underlying this manuscript will be shared by the corresponding author upon reasonable request. References Tilsed, J. V. et al. ESTES guidelines: acute mesenteric ischaemia. Eur. J. Trauma. Emerg. Surg. 42 (2), 253–270 (2016). Yassin, M. M. et al. Lower limb ischemia-reperfusion injury triggers a systemic inflammatory response and multiple organ dysfunction. World J. Surg. 26 (1), 115–121 (2002). Hensel, M. et al. Hyperprocalcitonemia in patients with noninfectious SIRS and pulmonary dysfunction associated with cardiopulmonary bypass. Anesthesiology 89 (1), 93–104 (1998). Sun, D. L. et al. Accuracy of the serum intestinal fatty-acid-binding protein for diagnosis of acute intestinal ischemia: a meta-analysis. Sci. Rep. 6 , 34371 (2016). Chen, J. et al. IL-6 as biomarkers of intestinal barrier dysfunction in neonates with necrotizing enterocolitis and SPF BALB/c mouse models. J. Int. Med. Res. 52 (6), 3000605241254788. 10.1177/03000605241254788 (2024). PMID: 38867509; PMCID: PMC11179468. Venkateswaran, R. V., Charman, S. C., Goddard, M. & Large, S. R. Lethal mesenteric ischaemia after cardiopulmonary bypass: a common complication? Eur. J. Cardiothorac. Surg. 22 (4), 534–538 (2002). Zou, L. et al. Intestinal fatty acid-binding protein as a predictor of prognosis in postoperative cardiac surgery patients. Med. (Baltim). 97 (33), e11782 (2018). Brunkhorst, F. M. et al. S3 Guideline Sepsis-prevention, diagnosis, therapy, and aftercare: Long version. Med. Klin. Intensivmed Notfmed . 115 (Suppl 2), 37–109 (2020). German. Vincent, J. L. et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med. 22 (7), 707–710 (1996). Knaus, W. A. et al. APACHE-acute physiology and chronic health evaluation: a physiologically based classification system. Crit. Care Med. 9 (8), 591–597 (1981). Reintam Blaser, A. et al. Gastrointestinal function in intensive care patients: terminology, definitions and management. Recommendations of the ESICM Working Group on Abdominal Problems. Intensive Care Med. 38 (3), 384–394 (2012). Cockcroft, D. W. & Gault, M. H. Prediction of creatinine clearance from serum creatinine. Nephron 16 (1), 31–41 (1976). The Society of Thoracic Surgeons. Executive Summary: Society of Thoracic Surgeons Spring 2007 Report (The Society of Thoracic Surgeons, 2007). Roques, F. et al. Risk factors and outcome in European cardiac surgery: analysis of the EuroSCORE multinational database of 19030 patients. Eur J Cardiothorac Surg. ; 15(06):816–822, discussion 822–823. (1999). Groesdonk, H. V. et al. Risk factors for nonocclusive mesenteric ischemia after elective cardiac surgery. J. Thorac. Cardiovasc. Surg. 145 (6), 1603–1610 (2013). Chaudhuri, N. et al. Intestinal ischaemia following cardiac surgery: a multivariate risk model. Eur. J. Cardiothorac. Surg. 29 (6), 971–977 (2006). Al-Diery, H. et al. The Pathogenesis of Nonocclusive Mesenteric Ischemia: Implications for Research and Clinical Practice. J. Intensive Care Med. 34 (10), 771–781 (2019). Sekino, M. et al. Intestinal fatty acid-binding protein level as a predictor of 28-day mortality and bowel ischemia in patients with septic shock: A preliminary study. J. Crit. Care . 42 , 92–100 (2017). Okada, K. et al. I-FABP levels in patients with chronic renal failure. J. Surg. Res. 230 , 94–100 (2018). Collins, G. S. et al. TRIPOD + AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385 , e078378. 10.1136/bmj-2023-078378 (2024). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx SupplementaryTRIPODAIStatement.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5382002","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":387664590,"identity":"f1d243a9-6cb2-42bf-8ee6-ebcd3f3c90d2","order_by":0,"name":"Zulfugar T. Taghiyev","email":"data:image/png;base64,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","orcid":"","institution":"Cardiovascular surgery, University Hospital Giessen","correspondingAuthor":true,"prefix":"","firstName":"Zulfugar","middleName":"T.","lastName":"Taghiyev","suffix":""},{"id":387664593,"identity":"d25afe8a-674e-4541-9734-7207d1f7b485","order_by":1,"name":"Carina Leweling","email":"","orcid":"","institution":"Cardiovascular surgery, University Hospital Giessen","correspondingAuthor":false,"prefix":"","firstName":"Carina","middleName":"","lastName":"Leweling","suffix":""},{"id":387664595,"identity":"9f504a92-fb41-40d4-9daa-ec5b33ec02c9","order_by":2,"name":"Lili-Marie Beier","email":"","orcid":"","institution":"Cardiovascular surgery, University Hospital Giessen","correspondingAuthor":false,"prefix":"","firstName":"Lili-Marie","middleName":"","lastName":"Beier","suffix":""},{"id":387664598,"identity":"0898b0d6-2f4a-4080-9db9-9a06ab97c5cb","order_by":3,"name":"Kevin M. Sadowski","email":"","orcid":"","institution":"Cardiovascular surgery, University Hospital Giessen","correspondingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"M.","lastName":"Sadowski","suffix":""},{"id":387664599,"identity":"02081885-9693-4ca7-bf5e-1a887807d011","order_by":4,"name":"Sophia Gunkel","email":"","orcid":"","institution":"Cardiovascular surgery, University Hospital Giessen","correspondingAuthor":false,"prefix":"","firstName":"Sophia","middleName":"","lastName":"Gunkel","suffix":""},{"id":387664600,"identity":"956d3910-a6d5-4a91-8cdb-d0fd5aa7c771","order_by":5,"name":"Borros M Arneth","email":"","orcid":"","institution":"Institute of Laboratory Medicine, Universities of Giessen and Marburg","correspondingAuthor":false,"prefix":"","firstName":"Borros","middleName":"M","lastName":"Arneth","suffix":""},{"id":387664601,"identity":"503ad9fd-f11a-4d70-88ab-595845649318","order_by":6,"name":"Chrysanthi Skevaki","email":"","orcid":"","institution":"Institute of Laboratory Medicine, Universities of Giessen and Marburg","correspondingAuthor":false,"prefix":"","firstName":"Chrysanthi","middleName":"","lastName":"Skevaki","suffix":""},{"id":387664602,"identity":"c3c89804-6b23-4e09-8bc5-f94d6067f24a","order_by":7,"name":"Johannes Kalder","email":"","orcid":"","institution":"Cardiovascular surgery, University Hospital Giessen","correspondingAuthor":false,"prefix":"","firstName":"Johannes","middleName":"","lastName":"Kalder","suffix":""},{"id":387664603,"identity":"ddbaf15e-cdc2-4f40-85f0-14269df2a5d2","order_by":8,"name":"Paula R. Keschenau","email":"","orcid":"","institution":"Cardiovascular surgery, University Hospital Giessen","correspondingAuthor":false,"prefix":"","firstName":"Paula","middleName":"R.","lastName":"Keschenau","suffix":""},{"id":387664604,"identity":"590364af-a9ea-4d32-8d30-6123660f85d5","order_by":9,"name":"Andreas Boening","email":"","orcid":"","institution":"Cardiovascular surgery, University Hospital Giessen","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Boening","suffix":""}],"badges":[],"createdAt":"2024-11-03 12:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5382002/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5382002/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71806004,"identity":"ed016ad6-3899-4cb7-8eef-5c9848fdd963","added_by":"auto","created_at":"2024-12-18 17:34:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1224880,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of sampling time points: \u003cstrong\u003eT\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/sub\u003e\u003csub\u003e \u003c/sub\u003ebefore surgery; \u003cstrong\u003eT\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e after protamine administration (after ECC); \u003cstrong\u003eT\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003csub\u003e \u003c/sub\u003eat ICU admission; \u003cstrong\u003eT\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e 12h after ICU admission; \u003cstrong\u003eT\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e 36h after ICU admission\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5382002/v1/c7d560a08edcf98e8056e247.png"},{"id":71806000,"identity":"37984baa-fed6-466d-a470-3b96bdce7006","added_by":"auto","created_at":"2024-12-18 17:34:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1499186,"visible":true,"origin":"","legend":"\u003cp\u003eGraphs comparing the levels of lactate (\u003cstrong\u003ea\u003c/strong\u003e), levels of IL-6 (\u003cstrong\u003eb\u003c/strong\u003e) and levels of I-FABP (\u003cstrong\u003ec\u003c/strong\u003e) in non-survivors and survivors. P-values for comparison: ns, insignificant; *, \u0026lt;0.05; **, \u0026lt;0.01; ***, \u0026lt;0.001. Mean ± SEM\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5382002/v1/81d673646bff8804e1d47483.png"},{"id":71806005,"identity":"a49600af-d3c9-4dea-9dba-be2b4ada16d2","added_by":"auto","created_at":"2024-12-18 17:34:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":773149,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristics analyses of mortality prediction for three parameters at different time points (\u003cstrong\u003ea-d\u003c/strong\u003e)\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5382002/v1/a6fd0ee7f31252731b571f50.png"},{"id":96917221,"identity":"3e1a9dfe-49cc-42a9-9089-3271c193f7b8","added_by":"auto","created_at":"2025-11-27 14:09:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4855022,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5382002/v1/7399c7bc-9b34-4949-a7ac-d6f85c6bdd2c.pdf"},{"id":71806003,"identity":"23d6f142-7989-4f78-98d9-56e6bb5fa979","added_by":"auto","created_at":"2024-12-18 17:34:20","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":19375,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5382002/v1/300dc8222f75b1fa6a52e2bf.docx"},{"id":71806001,"identity":"190d26f2-5a67-4110-afca-34810405a5a4","added_by":"auto","created_at":"2024-12-18 17:34:19","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":1491376,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTRIPODAIStatement.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5382002/v1/7906ddce916c8b0be2e1904a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intestinal Damage Marker as a Potential Predictor of Early Mortality after Cardiac Surgery","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCardiac surgery carries a significant risk of postoperative complications that may result in early death. Reduced blood flow to the intestines and latent enterocyte injury during heart surgery can lead to sepsis, multiple organ dysfunction syndrome, and poor outcomes. It is known that mesenteric ischemia triggers a gut-derived, overwhelming systemic inflammatory response syndrome (SIRS) and multiple organ dysfunction\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, and Hensel \u003cem\u003eet al.\u003c/em\u003e (1998) found that the occurrence of SIRS was about 40% after interventions such as cardiac surgery with cardiopulmonary bypass\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDiagnosing intestinal ischemia following heart surgery can be challenging due to the unclear clinical symptoms and unconvincing blood laboratory findings in critically ill patients. Intestinal fatty acid-binding protein (I-FABP) has recently been recognized as a promising biomarker for the early diagnosis of intestinal damage\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. I-FABP is a compact protein present in the enterocytes of the small intestine. It is discharged into the circulation when the intestinal epithelial cells are harmed, for example, during ischemic events. Increased concentrations of I-FABP in the blood have been associated with several disorders related to reduced blood supply to the intestines, suggesting its potential use in promptly detecting intestinal injury\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMultiple studies have shown the correlation between elevated I-FABP levels and the incidence of gastrointestinal problems after heart surgery. Although rare, these consequences considerably raise the chances of illness and premature death. Venkateswaren \u003cem\u003eet al.\u003c/em\u003e (2002) found that 11% of deaths after cardiopulmonary bypass were due to or associated with mesenteric ischemia, with 96% of them being due to nonocclusive mesenteric ischemia (NOMI)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Zou et al. (2018) conducted a recent study which revealed that individuals with elevated postoperative levels of I-FABP were at a greater risk of experiencing gastrointestinal issues and had a higher likelihood of early postoperative death\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn perioperative care, the clinical outcome is based on the assessment of a patient's health after surgery. It mainly focuses on early mortality, which is defined as hospital death after the surgical intervention. The present study seeks to establish whether increased levels of intestinal fatty acid-binding protein (I-FABP) in the blood during the time surrounding a surgical procedure might be used as a biomarker to predict which individuals are more likely to die early. The hypothesis is that increased perioperative blood I-FABP levels are associated with increased risk of gastrointestinal complications and adverse clinical outcomes. This suggests that patients with high I-FABP levels may need closer monitoring and more aggressive treatment to reduce the risk of early postoperative mortality.\u003c/p\u003e \u003cp\u003eAccordingly, the purpose of this study is to investigate the correlation between postoperative I-FABP levels and early mortality in patients undergoing cardiac surgery. This research aims to validate the prognostic significance of postoperative I-FABP levels for early mortality. The findings might improve patient monitoring and intervention techniques in clinical practice.\u003c/p\u003e"},{"header":"PATIENTS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eScreening and patient cohort\u003c/h2\u003e \u003cp\u003eIn this prospective observational study, 500 of 929 consecutive patients undergoing cardiac surgery at a single institution were enrolled from Mar. 2022 to Dec. 2023. Patients with chronic organ dysfunction (e.g. hepatic or renal dysfunction), confirmed or strongly suspected infection before operation (e.g. endocarditis), immunodeficiency, and cases in which informed consent to participate in the study could not be obtained were all excluded. All patients received standard surgery and medical therapy, including intensive monitoring postoperatively. Moreover, all patients were followed until discharge or hospital death.\u003c/p\u003e \u003cp\u003e All methods were performed in accordance with the relevant guidelines and regulations. Sepsis diagnosis adhered to the criteria established by the S3 Sepsis Guideline of the German Sepsis Society and the German Interdisciplinary Society for Intensive and Emergency Medicine\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Organ dysfunction was assessed using the Sequential Organ Failure Assessment (SOFA) score\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The APACHE II (Acute Physiology and Chronic Health Evaluation II) score is used to predict mortality and morbidity in intensive care units (ICU)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The Acute Gastrointestinal Injury (AGI) grading system is used in medicine to categorize the extent of gastrointestinal dysfunction in severely unwell individuals. The European Society of Intensive Care Medicine (ESICM) working committee on abdominal disorders established the approach. The use of the AGI grading system is critical for evaluating individuals who are at heightened risk or already facing gastrointestinal issues caused by diverse illnesses such as sepsis, shock, and organ failure\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the present study, the predictive models described comply fully with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e (\u003cb\u003eSupplementary TRIPOD\u0026thinsp;+\u0026thinsp;AI Statement\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample collection and biochemical measurements\u003c/h3\u003e\n\u003cp\u003eAfter the inclusion of patients in the study, blood samples for analysis of I-FABP, interleukin (IL)-6, and lactate were taken pre-, peri-, and postoperatively according to the following sampling plan: 1. T\u003csub\u003e0\u003c/sub\u003e - preoperative baseline samples; 2. T\u003csub\u003e1\u003c/sub\u003e - intraoperatively, after the end of extracorporeal circulation (ECC) and protamine administration; 3. T\u003csub\u003e2\u003c/sub\u003e - postoperatively at admission to the ICU; 4. T\u003csub\u003e3\u003c/sub\u003e \u0026minus;\u0026thinsp;12 h after ICU admission; 5. T\u003csub\u003e4\u003c/sub\u003e \u0026minus;\u0026thinsp;36 h after ICU admission (T4). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e gives an overview of the sampling plan.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEach blood sample (500-\u0026micro;l aliquot) was taken under the standard conditions alongside other routine pre- and perioperative laboratory assessments. All plasma samples for the I-FABP measurement were centrifuged within 30 min after collection (at 3000 rpm for 10 min at 4˚C) and stored in aliquots at \u0026minus;\u0026thinsp;80˚C until they were analyzed at the Clinical Research Center laboratory of the University of Marburg. I-FABP levels were measured from plasma samples by ELISA in relative units per mL (RU/mL). High-sensitivity commercial immunoassay kits (HK406 Hycult\u0026reg;Biotech) were utilized to measure plasma I-FABP levels in the blood following the manufacturer's protocols. Lactate and IL-6 levels were quantified using certified and standardized protocols within an accredited in-house laboratory.\u003c/p\u003e\n\u003ch3\u003eTarget population\u003c/h3\u003e\n\u003cp\u003e Out of 500 consecutive patients who agreed to participate in the research and had heart surgery with ECC at the University Hospital, the study included 101 individuals at high risk for systemic inflammation. This was determined by their lactic acidosis levels being more than 4 mmol/L and their IL-6 levels over 600 pg/mL. The target population was then divided into two distinct groups: survivors and non-survivors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e The patients\u0026rsquo; characteristics were obtained from their digital files. Study data were collected and managed using REDCap v13.6.0 (PHP v7.4.3 Linux/Unix OS, MarianDB v10.3.38) electronic data capture tools hosted at the University Hospital Giessen.\u003c/p\u003e \u003cp\u003eThe statistical analyses were conducted using Statistical Package for the Social Sciences (SPSS\u0026reg;) version 26.0 for Mac OS (IBM\u0026reg; Corporation released 2019, Armonk, New York, United States), GraphPad Prism version 8.0.0 for Mac OS (GraphPad Software released 2018, San Diego, California USA) for graphical illustration, and NCCS Statistical Analysis and Graphics software version 23.0.2 (released 2023, NCSS, LLC., Kaysville, Utah, USA) for analysis of receiver operating characteristic following appropriate coding procedures. Continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or \u0026plusmn;\u0026thinsp;standard error of the mean (SEM), while categorical variables are presented as frequencies and percentages. Inter-group disparities across various time points were assessed by one-way variance analysis (ANOVA), with Tukey's post hoc test applied in instances of observed differences. The normality of data distribution within each group was evaluated using the Shapiro-Wilk test. Student's t-test (unpaired) was used to compare normally distributed variables.\u003c/p\u003e \u003cp\u003eIn contrast, non-normally distributed variables were analyzed using the Mann-Whitney U-test or the Wilcoxon-signed-rank test. Furthermore, comparisons between groups were made using Pearson's chi-squared or Fisher's exact test to ascertain measurement independence. A standard confidence level of 95% was set, and statistical significance was determined at a p-value less than 0.05 (two-tailed). In multiple comparisons, adjustments were made using the Bonferroni correction method. The main research question was to determine whether elevated blood I-FABP levels during the perioperative period may be used to distinguish individuals who are at risk of early death. To tackle this issue, we conducted experiments to examine the following hypotheses: The null hypothesis (H0) states that the area under the receiver operating characteristics curve (AUC) is exactly 0.5, which suggests that serum I-FABP levels do not have any power to discriminate early death. Alternative hypothesis (Ha): The AUC is not equal to 0.5, indicating that serum I-FABP levels are not strong enough to distinguish early death. This approach assesses the predictive capability of I-FABP levels by calculating the AUC and comparing it to the value of the null hypothesis. In addition, a univariate and multivariable logistic regression analysis was performed to create a model predicting mortality in the analyzed cohort.\u003c/p\u003e \u003c/div\u003e\n"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eDemographic data and preoperative morbidity\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e provides detailed information on the total population in this study group, including the demographic parameters, clinical data, severity scores, and outcomes compared with the target population. The patients in the target group consisted of 29 adult females and 72 adult males with a mean age of 66.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3 y, a mean STS-Prom score of 5.93\u0026thinsp;\u0026plusmn;\u0026thinsp;8.93%, and a EuroSCORE II of 6.32\u0026thinsp;\u0026plusmn;\u0026thinsp;7.02%.\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of the study population.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable, n (%) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal Population\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;500)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTarget Population\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;101)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.714\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, kg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.11\u0026thinsp;\u0026plusmn;\u0026thinsp;5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.28\u0026thinsp;\u0026plusmn;\u0026thinsp;4.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.774\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, y\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.5\u0026thinsp;\u0026plusmn;\u0026thinsp;26.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOPD Gold III-IV, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.070\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNYHA class III- IV, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCCS class III-IV, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.254\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes on insulin, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.175\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbA1c, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.42\u0026thinsp;\u0026plusmn;\u0026thinsp;3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.234\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEjection fraction, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.067\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerum creatinine, mg/dl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eeGFR, ml/min/1.73 m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2 *\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.8\u0026thinsp;\u0026plusmn;\u0026thinsp;26.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.0\u0026thinsp;\u0026plusmn;\u0026thinsp;27.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSTS-Prom score, predicted mortality, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.29\u0026thinsp;\u0026plusmn;\u0026thinsp;7.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.93\u0026thinsp;\u0026plusmn;\u0026thinsp;8.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEuroSCORE II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.06\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.32\u0026thinsp;\u0026plusmn;\u0026thinsp;7.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e* Cockcroft-Gault Eq. 1\u003csup\u003e2\u003c/sup\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eValues are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n (%).\u003c/p\u003e\n \u003cp\u003eAbbreviations: \u0026nbsp;BMI= body mass-index; COPD= chronic obstructive pulmonary disease; NYHA= New York Heart Association; CCS= Canadian Cardiovascular Society; eGFR= estimated glomerular filtration rate; STS= Society of Thoracic Surgeons\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e presents the characteristics of the study group divided into non-survivors and survivors based on in-hospital mortality. The mean APACHE II score of the target population on admission to the ICU was 20.4 \u0026plusmn; 5.4 (95%CI [19.3 to 21.5]), and the mean SOFA score was 7.8 \u0026plusmn; 2.7 (95%CI [7.2 to 8.3]). The Acute Gastrointestinal Injury (AGI) score was used to assess gastrointestinal dysfunction in patients in the ICU. The occurrence rate of AGI grade \u0026le;II during the ICU period was 80%. A portion (30%) of these patients exhibited AGI grade \u0026ge;III the following days. The prevalence of AGI grade IV was 12%. The AGI score grad IV distribution showed significant differences between survivors and non-survivors (p=0.000) (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Characteristics of the target population.\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"553\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Variable, n (%) or mean \u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvivors\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=84)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-survivors\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=17)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreoperative characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eFemale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e22 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e7 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.213\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e28.9 \u0026plusmn; 4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e25.6 \u0026plusmn; 3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eAge, y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e66.0 \u0026plusmn; 13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e68.4 \u0026plusmn; 8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.475\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eEjection fraction, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e51.2 \u0026plusmn; 12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e47.5 \u0026plusmn; 17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.374\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSTS-Prom score, predicted mortality, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4.7 \u0026plusmn; 8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e7.2 \u0026plusmn; 9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.290\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eEuroSCORE II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5.3 \u0026plusmn; 5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e12.0 \u0026plusmn; 11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntraoperative characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eCombined surgery, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e33 (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e9 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.668\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eeGFR, mL/min/1.73 m\u003csup\u003e2 *\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e76.1 \u0026plusmn; 27.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e62.9 \u0026plusmn; 27.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.078\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eCPB time, mean \u0026plusmn; SD, hh: mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2:24 \u0026plusmn; 1:45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2:37 \u0026plusmn; 1:36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.672\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eCross-clamp time, mean \u0026plusmn; SD, hh: mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1:25 \u0026plusmn; 0:33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1:33 \u0026plusmn; 1:08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.489\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePostoperative characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eNeed for assist devices (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e10 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e7 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eICU stay, days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e7.6 \u0026plusmn; 10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e12.5 \u0026plusmn; 22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.168\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eInvasive ventilation, h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e85.7 \u0026plusmn; 234.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e99.4 \u0026plusmn; 132.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.844\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eDialysis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e15 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e9 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eRe-thoracotomy, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e10 (59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eAPACHE II score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e20.2 \u0026plusmn; 5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e20.5 \u0026plusmn; 5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.855\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSOFA score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e7.6 \u0026plusmn; 2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e8.4 \u0026plusmn; 2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.302\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eAGI Grade IV, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e7 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eNorepinephrine support, \u0026micro;g/kg\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e371.8 \u0026plusmn; 694.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1038.3 \u0026plusmn; 1674.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e* Cockcroft-Gault Equation\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eValues are mean \u0026plusmn; SD or n (%).\u003c/p\u003e\n \u003cp\u003eAbbreviations: BMI= body mass-index; STS= Society of Thoracic Surgeons; eGFR= estimated glomerular filtration rate; CPB=cardiopulmonary bypass; ICU=intensive care unit; APACHE II= Acute Physiology and Chronic Health Evaluation II; SOFA = Sequential Organ Failure Assessment\u003c/p\u003e\n \u003cp\u003eThe median length of ICU stay was 8.6 \u0026plusmn; 13.0 days (95% CI [6.0 to 11.1]), and the in-hospital mortality rate was 17%.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIn the ICU, dialysis was applied to 24 patients, accounting for 25% of the target group. A mechanical assist device was implanted in 17% of the target population. Based on the Sepsis-3 criteria\u003csup\u003e8\u003c/sup\u003e, 40% of patients were diagnosed with sepsis upon admission to the ICU, and 42% of patients developed septic shock within the first 12 hours after being admitted to the ICU.\u003c/p\u003e\n \u003cp\u003e\u003cu\u003ePrediction of mortality\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003eUnivariate and multivariable logistic regression analyses were performed to create a model predicting in-hospital mortality in the target cohort. Only the re-thoracotomy was a significant predictor of death. In the target group, re-thoracotomy was performed due to bleeding in 50% (n=5), hemopericardium with tamponade in 40% (n=4), and open thorax with assist device implantation in 10% (n=1). In contrast, lower patient age, lower BMI values, and absence of diabetes mellitus showed a significant survival advantage. The results of the univariate and multivariable logistic regression analyses are presented in \u003cstrong\u003eTable 3\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eLogistic regression analyses of the variables associated with mortality after cardiac surgery.\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 240px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSimple logistic regression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultiple logistic regression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eOR [95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eOR [95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eBody-Mass-Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.84 [0.726 to 0.965]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e0.67 [0.449 to 1.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eMultiple surgeries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e35.21 [1.180 to 1050.251]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eNeed for Asisst Device (Impella, ECMO, IABP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e6.58 [2.064 to 20.960]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eRethoracomtomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e118.75 [13.195 to 1065.455]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e58.57 [10.666 to 321.618]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eLeft Ventricular Ejection Fraction\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.97 [0.928 to 1.003]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eProlonged intubation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e1.00 [0.988 to 1.009]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.77 [0.593 to 0.997]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e0.77 [0.600 to 0.971]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eDiabetes mellitus II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.01 [0.200 to 0.935]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e0.01 [0.200 to 0.619]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eNeed for Vasopressors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e1.00 [1.000 to 1.001]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eEuroScore II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e1.11 [1.035 to 1.186]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eHemodialysis after surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e5.18 [1.716 to 15.609]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eEstimated Glomerular Filtration Rate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.98 [0.960 to 1.002]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eSTS-Score, risk of mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e1.17 [1.018 to 1.349]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eCardiopulmonary Bypass -Time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.99 [0.959 to 1.026]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eCross-Clamp-Time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e1.00 [0.990 to 1.017]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eValues are mean \u0026plusmn; SD or n (%).\u003c/p\u003e\n \u003cp\u003eAbbreviations: \u0026nbsp;ECMO=Extracorporeal membrane oxygenation; IABP=intra-aortic balloon pump; STS= Society of Thoracic Surgeons\u003c/p\u003e\n \u003cp\u003e\u003cu\u003eVariations in I-FABP values between survivors and non-survivors\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003eBlood samples taken at defined points in time were used to analyze changes in I-FABP levels. As shown in \u003cstrong\u003eFigure 2\u003c/strong\u003e, I-FABP levels were higher in non-survivors with statistically significant differences at ICU admission (T\u003csub\u003e2\u003c/sub\u003e), 12 h after ICU admission (T\u003csub\u003e3\u003c/sub\u003e), and 36 h after ICU admission (T\u003csub\u003e4\u003c/sub\u003e): 3156.48 \u0026plusmn; 5129.63 vs. 11757.62 \u0026plusmn; 19275.97 (p=0.001); 1134.89 \u0026plusmn; 1071.35 vs. 7887.64 \u0026plusmn; 17317.52 (p=0.000); and 506.42 \u0026plusmn; 419.27 vs. 28391.34 \u0026plusmn; 67226.36 (p=0.000), respectively (\u003cstrong\u003eFigure\u0026nbsp;2c\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eWe found a positive correlation between IL-6 and lactate immediately after protamine administration (after ECC) (Spearman\u0026apos;s Rho= 0.519, p=0.000). Also, a weak correlation of lactate and I-FABP was detected at ICU admission (Spearman\u0026apos;s Rho= 0.207) and 36 h after ICU administration (Spearman\u0026apos;s Rho= 0.291), respectively, p=0.043 and p=0.040. However, I-FABP did not correlate with IL-6 at any time point measured.\u003c/p\u003e\n \u003cp\u003e\u003cu\u003eI-FABP levels and prediction of mortality\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003eTo study the feasibility of using I-FABP as a mortality biomarker, receiver operating characteristics (ROC) analyses were performed. Furthermore, the AUC between IL-6 and lactate at different time points was compared (\u003cstrong\u003eFigure 3\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIn the ROC curve analysis, the I-FABP value measured 12 h after ICU admission showed an ability to predict in-hospital mortality with an AUC of 0.6984 (95%CI [0.4933-0.8300], p=0.019). The optimal threshold value for the baseline I-FABP was \u0026gt;2527.3 pg/ml, exhibiting a specificity of 91.3%, a sensitivity of 50%, and an accuracy of 84.4%. The accuracies of the predictors with the optimal cut-off values derived from the ROC curves are shown in \u003cstrong\u003eTable 4\u003c/strong\u003e in detail.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Diagnostic accuracy of I-FABP, IL-6, and lactate for predicting mortality after cardiac surgery.\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"762\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime points\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCut-off\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR +\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAfter protamine administration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eI-FABP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ge; 4140.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.255 to 0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e82.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eIL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ge; 76.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.240 to 0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e13.684\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eLactate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ge; 1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.430 to 0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e51.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e24.267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eICU admission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eI-FABP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ge; 4335.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.458 to 0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e84.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e53.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eIL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ge; 831.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.379 to 0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e57.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e65.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e25.641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eLactate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ge; 6.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.321 to 0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e84.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e21.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12 h after ICU admission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eI-FABP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ge; 2527.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.493 to 0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e91.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e57.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e104.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eIL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ge; 5192.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.298 to 0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e12. 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e98.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e112.857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eLactate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ge; 19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.356 to 0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e12. 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e97.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e55.714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e36 h after ICU admission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eI-FABP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ge; 1413.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.388 to 0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e30.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e96.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e76.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e106.666\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eIL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ge; 1946.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.317 to 0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e98.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e115.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e134.545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eLactate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ge; 23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.279 to 0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e98.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e57.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e61.667\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eAbbreviations: \u0026nbsp;I-FABP=intestinal fatty acid-binding protein; IL-6=interleukin 6; AUC= an area under the curve; CI=confidence interval, L.R.=likelihood ratio; DOR=diagnostic odds ratio.\u003c/p\u003e\n \u003cp\u003e\u003cu\u003eUnivariate analysis\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003eIn the univariate regression analysis, a significant negative association between postoperative mortality on the basis of I-FABP for values greater than the threshold was found for estimated glomerular filtration rate at\u0026nbsp;ICU admission (OR = 0.98 [0.955 to 0.996]) and 12 h after ICU admission (OR= 0.97 [0.951 to 0.996]) (\u003cstrong\u003eSupplementary Table\u003c/strong\u003e \u003cstrong\u003e1\u003c/strong\u003e). Assuming that biomarkers eliminated renally may vary in blood concentration depending on renal clearance, a regression analysis was conducted with eGFR as a confounder. The results indicated that eGFR did not significantly impact mortality, respectively p=0.677 at ICU admission and p=0.211 at 12h after ICU Admission. We also found that multiple surgeries (OR =2.73 [1.000 to 7.474]) and cross-clamp time (OR= 1.02 [1.000 to 1.026]) at ICU admission were significantly associated with an increased I-FABP value (\u0026gt;threshold). Thirty-six hours after ICU admission, re-do surgeries (OR=7.08 [1.364 to 36.775]) and cardiac assist device implantation after surgery (OR= 4.18 [1.150 to 15.164]) were significantly associated with increased I-FABP (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe primary aim of this study was to assess the utility of I-FABP as a biomarker for the prediction of in-hospital mortality. Our investigation demonstrated a strong association between elevated levels of I-FABP and an increased probability of mortality following cardiosurgical procedures. Non-survivors had significantly elevated levels of I-FABP at three critical time points: upon admission to the ICU, 12 hours after admission, and 36 hours after admission. The ROC curve analysis further confirmed the effectiveness of I-FABP as a predictor of mortality. The highest level of stratification was recorded 12 hours after admission to the ICU, with an AUC of 0.6984.\u003c/p\u003e\n\u003cp\u003eIn our study, I-FABP demonstrated high specificity but moderate sensitivity. In scenarios where there are no highly sensitive or specific alternatives available for predicting mortality, I-FABP, despite its moderate sensitivity, could prove valuable. As part of a multimodal approach, this biomarker can be used in conjunction with other tests, such as lactate or interleukin-6, as well as the patient\u0026apos;s clinical status to compensate for its low sensitivity.\u003c/p\u003e\n\u003cp\u003eLogistic regression analysis demonstrated that patients with a lower BMI, lower age, and no diabetes mellitus had a reduced mortality risk. On the other hand, the need for re-thoracotomy was identified as a significant predictor of mortality. Correlation analysis revealed a\u0026nbsp;significant association between I-FABP and lactate levels upon ICU admission and 36 hours later. This suggests that I-FABP might be a valuable biomarker alongside other clinical markers.\u003c/p\u003e\n\u003cp\u003eWe used several clinical factors, such as severity scores (STS score, EUROscore II, APACHE II, and SOFA)\u003csup\u003e9,10,13,14\u003c/sup\u003e, to assess patients\u0026apos; illness severity. However,\u0026nbsp;these scoring systems have limitations and may not provide a comprehensive picture of a patient\u0026apos;s condition; additionally, they may not accurately predict outcomes for all patients. However, Groesdonk et al. (2013) have identified several risk factors for the development of mesenteric ischemia: case complexity, duration of cardiopulmonary bypass time (\u0026gt;100 min), and use of vasopressors have showed the highest (between 18- and 153-fold) odds ratios for postoperative mesenteric ischemia\u003csup\u003e15\u003c/sup\u003e. Other important risk factors include higher age, prolonged postoperative ventilation time, renal failure requiring intermittent dialysis, and postoperative atrial fibrillation\u003csup\u003e16,17\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eUpon comparing our data with other recent research, we noted a constant correlation between I-FABP and mortality, indicating that I-FABP can serve as a reliable marker for predicting mortality. An investigation conducted by Lei Zou et al. (2018) revealed that elevated levels of I-FABP were linked to higher mortality rates in critically ill patients\u003csup\u003e7\u003c/sup\u003e. The study also found that the AUC values were comparable, suggesting a modest degree of predictive capability. The study by Sekino et al. examined the relationship between I-FABP levels and 28-day mortality in patients with septic shock. The findings revealed that higher levels of I-FABP at the time of admission to the ICU were linked to an increased probability of dying within 28 days. The study highlighted the significance of I-FABP as a marker of intestinal injury and its ability to predict death in septic shock patients, regardless of mesenteric ischemia\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFurthermore, Okada et al. (2018) discovered that I-FABP could predict acute kidney damage, as I-FABP negatively correlated with the estimated glomerular filtration rate and consequent death in critically sick patients, providing additional evidence for its usefulness as a biomarker in many clinical scenarios\u003csup\u003e19\u003c/sup\u003e. Our study also found a negative correlation between high levels of I-FABP and the estimated glomerular filtration rate at ICU admission and 12 hours after ICU admission. We interpret this as being consistent with a potential protection against postoperative mortality.\u003c/p\u003e\n\u003cp\u003eOur research provides novel perspectives by concentrating on a distinct group of individuals who underwent cardiac surgery in whom the occurrence of postoperative complications, such as intestinal damage, is elevated. The unique circumstances of cardiac surgery, which involve an increased risk of mesenteric malperfusion damage, might clarify the notable correlation between I-FABP and mortality that we found in our cohort.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cu\u003eStudy limitations\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe study is limited because it is a non-randomized analysis of prospectively collected registry data from two relatively small cohorts of patients at a single center. The two treatment groups differed significantly in preoperative parameters.\u0026nbsp;\u003c/p\u003e"},{"header":"Summary and conclusions","content":"\u003cp\u003eThis study highlights the capacity of I-FABP to serve as a predictive biomarker in patients undergoing cardiac surgery, specifically in identifying those who are more likely to experience death in the immediate postoperative period.\u0026nbsp;I-FABP has the potential to enable accurate determination of the need for diagnostic measurements (contrast CT, angiography) and for medical therapies such as arterial infusion therapy. Focusing on avoiding enterocyte damage during surgery and promptly diagnosing intestinal ischemia may be necessary to improve clinical results. Timely identification of individuals at risk for such consequences is essential for prompt management and enhanced survival rates.\u003c/p\u003e\n\u003cp\u003eFuture research should focus on confirming these findings in more extensive, multicenter trials and investigating the possibility of using I-FABP as a target for treatments aimed at decreasing postoperative mortality.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnalysis of variance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConfidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCPB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCardiopulmonary bypass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eECC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExtracorporeal circulation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEstimated glomerular filtration rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntensive care unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI-FABP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntestinal fatty acid-binding protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInterleukin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLVEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLeft ventricular ejection fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandard error of mean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study obtained approval from the Institutional Review Board and Ethics Committee of Justus-Liebig-University Giessen (Registration Number: GI AZ 293/20, Registration Date: March 11, 2021), Clinical trial registration: www.clinicaltrials.gov. Unique identifier: NCT06365827, Registration Date: April 9, 2024. All patients provided written informed consent to participate in this prospective register study.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eWe want to acknowledge the editorial assistance and language editing services of Elizabeth Martinson, Ph.D.\u003c/p\u003e\n\u003cp\u003eWe would also like to acknowledge Katharina Parzefal and Merle J. Horrelt for conducting all laboratory measurements at the Clinical Research Center laboratory of the University of Marburg.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Clinician Scientist Program of Justus-Liebig University Giessen (JLU-CAREER), funded by the German Research Council (DFG, GEPRIS project: 413584448)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZulfugar T. Taghiyev: Conceptualization; data collection and curation; investigation; methodology; supervision; validation; visualization; formal analysis; writing\u0026mdash;original draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCarina Leweling: Data\u0026ndash;collecting, validation and curation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLili-Marie Beier: Data\u0026ndash;collecting, validation and curation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMike Sadowski: Data\u0026ndash;collecting, validation and curation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSophia Gunkel: Data\u0026ndash;collecting, validation and curation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBorros M. Arneth: Administration of laboratory analysis, review and editing.\u003c/p\u003e\n\u003cp\u003eChrysanthi Skevaki: Administration of laboratory analysis, review and editing.\u003c/p\u003e\n\u003cp\u003eJohannes Kalder: Conceptualization, methodology, supervision, review and editing.\u003c/p\u003e\n\u003cp\u003ePaula Keschenau: Conceptualization, methodology, supervision, review and editing.\u003c/p\u003e\n\u003cp\u003eAndreas Boening: Administration, supervision, data curation, validation, review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this manuscript will be shared by the corresponding author upon reasonable request.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTilsed, J. V. et al. ESTES guidelines: acute mesenteric ischaemia. \u003cem\u003eEur. J. Trauma. Emerg. Surg.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (2), 253\u0026ndash;270 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYassin, M. M. et al. Lower limb ischemia-reperfusion injury triggers a systemic inflammatory response and multiple organ dysfunction. \u003cem\u003eWorld J. Surg.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e (1), 115\u0026ndash;121 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHensel, M. et al. Hyperprocalcitonemia in patients with noninfectious SIRS and pulmonary dysfunction associated with cardiopulmonary bypass. \u003cem\u003eAnesthesiology\u003c/em\u003e \u003cb\u003e89\u003c/b\u003e (1), 93\u0026ndash;104 (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun, D. L. et al. Accuracy of the serum intestinal fatty-acid-binding protein for diagnosis of acute intestinal ischemia: a meta-analysis. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 34371 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, J. et al. IL-6 as biomarkers of intestinal barrier dysfunction in neonates with necrotizing enterocolitis and SPF BALB/c mouse models. \u003cem\u003eJ. Int. Med. Res.\u003c/em\u003e \u003cb\u003e52\u003c/b\u003e (6), 3000605241254788. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/03000605241254788\u003c/span\u003e\u003cspan address=\"10.1177/03000605241254788\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024). PMID: 38867509; PMCID: PMC11179468.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkateswaran, R. V., Charman, S. C., Goddard, M. \u0026amp; Large, S. R. Lethal mesenteric ischaemia after cardiopulmonary bypass: a common complication? \u003cem\u003eEur. J. Cardiothorac. Surg.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e (4), 534\u0026ndash;538 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou, L. et al. Intestinal fatty acid-binding protein as a predictor of prognosis in postoperative cardiac surgery patients. \u003cem\u003eMed. (Baltim).\u003c/em\u003e \u003cb\u003e97\u003c/b\u003e (33), e11782 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrunkhorst, F. M. et al. S3 Guideline Sepsis-prevention, diagnosis, therapy, and aftercare: Long version. \u003cem\u003eMed. Klin. Intensivmed Notfmed\u003c/em\u003e. \u003cb\u003e115\u003c/b\u003e (Suppl 2), 37\u0026ndash;109 (2020). German.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVincent, J. L. et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. \u003cem\u003eIntensive Care Med.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e (7), 707\u0026ndash;710 (1996).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnaus, W. A. et al. APACHE-acute physiology and chronic health evaluation: a physiologically based classification system. \u003cem\u003eCrit. Care Med.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (8), 591\u0026ndash;597 (1981).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReintam Blaser, A. et al. Gastrointestinal function in intensive care patients: terminology, definitions and management. Recommendations of the ESICM Working Group on Abdominal Problems. \u003cem\u003eIntensive Care Med.\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e (3), 384\u0026ndash;394 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCockcroft, D. W. \u0026amp; Gault, M. H. Prediction of creatinine clearance from serum creatinine. \u003cem\u003eNephron\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e (1), 31\u0026ndash;41 (1976).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe Society of Thoracic Surgeons. \u003cem\u003eExecutive Summary: Society of Thoracic Surgeons Spring 2007 Report\u003c/em\u003e (The Society of Thoracic Surgeons, 2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoques, F. et al. Risk factors and outcome in European cardiac surgery: analysis of the EuroSCORE multinational database of 19030 patients. Eur J Cardiothorac Surg. ; 15(06):816\u0026ndash;822, discussion 822\u0026ndash;823. (1999).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGroesdonk, H. V. et al. Risk factors for nonocclusive mesenteric ischemia after elective cardiac surgery. \u003cem\u003eJ. Thorac. Cardiovasc. Surg.\u003c/em\u003e \u003cb\u003e145\u003c/b\u003e (6), 1603\u0026ndash;1610 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaudhuri, N. et al. Intestinal ischaemia following cardiac surgery: a multivariate risk model. \u003cem\u003eEur. J. Cardiothorac. Surg.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e (6), 971\u0026ndash;977 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Diery, H. et al. The Pathogenesis of Nonocclusive Mesenteric Ischemia: Implications for Research and Clinical Practice. \u003cem\u003eJ. Intensive Care Med.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e (10), 771\u0026ndash;781 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSekino, M. et al. Intestinal fatty acid-binding protein level as a predictor of 28-day mortality and bowel ischemia in patients with septic shock: A preliminary study. \u003cem\u003eJ. Crit. Care\u003c/em\u003e. \u003cb\u003e42\u003c/b\u003e, 92\u0026ndash;100 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkada, K. et al. I-FABP levels in patients with chronic renal failure. \u003cem\u003eJ. Surg. Res.\u003c/em\u003e \u003cb\u003e230\u003c/b\u003e, 94\u0026ndash;100 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollins, G. S. et al. TRIPOD\u0026thinsp;+\u0026thinsp;AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. \u003cem\u003eBMJ\u003c/em\u003e \u003cb\u003e385\u003c/b\u003e, e078378. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj-2023-078378\u003c/span\u003e\u003cspan address=\"10.1136/bmj-2023-078378\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"intestinal fatty acid-binding protein (I-FABP), mortality, cardiac surgery, enterocyte injury, mesenteric ischemia","lastPublishedDoi":"10.21203/rs.3.rs-5382002/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5382002/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e: Known associations between enterocyte injury and early mortality in adult patients following cardiac surgery highlight the critical role of timely detection and intervention. Elevated postoperative levels of intestinal fatty acid-binding protein (I-FABP) have been identified as a promising indicator for early identification of intestinal damage, potentially enhancing the risk prediction and treatment of critically ill patients after surgery. This study investigates the correlation between postoperative I-FABP levels and early mortality in patients at higher risk for early mortality undergoing cardiac surgery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: 500 consecutive patients undergoing cardiac surgery with extracorporeal circulation were enrolled. Blood samples were collected at five time points perioperatively. The target population included 101 patients at high risk for systemic inflammation identified by lactic acidosis \u0026gt;4 mmol/L and IL-6 \u0026gt;600 pg/mL; these were categorized as survivors and non-survivors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The mean age of patients in the target group was 66.5±12.3 y. Notably, 42% of patients developed septic shock within 12 hours of intensive care unit (ICU) admission, and the in-hospital mortality rate was 17%. Elevated serum I-FABP levels were significantly associated with non-survivors (MD 6945 pg/ml, 95%CI [2990.3 to 10899.8]; p=0.001), where the optimal threshold value for the I-FABP with \u0026gt;2527.3 pg/ml measured 12 h post-ICU admission predicted mortality with an AUC of 0.698 (95%CI [0.493-0.830], p=0.019). Univariate and multivariable logistic regression identified re-thoracotomy as a significant predictor of mortality, whereas lower age and body mass index indicated a survival advantage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eSerum I-FABP level at 12 h after ICU admission was able to identify patients with a high risk of mortality, with \u0026gt;2527.3 pg/ml as the optimal cut-off value. Even if lactate and IL-6 levels are high, they cannot discriminate between patients with/without early death risk.\u003c/p\u003e","manuscriptTitle":"Intestinal Damage Marker as a Potential Predictor of Early Mortality after Cardiac Surgery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 17:34:13","doi":"10.21203/rs.3.rs-5382002/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a3c6c63-b7d8-454b-8779-8f0c544407b4","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41309960,"name":"Biological sciences/Chemical biology/Proteins/Blood proteins"},{"id":41309961,"name":"Health sciences/Biomarkers"}],"tags":[],"updatedAt":"2025-11-26T09:38:54+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-18 17:34:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5382002","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5382002","identity":"rs-5382002","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}