Role of immature granulocytes in monitoring sepsis treatment

Role of immature granulocytes in monitoring sepsis treatment | 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 Role of immature granulocytes in monitoring sepsis treatment Mustafa Deniz, Zahide Sahin Yildirim, Zuleyha Erdin, Murat Alisik, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6066578/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Apr, 2025 Read the published version in BMC Anesthesiology → Version 1 posted 10 You are reading this latest preprint version Abstract Background Sepsis is an organ dysfunction that impairs response to infection. Inflammatory biomarkers have been used to diagnose and monitor sepsis. The aim of the present study was to determine the role of immature granulocytes (IGs) in monitoring sepsis treatment. Methods This two-center, prospective, observational study included patients diagnosed with sepsis according to the Sepsis-3 criteria, who were followed-up in the adult intensive care units of the Bolu Izzet Baysal State Hospital and Bolu Izzet Baysal Training and Research Hospital (Bolu Merkez/Bolu, Türkiye). Laboratory investigation results, demographic information, treatment responses, and mortality were recorded. Patients were divided into 2 groups according to treatment: appropriate (group 1); and inappropriate (group 2). Differences in the number of IGs and IG% were compared. Differences with P < 0.05 were considered to be statistically significant for all analyses. Results The study included 87 patients from 2 centers. The most common comorbidities were hypertension (54%) and 28-day mortality (37.9%). Empirical antibiotic therapy (43.7%) was appropriate for 38 patients (group 1) and 49 patients when the treatment was incorrect or inadequate (group 2). There were no significant differences between the groups in terms of laboratory investigation results on the day of treatment initiation. IG count and IG% on day 3 of treatment were significantly higher in group 2. Mortality was higher in patients with a high IG count (IG %) and in group 2. Conclusion IG% was a simple, inexpensive, and useful test for monitoring sepsis treatment and, in addition, IG count was also effective in predicting mortality. Immature granulocytes Sepsis Treatment Intensive care unit Pronosis Figures Figure 1 Figure 2 Background Sepsis is an organ dysfunction characterized by a dysregulated host response to infection(s) [ 1 ]. Although its associated mortality rate is high, early diagnosis and appropriate treatment can improve outcomes [ 2 ]. Diagnosis is made according to the Sepsis-3 criteria. Although biomarkers, such as procalcitonin (PCT), C-reactive protein (CRP), and interleukin (IL)-6, are helpful for diagnosis, they are relatively expensive tests and not always available [ 3 ]. When necessary, fluid resuscitation, vasopressors, source control, oxygenation, and early empirical antibiotic therapy are the main approaches to sepsis management [ 4 ]. Excessive or incorrect use of empirical antibiotics, however, should be considered in terms of treatment optimization and the development of antibiotic resistance [ 3 ]. Immature granulocytes (IGs) are a subset of neutrophils (metamyelocytes, myelocytes, and promyelocytes), and are produced in the bone marrow prompted by stimuli such as inflammation or infection [ 5 ]. IGs are less mature cells than band cells and are usually present at lower levels. The ability to automatically determine percentage of IGs using modern analyzers enables faster and more precise measurement of left shift. Although many studies have been performed to determine the optimal cut-off value, there is no clear consensus [ 6 ]. As such, this study aimed to determine the role of IG% in predicting the response to microbiological treatment(s) for sepsis. Methods The present investigation was a two-center, prospective, observational study. Patients diagnosed with sepsis in the adult care units of the Bolu Izzet Baysal State Hospital and Bolu Izzet Baysal Training and Research Hospital (Bolu Merkez/Bolu, Türkiye) were followed up. Patients > 18 years of age, who were diagnosed with sepsis according to the Sepsis-3 criteria, were included in this study, which was approved by the Bolu Abant Izzet Baysal University Clinical Research Ethics Committee (Decision No.2023/314). Variables such as age, sex, comorbidities, and Acute Physiologic Assessment and Chronic Health Evaluation (APACHE) II and Sequential Organ Failure Assessment (SOFA) scores, were recorded. Hemogram parameters, such as hemoglobin, white blood cell count, platelet count, IG count, and IG%, and biochemical parameters, such as albumin, bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine, sodium, and potassium levels, were recorded on the day of diagnosis and antibiotic initiation. Inflammatory markers, such as PCT and CRP, vasoactive agent use, and mechanical ventilation requirements, were recorded at the time of diagnosis. Mortality status on day 28, hospital mortality status, and length of intensive care unit stay were recorded. The main aim of this study was to determine the role of IG% in predicting the response to antibiotic therapy in patients diagnosed with sepsis. When patients are diagnosed with sepsis, empirical antibiotic treatment is often initiated. If blood culture growth and antibiogram results are compatible with the antibiotherapy administered to the patient, empirical antibiotherapy is continued; if not, either the treatment strategy is altered or antibiotics are added to the regimen. Failure to improve hemodynamic and clinical deterioration or regression of inflammatory biomarkers requires a change in empirical antibiotherapy or switch to broad spectrum agents. Using defined variables, patients in whom empirical antibiotherapy was continued as “continuation of current treatment” were allocated to group 1, while those in whom antibiotics were changed or added 3–6 days after empirical treatment were allocated to the “group requiring treatment change” (group 2). Values measured on day 3 were used to predict treatment response. In the authors’ hospitals, culture sample results are reported on day 6 at the latest. IG and IG% were measured on the day empirical antibiotic therapy was started and on day 3 of treatment using an automated hematology analyzer (XN1000, Sysmex Corporation, Kobe, Japan). IG% measured on day 3 was compared with that measured on the day of treatment initiation (IG% day 3/IG% day 1). In addition, IG% ratios were compared between the 2 groups. Statistical analyses All statistical analyses were performed using SPSS version 22 (IBM Corp., Armonk, NY, USA). Categorical variables are expressed as frequency and percentage, while continuous variables are expressed as mean ± standard deviation (SD) or median (interquartile range [IQR] i.e., Q1 to Q3]) depending on the normality of distribution, which was assessed using the Kolmogorov–Smirnov test. Comparisons between groups 1 and 2 were performed using the chi-squared test or Fisher’s exact test for categorical variables, and the independent samples t -test or Mann–Whitney U test for continuous variables, as appropriate. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive value of IG%, IG% on days 0 and 3, and IG ratio for mortality. The area under the ROC curve (AUC) and corresponding 95% confidence interval (CI) for AUC, sensitivity, and specificity are reported. The optimal cut-off values were determined using the Youden index. Differences with P < 0.05 were considered to be statistically significant for all analyses. Results A total of 87 patients from 2 centers were included, and the data were prospectively analyzed. Empirical antibiotic treatment was continued in 38 patients, who comprised group 1. Forty-nine patients in whom empirical antibiotic treatment was changed or antibiotics were added within 3–6 days comprised group 2. Hypertension was the most common comorbidity (n = 47 [54%]), followed by neurological diseases (n = 32 [36.8%]), and cardiac diseases (n = 30 [30%]). At the time of diagnosis, 42 (48.3%) patients received vasoactive agent support and 66 (76.9%) received mechanical ventilator support. The 28-day and in-hospital mortality rates were 37.9% and 52.9%, respectively (Table 1 ). Table 1 Demografics of patients N: 87 (%) Gender Female 41 (%47.1) Male 46 (%52.9) DM 28 (%32.2) HT 47 (%54) Cardiac disease 30 (%34.5) COPD 20 (%23) Malignancy 15 (%17.2) CKD 11 (%12.6) Neurologic disease 32 (%36.8) Vasoactive drugs 42 (%48.3) MV 66 (%76.9) 28. day mortality live 54 (%62.1) dead 33 (%37.9) Hospital mortality discharged 41 (%47.1) dead 46 (%52.9) AB changed No 38 (%43.7) Yes 49 (%56.3) DM: Diabetes Mellitus, HT: Hypertansion, COPD: chronic Obstructive Pulmonary Disease, CKD: Chronic Kidney Disease, MV: Mechanical Ventilation, AB changed: Empirical antibiotic changed When patients in group 1 and group 2 were compared according to the need for change(s) in antibiotic therapy, there was no significant difference between APACHE II, SOFA score, complete blood count/biochemical parameters, and inflammatory biomarkers in either group of patients with sepsis. The number of IGs at diagnosis was higher in group 2 ( P = 0.021). The IG% at diagnosis did not differ between the groups. In blood samples collected on day 3 of treatment, the IG count ( P = 0.001) and IG% ( P = 0.002) were higher in group 2 than those in group 1. The ratio of IG% on day 3 after treatment initiation to IG% on the day of diagnosis (i.e., IG ratio) was also higher in group 2 ( P = 0.005) (Table 2 , Fig. 1 ). Table 2 APACHE ll, SOFAs and blood tests of groups Grup 1 (N = 38) Grup 2 (N = 49) p value Age 77 (63-82.3) 77 (66–85) 0.449 APACHE ll 27.5 (24–31) 29 (25–32) 0.215 SOFAs 9.5 (7–11) 10 (7–12) 0.413 Hemoglobin (g/dL) 10.1 (8.9–12.2) 10 (8.8–12.1) 0.784 White blood cell (K/uL) 11.1 (8.5–16.3) 13.7 (10.1–19.8) 0.079 Platelets (K/uL) 228.5 (152.5-301.8) 239 (140–354) 0.584 Albumin (g/L) 29 (25-32.5) 28 (22.5–32.5) 0.479 Bilirubin (mg/dL) 0.7 (0.4–1.1) 0.5 (0.3–1.1) 0.261 Alanine amino transferase (U/L) 15.5 (11-45.8) 24 (14–57) 0.079 Aspartat amino transferase (U/L) 35 (18-60.8) 44 (22–76) 0.213 Creatinine (mg/dL) 1.1 (0.7–2.18) 1.2 (0.75–2.4) 0.575 Sodium (mmoL/L) 139 (135-144.3) 138 (136.5–142) 0.935 Potassium (mmoL/L) 4.25 (3.38–5.13) 4.2 (3.65–4.95) 0.942 Procalcitonin ( µg/L) 0.6 (0.1–3.9) 1 (0.3–3.1) 0.613 C-Reactive Protein (mg/L) 97.5 (67–147) 140 (61-217.5) 0.06 IG% 0.65 (0.48–1.43) 0.9 (0.6–1.5) 0.099 IG% 3. day 0.7 (0.48–1.18) 1.3 (0.7–2.8) 0.002 IG ratio 0.89 (0.67–1.35) 1.25 (1-1.9) 0.005 IG count (10 9 /L) 0.08 (0.05–0.17) 0.13 (0.08–0.27) 0.021 IG count 3. day (10 9 /L) 0.06(0.04–0.15) 0.15(0.08–0.33) 0.001 Length of stay (day) 10 (7-25.8) 11 (7-28.5) 0.586 APACHE ll: Acute Physiology and Chronic Health Evaluation ll, SOFA s: Sequential Organ Failure Assessment score, IG: Immature granulocyte, IG Ratio: Immature granulocyte % day 3/ Immature granulocyte % day 0 The number of patients with malignancy was higher in group 2 ( P = 0.01), as was the number of patients requiring mechanical ventilation support ( P = 0.003). However, no significant differences were found between the groups in terms of other comorbidities and sex. The 28-day intensive care unit ( P = 0.049) and hospital ( P = 0.008) mortality rates were higher in group 2 ( Table 3 ) . Table 3 Comorbidity, vasoactive drug use, mechanical ventilation support and prognosis of groups Grup 1. (N = 38) N(%) Grup 2. (N = 49) N(%) p value Gender Female 16 (42.1%) 25 (51%) 0.409 Male 22 (57.9%) 24 (49%) DM 15 (39.5%) 13 (26.5%) 0.200 HT 23 (60.5%) 24 (49%) 0.284 Cardiac disease 17 (44.7%) 13 (26.5%) 0.076 COPD 10 (26.3%) 10 (20.4%) 0.516 Malignancy 2 (5.3%) 13 (26.5%) 0.01 CKD 6 (15.8%) 5 (10.2%) 0.437 Neurologic disease 16 (42.1%) 16 (32.7%) 0.364 Vasoactive drug 14 (36.8%) 28 (57.1%) 0.06 MV 23 (60.5%) 43 (87.8%) 0.003 28. day mortality live 28 (73.7%) 26 (53.1%) 0.049 dead 10 (26.3%) 23 (46.9%) Hospital mortality discharged 24 (63.2%) 17 (34.7%) 0.008 dead 14 (36.8%) 32 (65.3%) DM: Diabetes Mellitus, HT: Hypertansion, COPD: Chronic Obstructive Pulmonary Disease, CKD: Chronic Kidney Disease, MV: Mechanical Ventilation Descriptives are presented as Number (percentage) (N(%)) and compared using the Mann-Whitney U or Chi-square tests respectively. When discharge and intensive care unit mortality were analyzed, APACHE II and SOFA scores, ALT, AST, IG%, IG count, and IG count on day 3 were higher in the deceased patient group. Albumin levels were lower in patients who died ( P = 0.007) (Table 4 ). When demographic data were compared, there were more patients with malignancy ( P = 0.012) and (p = 0.049) higher mortality rate in group 2 (Table 5 ). Table 4 APACHE II, SOFAs, blood tests Discharged (N = 54) Dead (N = 33) p value Age 77 (65-83.3) 77 (64.5–88.5) 0.584 APACHE ll 27 (22.8–30.3) 31 (26–33) 0.003 SOFAs 9 (7–11) 10 (8.5–12) 0.015 Hemoglobin (g/dL) 10.35 (8.73–12.25) 10 (8.8–11.5) 0.674 White blood Cell (K/uL) 12.25 (8.9-17.75) 14.3 (8.85–21.2) 0.327 Platelets (K/uL) 237.5 (156.3-334.5) 210 (134–387) 0.776 Albumin (g/dL) 29.5 (25-34.8) 25 (21–31) 0.007 Bilirubin (mg/dL) 0.6 (0.3–1.1) 0.5 (0.4–1.1) 0.937 Alanine amino trasferase (U/L) 16.5 (12-38.3) 25 (17.5–85) 0.027 Aspartat amino transferase (U/L) 32.5 (18.8–54) 46 (30.5-142.5) 0.014 Creatinine (mg/dL) 1 (0.7–2.1) 1.6 (0.9–2.55) 0.057 Sodium (mmoL/L) 139 (136.8–142) 138 (136–145) 0.766 Potassium (mmoL/L) 4.4 (3.4–5.03) 4.1 (3.7-5) 0.934 Procalcitonin ( µg/L) 0.45 (0.1–2.8) 1.7 (0.35–3.75) 0.059 C-Reactive Protein (mg/L) 122.5 (48.3-157.5) 144 (73.5–233) 0.192 IG % 0.7 (0.5–1.23) 0.9 (0.6–2.55) 0.039 IG 3. day % 0.85 (0.58–1.5) 1.3 (0.6–3.45) 0.072 IG ratio 1.09 (0.83–1.5) 1.09 (0.83–1.9) 0.707 IG count (10 9 /L) 0.09 (0.05–0.15) 0.18 (0.07–0.34) 0.033 IG count 3. day (10 9 /L) 0.08 (0.04–0.19) 0.16 (0.07–0.39) 0.014 APACHE ll: Acute Physiology and Chronic Health Evaluation ll, SOFA s: Sequential Organ Failure Assessment score, IG: Immature granulocyte, IG Ratio: Immature granulocyte % day 3/ Immature granulocyte % day 0 Table 5 Demographics, MV, and Vasoactive use differences between dead and discharged patients Discharged N:54(%) Dead N:33(%) Gender Female 30 (55.6%) 11 (33.3%) 0.044 Male 24 (44.4%) 22 (66.7%) DM 19 (35.2%) 9 (27.3%) 0.443 HT 32 (59.3%) 15 (45.5%) 0.210 Cardiac disease 22 (40.7%) 8 (24.2%) 0.116 COPD 11 (20.4%) 9 (27.3%) 0.458 Malignancy 5 (9.3%) 10 (30.3%) 0.012 CKD 7 (13%) 4 (12.1%) 0.909 Neurologic disease 23 (42.6%) 9 (27.3%) 0.150 Vasoactive drugs 26 (48.1%) 16 (48.5%) 0.979 MV 39 (72.2%) 27 (81.8%) 0.310 AB changed 26 (48.1%) 23 (69.7%) 0.049 DM: Diabetes Mellitus, HT: Hypertansion, COPD: chronic Obstructive Pulmonary Disease, CKD: Chronic Kidney Disease, MV: Mechanical Ventilation, AB changed: Empirical antibiotic changed Descriptives are presented as Number (percentage) (N(%)) and compared using the Mann-Whitney U or Chi-square tests respectively. ROC analysis performed to define the adequacy of treatment yielded an AUC of 0.603 (95% CI: 0.479–0.727), a sensitivity of 0.653, and a specificity of 0.605 were found, with a cut-off value of 0.75 for IG% ( P = 0.099). With a cut-off value of 0.75 for IG% on day 3, the AUC was 0.692 (95% CI:0.58–0.805), sensitivity was 0.735, and specificity was 0.579 ( P = 0.002). For the IG ratio, the AUC was 0.676 (95% CI: 0.56–0.791)), sensitivity was 0.837, and specificity was 0.526 ( P = 0.005), with a cut-off value of 0.915 (Fig. 2 ) Discussion Biomarkers are useful to diagnose, monitor treatment response, and predict prognosis of sepsis or suspected sepsis. Biomarkers must be highly specific, sensitive, reproducible and cost effective [ 7 ]. Many biomarkers have been investigated for their role in differentiating conditions such as infection, trauma, surgery, malignancy, and ischemia [ 8 ]. The IG count and IG% have recently been investigated for their roles in reflecting infection status. Yazla et al. [ 9 ] found that IG count and IG% were significant biomarkers for predicting the diagnosis of complicated acute appendicitis in patients undergoing surgery for acute appendicitis. In a similar study, Durak et al. [ 10 ] reported that IG count was an effective biomarker for predicting mesenteric ischemia and intestinal necrosis in patients undergoing laparotomy. Porizka et al. [ 11 ] reported that IG% could be used to differentiate between infective and non-infective systemic inflammatory response syndromes (i.e.,“SIRS”) in patients undergoing cardiac surgery. Jeon et al. [ 12 ] evaluated the IG% to be moderately effective in predicting sepsis in patients with burns and recommended it as an auxiliary test due to its cost and ease of routine use. They reported that IGs were effective markers for differentiating bacterial pneumonia in patients with severe acute respiratory syndrome coronavirus 2 (i.e., “SARS-CoV-2”) infection [ 13 ]. In a study conducted in a non-septic intensive care unit, blood tests were performed for 7 days, and the IG count and IG% were found to have high sensitivity and specificity in the early recognition of sepsis [ 3 ]. In a similar study, IG% was defined as a useful and effective marker of the development of infections and septic shock [ 14 ]. Ayres et al. [ 15 ] found that a low IG% demonstrated high specificity in excluding sepsis and reported that it was a useful additional marker. In a study comparing blood culture-positive groups, IG% was found to be effective for the early detection of bacteremia [ 16 ]. Although the IG count and IG% have supported the diagnosis of sepsis in previous studies, there has been heterogeneity in some results [ 17 ]. The half-life of IGs is 3 h, and the capacity of this marker to better reflect the state of inflammation compared with other markers with long half-lives is remarkable [ 18 ]. In our study, blood samples collected on the day of the empirical treatment were analyzed. There was no significant difference in PCT and CRP levels between the groups in which empirical treatment was appropriate and a response to treatment was obtained (i.e., group 1) and the group in which empirical treatment was inadequate (i.e., group 2). Again, there was no significant difference in IG% on the day of treatment initiation in either group. In blood samples obtained on day 3 of empirical treatment, IG% was significantly higher in the group in which antibiotics were added due to inadequate empirical treatment or antibiotic therapy was changed according to the culture results. When we compared IG% on day 3 with IG% on the day of treatment initiation, this ratio was significantly higher in the inadequate treatment group (group 2) than in the adequate treatment group (group 1). When we analyzed the number of IGs, the mean number of IGs decreased on day 3 in the adequate treatment group, whereas the mean number of IGs increased in the inadequate treatment group. In reviewing the literature, IG% and IG counts have been investigated in terms of diagnosis and prognosis of sepsis. However, it attracted our attention that the power of this hemogram parameter, which is inexpensive, easy to measure and useful, in monitoring the response to treatment has not been investigated. Our results encouraged us to investigate their roles in treatment monitoring. We believe that IG% and IG count, which are inexpensive and useful parameters, may be effective at follow-up, although further studies are required. In group 2 (in which the response to treatment was inadequate), the 28-day and hospital mortality rates were significantly higher. Again, when we compared patients who were discharged with those who were deceased, mortality was higher in the group in which antibiotics were changed; more specifically, in which treatment was inadequate. In this context, we agree that antibiotic treatment and follow-up are especially important for sepsis. The IG% and the number of IGs were higher on the day of treatment initiation and on day 3. Similar to most studies, we believe that IG% and IG counts are effective predictors of mortality. Our study had several limitations. First, although this was a two-center study, the sample size was relatively small. The treatment of sepsis is a multidisciplinary approach, and we may not have standardized fluid therapy, doses of inotropes and vasoactive agents, corticosteroid support, mechanical ventilation modes and pressures, or nutritional status. In conclusion, patients treated for sepsis either recover or die. In situations in which antibiotic therapy, fluid therapy, and vasoactive agent support are involved, we believe that IG% and IG count are inexpensive, effective, and useful hemogram parameters for monitoring the bacteriological treatment of sepsis. We believe that additional studies should be performed to fill this knowledge gap in the literature. Abbreviations ALT, alanine aminotransferase; APACHE, Acute Physiologic Assessment and Chronic Health Evaluation; AST, aspartate aminotransferase; CRP, C-reactive protein; IG, immature granulocytes; IL, interleukin; PCT, procalcitonin; SOFA, and Sequential Organ Failure Assessment Declarations Ethics approval and consent to participate: The Bolu Abant İzzet Baysal University Clinical Research Ethics Committee approved this study with the ethical code 2023/314. Written informed consent was received from all subjects or their care givers before beginning the study. All methods were carried out in accordance with relevant guidelines and regulations or Declaration of Helsinki. Consent for publication: Availability of data and materials: The datasets used and/ or analyzed during the current study are available from the corresponding author in reasonable request. Competing interests: The authors declare no competing interests Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ contributions: MD, ZSY, ZE, RE, MA and MY equally contributed to the conception and design of the research. MD, ZSY, MA, RE, ZE and MY contributed to the acquisition of the data. MD, ZSY, MA, ZE, RE and MY contributed to the analysis and interpretation of the data. MD, ZSY, ZE, RE, MA and MY drafted the manuscript. All authors read and approved the final manuscript. Acknowledgements: Not applicable References Singer M, Deutschman CS, Seymour CW, et al. 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Ayres LS, Sgnaolin V, Munhoz TP. Immature granulocytes index as early marker of sepsis. Int J Lab Hematol. 2019;41(3):392-6. https://doi: 10.1111/ijlh.12990. Lakshmipriya V, Kavitha K, Yogalakshmi E, Sridevi M . The Clinical Utility of Automated Immature Granulocyte Measurement in the Early Diagnosis of Bacteremia. Cureus. 2024;16(2):e53660. https://doi: 10.7759/cureus.53660. Agnello L, Giglio RV, Bivona G, et al. Lo Sasso B, Ciaccio M. The Value of a Complete Blood Count (CBC) for Sepsis Diagnosis and Prognosis. Diagnostics (Basel). 2021;11(10):1881. https://doi: 10.3390/diagnostics11101881. Georgakopoulou VE, Makrodimitri S, Triantafyllou M, et al. Immature granulocytes: Innovative biomarker for SARS‑CoV‑2 infection. Mol Med Rep. 2022;26(1):217. https://doi: 10.3892/mmr.2022.12733. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Apr, 2025 Read the published version in BMC Anesthesiology → Version 1 posted Editorial decision: Revision requested 01 Apr, 2025 Reviews received at journal 23 Mar, 2025 Reviewers agreed at journal 22 Mar, 2025 Reviews received at journal 20 Mar, 2025 Reviewers agreed at journal 20 Mar, 2025 Reviewers invited by journal 19 Mar, 2025 Editor invited by journal 24 Feb, 2025 Editor assigned by journal 24 Feb, 2025 Submission checks completed at journal 24 Feb, 2025 First submitted to journal 19 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6066578","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":420651534,"identity":"0347a1f8-9787-48c6-a5fb-e0674d987442","order_by":0,"name":"Mustafa Deniz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYDCCAwwMEiDED+IkFJCiRbIBpMWAeC0MDAYHwCQROvhun0688XGHhb3x+dWJHx4YMMjzix3Ar0XyXO5my5lnJJjNbrzdLAF0mOHM2Qn4tRic4d0mzdsmwWZ24+wGkJYEg9vEaPnbJsFjPOPs5h/Ea2Fsk5Aw4O/dRpwtkmd4N1v2tkkYSNzg3WaRYCBB2C98Z3g33vjZVmfP3392880fFTby/NIEtCCABFilBLHKQYD/ACmqR8EoGAWjYCQBAOUIQvFQSn2CAAAAAElFTkSuQmCC","orcid":"","institution":"Bolu Izzet Baysal State Hospital","correspondingAuthor":true,"prefix":"","firstName":"Mustafa","middleName":"","lastName":"Deniz","suffix":""},{"id":420651536,"identity":"ca229b1e-3347-4d83-87eb-621aceab5f94","order_by":1,"name":"Zahide Sahin Yildirim","email":"","orcid":"","institution":"Abant Izzet Baysal University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zahide","middleName":"Sahin","lastName":"Yildirim","suffix":""},{"id":420651539,"identity":"af89dafc-b0d5-4cc3-a4d5-f05e6d993394","order_by":2,"name":"Zuleyha Erdin","email":"","orcid":"","institution":"Abant Izzet Baysal University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zuleyha","middleName":"","lastName":"Erdin","suffix":""},{"id":420651540,"identity":"0dc2ab38-7c85-4c77-bc54-43e051201edf","order_by":3,"name":"Murat Alisik","email":"","orcid":"","institution":"Bolu Abant Izzet Baysal University","correspondingAuthor":false,"prefix":"","firstName":"Murat","middleName":"","lastName":"Alisik","suffix":""},{"id":420651541,"identity":"2d0c1a95-e0a9-4728-baed-cf85021e68aa","order_by":4,"name":"Ridvan Erdin","email":"","orcid":"","institution":"Abant Izzet Baysal University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ridvan","middleName":"","lastName":"Erdin","suffix":""},{"id":420651542,"identity":"8270343e-1184-4782-ac8c-6c3ee9f75e33","order_by":5,"name":"Mustafa Yildirim","email":"","orcid":"","institution":"Bolu Izzet Baysal State Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mustafa","middleName":"","lastName":"Yildirim","suffix":""}],"badges":[],"createdAt":"2025-02-19 19:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6066578/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6066578/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12871-025-03072-4","type":"published","date":"2025-04-23T15:57:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":77616230,"identity":"b8c54130-e803-4df6-ac1b-258a80b18a7c","added_by":"auto","created_at":"2025-03-03 15:02:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47588,"visible":true,"origin":"","legend":"\u003cp\u003eImmature Granulocyte percentage, ratio and antibiotic change in 3-6 days\u003c/p\u003e\n\u003cp\u003estatus\u003c/p\u003e\n\u003cp\u003eDay 0: Immature granulocyte % on the day of empirical antibiotic initiation\u003c/p\u003e\n\u003cp\u003eDay 3: Immature granulocyte % on 3th day of empirical antibiotic initiation\u003c/p\u003e\n\u003cp\u003eRatio: Immature granulocyte % day 3/ Immature granulocyte % day 0\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6066578/v1/db3c5adbc450e80e74b281eb.png"},{"id":77616237,"identity":"7ac57cc7-0173-4676-8035-6000fb72be06","added_by":"auto","created_at":"2025-03-03 15:02:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":96186,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of IG % and IG ratios in predicting antibiotic changing status\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6066578/v1/37ecc24c45283aa874f4991a.png"},{"id":81569785,"identity":"c1c162c2-75af-49ea-bd3d-e0d784b17c2c","added_by":"auto","created_at":"2025-04-28 16:11:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1195611,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6066578/v1/f165a130-2e67-43b3-b278-4e7844f9d57b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Role of immature granulocytes in monitoring sepsis treatment","fulltext":[{"header":"Background","content":"\u003cp\u003eSepsis is an organ dysfunction characterized by a dysregulated host response to infection(s) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although its associated mortality rate is high, early diagnosis and appropriate treatment can improve outcomes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Diagnosis is made according to the Sepsis-3 criteria. Although biomarkers, such as procalcitonin (PCT), C-reactive protein (CRP), and interleukin (IL)-6, are helpful for diagnosis, they are relatively expensive tests and not always available [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. When necessary, fluid resuscitation, vasopressors, source control, oxygenation, and early empirical antibiotic therapy are the main approaches to sepsis management [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Excessive or incorrect use of empirical antibiotics, however, should be considered in terms of treatment optimization and the development of antibiotic resistance [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImmature granulocytes (IGs) are a subset of neutrophils (metamyelocytes, myelocytes, and promyelocytes), and are produced in the bone marrow prompted by stimuli such as inflammation or infection [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. IGs are less mature cells than band cells and are usually present at lower levels. The ability to automatically determine percentage of IGs using modern analyzers enables faster and more precise measurement of left shift. Although many studies have been performed to determine the optimal cut-off value, there is no clear consensus [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs such, this study aimed to determine the role of IG% in predicting the response to microbiological treatment(s) for sepsis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe present investigation was a two-center, prospective, observational study. Patients diagnosed with sepsis in the adult care units of the Bolu Izzet Baysal State Hospital and Bolu Izzet Baysal Training and Research Hospital (Bolu Merkez/Bolu, T\u0026uuml;rkiye) were followed up. Patients\u0026thinsp;\u0026gt;\u0026thinsp;18 years of age, who were diagnosed with sepsis according to the Sepsis-3 criteria, were included in this study, which was approved by the Bolu Abant Izzet Baysal University Clinical Research Ethics Committee (Decision No.2023/314).\u003c/p\u003e \u003cp\u003eVariables such as age, sex, comorbidities, and Acute Physiologic Assessment and Chronic Health Evaluation (APACHE) II and Sequential Organ Failure Assessment (SOFA) scores, were recorded. Hemogram parameters, such as hemoglobin, white blood cell count, platelet count, IG count, and IG%, and biochemical parameters, such as albumin, bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine, sodium, and potassium levels, were recorded on the day of diagnosis and antibiotic initiation. Inflammatory markers, such as PCT and CRP, vasoactive agent use, and mechanical ventilation requirements, were recorded at the time of diagnosis. Mortality status on day 28, hospital mortality status, and length of intensive care unit stay were recorded.\u003c/p\u003e \u003cp\u003eThe main aim of this study was to determine the role of IG% in predicting the response to antibiotic therapy in patients diagnosed with sepsis. When patients are diagnosed with sepsis, empirical antibiotic treatment is often initiated. If blood culture growth and antibiogram results are compatible with the antibiotherapy administered to the patient, empirical antibiotherapy is continued; if not, either the treatment strategy is altered or antibiotics are added to the regimen. Failure to improve hemodynamic and clinical deterioration or regression of inflammatory biomarkers requires a change in empirical antibiotherapy or switch to broad spectrum agents. Using defined variables, patients in whom empirical antibiotherapy was continued as \u0026ldquo;continuation of current treatment\u0026rdquo; were allocated to group 1, while those in whom antibiotics were changed or added 3\u0026ndash;6 days after empirical treatment were allocated to the \u0026ldquo;group requiring treatment change\u0026rdquo; (group 2). Values measured on day 3 were used to predict treatment response. In the authors\u0026rsquo; hospitals, culture sample results are reported on day 6 at the latest. IG and IG% were measured on the day empirical antibiotic therapy was started and on day 3 of treatment using an automated hematology analyzer (XN1000, Sysmex Corporation, Kobe, Japan). IG% measured on day 3 was compared with that measured on the day of treatment initiation (IG% day 3/IG% day 1). In addition, IG% ratios were compared between the 2 groups.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using SPSS version 22 (IBM Corp., Armonk, NY, USA). Categorical variables are expressed as frequency and percentage, while continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (interquartile range [IQR] i.e., Q1 to Q3]) depending on the normality of distribution, which was assessed using the Kolmogorov\u0026ndash;Smirnov test. Comparisons between groups 1 and 2 were performed using the chi-squared test or Fisher\u0026rsquo;s exact test for categorical variables, and the independent samples \u003cem\u003et\u003c/em\u003e-test or Mann\u0026ndash;Whitney U test for continuous variables, as appropriate. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive value of IG%, IG% on days 0 and 3, and IG ratio for mortality. The area under the ROC curve (AUC) and corresponding 95% confidence interval (CI) for AUC, sensitivity, and specificity are reported. The optimal cut-off values were determined using the Youden index. Differences with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to be statistically significant for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 87 patients from 2 centers were included, and the data were prospectively analyzed. Empirical antibiotic treatment was continued in 38 patients, who comprised group 1. Forty-nine patients in whom empirical antibiotic treatment was changed or antibiotics were added within 3\u0026ndash;6 days comprised group 2. Hypertension was the most common comorbidity (n\u0026thinsp;=\u0026thinsp;47 [54%]), followed by neurological diseases (n\u0026thinsp;=\u0026thinsp;32 [36.8%]), and cardiac diseases (n\u0026thinsp;=\u0026thinsp;30 [30%]). At the time of diagnosis, 42 (48.3%) patients received vasoactive agent support and 66 (76.9%) received mechanical ventilator support. The 28-day and in-hospital mortality rates were 37.9% and 52.9%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemografics of patients\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 41.6649%;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 25.2147%;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 23.1921%;\"\u003e\n \u003cp\u003eN: 87 (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 41.6649%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.2147%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.1921%;\"\u003e\n \u003cp\u003e41 (%47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 41.6649%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.2147%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.1921%;\"\u003e\n \u003cp\u003e46 (%52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 41.6649%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.2147%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.1921%;\"\u003e\n \u003cp\u003e28 (%32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 41.6649%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.2147%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.1921%;\"\u003e\n \u003cp\u003e47 (%54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 41.6649%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardiac disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.2147%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.1921%;\"\u003e\n \u003cp\u003e30 (%34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 41.6649%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOPD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.2147%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.1921%;\"\u003e\n \u003cp\u003e20 (%23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 41.6649%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalignancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.2147%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.1921%;\"\u003e\n \u003cp\u003e15 (%17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 41.6649%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.2147%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.1921%;\"\u003e\n \u003cp\u003e11 (%12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 41.6649%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeurologic disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.2147%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.1921%;\"\u003e\n \u003cp\u003e32 (%36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 41.6649%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVasoactive drugs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.2147%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.1921%;\"\u003e\n \u003cp\u003e42 (%48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 41.6649%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.2147%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.1921%;\"\u003e\n \u003cp\u003e66 (%76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 41.6649%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e28. day mortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.2147%;\"\u003e\n \u003cp\u003elive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.1921%;\"\u003e\n \u003cp\u003e54 (%62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 41.6649%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.2147%;\"\u003e\n \u003cp\u003edead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.1921%;\"\u003e\n \u003cp\u003e33 (%37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 41.6649%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospital mortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.2147%;\"\u003e\n \u003cp\u003edischarged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.1921%;\"\u003e\n \u003cp\u003e41 (%47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 41.6649%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.2147%;\"\u003e\n \u003cp\u003edead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.1921%;\"\u003e\n \u003cp\u003e46 (%52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 41.6649%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAB changed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.2147%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.1921%;\"\u003e\n \u003cp\u003e38 (%43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 41.6649%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.2147%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.1921%;\"\u003e\n \u003cp\u003e49 (%56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 92.9033%;\"\u003eDM: Diabetes Mellitus, HT: Hypertansion, COPD: chronic Obstructive Pulmonary Disease, CKD: Chronic Kidney Disease, MV: Mechanical Ventilation, AB changed: Empirical antibiotic changed\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\u003cp\u003eWhen patients in group 1 and group 2 were compared according to the need for change(s) in antibiotic therapy, there was no significant difference between APACHE II, SOFA score, complete blood count/biochemical parameters, and inflammatory biomarkers in either group of patients with sepsis. The number of IGs at diagnosis was higher in group 2 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021). The IG% at diagnosis did not differ between the groups. In blood samples collected on day 3 of treatment, the IG count (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and IG% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) were higher in group 2 than those in group 1. The ratio of IG% on day 3 after treatment initiation to IG% on the day of diagnosis (i.e., IG ratio) was also higher in group 2 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAPACHE ll, SOFAs and blood tests of groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrup 1 (N\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrup 2 (N\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (63-82.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (66\u0026ndash;85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAPACHE ll\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.5 (24\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (25\u0026ndash;32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOFAs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.5 (7\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (7\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHemoglobin (g/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.1 (8.9\u0026ndash;12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8.8\u0026ndash;12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite blood cell (K/uL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.1 (8.5\u0026ndash;16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.7 (10.1\u0026ndash;19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlatelets (K/uL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228.5 (152.5-301.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e239 (140\u0026ndash;354)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlbumin (g/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (25-32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (22.5\u0026ndash;32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBilirubin (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.4\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 (0.3\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlanine amino transferase (U/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.5 (11-45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (14\u0026ndash;57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAspartat amino transferase (U/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (18-60.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (22\u0026ndash;76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 (0.7\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 (0.75\u0026ndash;2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSodium (mmoL/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139 (135-144.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (136.5\u0026ndash;142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePotassium (mmoL/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.25 (3.38\u0026ndash;5.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2 (3.65\u0026ndash;4.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProcalcitonin\u003c/b\u003e (\u003cb\u003e\u0026micro;g/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6 (0.1\u0026ndash;3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.3\u0026ndash;3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC-Reactive Protein (mg/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.5 (67\u0026ndash;147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140 (61-217.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIG%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65 (0.48\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9 (0.6\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIG% 3. day\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.48\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3 (0.7\u0026ndash;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIG ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89 (0.67\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25 (1-1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIG count (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08 (0.05\u0026ndash;0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13 (0.08\u0026ndash;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIG count 3. day (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06(0.04\u0026ndash;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15(0.08\u0026ndash;0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength of stay (day)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (7-25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (7-28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAPACHE ll: Acute Physiology and Chronic Health Evaluation ll, SOFA s: Sequential Organ Failure Assessment score, IG: Immature granulocyte, IG Ratio: Immature granulocyte % day 3/ Immature granulocyte % day 0\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe number of patients with malignancy was higher in group 2 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), as was the number of patients requiring mechanical ventilation support (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). However, no significant differences were found between the groups in terms of other comorbidities and sex. The 28-day intensive care unit (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049) and hospital (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) mortality rates were higher in group 2 \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComorbidity, vasoactive drug use, mechanical ventilation support and prognosis of groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrup 1. (N\u0026thinsp;=\u0026thinsp;38) N(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGrup 2. (N\u0026thinsp;=\u0026thinsp;49) N(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (42.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (57.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (60.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiac disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (44.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCOPD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMalignancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCKD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeurologic disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (42.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (32.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVasoactive drug\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (60.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (87.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e28. day mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (73.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (53.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (46.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospital mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edischarged\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (63.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (34.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (65.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eDM: Diabetes Mellitus, HT: Hypertansion, COPD: Chronic Obstructive Pulmonary Disease, CKD: Chronic Kidney Disease, MV: Mechanical Ventilation\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eDescriptives are presented as Number (percentage) (N(%)) and compared using the Mann-Whitney U or Chi-square tests respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen discharge and intensive care unit mortality were analyzed, APACHE II and SOFA scores, ALT, AST, IG%, IG count, and IG count on day 3 were higher in the deceased patient group. Albumin levels were lower in patients who died (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). When demographic data were compared, there were more patients with malignancy (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012) and (p\u0026thinsp;=\u0026thinsp;0.049) higher mortality rate in group 2 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAPACHE II, SOFAs, blood tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDischarged (N\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead (N\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (65-83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (64.5\u0026ndash;88.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAPACHE ll\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (22.8\u0026ndash;30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (26\u0026ndash;33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOFAs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (7\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8.5\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHemoglobin (g/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.35 (8.73\u0026ndash;12.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8.8\u0026ndash;11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite blood Cell (K/uL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.25 (8.9-17.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.3 (8.85\u0026ndash;21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlatelets (K/uL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e237.5 (156.3-334.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210 (134\u0026ndash;387)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlbumin (g/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.5 (25-34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (21\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBilirubin (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6 (0.3\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 (0.4\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlanine amino trasferase (U/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.5 (12-38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (17.5\u0026ndash;85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAspartat amino transferase (U/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.5 (18.8\u0026ndash;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (30.5-142.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.7\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6 (0.9\u0026ndash;2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSodium (mmoL/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139 (136.8\u0026ndash;142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (136\u0026ndash;145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePotassium (mmoL/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4 (3.4\u0026ndash;5.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1 (3.7-5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProcalcitonin\u003c/b\u003e (\u003cb\u003e\u0026micro;g/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45 (0.1\u0026ndash;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7 (0.35\u0026ndash;3.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC-Reactive Protein (mg/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122.5 (48.3-157.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (73.5\u0026ndash;233)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIG %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.5\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9 (0.6\u0026ndash;2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIG 3. day %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.58\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3 (0.6\u0026ndash;3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIG ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09 (0.83\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09 (0.83\u0026ndash;1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIG count (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09 (0.05\u0026ndash;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18 (0.07\u0026ndash;0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIG count 3. day (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08 (0.04\u0026ndash;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16 (0.07\u0026ndash;0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAPACHE ll: Acute Physiology and Chronic Health Evaluation ll, SOFA s: Sequential Organ Failure Assessment score, IG: Immature granulocyte, IG Ratio: Immature granulocyte % day 3/ Immature granulocyte % day 0\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics, MV, and Vasoactive use differences between dead and discharged patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDischarged N:54(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDead N:33(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (55.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (35.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (59.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiac disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (40.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCOPD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMalignancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (30.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCKD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeurologic disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVasoactive drugs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (48.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (48.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (72.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (81.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAB changed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (48.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (69.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eDM: Diabetes Mellitus, HT: Hypertansion, COPD: chronic Obstructive Pulmonary Disease, CKD: Chronic Kidney Disease, MV: Mechanical Ventilation, AB changed: Empirical antibiotic changed\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eDescriptives are presented as Number (percentage) (N(%)) and compared using the Mann-Whitney U or Chi-square tests respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eROC analysis performed to define the adequacy of treatment yielded an AUC of 0.603 (95% CI: 0.479\u0026ndash;0.727), a sensitivity of 0.653, and a specificity of 0.605 were found, with a cut-off value of 0.75 for IG% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.099). With a cut-off value of 0.75 for IG% on day 3, the AUC was 0.692 (95% CI:0.58\u0026ndash;0.805), sensitivity was 0.735, and specificity was 0.579 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). For the IG ratio, the AUC was 0.676 (95% CI: 0.56\u0026ndash;0.791)), sensitivity was 0.837, and specificity was 0.526 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), with a cut-off value of 0.915 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBiomarkers are useful to diagnose, monitor treatment response, and predict prognosis of sepsis or suspected sepsis. Biomarkers must be highly specific, sensitive, reproducible and cost effective [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Many biomarkers have been investigated for their role in differentiating conditions such as infection, trauma, surgery, malignancy, and ischemia [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The IG count and IG% have recently been investigated for their roles in reflecting infection status.\u003c/p\u003e \u003cp\u003eYazla et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] found that IG count and IG% were significant biomarkers for predicting the diagnosis of complicated acute appendicitis in patients undergoing surgery for acute appendicitis. In a similar study, Durak et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] reported that IG count was an effective biomarker for predicting mesenteric ischemia and intestinal necrosis in patients undergoing laparotomy. Porizka et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] reported that IG% could be used to differentiate between infective and non-infective systemic inflammatory response syndromes (i.e.,\u0026ldquo;SIRS\u0026rdquo;) in patients undergoing cardiac surgery. Jeon et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] evaluated the IG% to be moderately effective in predicting sepsis in patients with burns and recommended it as an auxiliary test due to its cost and ease of routine use. They reported that IGs were effective markers for differentiating bacterial pneumonia in patients with severe acute respiratory syndrome coronavirus 2 (i.e., \u0026ldquo;SARS-CoV-2\u0026rdquo;) infection [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn a study conducted in a non-septic intensive care unit, blood tests were performed for 7 days, and the IG count and IG% were found to have high sensitivity and specificity in the early recognition of sepsis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In a similar study, IG% was defined as a useful and effective marker of the development of infections and septic shock [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Ayres et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] found that a low IG% demonstrated high specificity in excluding sepsis and reported that it was a useful additional marker. In a study comparing blood culture-positive groups, IG% was found to be effective for the early detection of bacteremia [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Although the IG count and IG% have supported the diagnosis of sepsis in previous studies, there has been heterogeneity in some results [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe half-life of IGs is 3 h, and the capacity of this marker to better reflect the state of inflammation compared with other markers with long half-lives is remarkable [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In our study, blood samples collected on the day of the empirical treatment were analyzed. There was no significant difference in PCT and CRP levels between the groups in which empirical treatment was appropriate and a response to treatment was obtained (i.e., group 1) and the group in which empirical treatment was inadequate (i.e., group 2). Again, there was no significant difference in IG% on the day of treatment initiation in either group. In blood samples obtained on day 3 of empirical treatment, IG% was significantly higher in the group in which antibiotics were added due to inadequate empirical treatment or antibiotic therapy was changed according to the culture results. When we compared IG% on day 3 with IG% on the day of treatment initiation, this ratio was significantly higher in the inadequate treatment group (group 2) than in the adequate treatment group (group 1). When we analyzed the number of IGs, the mean number of IGs decreased on day 3 in the adequate treatment group, whereas the mean number of IGs increased in the inadequate treatment group.\u003c/p\u003e \u003cp\u003eIn reviewing the literature, IG% and IG counts have been investigated in terms of diagnosis and prognosis of sepsis. However, it attracted our attention that the power of this hemogram parameter, which is inexpensive, easy to measure and useful, in monitoring the response to treatment has not been investigated. Our results encouraged us to investigate their roles in treatment monitoring. We believe that IG% and IG count, which are inexpensive and useful parameters, may be effective at follow-up, although further studies are required.\u003c/p\u003e \u003cp\u003eIn group 2 (in which the response to treatment was inadequate), the 28-day and hospital mortality rates were significantly higher. Again, when we compared patients who were discharged with those who were deceased, mortality was higher in the group in which antibiotics were changed; more specifically, in which treatment was inadequate. In this context, we agree that antibiotic treatment and follow-up are especially important for sepsis. The IG% and the number of IGs were higher on the day of treatment initiation and on day 3. Similar to most studies, we believe that IG% and IG counts are effective predictors of mortality.\u003c/p\u003e \u003cp\u003eOur study had several limitations. First, although this was a two-center study, the sample size was relatively small. The treatment of sepsis is a multidisciplinary approach, and we may not have standardized fluid therapy, doses of inotropes and vasoactive agents, corticosteroid support, mechanical ventilation modes and pressures, or nutritional status.\u003c/p\u003e \u003cp\u003eIn conclusion, patients treated for sepsis either recover or die. In situations in which antibiotic therapy, fluid therapy, and vasoactive agent support are involved, we believe that IG% and IG count are inexpensive, effective, and useful hemogram parameters for monitoring the bacteriological treatment of sepsis. We believe that additional studies should be performed to fill this knowledge gap in the literature.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u0026nbsp;ALT, alanine aminotransferase; APACHE, Acute Physiologic Assessment and Chronic Health Evaluation; AST, aspartate aminotransferase; CRP, C-reactive protein;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eIG, immature granulocytes; IL, interleukin; PCT, procalcitonin; SOFA, and Sequential Organ Failure Assessment \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate:\u0026nbsp;The Bolu Abant İzzet Baysal University Clinical Research Ethics Committee approved this study with the ethical code 2023/314. Written informed consent was received from all subjects or their care givers before beginning the study. All methods were carried out in accordance with relevant guidelines and regulations or Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eConsent for publication:\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials:\u0026nbsp;The datasets used and/ or analyzed during the current study are available from the corresponding author in reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests:\u0026nbsp;The authors declare no competing interests\u003c/p\u003e\n\u003cp\u003eFunding:\u0026nbsp;This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;MD, ZSY, ZE, RE, MA and MY equally contributed to the conception and design of the research. MD, ZSY, MA, RE, ZE and MY contributed to the acquisition of the data. MD, ZSY, MA, ZE, RE and MY contributed to the analysis and interpretation of the data. MD, ZSY, ZE, RE, MA and MY drafted the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements: Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSinger M, Deutschman CS, Seymour CW, et al. The Third International Consensus definitions for sepsis and septic shock (Sepsis-3) JAMA. 2016;315(8):801\u0026ndash;10. https://doi: 10.1001/jama.2016.0287\u003c/li\u003e\n\u003cli\u003eEvans L, Rhodes A, Alhazzani W, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med. 2021;47(11):1181-247. https://doi: 10.1007/s00134-021-06506-y\u003c/li\u003e\n\u003cli\u003eBhansaly P, Mehta S, Sharma N, Gupta E, Mehta S, Gupta S. Evaluation of Immature Granulocyte Count as the Earliest Biomarker for Sepsis. Indian J Crit Care Med. 2022;26(2):216-23. https://doi: 10.5005/jp-journals-10071-23920.\u003c/li\u003e\n\u003cli\u003eEvans T. Diagnosis and management of sepsis. Clin Med (Lond). 2018;18(2):146-9. https://doi: 10.7861/clinmedicine.18-2-146.\u003c/li\u003e\n\u003cli\u003eAmulic B, Cazalet C, Hayes GL, et al. Neutrophil function: from mechanisms to disease. Annu Rev Immunol 2012;30:459-89. https://doi.org/10.1146/annurev-immunol-020711-074942\u003c/li\u003e\n\u003cli\u003eFarkas JD. The complete blood count to diagnose septic shock. J Thorac Dis. 2020;12(Suppl 1):16-21. https://doi: 10.21037/jtd.2019.12.63.\u003c/li\u003e\n\u003cli\u003eBarichello, T., Generoso, J.S., Singer, M. et al. Biomarkers for sepsis: more than just fever and leukocytosis\u0026mdash;a narrative review. \u003cem\u003eCrit Care\u003c/em\u003e 2022;26(1):14. https://doi.org/10.1186/s13054-021-03862-5\u003c/li\u003e\n\u003cli\u003eChakraborty RK, Burns B. Systemic inflammatory response syndrome. In: \u003cem\u003eStatPearls.\u003c/em\u003e Treasure Island (FL): StatPearls Publishing. Copyright \u0026copy; 2021, StatPearls Publishing LLC.; 2021\u003c/li\u003e\n\u003cli\u003eYazla M, Kadıoğlu B, Demirdelen H, Aksoy FM, \u0026Ouml;zkan E, Katipoğlu B. Predictive efficacy of immature granulocytes in acute complicated appendicitis. Rev Assoc Med Bras (1992). 2024;70(12):e20241178. https://doi: 10.1590/1806-9282.20241178.\u003c/li\u003e\n\u003cli\u003eDurak D, Turhan VB, Alkurt EG, Tutan MB, T Şahiner I. The role of immature granulocyte count and delta neutrophil index in the early prediction of mesenteric ischemia. Eur Rev Med Pharmacol Sci. 2022;26(12):4238-43. https://doi: 10.26355/eurrev_202206_29060.\u003c/li\u003e\n\u003cli\u003ePorizka M, Volny L, Kopecky P, Kunstyr J, Waldauf P, Balik M. Immature granulocytes as a sepsis predictor in patients undergoing cardiac surgery. Interact Cardiovasc Thorac Surg. 2019;28(6):845-51. https://doi: 10.1093/icvts/ivy360. \u003c/li\u003e\n\u003cli\u003eJeon K, Lee N, Jeong S, Park MJ, Song W. Immature granulocyte percentage for prediction of sepsis in severe burn patients: a machine leaning-based approach. BMC Infect Dis. 2021;21(1):1258. https://doi: 10.1186/s12879-021-06971-2.\u003c/li\u003e\n\u003cli\u003eDaix T, Jeannet R, Hernandez Padilla AC, Vignon P, Feuillard J, Fran\u0026ccedil;ois B. Immature granulocytes can help the diagnosis of pulmonary bacterial infections in patients with severe COVID-19 pneumonia. J Intensive Care. 2021;9(1):58. https://doi: 10.1186/s40560-021-00575-3.\u003c/li\u003e\n\u003cli\u003evan der Geest PJ, Mohseni M, Brouwer R, van der Hoven B, Steyerberg EW, Groeneveld AB. Immature granulocytes predict microbial infection and its adverse sequelae in the intensive care unit. J Crit Care. 2014;29(4):523-7. https://doi: 10.1016/j.jcrc.2014.03.033. \u003c/li\u003e\n\u003cli\u003eAyres LS, Sgnaolin V, Munhoz TP. Immature granulocytes index as early marker of sepsis. Int J Lab Hematol. 2019;41(3):392-6. https://doi: 10.1111/ijlh.12990. \u003c/li\u003e\n\u003cli\u003eLakshmipriya V, Kavitha K, Yogalakshmi E, Sridevi M\u003csup\u003e. \u003c/sup\u003eThe Clinical Utility of Automated Immature Granulocyte Measurement in the Early Diagnosis of Bacteremia. Cureus. 2024;16(2):e53660. https://doi: 10.7759/cureus.53660. \u003c/li\u003e\n\u003cli\u003eAgnello L, Giglio RV, Bivona G, et al. Lo Sasso B, Ciaccio M. The Value of a Complete Blood Count (CBC) for Sepsis Diagnosis and Prognosis. Diagnostics (Basel). 2021;11(10):1881. https://doi: 10.3390/diagnostics11101881.\u003c/li\u003e\n\u003cli\u003eGeorgakopoulou VE, Makrodimitri S, Triantafyllou M, et al. Immature granulocytes: Innovative biomarker for SARS‑CoV‑2 infection. Mol Med Rep. 2022;26(1):217. https://doi: 10.3892/mmr.2022.12733.\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":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Immature granulocytes, Sepsis, Treatment, Intensive care unit, Pronosis","lastPublishedDoi":"10.21203/rs.3.rs-6066578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6066578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSepsis is an organ dysfunction that impairs response to infection. Inflammatory biomarkers have been used to diagnose and monitor sepsis. The aim of the present study was to determine the role of immature granulocytes (IGs) in monitoring sepsis treatment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e This two-center, prospective, observational study included patients diagnosed with sepsis according to the Sepsis-3 criteria, who were followed-up in the adult intensive care units of the Bolu Izzet Baysal State Hospital and Bolu Izzet Baysal Training and Research Hospital (Bolu Merkez/Bolu, T\u0026uuml;rkiye). Laboratory investigation results, demographic information, treatment responses, and mortality were recorded. Patients were divided into 2 groups according to treatment: appropriate (group 1); and inappropriate (group 2). Differences in the number of IGs and IG% were compared. Differences with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to be statistically significant for all analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study included 87 patients from 2 centers. The most common comorbidities were hypertension (54%) and 28-day mortality (37.9%). Empirical antibiotic therapy (43.7%) was appropriate for 38 patients (group 1) and 49 patients when the treatment was incorrect or inadequate (group 2). There were no significant differences between the groups in terms of laboratory investigation results on the day of treatment initiation. IG count and IG% on day 3 of treatment were significantly higher in group 2. Mortality was higher in patients with a high IG count (IG %) and in group 2.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIG% was a simple, inexpensive, and useful test for monitoring sepsis treatment and, in addition, IG count was also effective in predicting mortality.\u003c/p\u003e","manuscriptTitle":"Role of immature granulocytes in monitoring sepsis treatment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-03 15:02:35","doi":"10.21203/rs.3.rs-6066578/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-01T10:24:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-23T08:48:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72084048066062427432939302912728200604","date":"2025-03-22T04:44:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-20T08:59:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274466627134047219679761123645591956893","date":"2025-03-20T05:43:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-20T01:05:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-02-24T10:21:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-24T09:33:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-24T09:29:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Anesthesiology","date":"2025-02-19T19:14:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cb0ea48e-9b24-45bb-945e-a003f2195615","owner":[],"postedDate":"March 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-28T16:04:57+00:00","versionOfRecord":{"articleIdentity":"rs-6066578","link":"https://doi.org/10.1186/s12871-025-03072-4","journal":{"identity":"bmc-anesthesiology","isVorOnly":false,"title":"BMC Anesthesiology"},"publishedOn":"2025-04-23 15:57:23","publishedOnDateReadable":"April 23rd, 2025"},"versionCreatedAt":"2025-03-03 15:02:35","video":"","vorDoi":"10.1186/s12871-025-03072-4","vorDoiUrl":"https://doi.org/10.1186/s12871-025-03072-4","workflowStages":[]},"version":"v1","identity":"rs-6066578","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6066578","identity":"rs-6066578","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}