Delta Neutrophil Index in Coronary Artery Bypass Surgery: An Innovation in Postoperative Mortality Assessment

Delta Neutrophil Index in Coronary Artery Bypass Surgery: An Innovation in Postoperative Mortality Assessment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Delta Neutrophil Index in Coronary Artery Bypass Surgery: An Innovation in Postoperative Mortality Assessment Abdulkadir Bilgiç, Burak Toprak, Hamide Kaya, Çise Kanat Toprak This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5274128/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2025 Read the published version in Journal of Inflammation Research → Version 1 posted You are reading this latest preprint version Abstract a. Background: Recently, the role of inflammatory markers in assessing the severity of CAD in the early stages has garnered interest. Currently, there are no specific inflammatory biomarkers routinely used for predicting postoperative mortality in patients undergoing coronary artery bypass grafting (CABG). In this study, we evaluated the significance of postoperative DNI as a prognostic marker for early mortality in patients undergoing coronary artery bypass grafting (CABG). b. Aims: The aim of this study is to determine the significance of the delta neutrophil index (DNI), which reflects the proportion of immature granulocytes, as a prognostic marker for early postoperative mortality in coronary artery bypass grafting (CABG). c. Methods: This rigorously designed retrospective cohort study, conducted at a high-volume tertiary care center specializing in cardiovascular surgery, included a robust patient cohort to ensure comprehensive data analysis and reliable conclusions. The study included a consecutive series of 446 patients who underwent coronary artery bypass grafting (CABG) between January 1, 2022, and August 1, 2023. d. Results: Mortality was found to be associated with Pre-DNI (p < 0.05). A 1-unit increase in Pre-DNI measurement was associated with a 2.61-fold (95% Confidence Interval: 1.54–4.45) increase in the risk of death. Additionally, mortality was also associated with Post-DNI (p < 0.05). A 1-unit increase in Post-DNI measurement was associated with a 10.21-fold (95% Confidence Interval: 5.08–20.05) increase in the risk of death. e. Conclusions: This study unequivocally establishes that elevated DNI values serve as potent independent predictors of postoperative mortality, underscoring the clinical utility of DNI as a key component in the perioperative risk stratification for CABG patients. Both preoperative and postoperative DNI were significantly associated with mortality, highlighting the valuable role of DNI in risk assessment necessary for perioperative and postoperative management. This highlights the dual utility of DNI in not only predicting but also monitoring patient outcomes throughout the perioperative period. Incorporating DNI into routine clinical practice could provide a more personalized approach to postoperative care, potentially improving patient survival and reducing complication rates in CABG surgery. Delta Neutrophil Index Coronary Artery Bypass Grafting Postoperative Mortality Inflammatory Markers Key Points a. What is known about the topic? It is well-established that inflammatory responses play a significant role in the outcomes of cardiac surgeries, including coronary artery bypass grafting (CABG). Various inflammatory markers, such as C-reactive protein (CRP) and white blood cell (WBC) counts, have been studied in relation to postoperative outcomes. However, no specific inflammatory biomarker is routinely used in clinical practice to predict postoperative mortality in CABG patients. The delta neutrophil index (DNI), which measures the proportion of immature granulocytes, has been suggested in recent studies as a promising marker for inflammation and may serve as an indicator of poor prognosis in critically ill patients, but its use in CABG remains underexplored. b. What does this study add? This study introduces the delta neutrophil index (DNI) as a novel prognostic marker for predicting early postoperative mortality in patients undergoing coronary artery bypass grafting (CABG). It provides the first clinical evidence that both preoperative and postoperative DNI values are significantly associated with increased mortality risk. The findings suggest that DNI could be a valuable tool for risk stratification, enabling more precise perioperative and postoperative management of CABG patients, particularly in identifying those at higher risk for poor outcomes who may otherwise go unnoticed using traditional markers. Introduction Coronary artery disease (CAD) is the leading cause of cardiovascular mortality worldwide, resulting in over 4.5 million deaths in developing countries. 1 Coronary artery bypass grafting (CABG) has been shown to improve overall health-related quality of life and survival. 2,3 Outcomes related to cardiac surgery, particularly CABG, have been extensively reported in the United States, Canada, and Western Europe. 4–8 Recently, there has been increasing interest in the role of inflammatory markers in assessing severity early after CABG. CABG leads to an excessive inflammatory response in the postoperative phase. 9 Despite experimental and clinical evidence of the relationship between inflammation and adverse outcomes 9 , no specific inflammatory biomarkers are routinely used to predict postoperative mortality in CABG patients. What if we could reliably predict postoperative complications with a simple blood test? The absence of a standardized marker underscores the need for identifying novel prognostic tools to improve postoperative management and patient survival. Immature granulocytes are a practical marker of local and systemic inflammation. 10,11,12 The use of specific automated blood cell analysis devices allows for the rapid determination of the delta neutrophil index (DNI), which reflects the fraction of circulating immature granulocytes, in conjunction with a complete blood count (CBC). 11,13,14,15 In this study, we assessed the importance of early postoperative DNI as a prognostic marker for mortality in CABG patients. To our knowledge, this is the first study to evaluate the relationship between DNI and postoperative mortality in a clinical setting. Methods This retrospective, observational cohort study was conducted at Mersin University Medical Faculty Training and Research Hospital, a tertiary academic hospital specializing in cardiovascular surgery. The study included consecutive patients who underwent coronary artery bypass grafting (CABG) between January 1, 2022, and August 1, 2023. a. Data Collection - Study Design: A nested case-control design within the cohort was employed. When the odds ratio (OR) for delta neutrophil index (DNI) and other factors associated with mortality was set at 1.5 (the minimum clinically significant level), the width of the confidence interval was considered to be 25%. Based on these parameters, the required sample size was determined to be 445. The number of patients who died was matched in a 1:4 ratio to those who survived. - Data Collection: Data on patients' demographics, laboratory test results, operation time, left ventricular ejection fraction (EF), and presence of multi-vessel disease were reviewed. Venous blood samples were collected at admission and postoperatively on a daily basis in vacuum tubes containing ethylenediaminetetraacetic acid (EDTA). Complete blood count (CBC) measurements were performed at multiple time points. DNI, white blood cell (WBC) count, hemoglobin level, and platelet count were analyzed by an automated blood cell analyzer. b. Data Analysis - Statistical Analysis: For continuous measurements, mean and standard deviation, median, minimum, and maximum values were used. Frequencies and percentages were used for categorical variables. Student’s t-test was applied for comparisons of age, EF, and biochemical measurements based on mortality status, while paired t-test was used for comparing repeated measurements. Chi-Square test was applied to examine the relationship between mortality status and variables such as gender, diabetes mellitus (DM), and hypertension (HT). Odds ratios and 95% confidence intervals were provided for parameters believed to be associated with mortality, including age, gender, EF, DM, HT, and biochemical parameters. Statistical significance was set at p<0.05. - Software: IBM SPSS 21 and MedCalc statistical software were used for data evaluation. Parametric tests were applied to continuous measurements without normality testing, due to the applicability of the Central Limit Theorem. 16 c. Data Availability Statement Datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. d. Ethical Approval Ethical approval for the study was obtained from the Mersin University Ethics Committee with the decision numbered 2024/472 and dated 22/05/2024. e. Declaration of Helsinki The study and the writing of the article were prepared in accordance with the Declaration of Helsinki. f. İnformed Written Consent Informed written consent was obtained in the surgical consent form before the subjects were included in the study. Results A total of 446 diagnosed patients were included in the study. The basic characteristics and clinical data are presented in Table 1. Table 1. Distribution of Socio-Demographic Characteristics in Patients Undergoing Open Heart Vascular Surgery (n=446) Characteristic Mean±SD Median(Min-Mak) Age (year) 64.7±9.8 66(26-85) Count (n) Percentage (%) Gender Male 312 70 Female 134 30 DM No 192 43 Yes 254 57 HT No 280 62,8 Yes 166 37,2 Mortality Alive 350 78,5 Exitus 96 21,5 (x̄ ±SS) Median (Min-Maks.) EF 52.64±7.09 55(29-65) PREOP Creatinine(mg/dL) 0.96±0.57 0.88(0.44-9.75) Ure (mg/dL) 38.91±15.65 35.6(16.85-114.85) DNI 0.48±0.27 0.4(0.10-5.1) NEU(103mcL) 5.58±1.34 5.02(1.01-14.95) LYM(103mcL) 2.02±0.78 1.94(0.32-5.79) PLT(103mcL) 238.03±63.03 233(79-519) CRP(mg/L) 23.54±18.78 8.89(0.43-413.27) Albumin(mg/L) 37.75±3.94 38.32(24.15-46.4) SD: Standard Deviation, p-value: Student's t-test was used for continuous variables, and the Chi-Square test was used for categorical variables. CRP: C reactive protein, PLT: Platelets, NEU: Neutrophil , LYM: lymphocyte According to Table 1; In this study, the demographic and clinical characteristics of a total of 446 patients were examined. Age: The age range of the patients was from a minimum of 26 years to a maximum of 85 years. The mean age was 64.7 ± 9.8 years, with a median age of 66 years. Gender: Of the patients included in the study, 70% were male, and 30% were female. Diabetes Mellitus (DM): Diabetes mellitus was detected in 57% of the patients. Hypertension (HT): Hypertension was present in 37.2% of the patients. Mortality: 21.5% of the patients in the study experienced death. Left Ventricular Ejection Fraction (EF): EF ranged from a minimum of 29% to a maximum of 65%. The mean EF was 52.64 ± 7.09%, with a median EF of 55%. These data provide a comprehensive overview of the general distribution of patients' age, gender, diabetes mellitus, hypertension, mortality rate, and left ventricular ejection fraction. Preoperative Measurements The laboratory findings obtained during preoperative evaluations were as follows: Creatinine: Ranged from a minimum of 0.44 mg/dL to a maximum of 9.75 mg/dL. The mean creatinine level was 0.96 ± 0.57 mg/dL, with a median value of 0.88 mg/dL. Urea: Ranged from a minimum of 16.85 mg/dL to a maximum of 114.85 mg/dL. The mean urea level was 38.91 ± 15.65 mg/dL, with a median value of 35.6 mg/dL. Delta Neutrophil Index (DNI): Ranged from a minimum of 0.10 to a maximum of 5.1. The mean DNI value was 0.48 ± 0.27, with a median value of 0.4. Neutrophils: Ranged from a minimum of 1.01 × 10³/µL to a maximum of 14.95 × 10³/µL. The mean neutrophil count was 5.58 ± 1.34 × 10³/µL, with a median value of 5.02 × 10³/µL. Lymphocytes: Ranged from a minimum of 0.32 × 10³/µL to a maximum of 5.79 × 10³/µL. The mean lymphocyte count was 2.02 ± 0.78 × 10³/µL, with a median value of 1.94 × 10³/µL. Platelets (PLT): Ranged from a minimum of 79 × 10³/µL to a maximum of 519 × 10³/µL. The mean PLT value was 238.03 ± 63.03 × 10³/µL, with a median value of 233 × 10³/µL. C-Reactive Protein (CRP): Ranged from a minimum of 0.43 mg/L to a maximum of 413.27 mg/L. The mean CRP level was 23.54 ± 18.78 mg/L, with a median value of 8.89 mg/L. Albumin: Ranged from a minimum of 24.15 mg/dL to a maximum of 46.4 mg/dL. The mean albumin level was 37.75 ± 3.94 mg/dL, with a median value of 38.32 mg/dL. In our study, the Delta Neutrophil Index (DNI) was found to be significantly associated with mortality. Patients with elevated DNI levels had a higher risk of postoperative mortality compared to those with lower DNI levels. Particularly in patients undergoing open-heart surgery, elevated preoperative DNI levels indicated an overactive inflammatory response post-surgery, which was associated with an increased mortality rate. In conclusion, elevated DNI levels were identified as a critical factor contributing to increased mortality risk in surgical patients. Proactive management and closer postoperative monitoring of patients with high DNI values could be essential in reducing mortality rates in this population. Table 2. Assessment of Differences and Associations in Socio-Demographic and Biochemical Measurements According to Mortality Status (n=446) Alive (n=350) Exitus (n=96) Features Mean±SD Mean±SD p-value*/*** Age (year) 63.88±9.52 64.64±12.72 0.52 EF 53.12±6.56 50.26±9.01 0.01 Pre-Creatinine(mg/dL) 0.94±0.54 1.1±0.35 0.21 Post-Creatinine(mg/dL) 0.99±0.56 1.35±0.57 <0.0001 p value** 0.002 <0.0001 Pre-Ure (mg/dL) 38.37±16.96 42.71±13.81 0.02 Post-Ure (mg/dL) 36.86±13.71 53.01±23.18 <0.0001 p value** 0.02 0.0001 Pre-IG 0.42±0.27 0.88±0.63 0.001 Post-IG 0.58±0.28 1.66±1.19 <0.0001 p value** <0.0001 <0.0001 Pre-NEU(103mcL) 5.56±2.41 5.66±2.66 0.74 Post- NEU(103mcL) 10.03±3.87 12.76±5.18 <0.0001 p value** <0.0001 <0.0001 Pre-LYM(103mcL) 2.03±0.68 2.14±1.21 0.43 Post-LYM(103mcL) 1.13±0.48 1.53±1.02 0.02 p value** <0.0001 <0.0001 Pre-PLT(103mcL) 237.11±69.32 232.74±75.95 0.6 Post-PLT(103mcL) 156.68±48.83 138.81±71.83 0.06 p value** <0.0001 <0.0001 Pre-CRP(mg/L) 19.06±17.11 26.99±22.39 0.36 Post-CRP(mg/L) 149.32±57.51 134.71±53.34 0.24 p value** <0.0001 <0.0001 Pre-Albumin(mg/L) 38.17±3.47 35.36±5.59 0.003 Post-Albumin(mg/L) 28.65±12.55 23.84±4.32 0.02 p value** <0.0001 <0.0001 n(%) n(%) Gender Male 254(72.6) 58(60.4) 0.02*** Female 96(27.4) 38(39.6) DM+ 218(62.3) 36(37.5) <0.0001*** HT+ 136(38.9) 30(31.3) 0.17*** *Student's t test,**Paired t test,***Chi-Square test (p<0.05 significance), p-value: Student's t-test was used for continuous variables, paired t-test for repeated measures, and Chi-Square test for categorical variables. According to Table 2; Socio-Demographic and Biochemical Measurements Based on Mortality Status The relationships between mortality status and socio-demographic and biochemical parameters were evaluated as follows: Age: The difference in average age based on mortality status was not statistically significant (p>0.05). The mean age of deceased patients was 64.64±12.72 years, whereas the mean age of surviving patients was 63.88±9.52 years. Gender: The association between mortality and gender was significant (p<0.05). Among deceased patients, 60.4% were male and 39.6% were female, while the gender distribution among survivors varied. Ejection Fraction (EF): There was a significant difference in mean EF between mortality statuses (p<0.05). The average EF in deceased patients was 50.26±9.01, compared to 53.12±6.56 in surviving patients. Diabetes Mellitus (DM): A significant relationship was found between mortality status and the presence of DM (p<0.05). DM was present in 37.75% of deceased patients, whereas the prevalence was 62.32% in survivors. Hypertension (HT): The relationship between mortality status and the presence of HT was not statistically significant (p>0.05). These findings indicate significant relationships between mortality and gender, EF, and the presence of DM, while the relationships with age and HT were not found to be significant. Postoperative Biochemical Parameters The measurement values of postoperative biochemical parameters based on mortality status are presented in Table 2. According to the results obtained, the differences between mortality and biochemical parameters are as follows: Creatinine (mg/dL): A significant relationship was found between mortality and postoperative creatinine values (p<0.05). Urea (mg/dL): A significant difference in postoperative urea values was observed based on mortality status (p<0.05). Neutrophil (NEU, 10³/µL): The relationship between mortality and postoperative neutrophil levels was significant (p<0.05). Lymphocyte (LYM, 10³/µL): A significant difference in postoperative lymphocyte levels was found based on mortality status (p<0.05). Delta Neutrophil Index (DNI): A significant relationship was found between mortality and postoperative DNI measurement values (p<0.05). Albumin (mg/L): The relationship between mortality and postoperative albumin levels was significant (p0.05). C-Reactive Protein (CRP, mg/L): The relationship between mortality and postoperative CRP values was not significant (p>0.05). These results indicate that in the postoperative period, creatinine, urea, neutrophil, lymphocyte, DNI, and albumin measurement values are significantly related to mortality. Conversely, no significant differences were observed in platelet and CRP measurements concerning mortality. Preoperative and Postoperative Biochemical Parameters in Deceased Patients The preoperative and postoperative biochemical parameter measurement values for deceased patients are presented in Table 2. According to the findings: Creatinine (mg/dL): A significant difference was observed between preoperative and postoperative creatinine values (p<0.05). Urea (mg/dL): A significant difference was found between preoperative and postoperative urea values (p<0.05). Delta Neutrophil Index (DNI): A significant difference was detected between preoperative and postoperative DNI values (p<0.05). Neutrophil (NEU, 10³/µL): A significant difference was observed between preoperative and postoperative neutrophil levels (p<0.05). Lymphocyte (LYM, 10³/µL): A significant difference was found between preoperative and postoperative lymphocyte values (p<0.05). Platelet (PLT, 10³/µL): A significant difference was observed between preoperative and postoperative platelet values (p<0.05). C-Reactive Protein (CRP, mg/L): A significant difference was detected between preoperative and postoperative CRP levels (p<0.05). Albumin (mg/L): A significant difference was found between preoperative and postoperative albumin values (p<0.05). Preoperative and Postoperative Biochemical Parameters in Surviving Patients The preoperative and postoperative biochemical parameter measurement values for surviving patients are presented in Table 2. According to these results: Creatinine (mg/dL): A significant difference was observed between preoperative and postoperative creatinine values (p<0.05). Urea (mg/dL): A significant difference was found between preoperative and postoperative urea values (p<0.05). Delta Neutrophil Index (DNI): A significant difference was detected between preoperative and postoperative DNI values (p<0.05). Neutrophil (NEU, 10³/µL): A significant difference was observed between preoperative and postoperative neutrophil levels (p<0.05). Lymphocyte (LYM, 10³/µL): A significant difference was found between preoperative and postoperative lymphocyte values (p<0.05). Platelet (PLT, 10³/µL): A significant difference was observed between preoperative and postoperative platelet values (p<0.05). C-Reactive Protein (CRP, mg/L): A significant difference was detected between preoperative and postoperative CRP levels (p<0.05). Albumin (mg/L): A significant difference was found between preoperative and postoperative albumin values (p<0.05). These findings suggest significant relationships between mortality and various socio-demographic and biochemical parameters. Notably, left ventricular ejection fraction (EF), diabetes mellitus status, and postoperative biochemical measurements (creatinine, urea, neutrophils, lymphocytes, DNI, and albumin) were critical factors influencing mortality risk. In conclusion, monitoring these parameters preoperatively and postoperatively may provide essential insights into patient management and outcomes. A proactive approach in managing patients with elevated risk factors could improve clinical outcomes and reduce mortality rates in this population. Table 3: Assessment of the Association Between Mortality and Age, Gender, and Chronic Disease Status(n=446) Variables Odds ratio 95% CI p-value Age 1.1 0.98-1.03 0.52 Ejection Fraction (EF) 0.95 0.92-0.98 0.003 Gender (Risk: Male) 1.73 1.08-2.78 0.02 Diabetes Mellitus (DM) (Risk: Present) 2.75 1.73-4.39 <0.0001 Hypertension (HT) (Risk: Present) 1.39 0.86-2.26 0.17 CI : Confidance Interval, p-value: Logistic regression analysis was performed to evaluate the effects on mortality. According to Table 3; Evaluation of Factors Affecting Mortality The following results were obtained when evaluating factors associated with mortality: Age: Mortality was found to be unrelated to age (p>0.05). Ejection Fraction (EF): A relationship between mortality and EF was observed (p<0.05). A 1-unit increase in EF measurement reduces the risk of death by 0.95 times (95% Confidence Interval: 0.92-0.98). Gender: Mortality was found to be associated with gender (p<0.05). The likelihood of death was 1.78 times higher in male patients. Male gender was found to increase the risk of mortality by 1.78 times (95% Confidence Interval: 1.08-2.78) compared to female gender. Diabetes Mellitus (DM): Mortality was determined to be associated with DM (p<0.05). The probability of death was 2.75 times higher in patients with DM. The presence of DM was found to increase the risk of mortality by 2.75 times (95% Confidence Interval: 1.73-4.39). Hypertension (HT): Mortality was found to be unrelated to HT exposure (p>0.05). This study reveals that age is not significantly related to mortality, while a notable inverse relationship exists between Ejection Fraction (EF) and mortality. Specifically, each 1-unit increase in EF reduces the risk of death by 5%. Furthermore, being male increases the mortality risk by 1.73 times, and the presence of Diabetes Mellitus (DM) raises the likelihood of death by 2.75 times. These findings underscore the importance of considering demographic factors and chronic disease statuses, such as gender and DM, in mortality risk assessments. Understanding these associations can inform clinical decision-making and improve patient management strategies in the context of open-heart surgery. Table 4: Assessment of the Association Between Preoperative Biochemical Parameters and Mortality(n=446) Variables Odds ratio 95% CI p-value Pre-Creatinine(mg/dL) 1.13 0.85-1.51 0.41 Pre-Ure (mg/dL) 1.02 1.001-1.03 0.03 Pre-DNI 2.61 1.54-4.45 <0.0001 Pre-NEU(103mcL) 1.02 0.93-1.11 0.74 Pre-LYM(103mcL) 1.16 0.89-1.52 0.28 Pre-PLT(103mcL) 0.99 0.98-1.01 0.6 Pre-CRP(mg/L) 1.01 0.99-1.001 0.19 Pre-Albumin(mg/L) -0.84 0.78-0.92 <0.0001 CI: Confidence Interval, p-value: Logistic regression analysis was performed to evaluate the effect of preoperative and postoperative biochemical parameters on mortality. According to Table 4; Association Between Preoperative Biochemical Measurement Factors and Mortality The effects of preoperative biochemical measurement factors on mortality were evaluated, yielding the following results: Creatinine (mg/dL): Mortality was found to be unrelated to preoperative creatinine levels (p>0.05). Preoperative Neutrophils (NEU, 10³/µL): Mortality was determined to be unrelated to preoperative neutrophil levels (p>0.05). Preoperative Lymphocytes (LYM, 10³/µL): Mortality was found to be unrelated to preoperative lymphocyte levels (p>0.05). Preoperative Platelets (PLT, 10³/µL): Mortality was found to be unrelated to preoperative platelet levels (p>0.05). Preoperative C-Reactive Protein (CRP, mg/L): Mortality was observed to be unrelated to preoperative CRP levels (p>0.05). Preoperative Urea (mg/dL): Mortality was found to be associated with preoperative urea levels (p<0.05). A 1-unit increase in preoperative urea measurement increases the risk of death by 1.02 times (95% Confidence Interval: 1.001-1.03). Preoperative Delta Neutrophil Index (DNI): Mortality was determined to be associated with preoperative DNI (p<0.05). A 1-unit increase in preoperative DNI measurement increases the risk of death by 2.61 times (95% Confidence Interval: 1.54-4.45). Preoperative Albumin (mg/L): Mortality was observed to be associated with preoperative albumin levels (p<0.05). A 1-unit increase in preoperative albumin measurement reduces the risk of death by 0.84 times (95% Confidence Interval: 0.78-0.92). This analysis highlights the significant association between preoperative biochemical parameters and mortality. Notably, preoperative Urea and Delta Neutrophil Index (DNI) levels were identified as critical factors, with each 1-unit increase in Urea raising the death risk by 2% and each 1-unit increase in DNI elevating the risk by 2.61 times. Furthermore, higher preoperative Albumin levels were associated with a reduced mortality risk, reinforcing the role of nutritional status in surgical outcomes. These findings emphasize the importance of integrating biochemical assessments into preoperative evaluations for enhanced risk stratification. Table 5: Assessment of the Association Between Postoperative Biochemical Parameters and Mortality(n=446) Variables Odds ratio 95% CI p-value Post-Creatinine(mg/dL) 2.65 1.5-4.55 <0.0001 Post-Ure (mg/dL) 1.05 1.03-1.07 <0.0001 Post-DNI 10.21 5.08-20.5 <0.0001 Post- NEU(103mcL) 1.14 1.08-1.21 <0.0001 Post-LYM(103mcL) 1.85 1.29-2.64 0.001 Post-PLT(103mcL) 0.98 0.97-0.99 0.02 Post-CRP(mg/L) 0.99 0.98-1.01 0.25 Post-Albumin(mg/L) -0.67 0.59-0.76 <0.0001 CI: confidence interval, p-value: Logistic regression analysis was performed to evaluate the effect of preoperative and postoperative biochemical parameters on mortality. According to Table 5; Association Between Postoperative Biochemical Measurement Factors and Mortality The effects of postoperative biochemical measurement factors on mortality were evaluated, yielding the following results: Postoperative C-Reactive Protein (CRP, mg/L): Mortality was found to be unrelated to postoperative CRP levels (p>0.05). Postoperative Creatinine (mg/dL): Mortality was found to be associated with postoperative creatinine levels (p<0.05). A 1-unit increase in postoperative creatinine measurement increases the risk of death by 2.65 times (95% Confidence Interval: 1.5-4.55). Postoperative Urea (mg/dL): Mortality was found to be associated with postoperative urea levels (p<0.05). A 1-unit increase in postoperative urea measurement increases the risk of death by 1.05 times (95% Confidence Interval: 1.03-1.07). Postoperative Delta Neutrophil Index (DNI): Mortality was found to be associated with postoperative DNI (p<0.05). A 1-unit increase in postoperative DNI measurement increases the risk of death by 10.21 times (95% Confidence Interval: 5.08-20.05). Postoperative Neutrophils (NEU, 10³/µL): Mortality was found to be associated with postoperative neutrophil levels (p<0.05). A 1-unit increase in postoperative neutrophil measurement increases the risk of death by 1.14 times (95% Confidence Interval: 1.08-1.21). Postoperative Lymphocytes (LYM, 10³/µL): Mortality was found to be associated with postoperative lymphocyte levels (p<0.05). A 1-unit increase in postoperative lymphocyte measurement increases the risk of death by 1.85 times (95% Confidence Interval: 1.29-2.64). Postoperative Platelets (PLT, 10³/µL): Mortality was found to be associated with postoperative platelet levels (p<0.05). A 1-unit increase in postoperative platelet measurement decreases the risk of death by 0.98 times (95% Confidence Interval: 0.97-0.99). Postoperative Albumin (mg/L): Mortality was found to be associated with postoperative albumin levels (p<0.05). A 1-unit increase in postoperative albumin measurement decreases the risk of death by 0.67 times (95% Confidence Interval: 0.59-0.76). The findings from this analysis underscore the critical role of postoperative biochemical parameters in predicting mortality. Specifically, each 1-unit increase in postoperative Creatinine and Urea significantly raises the risk of death, by 2.65 and 5% respectively. The Post-Delta Neutrophil Index (DNI) is particularly noteworthy, with a dramatic increase in mortality risk of 10.21 times for each unit increase, highlighting its potential as a robust prognostic marker. Additionally, elevated postoperative Lymphocyte and Neutrophil levels are associated with increased mortality risk, while higher Platelet and Albumin levels contribute to a decreased risk. These insights emphasize the importance of monitoring postoperative biochemical parameters for improving risk stratification and patient management strategies. Discussion In this single-center retrospective study, it was determined that the preoperative and postoperative Delta Neutrophil Index (DNI) is a good predictor of mortality after Coronary Artery Bypass Grafting (CABG). Patients undergoing coronary artery surgical revascularization are exposed to various physiological effects. The CABG surgery, one of the most frequently performed operations since the introduction of the Cardiopulmonary Bypass (CPB) machine, has gained significant attention due to its potential to lead to prolonged ventilator weaning, increased renal dysfunction, stroke, deep sternal infections, and death. 17 These outcomes are thought to be largely associated with systemic inflammation caused by CPB machines. 18,19 However, systemic inflammation that arises after CABG procedures is influenced by many factors beyond CPB machines. Tissue damage and contact of non-endothelial surfaces with blood are known primary triggers of Systemic Inflammatory Response Syndrome (SIRS). Nevertheless, current evidence indicates that the mechanical process of extracorporeal circulation and the CPB itself play an important role. 20 Using an in vivo acute inflammatory model like bypass surgery allows for a more realistic assessment of genotype function compared to in vitro experiments and may reflect clinically significant changes. Neutrophils are critical cells in innate immunity and mediate tissue damage following ischemia-reperfusion injury. 21 Patients in the high DNI group demonstrated worse postoperative outcomes compared to those in the low DNI group. Given the significant relationship between inflammatory responses and postoperative outcomes in cardiac surgery, an objective inflammatory index has been developed to improve risk stratification in cardiac surgery. 22 DNI may represent the degree of inflammation and physical stress caused by surgical stimulation, serving as a valuable prognostic indicator in surgical patients. Our findings corroborate this hypothesis, as elevated DNI levels were significantly associated with increased mortality rates, highlighting its relevance not only in assessing surgical stress but also in guiding postoperative patient management. Given its predictive power, the incorporation of DNI into existing risk models could revolutionize patient monitoring protocols, enabling earlier interventions and reducing the burden of postoperative complications. Future research should focus on integrating DNI into comprehensive risk models that incorporate both inflammatory and hemodynamic parameters for enhanced predictive accuracy. Therefore, we assessed the impact of DNI on outcomes after CABG. In the present study, mortality was associated with Pre-IG (p<0.05). A 1-unit increase in Pre-IG measurement increased the risk of death by 2.61 (95% CI: 1.54-4.45). Mortality was also associated with Post-IG (p<0.05). A 1-unit increase in Post-IG measurement increased the risk of death by 10.21 (95% CI: 5.08-20.05). Notably, preoperative and postoperative DNI showed significant relationships with mortality. These findings suggest that measuring pre-DNI and post-DNI together may be useful for better risk classification and screening of high-risk patients. However, these are not definitive diagnostic markers and should be used alongside other clinical evaluations. DNI was significantly associated with well-known risk factors for poor prognosis after cardiac surgery, indicating that DNI may be influenced by the patient's underlying condition and that it can accurately represent this. Previous studies reported that the inflammatory response after cardiac surgery peaks within 48 hours and shows a tendency to decrease. 23,24 In contrast, our study highlights the significant predictive value of both preoperative and postoperative DNI, which continued to show strong associations with mortality beyond the 48-hour mark, emphasizing its utility in extended postoperative monitoring. DNI may be a valuable indicator in identifying patients who are not in the high-risk group preoperatively but have a poor recovery process postoperatively. In this context, the current study observed a higher incidence of postoperative hospital morbidity in the high DNI group compared to the low DNI group. Consistent with the results of this study, the benefit of the Delta Neutrophil Index in predicting 30-day mortality in patients with ST-segment elevation myocardial infarction has been demonstrated. 25 Additionally, in terms of sepsis, Park et al. revealed that DNI >6.5% within the first 24 hours after admission to the intensive care unit is a good diagnostic marker for severe sepsis and septic shock. 10 Similarly, our findings demonstrate that a higher DNI, both pre- and postoperatively, significantly correlates with mortality in CABG patients, suggesting that DNI can serve as a crucial early marker for predicting adverse outcomes in a broader surgical context. Previous studies have proposed potential mechanisms to explain the rapid and early release of immature granulocytes. In cases of sterile inflammation, such as OPCAB, the mechanism of increasing immature granulocytes likely resembles that in sepsis. For example, in severe inflammation, the large consumption and destruction of mature cells may lead to a rapid expansion of circulating neutrophils to compensate for the loss of active neutrophils. 26,27,28,29,30 This mechanism aligns with our observation that patients with higher DNI levels exhibited worse outcomes, suggesting that DNI could be a useful marker for detecting heightened inflammatory responses that contribute to mortality after CABG Our study extends this concept by showing that elevated DNI levels in CABG patients, reflective of heightened granulocyte production, are strongly predictive of mortality, reinforcing the importance of managing inflammatory responses in the postoperative period. Moreover, in myocardial reperfusion injury, reperfusion causes endothelial dysfunction, leading to vasoconstriction within the first few minutes, while increased leukocyte adhesion and flow contribute to impaired blood flow. 31 This could be a promising biomarker for predicting postoperative DNI mortality after reperfusion. Building on this, our results confirm that postoperative DNI is a robust predictor of mortality following CABG, particularly in patients undergoing reperfusion, suggesting its potential integration into routine postoperative risk assessments. In contrast to previous studies that used mortality several years after surgery, this study focused on relatively short-term outcomes like DNI. While CRP, WBC, and Neutrophil levels significantly increased in patients after surgery, DNI levels remained within normal ranges in survivors. These findings clearly demonstrate the activation of opposing immune-inflammatory pathways induced by CABG and confirm the importance of DNI as a risk stratification marker. Conclusions In conclusion, the increased Delta Neutrophil Index (DNI) value, reflecting the proportion of circulating immature granulocytes in the blood, has been found to be an independent predictor of postoperative mortality and poor clinical outcomes following Coronary Artery Bypass Grafting (CABG). Both preoperative and postoperative DNI were significantly associated with mortality, indicating the valuable roles of DNI in the risk assessment necessary for perioperative and postoperative management. This highlights the dual utility of DNI in not only predicting but also monitoring patient outcomes throughout the perioperative period. As a secondary outcome, DNI may serve as a valuable indicator for identifying patients who are not in the high-risk group according to current risk assessment scores but have a poor recovery process postoperatively, potentially leading to increased morbidity and mortality risk. a. Limitations of the Study This study has several potential limitations. Despite encompassing a relatively large patient cohort, the nature of its single-center observational retrospective design imposes certain constraints. This design has limited the ability to perform meaningful subgroup analyses and has restricted the examination of subgroups such as patients with heart failure, those with high EuroSCORE, and those requiring urgent or reoperation. Therefore, broader-scale studies including such high-risk patient groups are needed. The study did not identify the underlying pathophysiology of the relationship between Delta Neutrophil Index (DNI) and early complications in adult cardiac surgery. We aimed to minimize bias by using multivariate logistic regression analysis to account for variables that could affect cardiac surgeries performed with Cardiopulmonary Bypass (CPB) and early clinical outcomes. However, we believe that some variables, particularly CPB, may have influenced our results. We attempted to mitigate inter-center variability by grouping patients operated on by the same experienced surgical team using the same technique. Additionally, we evaluated deaths based on general causes without an in-depth analysis of the specific reasons for mortality. Larger-scale randomized controlled trials are needed to validate whether Delta Neutrophil Index is a simple and effective marker in clinical practice and to determine if it impacts clinical outcomes. Although the study is limited by its single-center design, the findings offer a robust foundation for future multicenter, randomized trials aimed at validating DNI as a critical component in perioperative management protocols. Abbreviations CABG : Coronary Artery Bypass Grafting CAD : Coronary Artery Disease CBC : Complete Blood Count CRP : C-Reactive Protein DNI : Delta Neutrophil Index DM : Diabetes Mellitus EF : Ejection Fraction HT : Hypertension IG: Immature Granulocytes PLT: Platelets WBC: White Blood Cell Declarations Author Contributions: Burak Toprak: Made substantial contributions to the study design, manuscript writing, conceptualization, and execution of the research. Abdulkadir Bilgiç: Substantially revised the manuscript and critically interpreted the results. Çise Kanat Toprak: Contributed to data collection and performed data analysis. Hamide Kaya: Contributed to the conceptualization phase of the research. Thanks We are grateful to Elif Ertaş from the Department of Biostatistics, Selçuk University, Turkey, for her expertise in statistical analysis. Conflict Of Interest Statement We have no conflict of interest. Statement On The Use Of Artificial Intelligence No artificial intelligence application was used. Financing No financing available. Clinical Trial Number: Not applicable. References Okrainec K, Banerjee DK, Eisenberg MJ. 2004. Coronary artery disease in the developing world. Am Heart J. 148:7-15. https://doi.org/10.1016/j.ahj.2003.12.029 Rumsfeld JS, Magid DJ, O'Brien M, McCarthy M Jr, MaWhinney S, Shroyer AL. 2001. Changes in health-related quality of life following coronary artery bypass graft surgery. Ann Thorac Surg. 72:2026-2032. https://doi.org/10.1016/s0003-4975(01)03150-3 Serruys PW, Morice MC, Kappetein AP, Colombo A, Holmes DR, Mack MJ, Stahle E. 2009. Percutaneous coronary intervention versus coronary-artery bypass grafting for severe coronary artery disease. N Engl J Med. 360:961-972. https://doi.org/10.1056/NEJMoa0804626 Shroyer AL, Coombs LP, Peterson ED, Eiken MC, DeLong ER, Chen A, Ferguson TB Jr. 2003. The Society of Thoracic Surgeons: 30-day operative mortality and morbidity risk models. Ann Thorac Surg. 75:1856-1864. https://doi.org/10.1016/s0003-4975(03)00256-9 Agabiti N, Ancona C, Forastiere F, Arca M, Perucci CA. 2003. Evaluating outcomes of hospital care following coronary artery bypass surgery in Rome, Italy. Eur J Cardiothorac Surg. 23:599-607. https://doi.org/10.1016/s1010-7940(03)00041-7 Tu JV, Naylor CD. 1996. Coronary artery bypass mortality rates in Ontario: A Canadian approach to quality assurance in cardiac surgery. Circulation. 94:2429-2433. https://doi.org/10.1161/01.cir.94.10.2429 Ferguson TB Jr, Hammill BG, Peterson ED, DeLong ER, Grover FL. 2002. A decade of change--risk profiles and outcomes for isolated coronary artery bypass grafting procedures 1990-1999: a report from the STS National Database Committee and the Duke Clinical Research Institute. Ann Thorac Surg. 73:480-489. https://doi.org/10.1016/s0003-4975(01)03338-7 Hannan EL, Zhong Y, Lahey SJ, Culliford AT, Gold JP, Smith CR, Higgins RS. 2011. 30-day readmissions after coronary artery bypass graft surgery in New York State. JACC Cardiovasc Interv. 4:569-576. https://doi.org/10.1016/j.jcin.2011.02.012 Brull DJ, Montgomery HE, Sanders J, Dhamrait S, Luong L, Rumley A, Lowe GDO, Humphries SE. 2001. Interleukin-6 gene -174G>C and -572G>C promoter polymorphisms are strong predictors of plasma interleukin-6 levels after coronary artery bypass surgery. Arterioscler Thromb Vasc Biol. 21:1458-1463. https://doi.org/10.1161/hq0901.093394 Park BH, Kwon J, Lee J. 2011. Delta neutrophil index as an early marker of disease severity in critically ill patients with sepsis. BMC Infect Dis. 11:299. https://doi.org/10.1186/1471-2334-11-299 Yune HY, Kim YH, Lee YH. 2015. Delta neutrophil index as a promising prognostic marker in out-of-hospital cardiac arrest. PLoS One. 10:e0120677. https://doi.org/10.1371/journal.pone.0120677 Kim OH, Kim YH, Ryu HJ, Lee J, Kim H, Nahm CH, Cho HS, Choi JW. 2016. The use of delta neutrophil index and myeloperoxidase index for predicting acute complicated appendicitis in children. PLoS One. 11:e0148799. https://doi.org/10.1371/journal.pone.0148799 Kim H, Lee JH, Kim JS, Moon SY, Ko DR, Lee J, Kim YH. 2017. Usefulness of the delta neutrophil index as a prognostic marker of acute cholangitis in emergency departments. Shock. 47:303-312. https://doi.org/10.1097/shk.0000000000000732 Ko DR, Lee J, Kim H, Kim YH, Ahn KJ, Park YS, Kim TH. 2017. Usefulness of the delta neutrophil index as an ancillary test in the emergency department for the early diagnosis of suspected acute promyelocytic leukemia. Leuk Lymphoma. 58:1-8. https://doi.org/10.1080/10428194.2017.1310047 Nahm CH, Choi JW, Lee J. 2008. Delta neutrophil index in automated immature granulocyte counts for assessing disease severity of patients with sepsis. Ann Clin Lab Sci. 38:241-246. https://www.ncbi.nlm.nih.gov/pubmed/18715889 Norman G. 2010. Likert scales, levels of measurement and the “laws” of statistics. Adv Health Sci Educ Theory Pract. 15:625-632. https://doi.org/10.1007/s10459-010-9222-y Shahian DM, O’Brien SM, Filardo G, Ferraris VA, Haan CK, Rich JB, Normand SL, DeLong ER, Shewan CM, Dokholyan RS, Peterson ED. 2009. The Society of Thoracic Surgeons 2008 cardiac surgery risk models: part 1--coronary artery bypass grafting surgery. Ann Thorac Surg. 88:S2-22. https://doi.org/10.1016/j.athoracsur.2009.05.055 Brewer R, Theurer PF, Cogan CM, Bell GF, Prager RL, Paone G. 2014. Morbidity but not mortality is decreased after off-pump coronary artery bypass surgery. Ann Thorac Surg. 97:831-836. https://doi.org/10.1016/j.athoracsur.2013.11.059 Bicer M, Senturk T, Yanar M, Tutuncu A, Oral AY, Ulukaya E, Akyol S, Yildiz Y, Ulukent SC, Yuksek T, Baysal T. 2014. Effects of off-pump versus on-pump coronary artery bypass grafting: apoptosis, inflammation and oxidative stress. Heart Surg Forum. 17:E271-276. https://doi.org/10.1532/hsf98.2013314 Whitten CW, Hill GE, Ivy R, Greilich PE, Lipton JM. 1998. Does the duration of cardiopulmonary bypass or aortic cross-clamp, in the absence of blood and/or blood product administration, influence the IL-6 response to cardiac surgery? Anesth Analg. 86:28-33. https://doi.org/10.1213/00000539-199801000-00006 Fang L, Moore XL, Dart AM, Wang LM. 2015. Systemic inflammatory response following acute myocardial infarction. J Geriatr Cardiol. 12:305-312. https://doi.org/10.11909/j.issn.1671-5411.2015.03.024 Laffey JG, Boylan JF, Cheng DC. 2002. The systemic inflammatory response to cardiac surgery: implications for the anesthesiologist. Anesthesiology. 97:215-252. https://doi.org/10.1097/00000542-200207000-00031 Boyle EM, Pohlman TH, Johnson MC, Verrier ED. 1997. Endothelial cell injury in cardiovascular surgery: the systemic inflammatory response. Ann Thorac Surg. 63:277-284. https://doi.org/10.1016/s0003-4975(97)00025-7 Chew MS, Brandslund I, Brix-Christensen V, Ravn HB, Hjortdal VE, Pedersen J, Hasenkam JM. 2001. Tissue injury and the inflammatory response to pediatric cardiac surgery with cardiopulmonary bypass: a descriptive study. Anesthesiology. 94:745-753. https://doi.org/10.1097/00000542-200105000-00010 Kong T, Kim TH, Park YS, Lee JH, Kim H, Ko DR, Park BH. 2017. Usefulness of the delta neutrophil index to predict 30-day mortality in patients with ST-segment elevation myocardial infarction. Sci Rep. 7:15718. https://doi.org/10.1038/s41598-017-15718-0 Hwang YJ, Ahn KJ, Lee JW, Kim SH, Na MJ. 2015. Newly designed delta neutrophil index-to-serum albumin ratio prognosis of early mortality in severe sepsis. Am J Emerg Med. 33:1577-1582. https://doi.org/10.1016/j.ajem.2015.07.056 Bermejo-Martin JF, Almansa R, Torres A, de la Fuente A, Gomez-Sanchez E, Gonzalez-Rivera M, Kelvin D, Roquilly A. 2016. Defining immunological dysfunction in sepsis: a requisite tool for precision medicine. J Infect. 72:525-536. https://doi.org/10.1016/j.jinf.2016.02.010 Alves-Filho JC, Spiller F, Cunha FQ. 2010. Neutrophil paralysis in sepsis. Shock. 34:15-21. https://doi.org/10.1097/shk.0b013e3181e7e61b Leliefeld PH, Wessels CM, Leenen LP, Koenderman L, Pillay J. 2016. The role of neutrophils in immune dysfunction during severe inflammation. Crit Care. 20:73. https://doi.org/10.1186/s13054-016-1235-4 Kong T, Lee J, Kim H, Kim TH, Park YS, Kim YH, Ko DR. 2017. Usefulness of the Delta Neutrophil Index to Predict 30-day Mortality in Patients with Upper Gastrointestinal Bleeding. Shock. 47:1-8. https://doi.org/10.1097/shk.0000000000000756 Arslan F, de Kleijn DP, Pasterkamp G. 2011. Innate immune signaling in cardiac ischemia. Nat Rev Cardiol. 8:292-300. https://doi.org/10.1038/nrcardio.2011.38 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Feb, 2025 Read the published version in Journal of Inflammation Research → 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5274128","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":369667830,"identity":"8dbcf26e-4096-4cea-9cd0-f4cb2a45c61e","order_by":0,"name":"Abdulkadir Bilgiç","email":"","orcid":"","institution":"Mersin University Faculty of Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Abdulkadir","middleName":"","lastName":"Bilgiç","suffix":""},{"id":369667831,"identity":"5542565c-9111-42f5-8529-1746d0df3d99","order_by":1,"name":"Burak Toprak","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACAwY2CM0PIhMKiNVyAEhLNoC0GJCixeAAhEsYmEsfS2D+uOOesfH51YkfHhgwyPOLHcCvxbIv7QDDwTPFZmY33m6WADrMcObsBAIOO8PewHCwLcHG7MbZDSAtCQa3idViPOPs5h9EamE7ANJiZsDfu404Wyx72BIOnD2TYCxxg3ebRYKBBGG/mPOwGT6o3JFg2N9/dvPNHxU28vzSBLSAwAHGBiApAVYpQVg5GIC18B8gUvUoGAWjYBSMOAAAwatF1DbeG6IAAAAASUVORK5CYII=","orcid":"","institution":"Mersin City Training and Research Hospital","correspondingAuthor":true,"prefix":"","firstName":"Burak","middleName":"","lastName":"Toprak","suffix":""},{"id":369667832,"identity":"7b0df2e2-1c82-437c-8344-1063df7667e6","order_by":2,"name":"Hamide Kaya","email":"","orcid":"","institution":"Mersin University Faculty of Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hamide","middleName":"","lastName":"Kaya","suffix":""},{"id":369667833,"identity":"37a5e848-b74d-4109-8ed2-dee712a1d987","order_by":3,"name":"Çise Kanat Toprak","email":"","orcid":"","institution":"Mersin University Faculty of Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Çise","middleName":"Kanat","lastName":"Toprak","suffix":""}],"badges":[],"createdAt":"2024-10-16 08:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5274128/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5274128/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.2147/JIR.S500508","type":"published","date":"2025-02-03T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75741291,"identity":"2c3bd76b-5d2b-4db1-ab26-45a764c47ada","added_by":"auto","created_at":"2025-02-07 16:41:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2154747,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5274128/v1/b535c0a6-e3eb-4bea-871a-d31e5bd40094.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Delta Neutrophil Index in Coronary Artery Bypass Surgery: An Innovation in Postoperative Mortality Assessment","fulltext":[{"header":"Key Points","content":"\u003cp\u003e\u003cstrong\u003ea. What is known about the topic?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt is well-established that inflammatory responses play a significant role in the outcomes of cardiac surgeries, including coronary artery bypass grafting (CABG). Various inflammatory markers, such as C-reactive protein (CRP) and white blood cell (WBC) counts, have been studied in relation to postoperative outcomes. However, no specific inflammatory biomarker is routinely used in clinical practice to predict postoperative mortality in CABG patients. The delta neutrophil index (DNI), which measures the proportion of immature granulocytes, has been suggested in recent studies as a promising marker for inflammation and may serve as an indicator of poor prognosis in critically ill patients, but its use in CABG remains underexplored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. What does this study add?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study introduces the delta neutrophil index (DNI) as a novel prognostic marker for predicting early postoperative mortality in patients undergoing coronary artery bypass grafting (CABG). It provides the first clinical evidence that both preoperative and postoperative DNI values are significantly associated with increased mortality risk. The findings suggest that DNI could be a valuable tool for risk stratification, enabling more precise perioperative and postoperative management of CABG patients, particularly in identifying those at higher risk for poor outcomes who may otherwise go unnoticed using traditional markers.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eCoronary artery disease (CAD) is the leading cause of cardiovascular mortality worldwide, resulting in over 4.5\u0026nbsp;million deaths in developing countries.\u003csup\u003e1\u003c/sup\u003e Coronary artery bypass grafting (CABG) has been shown to improve overall health-related quality of life and survival.\u003csup\u003e2,3\u003c/sup\u003e Outcomes related to cardiac surgery, particularly CABG, have been extensively reported in the United States, Canada, and Western Europe.\u003csup\u003e4\u0026ndash;8\u003c/sup\u003e Recently, there has been increasing interest in the role of inflammatory markers in assessing severity early after CABG. CABG leads to an excessive inflammatory response in the postoperative phase.\u003csup\u003e9\u003c/sup\u003e Despite experimental and clinical evidence of the relationship between inflammation and adverse outcomes\u003csup\u003e9\u003c/sup\u003e, no specific inflammatory biomarkers are routinely used to predict postoperative mortality in CABG patients. What if we could reliably predict postoperative complications with a simple blood test? The absence of a standardized marker underscores the need for identifying novel prognostic tools to improve postoperative management and patient survival. Immature granulocytes are a practical marker of local and systemic inflammation.\u003csup\u003e10,11,12\u003c/sup\u003e The use of specific automated blood cell analysis devices allows for the rapid determination of the delta neutrophil index (DNI), which reflects the fraction of circulating immature granulocytes, in conjunction with a complete blood count (CBC).\u003csup\u003e11,13,14,15\u003c/sup\u003e In this study, we assessed the importance of early postoperative DNI as a prognostic marker for mortality in CABG patients. To our knowledge, this is the first study to evaluate the relationship between DNI and postoperative mortality in a clinical setting.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis retrospective, observational cohort study was conducted at Mersin University Medical Faculty Training and Research Hospital, a tertiary academic hospital specializing in cardiovascular surgery. The study included consecutive patients who underwent coronary artery bypass grafting (CABG) between January 1, 2022, and August 1, 2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea. Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e- Study Design:\u0026nbsp;\u003c/strong\u003eA nested case-control design within the cohort was employed. When the odds ratio (OR) for delta neutrophil index (DNI) and other factors associated with mortality was set at 1.5 (the minimum clinically significant level), the width of the confidence interval was considered to be 25%. Based on these parameters, the required sample size was determined to be 445. The number of patients who died was matched in a 1:4 ratio to those who survived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e- Data Collection:\u0026nbsp;\u003c/strong\u003eData on patients\u0026apos; demographics, laboratory test results, operation time, left ventricular ejection fraction (EF), and presence of multi-vessel disease were reviewed. Venous blood samples were collected at admission and postoperatively on a daily basis in vacuum tubes containing ethylenediaminetetraacetic acid (EDTA). Complete blood count (CBC) measurements were performed at multiple time points. DNI, white blood cell (WBC) count, hemoglobin level, and platelet count were analyzed by an automated blood cell analyzer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e- Statistical Analysis:\u0026nbsp;\u003c/strong\u003eFor continuous measurements, mean and standard deviation, median, minimum, and maximum values were used. Frequencies and percentages were used for categorical variables. Student\u0026rsquo;s t-test was applied for comparisons of age, EF, and biochemical measurements based on mortality status, while paired t-test was used for comparing repeated measurements. Chi-Square test was applied to examine the relationship between mortality status and variables such as gender, diabetes mellitus (DM), and hypertension (HT). Odds ratios and 95% confidence intervals were provided for parameters believed to be associated with mortality, including age, gender, EF, DM, HT, and biochemical parameters. Statistical significance was set at p\u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e- Software:\u0026nbsp;\u003c/strong\u003eIBM SPSS 21 and MedCalc statistical software were used for data evaluation. Parametric tests were applied to continuous measurements without normality testing, due to the applicability of the Central Limit Theorem.\u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec. Data Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed. Ethical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was obtained from the Mersin University Ethics Committee with the decision numbered 2024/472 and dated 22/05/2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee. Declaration of Helsinki\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study and the writing of the article were prepared in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef. İnformed Written Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed written consent was obtained in the surgical consent form before the subjects were included in the study.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 446 diagnosed patients were included in the study. The basic characteristics and clinical data are presented in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Distribution of Socio-Demographic Characteristics in Patients Undergoing Open Heart Vascular Surgery (n=446)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"112%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian(Min-Mak)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e64.7\u0026plusmn;9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e66(26-85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCount (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e62,8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e37,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e78,5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eExitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e21,5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(x̄ \u0026plusmn;SS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (Min-Maks.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e52.64\u0026plusmn;7.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e55(29-65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePREOP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eCreatinine(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e0.96\u0026plusmn;0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e0.88(0.44-9.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eUre (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e38.91\u0026plusmn;15.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e35.6(16.85-114.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eDNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e0.48\u0026plusmn;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e0.4(0.10-5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eNEU(103mcL) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e5.58\u0026plusmn;1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e5.02(1.01-14.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eLYM(103mcL) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e2.02\u0026plusmn;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e1.94(0.32-5.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003ePLT(103mcL) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e238.03\u0026plusmn;63.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e233(79-519)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eCRP(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e23.54\u0026plusmn;18.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e8.89(0.43-413.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eAlbumin(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.449%;\"\u003e\n \u003cp\u003e37.75\u0026plusmn;3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.551%;\"\u003e\n \u003cp\u003e38.32(24.15-46.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSD:\u0026nbsp;\u003c/strong\u003eStandard Deviation, p-value: Student\u0026apos;s t-test was used for continuous variables, and the Chi-Square test was used for categorical variables.\u003cstrong\u003e\u0026nbsp;CRP:\u0026nbsp;\u003c/strong\u003eC reactive protein,\u003cstrong\u003e\u0026nbsp;PLT:\u0026nbsp;\u003c/strong\u003ePlatelets, \u003cstrong\u003eNEU:\u0026nbsp;\u003c/strong\u003eNeutrophil\u003cstrong\u003e, LYM:\u0026nbsp;\u003c/strong\u003elymphocyte\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAccording to Table 1;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the demographic and clinical characteristics of a total of 446 patients were examined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge:\u003c/strong\u003e The age range of the patients was from a minimum of 26 years to a maximum of 85 years. The mean age was 64.7 \u0026plusmn; 9.8 years, with a median age of 66 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGender:\u003c/strong\u003e Of the patients included in the study, 70% were male, and 30% were female.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiabetes Mellitus (DM):\u003c/strong\u003e Diabetes mellitus was detected in 57% of the patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypertension (HT):\u003c/strong\u003e Hypertension was present in 37.2% of the patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMortality:\u003c/strong\u003e 21.5% of the patients in the study experienced death.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLeft Ventricular Ejection Fraction (EF):\u003c/strong\u003e EF ranged from a minimum of 29% to a maximum of 65%. The mean EF was 52.64 \u0026plusmn; 7.09%, with a median EF of 55%.\u003c/p\u003e\n\u003cp\u003eThese data provide a comprehensive overview of the general distribution of patients\u0026apos; age, gender, diabetes mellitus, hypertension, mortality rate, and left ventricular ejection fraction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreoperative Measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe laboratory findings obtained during preoperative evaluations were as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCreatinine:\u0026nbsp;\u003c/strong\u003eRanged from a minimum of 0.44 mg/dL to a maximum of 9.75 mg/dL. The mean creatinine level was 0.96 \u0026plusmn; 0.57 mg/dL, with a median value of 0.88 mg/dL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUrea:\u003c/strong\u003e Ranged from a minimum of 16.85 mg/dL to a maximum of 114.85 mg/dL. The mean urea level was 38.91 \u0026plusmn; 15.65 mg/dL, with a median value of 35.6 mg/dL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDelta Neutrophil Index (DNI):\u003c/strong\u003e Ranged from a minimum of 0.10 to a maximum of 5.1. The mean DNI value was 0.48 \u0026plusmn; 0.27, with a median value of 0.4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeutrophils:\u003c/strong\u003e Ranged from a minimum of 1.01 \u0026times; 10\u0026sup3;/\u0026micro;L to a maximum of 14.95 \u0026times; 10\u0026sup3;/\u0026micro;L. The mean neutrophil count was 5.58 \u0026plusmn; 1.34 \u0026times; 10\u0026sup3;/\u0026micro;L, with a median value of 5.02 \u0026times; 10\u0026sup3;/\u0026micro;L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLymphocytes:\u003c/strong\u003e Ranged from a minimum of 0.32 \u0026times; 10\u0026sup3;/\u0026micro;L to a maximum of 5.79 \u0026times; 10\u0026sup3;/\u0026micro;L. The mean lymphocyte count was 2.02 \u0026plusmn; 0.78 \u0026times; 10\u0026sup3;/\u0026micro;L, with a median value of 1.94 \u0026times; 10\u0026sup3;/\u0026micro;L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlatelets (PLT):\u003c/strong\u003e Ranged from a minimum of 79 \u0026times; 10\u0026sup3;/\u0026micro;L to a maximum of 519 \u0026times; 10\u0026sup3;/\u0026micro;L. The mean PLT value was 238.03 \u0026plusmn; 63.03 \u0026times; 10\u0026sup3;/\u0026micro;L, with a median value of 233 \u0026times; 10\u0026sup3;/\u0026micro;L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC-Reactive Protein (CRP):\u003c/strong\u003e Ranged from a minimum of 0.43 mg/L to a maximum of 413.27 mg/L. The mean CRP level was 23.54 \u0026plusmn; 18.78 mg/L, with a median value of 8.89 mg/L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlbumin:\u003c/strong\u003e Ranged from a minimum of 24.15 mg/dL to a maximum of 46.4 mg/dL. The mean albumin level was 37.75 \u0026plusmn; 3.94 mg/dL, with a median value of 38.32 mg/dL.\u003c/p\u003e\n\u003cp\u003eIn our study, the Delta Neutrophil Index (DNI) was found to be significantly associated with mortality. Patients with elevated DNI levels had a higher risk of postoperative mortality compared to those with lower DNI levels. Particularly in patients undergoing open-heart surgery, elevated preoperative DNI levels indicated an overactive inflammatory response post-surgery, which was associated with an increased mortality rate.\u003c/p\u003e\n\u003cp\u003eIn conclusion, elevated DNI levels were identified as a critical factor contributing to increased mortality risk in surgical patients. Proactive management and closer postoperative monitoring of patients with high DNI values could be essential in reducing mortality rates in this population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Assessment of Differences and Associations in Socio-Demographic and Biochemical Measurements According to Mortality Status (n=446)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"634\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlive\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=350)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExitus\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=96)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeatures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\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: 161px;\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e63.88\u0026plusmn;9.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e64.64\u0026plusmn;12.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e53.12\u0026plusmn;6.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e50.26\u0026plusmn;9.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePre-Creatinine(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.94\u0026plusmn;0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1.1\u0026plusmn;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePost-Creatinine(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.99\u0026plusmn;0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1.35\u0026plusmn;0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePre-Ure (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e38.37\u0026plusmn;16.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e42.71\u0026plusmn;13.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePost-Ure (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e36.86\u0026plusmn;13.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e53.01\u0026plusmn;23.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePre-IG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.42\u0026plusmn;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.88\u0026plusmn;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePost-IG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.58\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1.66\u0026plusmn;1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePre-NEU(103mcL) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e5.56\u0026plusmn;2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e5.66\u0026plusmn;2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePost-\u0026nbsp;NEU(103mcL) \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e10.03\u0026plusmn;3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e12.76\u0026plusmn;5.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePre-LYM(103mcL) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e2.03\u0026plusmn;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2.14\u0026plusmn;1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePost-LYM(103mcL) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1.13\u0026plusmn;0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1.53\u0026plusmn;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePre-PLT(103mcL) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e237.11\u0026plusmn;69.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e232.74\u0026plusmn;75.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePost-PLT(103mcL) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e156.68\u0026plusmn;48.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e138.81\u0026plusmn;71.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePre-CRP(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e19.06\u0026plusmn;17.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e26.99\u0026plusmn;22.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePost-CRP(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e149.32\u0026plusmn;57.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e134.71\u0026plusmn;53.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePre-Albumin(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e38.17\u0026plusmn;3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e35.36\u0026plusmn;5.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\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: 161px;\"\u003e\n \u003cp\u003ePost-Albumin(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e28.65\u0026plusmn;12.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e23.84\u0026plusmn;4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e254(72.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e58(60.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e96(27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e38(39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eDM+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e218(62.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e36(37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eHT+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e136(38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e30(31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.17***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n\u003c/table\u003e\n\u003cp\u003e*Student\u0026apos;s t test,**Paired t test,***Chi-Square test (p\u0026lt;0.05 significance), p-value: Student\u0026apos;s t-test was used for continuous variables, paired t-test for repeated measures, and Chi-Square test for categorical variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAccording to Table 2;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocio-Demographic and Biochemical Measurements Based on Mortality Status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationships between mortality status and socio-demographic and biochemical parameters were evaluated as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge:\u003c/strong\u003e The difference in average age based on mortality status was not statistically significant (p\u0026gt;0.05). The mean age of deceased patients was 64.64\u0026plusmn;12.72 years, whereas the mean age of surviving patients was 63.88\u0026plusmn;9.52 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGender:\u003c/strong\u003e The association between mortality and gender was significant (p\u0026lt;0.05). Among deceased patients, 60.4% were male and 39.6% were female, while the gender distribution among survivors varied.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEjection Fraction (EF):\u003c/strong\u003e There was a significant difference in mean EF between mortality statuses (p\u0026lt;0.05). The average EF in deceased patients was 50.26\u0026plusmn;9.01, compared to 53.12\u0026plusmn;6.56 in surviving patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiabetes Mellitus (DM):\u003c/strong\u003e A significant relationship was found between mortality status and the presence of DM (p\u0026lt;0.05). DM was present in 37.75% of deceased patients, whereas the prevalence was 62.32% in survivors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypertension (HT):\u003c/strong\u003e The relationship between mortality status and the presence of HT was not statistically significant (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eThese findings indicate significant relationships between mortality and gender, EF, and the presence of DM, while the relationships with age and HT were not found to be significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePostoperative Biochemical Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe measurement values of postoperative biochemical parameters based on mortality status are presented in Table 2. According to the results obtained, the differences between mortality and biochemical parameters are as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCreatinine (mg/dL):\u003c/strong\u003e A significant relationship was found between mortality and postoperative creatinine values (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUrea (mg/dL):\u003c/strong\u003e A significant difference in postoperative urea values was observed based on mortality status (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeutrophil (NEU, 10\u0026sup3;/\u0026micro;L):\u003c/strong\u003e The relationship between mortality and postoperative neutrophil levels was significant (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLymphocyte (LYM, 10\u0026sup3;/\u0026micro;L):\u0026nbsp;\u003c/strong\u003eA significant difference in postoperative lymphocyte levels was found based on mortality status (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eDelta Neutrophil Index (DNI):\u003c/u\u003e\u003c/strong\u003e\u003cu\u003e\u0026nbsp;A significant relationship was found between mortality and postoperative DNI measurement values (p\u0026lt;0.05).\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlbumin (mg/L):\u0026nbsp;\u003c/strong\u003eThe relationship between mortality and postoperative albumin levels was significant (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlatelet (PLT, 10\u0026sup3;/\u0026micro;L):\u0026nbsp;\u003c/strong\u003eNo significant difference was found between postoperative platelet values and mortality status (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC-Reactive Protein (CRP, mg/L):\u003c/strong\u003e The relationship between mortality and postoperative CRP values was not significant (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eThese results indicate that in the postoperative period, creatinine, urea, neutrophil, lymphocyte, DNI, and albumin measurement values are significantly related to mortality. Conversely, no significant differences were observed in platelet and CRP measurements concerning mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreoperative and Postoperative Biochemical Parameters in Deceased Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe preoperative and postoperative biochemical parameter measurement values for deceased patients are presented in Table 2. According to the findings:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCreatinine (mg/dL):\u003c/strong\u003e A significant difference was observed between preoperative and postoperative creatinine values (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUrea (mg/dL):\u003c/strong\u003e A significant difference was found between preoperative and postoperative urea values (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eDelta Neutrophil Index (DNI):\u003c/u\u003e\u003c/strong\u003e\u003cu\u003e\u0026nbsp;A significant difference was detected between preoperative and postoperative DNI values (p\u0026lt;0.05).\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeutrophil (NEU, 10\u0026sup3;/\u0026micro;L):\u003c/strong\u003e A significant difference was observed between preoperative and postoperative neutrophil levels (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLymphocyte (LYM, 10\u0026sup3;/\u0026micro;L):\u003c/strong\u003e A significant difference was found between preoperative and postoperative lymphocyte values (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlatelet (PLT, 10\u0026sup3;/\u0026micro;L):\u0026nbsp;\u003c/strong\u003eA significant difference was observed between preoperative and postoperative platelet values (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC-Reactive Protein (CRP, mg/L):\u003c/strong\u003e A significant difference was detected between preoperative and postoperative CRP levels (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlbumin (mg/L):\u003c/strong\u003e A significant difference was found between preoperative and postoperative albumin values (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreoperative and Postoperative Biochemical Parameters in Surviving Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe preoperative and postoperative biochemical parameter measurement values for surviving patients are presented in Table 2. According to these results:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCreatinine (mg/dL):\u003c/strong\u003e A significant difference was observed between preoperative and postoperative creatinine values (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUrea (mg/dL):\u003c/strong\u003e A significant difference was found between preoperative and postoperative urea values (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eDelta Neutrophil Index (DNI):\u003c/u\u003e\u003c/strong\u003e\u003cu\u003e\u0026nbsp;A significant difference was detected between preoperative and postoperative DNI values (p\u0026lt;0.05).\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeutrophil (NEU, 10\u0026sup3;/\u0026micro;L):\u003c/strong\u003e A significant difference was observed between preoperative and postoperative neutrophil levels (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLymphocyte (LYM, 10\u0026sup3;/\u0026micro;L):\u003c/strong\u003e A significant difference was found between preoperative and postoperative lymphocyte values (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlatelet (PLT, 10\u0026sup3;/\u0026micro;L):\u0026nbsp;\u003c/strong\u003eA significant difference was observed between preoperative and postoperative platelet values (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC-Reactive Protein (CRP, mg/L):\u003c/strong\u003e A significant difference was detected between preoperative and postoperative CRP levels (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlbumin (mg/L):\u003c/strong\u003e A significant difference was found between preoperative and postoperative albumin values (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003eThese findings suggest significant relationships between mortality and various socio-demographic and biochemical parameters. Notably, left ventricular ejection fraction (EF), diabetes mellitus status, and postoperative biochemical measurements (creatinine, urea, neutrophils, lymphocytes, DNI, and albumin) were critical factors influencing mortality risk.\u003c/p\u003e\n\u003cp\u003eIn conclusion, monitoring these parameters preoperatively and postoperatively may provide essential insights into patient management and outcomes. A proactive approach in managing patients with elevated risk factors could improve clinical outcomes and reduce mortality rates in this population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Assessment of the Association Between Mortality and Age, Gender, and Chronic Disease Status(n=446)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\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: 189px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.98-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eEjection Fraction (EF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.92-0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\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: 189px;\"\u003e\n \u003cp\u003eGender (Risk: Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1.08-2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eDiabetes Mellitus (DM) (Risk: Present)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1.73-4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eHypertension (HT) (Risk: Present)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.86-2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e: Confidance Interval, p-value: Logistic regression analysis was performed to evaluate the effects on mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAccording to Table 3;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation of Factors Affecting Mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following results were obtained when evaluating factors associated with mortality:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge:\u003c/strong\u003e Mortality was found to be unrelated to age (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEjection Fraction (EF):\u003c/strong\u003e A relationship between mortality and EF was observed (p\u0026lt;0.05). A 1-unit increase in EF measurement reduces the risk of death by 0.95 times (95% Confidence Interval: 0.92-0.98).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGender:\u003c/strong\u003e Mortality was found to be associated with gender (p\u0026lt;0.05). The likelihood of death was 1.78 times higher in male patients. Male gender was found to increase the risk of mortality by 1.78 times (95% Confidence Interval: 1.08-2.78) compared to female gender.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiabetes Mellitus (DM):\u003c/strong\u003e Mortality was determined to be associated with DM (p\u0026lt;0.05). The probability of death was 2.75 times higher in patients with DM. The presence of DM was found to increase the risk of mortality by 2.75 times (95% Confidence Interval: 1.73-4.39).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypertension (HT):\u003c/strong\u003e Mortality was found to be unrelated to HT exposure (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eThis study reveals that age is not significantly related to mortality, while a notable inverse relationship exists between Ejection Fraction (EF) and mortality. Specifically, each 1-unit increase in EF reduces the risk of death by 5%. Furthermore, being male increases the mortality risk by 1.73 times, and the presence of Diabetes Mellitus (DM) raises the likelihood of death by 2.75 times. These findings underscore the importance of considering demographic factors and chronic disease statuses, such as gender and DM, in mortality risk assessments. Understanding these associations can inform clinical decision-making and improve patient management strategies in the context of open-heart surgery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Assessment of the Association Between Preoperative Biochemical Parameters and Mortality(n=446)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\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: 189px;\"\u003e\n \u003cp\u003ePre-Creatinine(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.85-1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePre-Ure (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1.001-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePre-DNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.61\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1.54-4.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePre-NEU(103mcL) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.93-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePre-LYM(103mcL) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.89-1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePre-PLT(103mcL) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.98-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePre-CRP(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.99-1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePre-Albumin(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e-0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.78-0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eCI:\u003c/strong\u003eConfidence Interval, p-value: Logistic regression analysis was performed to evaluate the effect of preoperative and postoperative biochemical parameters on mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAccording to Table 4;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation Between Preoperative Biochemical Measurement Factors and Mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe effects of preoperative biochemical measurement factors on mortality were evaluated, yielding the following results:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCreatinine (mg/dL):\u003c/strong\u003e Mortality was found to be unrelated to preoperative creatinine levels (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreoperative Neutrophils (NEU, 10\u0026sup3;/\u0026micro;L):\u003c/strong\u003e Mortality was determined to be unrelated to preoperative neutrophil levels (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreoperative Lymphocytes (LYM, 10\u0026sup3;/\u0026micro;L):\u003c/strong\u003e Mortality was found to be unrelated to preoperative lymphocyte levels (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreoperative Platelets (PLT, 10\u0026sup3;/\u0026micro;L):\u003c/strong\u003e Mortality was found to be unrelated to preoperative platelet levels (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreoperative C-Reactive Protein (CRP, mg/L):\u0026nbsp;\u003c/strong\u003eMortality was observed to be unrelated to preoperative CRP levels (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreoperative Urea (mg/dL):\u003c/strong\u003e Mortality was found to be associated with preoperative urea levels (p\u0026lt;0.05). A 1-unit increase in preoperative urea measurement increases the risk of death by 1.02 times (95% Confidence Interval: 1.001-1.03).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreoperative Delta Neutrophil Index (DNI):\u003c/strong\u003e Mortality was determined to be associated with preoperative DNI (p\u0026lt;0.05). A 1-unit increase in preoperative DNI measurement increases the risk of death by 2.61 times (95% Confidence Interval: 1.54-4.45).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreoperative Albumin (mg/L):\u003c/strong\u003e Mortality was observed to be associated with preoperative albumin levels (p\u0026lt;0.05). A 1-unit increase in preoperative albumin measurement reduces the risk of death by 0.84 times (95% Confidence Interval: 0.78-0.92).\u003c/p\u003e\n\u003cp\u003eThis analysis highlights the significant association between preoperative biochemical parameters and mortality. Notably, preoperative Urea and Delta Neutrophil Index (DNI) levels were identified as critical factors, with each 1-unit increase in Urea raising the death risk by 2% and each 1-unit increase in DNI elevating the risk by 2.61 times. Furthermore, higher preoperative Albumin levels were associated with a reduced mortality risk, reinforcing the role of nutritional status in surgical outcomes. These findings emphasize the importance of integrating biochemical assessments into preoperative evaluations for enhanced risk stratification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Assessment of the Association Between Postoperative Biochemical Parameters and Mortality(n=446)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\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: 189px;\"\u003e\n \u003cp\u003ePost-Creatinine(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1.5-4.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePost-Ure (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1.03-1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePost-DNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e5.08-20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePost- NEU(103mcL) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1.08-1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePost-LYM(103mcL) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1.29-2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePost-PLT(103mcL) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.97-0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePost-CRP(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.98-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePost-Albumin(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e-0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.59-0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eCI:\u003c/strong\u003econfidence interval, p-value: Logistic regression analysis was performed to evaluate the effect of preoperative and postoperative biochemical parameters on mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAccording to Table 5;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation Between Postoperative Biochemical Measurement Factors and Mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe effects of postoperative biochemical measurement factors on mortality were evaluated, yielding the following results:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePostoperative C-Reactive Protein (CRP, mg/L):\u0026nbsp;\u003c/strong\u003eMortality was found to be unrelated to postoperative CRP levels (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePostoperative Creatinine (mg/dL):\u003c/strong\u003e Mortality was found to be associated with postoperative creatinine levels (p\u0026lt;0.05). A 1-unit increase in postoperative creatinine measurement increases the risk of death by 2.65 times (95% Confidence Interval: 1.5-4.55).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePostoperative Urea (mg/dL):\u0026nbsp;\u003c/strong\u003eMortality was found to be associated with postoperative urea levels (p\u0026lt;0.05). A 1-unit increase in postoperative urea measurement increases the risk of death by 1.05 times (95% Confidence Interval: 1.03-1.07).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePostoperative Delta Neutrophil Index (DNI):\u003c/strong\u003e Mortality was found to be associated with postoperative DNI (p\u0026lt;0.05). A 1-unit increase in postoperative DNI measurement increases the risk of death by 10.21 times (95% Confidence Interval: 5.08-20.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePostoperative Neutrophils (NEU, 10\u0026sup3;/\u0026micro;L):\u003c/strong\u003e Mortality was found to be associated with postoperative neutrophil levels (p\u0026lt;0.05). A 1-unit increase in postoperative neutrophil measurement increases the risk of death by 1.14 times (95% Confidence Interval: 1.08-1.21).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePostoperative Lymphocytes (LYM, 10\u0026sup3;/\u0026micro;L):\u003c/strong\u003e Mortality was found to be associated with postoperative lymphocyte levels (p\u0026lt;0.05). A 1-unit increase in postoperative lymphocyte measurement increases the risk of death by 1.85 times (95% Confidence Interval: 1.29-2.64).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePostoperative Platelets (PLT, 10\u0026sup3;/\u0026micro;L):\u003c/strong\u003e Mortality was found to be associated with postoperative platelet levels (p\u0026lt;0.05). A 1-unit increase in postoperative platelet measurement decreases the risk of death by 0.98 times (95% Confidence Interval: 0.97-0.99).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePostoperative Albumin (mg/L):\u003c/strong\u003e Mortality was found to be associated with postoperative albumin levels (p\u0026lt;0.05). A 1-unit increase in postoperative albumin measurement decreases the risk of death by 0.67 times (95% Confidence Interval: 0.59-0.76).\u003c/p\u003e\n\u003cp\u003eThe findings from this analysis underscore the critical role of postoperative biochemical parameters in predicting mortality. Specifically, each 1-unit increase in postoperative Creatinine and Urea significantly raises the risk of death, by 2.65 and 5% respectively. The Post-Delta Neutrophil Index (DNI) is particularly noteworthy, with a dramatic increase in mortality risk of 10.21 times for each unit increase, highlighting its potential as a robust prognostic marker. Additionally, elevated postoperative Lymphocyte and Neutrophil levels are associated with increased mortality risk, while higher Platelet and Albumin levels contribute to a decreased risk. These insights emphasize the importance of monitoring postoperative biochemical parameters for improving risk stratification and patient management strategies.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this single-center retrospective study, it was determined that the preoperative and postoperative Delta Neutrophil Index (DNI) is a good predictor of mortality after Coronary Artery Bypass Grafting (CABG). Patients undergoing coronary artery surgical revascularization are exposed to various physiological effects. The CABG surgery, one of the most frequently performed operations since the introduction of the Cardiopulmonary Bypass (CPB) machine, has gained significant attention due to its potential to lead to prolonged ventilator weaning, increased renal dysfunction, stroke, deep sternal infections, and death.\u003csup\u003e17\u003c/sup\u003e These outcomes are thought to be largely associated with systemic inflammation caused by CPB machines.\u003csup\u003e18,19\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, systemic inflammation that arises after CABG procedures is influenced by many factors beyond CPB machines. Tissue damage and contact of non-endothelial surfaces with blood are known primary triggers of Systemic Inflammatory Response Syndrome (SIRS). Nevertheless, current evidence indicates that the mechanical process of extracorporeal circulation and the CPB itself play an important role.\u003csup\u003e20\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing an in vivo acute inflammatory model like bypass surgery allows for a more realistic assessment of genotype function compared to in vitro experiments and may reflect clinically significant changes. Neutrophils are critical cells in innate immunity and mediate tissue damage following ischemia-reperfusion injury.\u003csup\u003e21\u003c/sup\u003e Patients in the high DNI group demonstrated worse postoperative outcomes compared to those in the low DNI group. Given the significant relationship between inflammatory responses and postoperative outcomes in cardiac surgery, an objective inflammatory index has been developed to improve risk stratification in cardiac surgery.\u003csup\u003e22\u003c/sup\u003e DNI may represent the degree of inflammation and physical stress caused by surgical stimulation, serving as a valuable prognostic indicator in surgical patients. Our findings corroborate this hypothesis, as elevated DNI levels were significantly associated with increased mortality rates, highlighting its relevance not only in assessing surgical stress but also in guiding postoperative patient management. Given its predictive power, the incorporation of DNI into existing risk models could revolutionize patient monitoring protocols, enabling earlier interventions and reducing the burden of postoperative complications. Future research should focus on integrating DNI into comprehensive risk models that incorporate both inflammatory and hemodynamic parameters for enhanced predictive accuracy. Therefore, we assessed the impact of DNI on outcomes after CABG. In the present study, mortality was associated with Pre-IG (p\u0026lt;0.05). A 1-unit increase in Pre-IG measurement increased the risk of death by 2.61 (95% CI: 1.54-4.45). Mortality was also associated with Post-IG (p\u0026lt;0.05). A 1-unit increase in Post-IG measurement increased the risk of death by 10.21 (95% CI: 5.08-20.05). Notably, preoperative and postoperative DNI showed significant relationships with mortality. These findings suggest that measuring pre-DNI and post-DNI together may be useful for better risk classification and screening of high-risk patients.\u003c/p\u003e\n\u003cp\u003eHowever, these are not definitive diagnostic markers and should be used alongside other clinical evaluations. DNI was significantly associated with well-known risk factors for poor prognosis after cardiac surgery, indicating that DNI may be influenced by the patient\u0026apos;s underlying condition and that it can accurately represent this.\u003c/p\u003e\n\u003cp\u003ePrevious studies reported that the inflammatory response after cardiac surgery peaks within 48 hours and shows a tendency to decrease.\u003csup\u003e23,24\u003c/sup\u003e In contrast, our study highlights the significant predictive value of both preoperative and postoperative DNI, which continued to show strong associations with mortality beyond the 48-hour mark, emphasizing its utility in extended postoperative monitoring. DNI may be a valuable indicator in identifying patients who are not in the high-risk group preoperatively but have a poor recovery process postoperatively. In this context, the current study observed a higher incidence of postoperative hospital morbidity in the high DNI group compared to the low DNI group.\u003c/p\u003e\n\u003cp\u003eConsistent with the results of this study, the benefit of the Delta Neutrophil Index in predicting 30-day mortality in patients with ST-segment elevation myocardial infarction has been demonstrated.\u003csup\u003e25\u003c/sup\u003e Additionally, in terms of sepsis, Park et al. revealed that DNI \u0026gt;6.5% within the first 24 hours after admission to the intensive care unit is a good diagnostic marker for severe sepsis and septic shock.\u003csup\u003e10\u003c/sup\u003e Similarly, our findings demonstrate that a higher DNI, both pre- and postoperatively, significantly correlates with mortality in CABG patients, suggesting that DNI can serve as a crucial early marker for predicting adverse outcomes in a broader surgical context. Previous studies have proposed potential mechanisms to explain the rapid and early release of immature granulocytes. In cases of sterile inflammation, such as OPCAB, the mechanism of increasing immature granulocytes likely resembles that in sepsis. For example, in severe inflammation, the large consumption and destruction of mature cells may lead to a rapid expansion of circulating neutrophils to compensate for the loss of active neutrophils.\u003csup\u003e26,27,28,29,30\u003c/sup\u003e This mechanism aligns with our observation that patients with higher DNI levels exhibited worse outcomes, suggesting that DNI could be a useful marker for detecting heightened inflammatory responses that contribute to mortality after CABG \u0026nbsp;Our study extends this concept by showing that elevated DNI levels in CABG patients, reflective of heightened granulocyte production, are strongly predictive of mortality, reinforcing the importance of managing inflammatory responses in the postoperative period. Moreover, in myocardial reperfusion injury, reperfusion causes endothelial dysfunction, leading to vasoconstriction within the first few minutes, while increased leukocyte adhesion and flow contribute to impaired blood flow.\u003csup\u003e31\u003c/sup\u003e This could be a promising biomarker for predicting postoperative DNI mortality after reperfusion. Building on this, our results confirm that postoperative DNI is a robust predictor of mortality following CABG, particularly in patients undergoing reperfusion, suggesting its potential integration into routine postoperative risk assessments.\u003c/p\u003e\n\u003cp\u003eIn contrast to previous studies that used mortality several years after surgery, this study focused on relatively short-term outcomes like DNI. While CRP, WBC, and Neutrophil levels significantly increased in patients after surgery, DNI levels remained within normal ranges in survivors. These findings clearly demonstrate the activation of opposing immune-inflammatory pathways induced by CABG and confirm the importance of DNI as a risk stratification marker.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, the increased Delta Neutrophil Index (DNI) value, reflecting the proportion of circulating immature granulocytes in the blood, has been found to be an independent predictor of postoperative mortality and poor clinical outcomes following Coronary Artery Bypass Grafting (CABG). Both preoperative and postoperative DNI were significantly associated with mortality, indicating the valuable roles of DNI in the risk assessment necessary for perioperative and postoperative management. This highlights the dual utility of DNI in not only predicting but also monitoring patient outcomes throughout the perioperative period.\u003c/p\u003e\n\u003cp\u003eAs a secondary outcome, DNI may serve as a valuable indicator for identifying patients who are not in the high-risk group according to current risk assessment scores but have a poor recovery process postoperatively, potentially leading to increased morbidity and mortality risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea. Limitations of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several potential limitations. Despite encompassing a relatively large patient cohort, the nature of its single-center observational retrospective design imposes certain constraints. This design has limited the ability to perform meaningful subgroup analyses and has restricted the examination of subgroups such as patients with heart failure, those with high EuroSCORE, and those requiring urgent or reoperation. Therefore, broader-scale studies including such high-risk patient groups are needed.\u003c/p\u003e\n\u003cp\u003eThe study did not identify the underlying pathophysiology of the relationship between Delta Neutrophil Index (DNI) and early complications in adult cardiac surgery. We aimed to minimize bias by using multivariate logistic regression analysis to account for variables that could affect cardiac surgeries performed with Cardiopulmonary Bypass (CPB) and early clinical outcomes. However, we believe that some variables, particularly CPB, may have influenced our results. We attempted to mitigate inter-center variability by grouping patients operated on by the same experienced surgical team using the same technique.\u003c/p\u003e\n\u003cp\u003eAdditionally, we evaluated deaths based on general causes without an in-depth analysis of the specific reasons for mortality. Larger-scale randomized controlled trials are needed to validate whether Delta Neutrophil Index is a simple and effective marker in clinical practice and to determine if it impacts clinical outcomes. Although the study is limited by its single-center design, the findings offer a robust foundation for future multicenter, randomized trials aimed at validating DNI as a critical component in perioperative management protocols.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003col style=\"list-style-type: upper-roman;\"\u003e\n \u003cli\u003e\u003cstrong\u003eCABG\u003c/strong\u003e: Coronary Artery Bypass Grafting \u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCAD\u003c/strong\u003e: Coronary Artery Disease \u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCBC\u003c/strong\u003e: Complete Blood Count \u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCRP\u003c/strong\u003e: C-Reactive Protein \u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDNI\u003c/strong\u003e: Delta Neutrophil Index \u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDM\u003c/strong\u003e: Diabetes Mellitus \u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEF\u003c/strong\u003e: Ejection Fraction \u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHT\u003c/strong\u003e: Hypertension \u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIG:\u003c/strong\u003e Immature Granulocytes \u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePLT:\u003c/strong\u003e Platelets \u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWBC:\u003c/strong\u003e White Blood Cell\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBurak Toprak:\u003c/strong\u003e Made substantial contributions to the study design, manuscript writing, conceptualization, and execution of the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbdulkadir Bilgi\u0026ccedil;:\u003c/strong\u003e Substantially revised the manuscript and critically interpreted the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026Ccedil;ise Kanat Toprak:\u003c/strong\u003e Contributed to data collection and performed data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHamide Kaya:\u003c/strong\u003e Contributed to the conceptualization phase of the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThanks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to Elif Ertaş from the Department of Biostatistics, Sel\u0026ccedil;uk University, Turkey, for her expertise in statistical analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict Of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement On The Use Of Artificial Intelligence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo artificial intelligence application was used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo financing available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOkrainec K, Banerjee DK, Eisenberg MJ. 2004. Coronary artery disease in the developing world. Am Heart J. 148:7-15. https://doi.org/10.1016/j.ahj.2003.12.029 \u003c/li\u003e\n\u003cli\u003eRumsfeld JS, Magid DJ, O\u0026apos;Brien M, McCarthy M Jr, MaWhinney S, Shroyer AL. 2001. Changes in health-related quality of life following coronary artery bypass graft surgery. Ann Thorac Surg. 72:2026-2032. https://doi.org/10.1016/s0003-4975(01)03150-3 \u003c/li\u003e\n\u003cli\u003eSerruys PW, Morice MC, Kappetein AP, Colombo A, Holmes DR, Mack MJ, Stahle E. 2009. Percutaneous coronary intervention versus coronary-artery bypass grafting for severe coronary artery disease. N Engl J Med. 360:961-972. https://doi.org/10.1056/NEJMoa0804626 \u003c/li\u003e\n\u003cli\u003eShroyer AL, Coombs LP, Peterson ED, Eiken MC, DeLong ER, Chen A, Ferguson TB Jr. 2003. The Society of Thoracic Surgeons: 30-day operative mortality and morbidity risk models. 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Ann Thorac Surg. 73:480-489. https://doi.org/10.1016/s0003-4975(01)03338-7 \u003c/li\u003e\n\u003cli\u003eHannan EL, Zhong Y, Lahey SJ, Culliford AT, Gold JP, Smith CR, Higgins RS. 2011. 30-day readmissions after coronary artery bypass graft surgery in New York State. JACC Cardiovasc Interv. 4:569-576. https://doi.org/10.1016/j.jcin.2011.02.012 \u003c/li\u003e\n\u003cli\u003eBrull DJ, Montgomery HE, Sanders J, Dhamrait S, Luong L, Rumley A, Lowe GDO, Humphries SE. 2001. Interleukin-6 gene -174G\u0026gt;C and -572G\u0026gt;C promoter polymorphisms are strong predictors of plasma interleukin-6 levels after coronary artery bypass surgery. Arterioscler Thromb Vasc Biol. 21:1458-1463. https://doi.org/10.1161/hq0901.093394 \u003c/li\u003e\n\u003cli\u003ePark BH, Kwon J, Lee J. 2011. Delta neutrophil index as an early marker of disease severity in critically ill patients with sepsis. BMC Infect Dis. 11:299. https://doi.org/10.1186/1471-2334-11-299 \u003c/li\u003e\n\u003cli\u003eYune HY, Kim YH, Lee YH. 2015. Delta neutrophil index as a promising prognostic marker in out-of-hospital cardiac arrest. PLoS One. 10:e0120677. https://doi.org/10.1371/journal.pone.0120677 \u003c/li\u003e\n\u003cli\u003eKim OH, Kim YH, Ryu HJ, Lee J, Kim H, Nahm CH, Cho HS, Choi JW. 2016. The use of delta neutrophil index and myeloperoxidase index for predicting acute complicated appendicitis in children. PLoS One. 11:e0148799. https://doi.org/10.1371/journal.pone.0148799 \u003c/li\u003e\n\u003cli\u003eKim H, Lee JH, Kim JS, Moon SY, Ko DR, Lee J, Kim YH. 2017. Usefulness of the delta neutrophil index as a prognostic marker of acute cholangitis in emergency departments. Shock. 47:303-312. https://doi.org/10.1097/shk.0000000000000732 \u003c/li\u003e\n\u003cli\u003eKo DR, Lee J, Kim H, Kim YH, Ahn KJ, Park YS, Kim TH. 2017. 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Ann Thorac Surg. 88:S2-22. https://doi.org/10.1016/j.athoracsur.2009.05.055 \u003c/li\u003e\n\u003cli\u003eBrewer R, Theurer PF, Cogan CM, Bell GF, Prager RL, Paone G. 2014. Morbidity but not mortality is decreased after off-pump coronary artery bypass surgery. Ann Thorac Surg. 97:831-836. https://doi.org/10.1016/j.athoracsur.2013.11.059 \u003c/li\u003e\n\u003cli\u003eBicer M, Senturk T, Yanar M, Tutuncu A, Oral AY, Ulukaya E, Akyol S, Yildiz Y, Ulukent SC, Yuksek T, Baysal T. 2014. Effects of off-pump versus on-pump coronary artery bypass grafting: apoptosis, inflammation and oxidative stress. Heart Surg Forum. 17:E271-276. https://doi.org/10.1532/hsf98.2013314 \u003c/li\u003e\n\u003cli\u003eWhitten CW, Hill GE, Ivy R, Greilich PE, Lipton JM. 1998. Does the duration of cardiopulmonary bypass or aortic cross-clamp, in the absence of blood and/or blood product administration, influence the IL-6 response to cardiac surgery? Anesth Analg. 86:28-33. https://doi.org/10.1213/00000539-199801000-00006 \u003c/li\u003e\n\u003cli\u003eFang L, Moore XL, Dart AM, Wang LM. 2015. Systemic inflammatory response following acute myocardial infarction. J Geriatr Cardiol. 12:305-312. https://doi.org/10.11909/j.issn.1671-5411.2015.03.024 \u003c/li\u003e\n\u003cli\u003eLaffey JG, Boylan JF, Cheng DC. 2002. The systemic inflammatory response to cardiac surgery: implications for the anesthesiologist. Anesthesiology. 97:215-252. https://doi.org/10.1097/00000542-200207000-00031 \u003c/li\u003e\n\u003cli\u003eBoyle EM, Pohlman TH, Johnson MC, Verrier ED. 1997. Endothelial cell injury in cardiovascular surgery: the systemic inflammatory response. Ann Thorac Surg. 63:277-284. https://doi.org/10.1016/s0003-4975(97)00025-7 \u003c/li\u003e\n\u003cli\u003eChew MS, Brandslund I, Brix-Christensen V, Ravn HB, Hjortdal VE, Pedersen J, Hasenkam JM. 2001. Tissue injury and the inflammatory response to pediatric cardiac surgery with cardiopulmonary bypass: a descriptive study. Anesthesiology. 94:745-753. https://doi.org/10.1097/00000542-200105000-00010 \u003c/li\u003e\n\u003cli\u003eKong T, Kim TH, Park YS, Lee JH, Kim H, Ko DR, Park BH. 2017. Usefulness of the delta neutrophil index to predict 30-day mortality in patients with ST-segment elevation myocardial infarction. Sci Rep. 7:15718. https://doi.org/10.1038/s41598-017-15718-0 \u003c/li\u003e\n\u003cli\u003eHwang YJ, Ahn KJ, Lee JW, Kim SH, Na MJ. 2015. Newly designed delta neutrophil index-to-serum albumin ratio prognosis of early mortality in severe sepsis. Am J Emerg Med. 33:1577-1582. https://doi.org/10.1016/j.ajem.2015.07.056 \u003c/li\u003e\n\u003cli\u003eBermejo-Martin JF, Almansa R, Torres A, de la Fuente A, Gomez-Sanchez E, Gonzalez-Rivera M, Kelvin D, Roquilly A. 2016. Defining immunological dysfunction in sepsis: a requisite tool for precision medicine. J Infect. 72:525-536. https://doi.org/10.1016/j.jinf.2016.02.010 \u003c/li\u003e\n\u003cli\u003eAlves-Filho JC, Spiller F, Cunha FQ. 2010. Neutrophil paralysis in sepsis. Shock. 34:15-21. https://doi.org/10.1097/shk.0b013e3181e7e61b \u003c/li\u003e\n\u003cli\u003eLeliefeld PH, Wessels CM, Leenen LP, Koenderman L, Pillay J. 2016. The role of neutrophils in immune dysfunction during severe inflammation. Crit Care. 20:73. https://doi.org/10.1186/s13054-016-1235-4 \u003c/li\u003e\n\u003cli\u003eKong T, Lee J, Kim H, Kim TH, Park YS, Kim YH, Ko DR. 2017. Usefulness of the Delta Neutrophil Index to Predict 30-day Mortality in Patients with Upper Gastrointestinal Bleeding. Shock. 47:1-8. https://doi.org/10.1097/shk.0000000000000756 \u003c/li\u003e\n\u003cli\u003eArslan F, de Kleijn DP, Pasterkamp G. 2011. Innate immune signaling in cardiac ischemia. Nat Rev Cardiol. 8:292-300. https://doi.org/10.1038/nrcardio.2011.38 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Delta Neutrophil Index, Coronary Artery Bypass Grafting, Postoperative Mortality, Inflammatory Markers","lastPublishedDoi":"10.21203/rs.3.rs-5274128/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5274128/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ea. Background:\u003c/h2\u003e \u003cp\u003eRecently, the role of inflammatory markers in assessing the severity of CAD in the early stages has garnered interest. Currently, there are no specific inflammatory biomarkers routinely used for predicting postoperative mortality in patients undergoing coronary artery bypass grafting (CABG). In this study, we evaluated the significance of postoperative DNI as a prognostic marker for early mortality in patients undergoing coronary artery bypass grafting (CABG).\u003c/p\u003e\u003ch2\u003eb. Aims:\u003c/h2\u003e \u003cp\u003eThe aim of this study is to determine the significance of the delta neutrophil index (DNI), which reflects the proportion of immature granulocytes, as a prognostic marker for early postoperative mortality in coronary artery bypass grafting (CABG).\u003c/p\u003e\u003ch2\u003ec. Methods:\u003c/h2\u003e \u003cp\u003eThis rigorously designed retrospective cohort study, conducted at a high-volume tertiary care center specializing in cardiovascular surgery, included a robust patient cohort to ensure comprehensive data analysis and reliable conclusions. The study included a consecutive series of 446 patients who underwent coronary artery bypass grafting (CABG) between January 1, 2022, and August 1, 2023.\u003c/p\u003e\u003ch2\u003ed. Results:\u003c/h2\u003e \u003cp\u003eMortality was found to be associated with Pre-DNI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A 1-unit increase in Pre-DNI measurement was associated with a 2.61-fold (95% Confidence Interval: 1.54\u0026ndash;4.45) increase in the risk of death. Additionally, mortality was also associated with Post-DNI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A 1-unit increase in Post-DNI measurement was associated with a 10.21-fold (95% Confidence Interval: 5.08\u0026ndash;20.05) increase in the risk of death.\u003c/p\u003e\u003ch2\u003ee. Conclusions:\u003c/h2\u003e \u003cp\u003eThis study unequivocally establishes that elevated DNI values serve as potent independent predictors of postoperative mortality, underscoring the clinical utility of DNI as a key component in the perioperative risk stratification for CABG patients. Both preoperative and postoperative DNI were significantly associated with mortality, highlighting the valuable role of DNI in risk assessment necessary for perioperative and postoperative management. This highlights the dual utility of DNI in not only predicting but also monitoring patient outcomes throughout the perioperative period. Incorporating DNI into routine clinical practice could provide a more personalized approach to postoperative care, potentially improving patient survival and reducing complication rates in CABG surgery.\u003c/p\u003e","manuscriptTitle":"Delta Neutrophil Index in Coronary Artery Bypass Surgery: An Innovation in Postoperative Mortality Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-24 08:28:32","doi":"10.21203/rs.3.rs-5274128/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":"68a321d0-353f-4625-aa4c-01e6fab66701","owner":[],"postedDate":"October 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-07T16:41:17+00:00","versionOfRecord":{"articleIdentity":"rs-5274128","link":"https://doi.org/10.2147/JIR.S500508","journal":{"identity":"journal-of-inflammation-research","isVorOnly":true,"title":"Journal of Inflammation Research"},"publishedOn":"2025-02-03 00:00:00","publishedOnDateReadable":"February 3rd, 2025"},"versionCreatedAt":"2024-10-24 08:28:32","video":"","vorDoi":"10.2147/JIR.S500508","vorDoiUrl":"https://doi.org/10.2147/JIR.S500508","workflowStages":[]},"version":"v1","identity":"rs-5274128","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5274128","identity":"rs-5274128","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}