Predictive Value of IL-8 for Mortality Risk in elderly sepsis Patients of Emergency Department

Predictive Value of IL-8 for Mortality Risk in elderly sepsis Patients of Emergency Department | 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 Predictive Value of IL-8 for Mortality Risk in elderly sepsis Patients of Emergency Department Xiangqun Zhang, Bing Wei, Ying Zhang, Yixuan Li, Yang Long, Junyu Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4005892/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives The present study aimed to explore the mortality risk of interleukin-8 (IL-8) among elderly patients diagnosed with sepsis upon admission to the emergency department. Methods A total of 273 elderly patients with sepsis were admitted to the emergency department of Beijing Chaoyang Hospital from January 2022 to April 2023. In total, 220 patients were included in the present study. Serum samples were obtained within 1 hour of admission to assess serum IL-8, white blood cell (WBC), procalcitonin (PCT), C-reactive protein (CRP), and lactic acid (LAC) levels, along with other relevant parameters. The Sequential Organ Failure Score (SOFA) and Acute Physiological and Chronic Health Assessment II (APACHE II) were recorded. Logistic regression analysis was employed to identify independent predictors of mortality within 28 days for elderly patients diagnosed with sepsis. Further, the capacity of these factors to predict 28-day mortality within this patient cohort was evaluated using Receiver Operating Characteristic (ROC) curve analysis. Results The levels of lactic acid (LAC), interleukin-8 (IL-8), procalcitonin (PCT), as well as the severity scores of APACHE II and SOFA, and the albumin (ALB) score, demonstrated notable and statistically significant distinctions between the non-survivor and survivor cohorts (P < 0.05). Through logistic regression analysis, it was determined that the SOFA score, APACHE II score, LAC, and IL-8 were all significant independent predictors for 28-day mortality in elderly sepsis patients (P < 0.05). The AUC of the ROC curve for IL-8 was calculated to be 0.701, indicating a moderately predictive performance. Sensitivity and specificity were reported as 74.5% and 63.4%, respectively, with a cut-off value of 14.497. In comparison, the AUC for LAC was marginally higher at 0.708, accompanied by a sensitivity of 76.5% and a specificity of 56.3%, with a corresponding cut-off value of 1.15. Further, the AUC for APACHE II was 0.761, indicating good predictive performance. The sensitivity and specificity for APACHE II were found to be 55.0% and 91.5%, with a cut-off value of 21.5. Nevertheless, the results of the statistical analysis reveal no significant difference in the predictive value between IL-8 and LAC (Z = 0.134, P = 0.894). However, IL-8 demonstrated a slightly higher specificity (63.4%) compared to LAC (56.3%). Moreover, the present findings indicate that the combined assessment of IL-8 and SOFA score (AUC 0.862, sensitivity 71.1%, specificity 93.0%; Z = 3.454, P = 0.005) demonstrated superior predictive value for mortality compared to using IL-8 alone. Conclusions IL-8 LAC, APACHE II, and SOFA can be considered independent predictors factors for mortality of elderly sepsis patients. Utilizing the combination of IL-8 and SOFA demonstrates a heightened predictive capability compared to using any single index alone. IL-8 aged patient sepsis mortalities Figures Figure 1 Figure 2 Introduction Sepsis is globally recognized as one of the most prevalent diseases, with a considerable impact on morbidity and mortality rates. It affects numerous individuals annually, resulting in the death of approximately one-third to one-fourth of diagnosed patients.(Fleischmann-Struzek, Mellhammar et al. 2020) Older people are generally more susceptible to this condition, as demonstrated in numerous studies conducted in different regions. For instance, Kaukonen et al. performed a retrospective review of sepsis patients admitted to ICUs in Australia and New Zealand. Findings were made that more than 65% of the sepsis patients were over 65 years of age.(Kaukonen, Bailey et al. 2014) A study conducted by Martin indicated that the incidence of sepsis among individuals aged 65 and older was 20.4% greater than that observed in the population under 65 years of age. (Martin, Mannino et al. 2006) With the aging of the global population, the incidence of sepsis is anticipated to increase rapidly. This upward trend places significant pressure on healthcare systems and escalates medical insurance costs. The early recognition of the severity of sepsis is crucial in this context since timely and appropriate treatment improves prognosis.(Evans, Rhodes et al. 2021) Despite extensive research efforts in this field over the past few decades, accurately recognizing the severity of sepsis at its onset remains challenging. This challenge stems from the ambiguous definition of sepsis syndrome and the inherent individual differences among patients. Among the many biomarkers used to predict the outcomes of sepsis, lactic acid level is one of the most investigated markers. Scholars in the field have already confirmed the correlation between lactate levels and mortality in sepsis patients.(Borthwick, Brunt et al. 2012) Nonetheless, lactate levels alone are neither sensitive nor specific enough to exclude or diagnose sepsis(Hernández, Ospina-Tascón et al. 2019). Sepsis screening tools play a crucial role in facilitating the early identification of the condition. However, the predictive sensitivity and specificity of these prediction tools vary. Currently, available options in the market often exhibit poor predictive value. (Bhattacharjee, Edelson et al. 2017) As such, new biomarkers must be continuously explored to rapidly diagnose sepsis and predict prognosis. IL-8, produced by various cells engaged in inflammatory responses such as monocytes and endothelial cells, belongs to the chemokine family of cytokines. Initially identified as a mediator for neutrophil chemotaxis and activation.(Kownatzki, Kapp et al. 1986, Walz, Peveri et al. 1987) IL-8 is involved in multiple pro-inflammatory functions.(Baggiolini, Dewald et al. 1994) Its primary role involves the recruitment of neutrophils to sites of inflammation while also stimulating the proliferation and differentiation of mononuclear macrophages,(Corre, Pineau et al. 1999) including the activation of immune cells and the facilitation of angiogenesis.(Moore, Arenberg et al. 1998, Matsushima, Yang et al. 2022) IL-8 receptors are expressed on monocytes, neutrophils, endothelial cells, microglia, and astrocytes. Following the binding of IL-8 to its receptors, cellular activation ensues, leading to various inflammatory responses(Liou, Chang et al. 2014) This specific cytokine plays a pivotal role in cases of infection, with previous reports indicating its potential application in predicting mortality in children or adults with sepsis.(Wong, Cvijanovich et al. 2008) In the present study, the assumption that serum IL-8 levels measured one hour into the emergency room serve as reliable biomarkers to predict the mortality rate of elderly sepsis patients was prospectively tested. This was achieved by comparing IL-8 expression levels between groups of surviving and deceased patients. Materials and Methods 1 Subjects A total of 273 sepsis patients aged 65 years and older were admitted to the emergency department of Beijing Chaoyang Hospital between January 2022 and April 2023. Among these patients, 23 individuals with incomplete clinical data upon admission were excluded from the study. Additionally, 11 patients were excluded due to having pre-specified excluded conditions (8 with tumors, 1 with a blood disease, and 2 with end-stage liver disease). Furthermore, 11 patients were lost to follow-up, and 8 patients or their relatives declined enrollment. Consequently, 220 elderly sepsis patients were included in the study (Fig. 1: Flow diagram depicting the process of patient inclusion.). Patient demographics including gender, age, past medical history, as well as vital signs such as blood pressure, heart rate, and body temperature, were meticulously recorded. Further, within one hour of admission, the levels of IL-8, WBC, CRP, PCT, Lactate (Lac), and blood gas index were measured. Prompt blood biochemistry and X-ray examinations were also conducted. SOFA score and APACHE II score were computed based on vital signs and laboratory findings. Subsequently, patients were followed up for 28 days. The endpoint event for the present study was the 28-day mortality. Patients under investigation were classified into two groups according to their survival outcomes within 28 days of admission: the survival group, which included 71 cases, and the death group, consisting of 149 cases. 2 Inclusion and exclusion criteria: The research enrolled patients who met the following criteria: (1) aged 65 or older, and (2) diagnosed with sepsis. Exclusion criteria included: (1) patients with advanced pathologies such as advanced or metastatic malignant tumors, end-stage liver or kidney disease; (2) recent hospitalization within 14 days before symptom onset; (3) a medical history including tuberculosis, severe immunosuppressive diseases, blood disorders, or systemic anticoagulation; or (4) previous treatment received outside the hospital. Additionally, patients who or whose families expressed opposition to participating in the study were also excluded. 3 Diagnostic Criteria Sepsis was diagnosed according to the latest version (3.0) of diagnostic criteria for sepsis published in the Journal of the American Medical Association in 2016(Singer, Deutschman et al. 2016). The academic community acknowledges that a SOFA score equal to or greater than 2 can aid in the diagnosis of sepsis. Clinical bedside quick SOFA score (respiratory rate ≥ 22 times /min, altered consciousness. systolic blood pressure ≤ 100 mm Hg) can be used to diagnose sepsis when the patient meets the criteria outlined in at least 2 of the qSOFA items. After meeting the described criteria, patients were assessed for organ dysfunction. 4 Ethics According to study protocol, prior approval was obtained from the Ethics Committee of Beijing Chaoyang Hospital, which is affiliated with Capital Medical University (approval number: 2021-Department 636). Informed consent was acquired from patients or their legal guardians before any treatments or tests, ensuring their comprehension and agreement. 5 Laboratory examination Within 1 hour of patient admission, a blood sample of 5–10 mL was collected utilizing either heparin or ethylenediamine tetraacetate (EDTA) as an anticoagulant. The collected blood samples were promptly stored at a temperature of -80°C. Subsequently, the level of circulating IL-8 in the plasma sample was quantified using the Human XL Cytokine Luminex (R) Performance Assay 46-plex Fixed Panel (LKTM014B, R&D) according to the manufacturer's instructions. In addition to the parameters mentioned, other relevant laboratory parameters including blood counts and routine biochemistry were measured in the clinical laboratory. 6 Statistical analysis All statistical analyses were performed using SPSS 16.0 (SPSS Inc., Chicago, IL, USA). Descriptive statistical methods were applied to both categorical and continuous variables. Normally distributed data were expressed as mean ± standard deviation (mean ± SD) and compared using independent-samples t-tests. For data that did not follow a normal distribution, medians and interquartile ranges (IQR) were utilized, and differences between compared groups were assessed using the Mann–Whitney test. Logistic regression analysis was conducted to identify independent predictors of 28-day mortality. Receiver operating characteristic (ROC) curves were constructed, and the area under the curve (AUC) was calculated. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also determined. The threshold value was ascertained utilizing the Youden method. The AUCs were compared using the Z-test. All statistical tests were two-tailed, and a significance level of P < 0.05 was considered statistically significant. Results 1. Baseline data of elderly sepsis patients The baseline data of the patients are shown in Table 1 . Among them, 149 patients died of sepsis and 71 patients were discharged after treatment. The results of the t-test show a significant difference in age between the non-survivor and survivor groups (72.19 ± 12.989 vs. 76.64 ± 11.213) (P = 0.009). There were also significant differences in Lac (1.1(0.8, 1.5) vs. 1.6 (1.2, 4.35)), PCT (0.05 (0.05, 0.21) vs. 0.05 (0.05, 1.93)), ALB (36.3 (30.0,41.8) vs. 32.9 (24.6, 38)), SOFA score (5 (3, 5) vs. 9 (6, 11.25)), and the APACHE2 score (15.5(11.25, 18) vs. 22 (17, 27)) IL-8 (10.85(5.13, 31.68) vs. 25.1 (11.29, 116.42)) between the two groups (P < 0.01) (Table 1 ). No statistical differences were identified in other indicators (Table 1 ). Table 1 Baseline data of emergency elderly patients with sepsis Survivor(n = 71) Non-Survivor(n = 149) P Age, years 72.19 ± 12.989 76.64 ± 11.213 0.009 Male, % 45(62.5) 87(58.4) 0.66 COPD, % 19(26.8) 35(23.3) 0.61 CVD, % 26(36.6) 44(29.3) 0.28 Hypertension, % 31(43) 69(45) 0.77 DM, % 28(36.8) 48(63.2) 0.29 CHF, % 9(12.7) 35(23.3) 0.07 CRD, % 5(7) 17(11.3) 0.47 MBP, mmHg 100.7(84.3,108.3) 100.83(90.0,116.25) 0.37 Respiratory rate, beats/min 20(15.75,22.0) 20.0(18.0,21.0 0.39 Temperature, °C 36.35(36,36.5) 34.4(36.2,36.6) 0.37 PaO2, mmHg 85(66.0,104.0) 77(65.0,93.5) 0.49 Lac, mmol/L 1.1(0.8,1.5) 1.6(1.2,4.35) 0 PCT, ng/mL 0.05(0.05,0.21) 0.05(0.05,1.93) 0.01 WBC, ×109/L 8.6(7.2,11.7) 9.0(7.3,11.8) 0.46 PaO2/FiO2 303.95(176.71,411.22 271.0(184.0,344.0) 0.22 ALB 36.3(30.0,41.8) 32.9(24.6,38) 0 CRP 22.5(8.0,83.0) 14.5(8.0,80.0) 0.74 SOFA score 5(3,5) 9(6,11.25) 0 APCHE2 15.5(11.25,18.0) 22(17,27) 0 IL-8 10.85(5.13,31.68) 25.1(11.29,116.42) 0 2. Logistic regression analysis was performed on the significant indicators in the univariate regression analysis. The non-survivor group had significant differences in Lac levels (P = 0.043, OR = 1.148, 95%CI: 1.004–1.313), IL-8(P = 0.001, OR = 1.011 95%CI: 1.005–1.017), SOFA score (P < 0.001, OR = 1.413, 95%CI: 1.212–1.648) and APACHE2 score (P < 0.001, OR = 1.133, 95%CI: 1.059–1.211). Such results reveal that the Lac level, IL-8 level, SOFA score, and APCHE2 score were independent risk factors for 28-day mortality in elderly sepsis patients (Table 2 ). 3. Prediction of the mortality in elderly sepsis patients Table 2 Binary logistic regression analysis of the mortality for elderly sepsis patients β S.E Wald P OR (95% CI) PCT -0.022 0.055 0.157 0.692 0.978(0.878–1.090) Lac 0.138 0.068 4.083 0.043 1.148(1.004–1.313) ALB -0.02 0.02 0.949 0.33 0.981(0.943–1.020) IL-8 0.01 0.003 12.112 0.001 1.011(1.005–1.017) SOFA 0.346 0.078 19.466 0 1.413(1.212–1.648) APACHE2 0.124 0.034 13.285 0 1.133(1.059–1.211) Constant -3.905 1.111 12.357 0 The analysis of predictive factors for mortality in elderly patients with sepsis is presented in Table 3 . The ROC curves illustrating the predictive effects for sepsis patients are displayed in Fig. 2. The AUC values of the Lac, PCT, IL-8, SOFA score, and APACHE2 score for 28-day mortality were 0.708 (95%CI:0.637–0.780, P < 0.001), 0.593 (95%CI: 0.514–0.671, P = 0.026), 0.701 (95%CI: 0.626–0.775, P < 0.001), 0.844(95%CI: 0.788–0.899, P < 0.001),0.761 (95%CI: 0.696–0.826, P < 0.001), respectively. The cut-off values for the Lac, PCT, IL-8, SOFA score, and APACHE2 score maximizing the composite of specificity and sensitivity in the prediction of sepsis patients in 28-day mortality were 1.15, 0.115,14.497, 5.5, and 21.5 (Table 3 and Fig. 2: Receiver operating characteristic (ROC) curves of biomarkers combined with severe score for predicting 28-day mortality in elderly sepsis patients). Table 3 Prediction of the mortality in elderly sepsis patients Variables AUC (95% CI) S.E P Sensitivity Specificity Cut off PPV NPV LR+ LR- Lac 0.708(0.637–0.780) 0.036 0 0.765 0.563 1.15 56.3 76.7 1.751 0.417 PCT 0.593(0.514–0.671) 0.04 0.026 0.463 0.732 0.115 73.6 46.3 1.728 0.734 IL-8 0.701(0.626–0.775) 0.038 0 0.745 0.634 14.497 62.5 74 2.036 0.402 SOFA 0.844(0.788–0.899) 0.028 0 0.812 0.789 5.5 79.2 81.3 3.848 0.238 APACHE2 0.761(0.696–0.826) 0.033 0 0.55 0.915 21.5 91.7 54.7 6.471 0.492 IL 8 + SOFA 0.862(0.808–0.915) 0.027 0 0.711 0.93 95.5 60.6 10.157 0.311 IL 8 + APACHE 0.797(0.734–0.860) 0.032 0 0.705 0.831 83.1 69.8 4.172 0.355 Lac + SOFA 0.850(0.795–0.905) 0.028 0 0.691 0.93 95.4 58.9 9.871 0.332 Lac + APACHE 0.793(0.732–0.854) 0.031 0 0.658 0.813 91.6 54.4 3.519 0.421 Subsequently, Lac IL-8, SOFA, and APACHE2 data were paired to predict mortality in elderly patients with sepsis. The findings reveal that the combination of IL-8 and SOFA exhibited the highest AUC of 86.2% and specificity of 93%. Additionally, the sensitivity of the SOFA score was the highest at 81.2%. Detailed results are presented in Table 3 . Further, the AUCs for predicting mortality among sepsis patients were compared, showing no significant differences between IL-8 and Lac in predicting 28-day mortality (Z = 0.134, P = 0.894). Discussion Sepsis is a serious medical condition characterized by organ dysfunction resulting from the host's dysregulated response to infection. International epidemiological data indicate that the mortality rate of patients with sepsis exceeds that of myocardial infarction, making it the leading cause of non-cardiac deaths in the ICU(Mayr, Yende et al. 2014). Among all pat ients with sepsis, older individuals are more susceptible to infections caused by gram-negative microorganisms. As such, the incidence of the disease is significantly higher in the elderly compared to younger individuals. A previous study conducted by Martin reported that adults over the age of 65 were 1.31 times more susceptible to acquiring a gram-negative infection in comparison to those under the age of 65. (Martin, Mannino et al. 2006) Additionally, elderly adults with infections frequently exhibit atypical symptoms, which can complicate the swift diagnosis and prompt initiation of treatment. Fever, a common clinical manifestation of infection and a frequently encountered sepsis-associated symptom is not present in roughly 30–50% of the elderly patients afflicted with infection(Ewig, Klapdor et al. 2012). Hence, early diagnosis and treatment of numerous elderly patients threatened by sepsis can be difficult to achieve. Given this context, the identification of safe and effective prognostic biomarkers in elderly sepsis patients holds significant importance for clinicians. These advancements could aid in the development and implementation of timely and effective treatment strategies to reduce mortality rates among elderly sepsis patients. Many researchers concur that Procalcitonin (PCT) serves as a highly sensitive and specific biomarker for detecting severe bacterial infections. Thus, PCT measurements offer valuable insights into clinical scenarios where they are most beneficial(Hoeboer, van der Geest et al. 2015, Mihajlovic, Brkic et al. 2017). The present experimental results suggest (and confirm) that PCT is meaningful in predicting 28-day mortality in elderly patients with sepsis ( P = 0.01). The AUC was determined to be 59.3%, with a sensitivity of 46.3% and specificity of 73.2%. These results generally align with the outcomes reported in previous related studies. Moreover, regarding 28-day mortality, The results of the binary logistic regression analysis reveal that Lac and IL-8, together with the SOFA and APACHE II scores, acted as independent predictors, whereas PCT did not. The AUC of the SOFA score was 0.844, surpassing that of APACHE II (0.761), Lac (0.740), and IL-8 (0.751). Previous studies have already demonstrated the effectiveness of the SOFA scoring system in predicting mortality among adult sepsis patients(Raith, Udy et al. 2017). This consistency is reflected in the present results, where the SOFA score exhibited the highest prognostic value for elderly patients with sepsis. The cytokine interleukin IL-8, known for its pro-inflammatory properties, is a key member of the chemokine family and plays a crucial role in the pathogenesis of sepsis. Numerous studies conducted over the years have shown that IL-8 can serve as a valuable biomarker for predicting infections and septic events. Kraft et al. confirmed that IL-8 demonstrates remarkable sensitivity and specificity as a biomarker for assessing burn size, particularly when its plasma concentration is below a threshold of 234 pg/ml. Moreover, at higher levels, plasma IL-8 concentration exhibits a strong correlation with the frequency of septic events. Moreover, IL-8 holds potential as a valuable biomarker for predicting infections and septic incidents in patients suffering from burn injuries(Kraft, Herndon et al. 2015). In another experiment conducted by Liu XW, patients with IL-8 levels exceeding normal were found to be more susceptible to acute lung injury(Liu, Ma et al. 2019). Further, it has been suggested that the initial levels of IL-8 can be one of the most prognostic factors for mortality in sepsis patients(Zhou, Cheng et al. 2015). In a study by Wong et al.(Liou, Chang et al. 2014) involving pediatric patients with serum IL-8 levels of 220 pg/ml or lower obtained within 24 hours of admission, the data demonstrated the effectiveness of IL-8 in predicting a significant probability of survival in children with septic shock. Therefore, one may infer that IL-8 serves as a suitable stratification tool for interventional trials related to pediatric septic shock. The present study aimed to verify the association between IL-8 levels and the prognosis of sepsis in elderly patients. In the experimental trials, findings were made that IL-8 in the non-survivor group was higher than in the survivor group [10.85(5.13,31.68) vs. 25.1(11.29-116.42), P = 0:00] and IL-8 emerged as a significant independent prognostic indicator for the 28-day mortality of elderly individuals, an outcome confirmed by logistic analysis [OR 1.011(1.005–1.017)), P = 0:001]. The sensitivity and specificity of IL-8 in predicting sepsis were 74.3% and 63.4% respectively, and the area under the ROC curve was 70.1%. Compared with the levels of blood lactic acid, there was no statistical significance (Z = 0.1337, P = 0.894) between IL-8 and lactic acid. Various related studies have evaluated lactate as an effective prognostic indicator for assessing sepsis patients(Gattinoni, Vasques et al. 2019, Grealish, Chiew et al. 2021). For instance, Grealish et al. investigated lactate values in critically ill patients admitted to the Emergency Department with or without diabetes and conducted a multivariable analysis with this patient sample. Their results demonstrated that lactate remained an independent predictor of ICU/in-hospital mortality in the non-diabetes group(Gattinoni, Vasques et al. 2019). In the present study, the sensitivity of IL-8 was 74.5%, while the cut-off value of IL-8 was 14.497pg/ml, nearly 76.5% for LAC. The specificity of IL-8 was 63.4%, which was slightly higher than that of lactic acid (56.3%). Both IL-8 and LAC were found to be independent risk factors for the prognosis of elderly patients with sepsis. Comparing their AUCs (Z = 0.134, P = 0.894), no statistically significant differences were found in the AUC values for IL-8 and LAC. Additionally, sensitivity and specificity analyses showed similar diagnostic value between IL-8 and LAC. In some respects, IL-8 may even be considered superior to lactate. The AUC of IL-8 in combination with the SOFA score was significantly higher when compared to that of IL-8 alone (Z = 3.454, P = 0.005). The AUC of IL-8 in combination with the SOFA score was slightly higher than that of SOFA (Z = 0.463, P = 0.644). The SOFA score was proposed by the European Society of Intensive Care Medicine (ESICM) in 1994 (Vincent, Moreno et al. 1996) and has since been associated with mortality rates in sepsis patients(Ferreira, Bota et al. 2001, Park, Lee et al. 2023). At present, it is widely utilized to assess the severity of sepsis and to perform prognostic evaluations(Oh, Roh et al. 2020). The present results are consistent with these claims and indicate that SOFA scores have the highest predictive value for sepsis patients. Compared with the SOFA score, the APACHE II score also demonstrated a high predictive value for 28-day mortality. While no single prediction biomarker stood out prominently, their predictive value increased significantly when combined with other prognostic indicators. Further findings were made that the utilization of plasma IL8 thresholds consistently aided in the identification of individuals at an elevated risk of mortality among elderly patients with sepsis. The results demonstrate that IL-8 could potentially serve as a prognostic factor for sepsis trials. After integrating it with the SOFA and APACHE II scoring systems, the prognostic value of IL-8 was significantly enhanced, providing valuable guidance for the clinical management and prognosis of sepsis. Utilizing these markers in the analysis of high-risk elderly patients could improve the efficacy and robustness of clinical trials while reducing the number of sepsis patients exposed to potentially harmful treatments unnecessarily. In the present literature review, several studies on IL-8 and its relationship with the prognosis of elderly patients with sepsis were discerned. The present study represents a pioneering effort in this field, and the conclusions hold significant importance for both the scholarly and medical communities. In future studies, the aim will be to further explore the risk stratification and predictive efficacy of IL-8 in the context of elderly septic patients to enhance the value of the present findings. Nonetheless, the present research has several limitations. First, the present study was limited to a single center and had a relatively small sample size. As such, large-scale, multi-center studies are needed to verify the results. Second, the dynamic correlation between IL-8 fluctuations and the prognosis of elderly sepsis was not actively monitored, necessitating further exploration in future studies. Lastly, while initial serological markers provide insight into the inflammatory response and disease severity at the disease's onset, they have limitations in predicting the ultimate prognosis comprehensively. Conclusion In summary, IL-8, lactate (LAC), SOFA, and APACHE II scores each independently demonstrate significance in predicting mortality among elderly patients with sepsis. Particularly noteworthy is the high predictive value observed with the combination of IL-8 and SOFA scores. This underscores the importance of early and prompt evaluation for guiding therapeutic interventions and predicting outcomes in this vulnerable population. Declarations Funding: Supported by Beijing Municipal Science and Technology Commission China, No. Z211100002921061 Conflicts of interest: All author reports no conflicts of interest in this work. Ethics: Ethics Committee of Beijing Chaoyang Hospital, which is affiliated with Capital Medical. University (approval number: 2021-Department 636). Informed consent was acquired from patients or their legal guardians before any treatments or tests, ensuring their comprehension and agreement. Availability of data and material : The authors confirm that the data supporting the findings of this study are available in the article. Code availability : not available. Consent for publication : Written informed consent for publication was obtained from all participants. Authors' contributions : Xiangqun Zhang have made substantial contributions to the conception and design of the work; and the acquisition, analysis, OR interpretation of data; OR the creation of new software used in the work; OR have drafted the work or substantively revised it Shubin Gun andJunyu Wang designed the work; Bing Wei, Ying Zhang, and YixuanLi helped to collect the data. References Baggiolini, M., B. Dewald and B. Moser (1994). "Interleukin-8 and related chemotactic cytokines--CXC and CC chemokines." Adv Immunol 55 : 97-179. Bhattacharjee, P., D. P. Edelson and M. M. Churpek (2017). "Identifying Patients With Sepsis on the Hospital Wards." Chest 151 (4): 898-907. Borthwick, H. A., L. K. Brunt, K. L. Mitchem and C. Chaloner (2012). "Does lactate measurement performed on admission predict clinical outcome on the intensive care unit? A concise systematic review." 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Pasticci, E. Duscio, F. Vassalli, L. G. Forni, D. Payen, M. Cressoni, A. Zanella, R. Latini, M. Quintel and J. J. Marini (2019). "Understanding Lactatemia in Human Sepsis. Potential Impact for Early Management." Am J Respir Crit Care Med 200 (5): 582-589. Grealish, M., A. L. Chiew, W. Varndell and B. Depczynski (2021). "The relationship between admission glucose and lactate with critical illness amongst adult patients presenting to the emergency department." Acta Diabetol 58 (10): 1343-1349. Hernández, G., G. A. Ospina-Tascón, L. P. Damiani, E. Estenssoro, A. Dubin, J. Hurtado, G. Friedman, R. Castro, L. Alegría, J. L. Teboul, M. Cecconi, G. Ferri, M. Jibaja, R. Pairumani, P. Fernández, D. Barahona, V. Granda-Luna, A. B. Cavalcanti, J. Bakker, G. Hernández, G. Ospina-Tascón, L. Petri Damiani, E. Estenssoro, A. Dubin, J. Hurtado, G. Friedman, R. Castro, L. Alegría, J. L. Teboul, M. Cecconi, M. Cecconi, G. Ferri, M. Jibaja, R. Pairumani, P. Fernández, D. Barahona, A. B. Cavalcanti, J. 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Ramirez, M. Tobar, F. García, F. Picoita, N. Remache, V. Granda, F. Paredes, E. Barzallo, P. Garcés, F. Guerrero, S. Salazar, G. Torres, C. Tana, J. Calahorrano, F. Solis, P. Torres, L. Herrera, A. Ornes, V. Peréz, G. Delgado, A. López, E. Espinosa, J. Moreira, B. Salcedo, I. Villacres, J. Suing, M. Lopez, L. Gomez, G. Toctaquiza, M. Cadena Zapata, M. A. Orazabal, R. Pardo Espejo, J. Jimenez, A. Calderón, G. Paredes, J. L. Barberán, T. Moya, H. Atehortua, R. Sabogal, G. Ortiz, A. Lara, F. Sanchez, A. Hernán Portilla, H. Dávila, J. A. Mora, L. E. Calderón, I. Alvarez, E. Escobar, A. Bejarano, L. A. Bustamante and J. L. Aldana (2019). "Effect of a Resuscitation Strategy Targeting Peripheral Perfusion Status vs Serum Lactate Levels on 28-Day Mortality Among Patients With Septic Shock: The ANDROMEDA-SHOCK Randomized Clinical Trial." Jama 321 (7): 654-664. Hoeboer, S. H., P. J. van der Geest, D. Nieboer and A. B. Groeneveld (2015). "The diagnostic accuracy of procalcitonin for bacteraemia: a systematic review and meta-analysis." Clin Microbiol Infect 21 (5): 474-481. Kaukonen, K. M., M. Bailey, S. Suzuki, D. Pilcher and R. Bellomo (2014). "Mortality related to severe sepsis and septic shock among critically ill patients in Australia and New Zealand, 2000-2012." Jama 311 (13): 1308-1316. Kownatzki, E., A. Kapp and S. Uhrich (1986). "Novel neutrophil chemotactic factor derived from human peripheral blood mononuclear leucocytes." Clin Exp Immunol 64 (1): 214-222. Kraft, R., D. N. Herndon, C. C. Finnerty, R. A. Cox, J. Song and M. G. Jeschke (2015). "Predictive Value of IL-8 for Sepsis and Severe Infections After Burn Injury: A Clinical Study." Shock 43 (3): 222-227. Liou, J. W., F. T. Chang, Y. Chung, W. Y. Chen, W. B. Fischer and H. J. Hsu (2014). "In silico analysis reveals sequential interactions and protein conformational changes during the binding of chemokine CXCL-8 to its receptor CXCR1." PLoS One 9 (4): e94178. Liu, X. W., T. Ma, Q. Cai, L. Wang, H. W. Song and Z. Liu (2019). "Elevation of Serum PARK7 and IL-8 Levels Is Associated With Acute Lung Injury in Patients With Severe Sepsis/Septic Shock." J Intensive Care Med 34 (8): 662-668. Martin, G. S., D. M. Mannino and M. Moss (2006). "The effect of age on the development and outcome of adult sepsis." Crit Care Med 34 (1): 15-21. Matsushima, K., D. Yang and J. J. Oppenheim (2022). "Interleukin-8: An evolving chemokine." Cytokine 153 : 155828. Mayr, F. B., S. Yende and D. C. Angus (2014). "Epidemiology of severe sepsis." Virulence 5 (1): 4-11. Mihajlovic, D., S. Brkic, A. Uvelin, B. Draskovic and V. Vrsajkov (2017). "Use of presepsin and procalcitonin for prediction of SeptiFast results in critically ill patients." J Crit Care 40 : 197-201. Moore, B. B., D. A. Arenberg, C. L. Addison, M. P. Keane, P. J. Polverini and R. M. Strieter (1998). "CXC chemokines mechanism of action in regulating tumor angiogenesis." Angiogenesis 2 (2): 123-134. Oh, Y., J. Roh, J. Lee, H. S. Chung, K. Lee and M. K. Lee (2020). "Sequential Organ Failure Assessment score as a predictor of mortality in ventilated patients with multidrug-resistant bacteremia." Acute Crit Care 35 (3): 169-178. Park, H., J. Lee, D. K. Oh, M. H. Park, C. M. Lim, S. M. Lee and H. Y. Lee (2023). "Serial evaluation of the serum lactate level with the SOFA score to predict mortality in patients with sepsis." Sci Rep 13 (1): 6351. Raith, E. P., A. A. Udy, M. Bailey, S. McGloughlin, C. MacIsaac, R. Bellomo and D. V. Pilcher (2017). "Prognostic Accuracy of the SOFA Score, SIRS Criteria, and qSOFA Score for In-Hospital Mortality Among Adults With Suspected Infection Admitted to the Intensive Care Unit." Jama 317 (3): 290-300. Singer, M., C. S. Deutschman, C. W. Seymour, M. Shankar-Hari, D. Annane, M. Bauer, R. Bellomo, G. R. Bernard, J. D. Chiche, C. M. Coopersmith, R. S. Hotchkiss, M. M. Levy, J. C. Marshall, G. S. Martin, S. M. Opal, G. D. Rubenfeld, T. van der Poll, J. L. Vincent and D. C. Angus (2016). "The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)." Jama 315 (8): 801-810. Vincent, J. L., R. Moreno, J. Takala, S. Willatts, A. De Mendonça, H. Bruining, C. K. Reinhart, P. M. Suter and L. G. Thijs (1996). "The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine." Intensive Care Med 22 (7): 707-710. Walz, A., P. Peveri, H. Aschauer and M. Baggiolini (1987). "Purification and amino acid sequencing of NAF, a novel neutrophil-activating factor produced by monocytes." Biochem Biophys Res Commun 149 (2): 755-761. Wong, H. R., N. Cvijanovich, D. S. Wheeler, M. T. Bigham, M. Monaco, K. Odoms, W. L. Macias and M. D. Williams (2008). "Interleukin-8 as a stratification tool for interventional trials involving pediatric septic shock." Am J Respir Crit Care Med 178 (3): 276-282. Zhou, M., S. Cheng, J. Yu and Q. Lu (2015). "Interleukin-8 for diagnosis of neonatal sepsis: a meta-analysis." PLoS One 10 (5): e0127170. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4005892","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":285010783,"identity":"58e5a0d7-99eb-46df-ab59-36e48d1860c9","order_by":0,"name":"Xiangqun Zhang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiangqun","middleName":"","lastName":"Zhang","suffix":""},{"id":285010784,"identity":"d5e53566-3ef0-4b4f-9626-6dc73a729dd1","order_by":1,"name":"Bing Wei","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Wei","suffix":""},{"id":285010785,"identity":"1e22f0d8-4bcb-4dd3-8508-ebfe413c6ff1","order_by":2,"name":"Ying Zhang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zhang","suffix":""},{"id":285010786,"identity":"ccbe337c-5cda-4c4e-8bc5-7f937bf8ae68","order_by":3,"name":"Yixuan Li","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yixuan","middleName":"","lastName":"Li","suffix":""},{"id":285010787,"identity":"2373a0c6-b611-4853-8a2a-4aa931bf42ff","order_by":4,"name":"Yang Long","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Long","suffix":""},{"id":285010788,"identity":"a0622da9-c989-4521-8bdd-51272045dae4","order_by":5,"name":"Junyu Wang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junyu","middleName":"","lastName":"Wang","suffix":""},{"id":285010789,"identity":"f5c70b32-9973-4aa0-ba24-8f4fbb00bfb0","order_by":6,"name":"Shubin Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDACCQY2hgQGCR774w0MDIwNxGuxkWM4c4AULQwMacYMNxKI1CI/u8fswcMdhxMbZ74xfPBzB4M8v9gB/FoM7pwxN0g8czixWTrH2LD3DIPhzNkJBLRI5JhJJLYdTmyTBjJ42xgSDG4T0CI/A6qlR/KM+c+/xGhhuAHWkmYsIcFjxkyULQY30soNEtts5Ax40oqlZdskCPtFfkbytoc/2yR4DNgPb/z4ts1Gnl+akMMQgMOAARRNpAD2ByQpHwWjYBSMgpEDAFL3Q5sja6/bAAAAAElFTkSuQmCC","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shubin","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2024-03-02 08:14:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4005892/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4005892/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53886282,"identity":"418578cb-d021-4c14-8d86-08f234ff9648","added_by":"auto","created_at":"2024-04-01 19:25:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65196,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4005892/v1/32c716713fcf0b758fe578e6.png"},{"id":53886283,"identity":"15e4fdc5-8c90-4340-a5ba-97156a4a64aa","added_by":"auto","created_at":"2024-04-01 19:25:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":241651,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4005892/v1/21e63df1a6ee0cec30448ef2.png"},{"id":61385605,"identity":"b6a8a2ed-937d-4268-badc-c9e6f32e3835","added_by":"auto","created_at":"2024-07-30 07:02:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":893143,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4005892/v1/b3c12e7f-95a2-4a28-ad73-4736d04b9249.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Value of IL-8 for Mortality Risk in elderly sepsis Patients of Emergency Department","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis is globally recognized as one of the most prevalent diseases, with a considerable impact on morbidity and mortality rates. It affects numerous individuals annually, resulting in the death of approximately one-third to one-fourth of diagnosed patients.(Fleischmann-Struzek, Mellhammar et al. 2020) Older people are generally more susceptible to this condition, as demonstrated in numerous studies conducted in different regions. For instance, Kaukonen et al. performed a retrospective review of sepsis patients admitted to ICUs in Australia and New Zealand. Findings were made that more than 65% of the sepsis patients were over 65 years of age.(Kaukonen, Bailey et al. 2014) A study conducted by Martin indicated that the incidence of sepsis among individuals aged 65 and older was 20.4% greater than that observed in the population under 65 years of age. (Martin, Mannino et al. 2006) With the aging of the global population, the incidence of sepsis is anticipated to increase rapidly. This upward trend places significant pressure on healthcare systems and escalates medical insurance costs.\u003c/p\u003e \u003cp\u003eThe early recognition of the severity of sepsis is crucial in this context since timely and appropriate treatment improves prognosis.(Evans, Rhodes et al. 2021) Despite extensive research efforts in this field over the past few decades, accurately recognizing the severity of sepsis at its onset remains challenging. This challenge stems from the ambiguous definition of sepsis syndrome and the inherent individual differences among patients. Among the many biomarkers used to predict the outcomes of sepsis, lactic acid level is one of the most investigated markers. Scholars in the field have already confirmed the correlation between lactate levels and mortality in sepsis patients.(Borthwick, Brunt et al. 2012) Nonetheless, lactate levels alone are neither sensitive nor specific enough to exclude or diagnose sepsis(Hern\u0026aacute;ndez, Ospina-Tasc\u0026oacute;n et al. 2019). Sepsis screening tools play a crucial role in facilitating the early identification of the condition. However, the predictive sensitivity and specificity of these prediction tools vary. Currently, available options in the market often exhibit poor predictive value. (Bhattacharjee, Edelson et al. 2017) As such, new biomarkers must be continuously explored to rapidly diagnose sepsis and predict prognosis.\u003c/p\u003e \u003cp\u003eIL-8, produced by various cells engaged in inflammatory responses such as monocytes and endothelial cells, belongs to the chemokine family of cytokines. Initially identified as a mediator for neutrophil chemotaxis and activation.(Kownatzki, Kapp et al. 1986, Walz, Peveri et al. 1987) IL-8 is involved in multiple pro-inflammatory functions.(Baggiolini, Dewald et al. 1994) Its primary role involves the recruitment of neutrophils to sites of inflammation while also stimulating the proliferation and differentiation of mononuclear macrophages,(Corre, Pineau et al. 1999) including the activation of immune cells and the facilitation of angiogenesis.(Moore, Arenberg et al. 1998, Matsushima, Yang et al. 2022) IL-8 receptors are expressed on monocytes, neutrophils, endothelial cells, microglia, and astrocytes. Following the binding of IL-8 to its receptors, cellular activation ensues, leading to various inflammatory responses(Liou, Chang et al. 2014) This specific cytokine plays a pivotal role in cases of infection, with previous reports indicating its potential application in predicting mortality in children or adults with sepsis.(Wong, Cvijanovich et al. 2008)\u003c/p\u003e \u003cp\u003eIn the present study, the assumption that serum IL-8 levels measured one hour into the emergency room serve as reliable biomarkers to predict the mortality rate of elderly sepsis patients was prospectively tested. This was achieved by comparing IL-8 expression levels between groups of surviving and deceased patients.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e1 Subjects\u003c/p\u003e \u003cp\u003eA total of 273 sepsis patients aged 65 years and older were admitted to the emergency department of Beijing Chaoyang Hospital between January 2022 and April 2023. Among these patients, 23 individuals with incomplete clinical data upon admission were excluded from the study. Additionally, 11 patients were excluded due to having pre-specified excluded conditions (8 with tumors, 1 with a blood disease, and 2 with end-stage liver disease). Furthermore, 11 patients were lost to follow-up, and 8 patients or their relatives declined enrollment. Consequently, 220 elderly sepsis patients were included in the study (Fig.\u0026nbsp;1: Flow diagram depicting the process of patient inclusion.). Patient demographics including gender, age, past medical history, as well as vital signs such as blood pressure, heart rate, and body temperature, were meticulously recorded. Further, within one hour of admission, the levels of IL-8, WBC, CRP, PCT, Lactate (Lac), and blood gas index were measured. Prompt blood biochemistry and X-ray examinations were also conducted. SOFA score and APACHE II score were computed based on vital signs and laboratory findings. Subsequently, patients were followed up for 28 days. The endpoint event for the present study was the 28-day mortality. Patients under investigation were classified into two groups according to their survival outcomes within 28 days of admission: the survival group, which included 71 cases, and the death group, consisting of 149 cases.\u003c/p\u003e \u003cp\u003e2 Inclusion and exclusion criteria:\u003c/p\u003e \u003cp\u003eThe research enrolled patients who met the following criteria: (1) aged 65 or older, and (2) diagnosed with sepsis. Exclusion criteria included: (1) patients with advanced pathologies such as advanced or metastatic malignant tumors, end-stage liver or kidney disease; (2) recent hospitalization within 14 days before symptom onset; (3) a medical history including tuberculosis, severe immunosuppressive diseases, blood disorders, or systemic anticoagulation; or (4) previous treatment received outside the hospital. Additionally, patients who or whose families expressed opposition to participating in the study were also excluded.\u003c/p\u003e \u003cp\u003e3 Diagnostic Criteria\u003c/p\u003e \u003cp\u003eSepsis was diagnosed according to the latest version (3.0) of diagnostic criteria for sepsis published in the Journal of the American Medical Association in 2016(Singer, Deutschman et al. 2016). The academic community acknowledges that a SOFA score equal to or greater than 2 can aid in the diagnosis of sepsis. Clinical bedside quick SOFA score (respiratory rate\u0026thinsp;\u0026ge;\u0026thinsp;22 times /min, altered consciousness. systolic blood pressure\u0026thinsp;\u0026le;\u0026thinsp;100 mm Hg) can be used to diagnose sepsis when the patient meets the criteria outlined in at least 2 of the qSOFA items. After meeting the described criteria, patients were assessed for organ dysfunction.\u003c/p\u003e \u003cp\u003e4 Ethics\u003c/p\u003e \u003cp\u003e According to study protocol, prior approval was obtained from the Ethics Committee of Beijing Chaoyang Hospital, which is affiliated with Capital Medical University (approval number: 2021-Department 636). Informed consent was acquired from patients or their legal guardians before any treatments or tests, ensuring their comprehension and agreement.\u003c/p\u003e \u003cp\u003e5 Laboratory examination\u003c/p\u003e \u003cp\u003eWithin 1 hour of patient admission, a blood sample of 5\u0026ndash;10 mL was collected utilizing either heparin or ethylenediamine tetraacetate (EDTA) as an anticoagulant. The collected blood samples were promptly stored at a temperature of -80\u0026deg;C. Subsequently, the level of circulating IL-8 in the plasma sample was quantified using the Human XL Cytokine Luminex (R) Performance Assay 46-plex Fixed Panel (LKTM014B, R\u0026amp;D) according to the manufacturer's instructions. In addition to the parameters mentioned, other relevant laboratory parameters including blood counts and routine biochemistry were measured in the clinical laboratory.\u003c/p\u003e \u003cp\u003e6 Statistical analysis\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using SPSS 16.0 (SPSS Inc., Chicago, IL, USA). Descriptive statistical methods were applied to both categorical and continuous variables. Normally distributed data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) and compared using independent-samples t-tests. For data that did not follow a normal distribution, medians and interquartile ranges (IQR) were utilized, and differences between compared groups were assessed using the Mann\u0026ndash;Whitney test.\u003c/p\u003e \u003cp\u003eLogistic regression analysis was conducted to identify independent predictors of 28-day mortality. Receiver operating characteristic (ROC) curves were constructed, and the area under the curve (AUC) was calculated. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also determined. The threshold value was ascertained utilizing the Youden method. The AUCs were compared using the Z-test. All statistical tests were two-tailed, and a significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e1. Baseline data of elderly sepsis patients\u003c/p\u003e \u003cp\u003eThe baseline data of the patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among them, 149 patients died of sepsis and 71 patients were discharged after treatment. The results of the t-test show a significant difference in age between the non-survivor and survivor groups (72.19\u0026thinsp;\u0026plusmn;\u0026thinsp;12.989 vs. 76.64\u0026thinsp;\u0026plusmn;\u0026thinsp;11.213) (P\u0026thinsp;=\u0026thinsp;0.009). There were also significant differences in Lac (1.1(0.8, 1.5) vs. 1.6 (1.2, 4.35)), PCT (0.05 (0.05, 0.21) vs. 0.05 (0.05, 1.93)), ALB (36.3 (30.0,41.8) vs. 32.9 (24.6, 38)), SOFA score (5 (3, 5) vs. 9 (6, 11.25)), and the APACHE2 score (15.5(11.25, 18) vs. 22 (17, 27)) IL-8 (10.85(5.13, 31.68) vs. 25.1 (11.29, 116.42)) between the two groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No statistical differences were identified in other indicators (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline data of emergency elderly patients with sepsis\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\u003eSurvivor(n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Survivor(n\u0026thinsp;=\u0026thinsp;149)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\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, years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.19\u0026thinsp;\u0026plusmn;\u0026thinsp;12.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.64\u0026thinsp;\u0026plusmn;\u0026thinsp;11.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale, %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45(62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87(58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66\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 \u003cp\u003e19(26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVD, %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension, %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31(43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69(45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \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 \u003cp\u003e28(36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCHF, %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRD, %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMBP, mmHg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.7(84.3,108.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.83(90.0,116.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRespiratory rate, beats/min\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(15.75,22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0(18.0,21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTemperature, \u0026deg;C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.35(36,36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.4(36.2,36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePaO2, mmHg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85(66.0,104.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77(65.0,93.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLac, mmol/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1(0.8,1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6(1.2,4.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePCT, ng/mL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05(0.05,0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05(0.05,1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWBC, \u0026times;109/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.6(7.2,11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.0(7.3,11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePaO2/FiO2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e303.95(176.71,411.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e271.0(184.0,344.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.3(30.0,41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.9(24.6,38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.5(8.0,83.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.5(8.0,80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOFA score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(3,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(6,11.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAPCHE2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.5(11.25,18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(17,27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.85(5.13,31.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.1(11.29,116.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e2. Logistic regression analysis was performed on the significant indicators in the univariate regression analysis. The non-survivor group had significant differences in Lac levels (P\u0026thinsp;=\u0026thinsp;0.043, OR\u0026thinsp;=\u0026thinsp;1.148, 95%CI: 1.004\u0026ndash;1.313), IL-8(P\u0026thinsp;=\u0026thinsp;0.001, OR\u0026thinsp;=\u0026thinsp;1.011 95%CI: 1.005\u0026ndash;1.017), SOFA score (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR\u0026thinsp;=\u0026thinsp;1.413, 95%CI: 1.212\u0026ndash;1.648) and APACHE2 score (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR\u0026thinsp;=\u0026thinsp;1.133, 95%CI: 1.059\u0026ndash;1.211). Such results reveal that the Lac level, IL-8 level, SOFA score, and APCHE2 score were independent risk factors for 28-day mortality in elderly sepsis patients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e3. Prediction of the mortality in elderly sepsis patients\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\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\u003e Binary logistic regression analysis of the mortality for elderly sepsis patients\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.978(0.878\u0026ndash;1.090)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLac\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.148(1.004\u0026ndash;1.313)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.981(0.943\u0026ndash;1.020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.011(1.005\u0026ndash;1.017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.413(1.212\u0026ndash;1.648)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.133(1.059\u0026ndash;1.211)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\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 \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analysis of predictive factors for mortality in elderly patients with sepsis is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The ROC curves illustrating the predictive effects for sepsis patients are displayed in Fig.\u0026nbsp;2. The AUC values of the Lac, PCT, IL-8, SOFA score, and APACHE2 score for 28-day mortality were 0.708 (95%CI:0.637\u0026ndash;0.780, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 0.593 (95%CI: 0.514\u0026ndash;0.671, P\u0026thinsp;=\u0026thinsp;0.026), 0.701 (95%CI: 0.626\u0026ndash;0.775, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 0.844(95%CI: 0.788\u0026ndash;0.899, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001),0.761 (95%CI: 0.696\u0026ndash;0.826, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. The cut-off values for the Lac, PCT, IL-8, SOFA score, and APACHE2 score maximizing the composite of specificity and sensitivity in the prediction of sepsis patients in 28-day mortality were 1.15, 0.115,14.497, 5.5, and 21.5 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;2: Receiver operating characteristic (ROC) curves of biomarkers combined with severe score for predicting 28-day mortality in elderly sepsis patients).\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\u003e Prediction of the mortality in elderly sepsis patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCut off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLR+\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLR-\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\u003eLac\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.708(0.637\u0026ndash;0.780)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e56.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e76.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.593(0.514\u0026ndash;0.671)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e73.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.701(0.626\u0026ndash;0.775)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOFA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.844(0.788\u0026ndash;0.899)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e79.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e81.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAPACHE2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.761(0.696\u0026ndash;0.826)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e91.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e54.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL 8\u0026thinsp;+\u0026thinsp;SOFA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.862(0.808\u0026ndash;0.915)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e95.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL 8\u0026thinsp;+\u0026thinsp;APACHE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.797(0.734\u0026ndash;0.860)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e83.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e69.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLac\u0026thinsp;+\u0026thinsp;SOFA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.850(0.795\u0026ndash;0.905)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e95.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLac\u0026thinsp;+\u0026thinsp;APACHE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.793(0.732\u0026ndash;0.854)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e91.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e54.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSubsequently, Lac IL-8, SOFA, and APACHE2 data were paired to predict mortality in elderly patients with sepsis. The findings reveal that the combination of IL-8 and SOFA exhibited the highest AUC of 86.2% and specificity of 93%. Additionally, the sensitivity of the SOFA score was the highest at 81.2%. Detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Further, the AUCs for predicting mortality among sepsis patients were compared, showing no significant differences between IL-8 and Lac in predicting 28-day mortality (Z\u0026thinsp;=\u0026thinsp;0.134, P\u0026thinsp;=\u0026thinsp;0.894).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSepsis is a serious medical condition characterized by organ dysfunction resulting from the host's dysregulated response to infection. International epidemiological data indicate that the mortality rate of patients with sepsis exceeds that of myocardial infarction, making it the leading cause of non-cardiac deaths in the ICU(Mayr, Yende et al. 2014).\u003c/p\u003e \u003cp\u003e \u003csup\u003eAmong all pat\u003c/sup\u003eients with sepsis, older individuals are more susceptible to infections caused by gram-negative microorganisms. As such, the incidence of the disease is significantly higher in the elderly compared to younger individuals. A previous study conducted by Martin reported that adults over the age of 65 were 1.31 times more susceptible to acquiring a gram-negative infection in comparison to those under the age of 65. (Martin, Mannino et al. 2006) Additionally, elderly adults with infections frequently exhibit atypical symptoms, which can complicate the swift diagnosis and prompt initiation of treatment. Fever, a common clinical manifestation of infection and a frequently encountered sepsis-associated symptom is not present in roughly 30\u0026ndash;50% of the elderly patients afflicted with infection(Ewig, Klapdor et al. 2012). Hence, early diagnosis and treatment of numerous elderly patients threatened by sepsis can be difficult to achieve.\u003c/p\u003e \u003cp\u003eGiven this context, the identification of safe and effective prognostic biomarkers in elderly sepsis patients holds significant importance for clinicians. These advancements could aid in the development and implementation of timely and effective treatment strategies to reduce mortality rates among elderly sepsis patients. Many researchers concur that Procalcitonin (PCT) serves as a highly sensitive and specific biomarker for detecting severe bacterial infections. Thus, PCT measurements offer valuable insights into clinical scenarios where they are most beneficial(Hoeboer, van der Geest et al. 2015, Mihajlovic, Brkic et al. 2017). The present experimental results suggest (and confirm) that PCT is meaningful in predicting 28-day mortality in elderly patients with sepsis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). The AUC was determined to be 59.3%, with a sensitivity of 46.3% and specificity of 73.2%. These results generally align with the outcomes reported in previous related studies.\u003c/p\u003e \u003cp\u003eMoreover, regarding 28-day mortality, The results of the binary logistic regression analysis reveal that Lac and IL-8, together with the SOFA and APACHE II scores, acted as independent predictors, whereas PCT did not. The AUC of the SOFA score was 0.844, surpassing that of APACHE II (0.761), Lac (0.740), and IL-8 (0.751). Previous studies have already demonstrated the effectiveness of the SOFA scoring system in predicting mortality among adult sepsis patients(Raith, Udy et al. 2017). This consistency is reflected in the present results, where the SOFA score exhibited the highest prognostic value for elderly patients with sepsis.\u003c/p\u003e \u003cp\u003eThe cytokine interleukin IL-8, known for its pro-inflammatory properties, is a key member of the chemokine family and plays a crucial role in the pathogenesis of sepsis. Numerous studies conducted over the years have shown that IL-8 can serve as a valuable biomarker for predicting infections and septic events. Kraft et al. confirmed that IL-8 demonstrates remarkable sensitivity and specificity as a biomarker for assessing burn size, particularly when its plasma concentration is below a threshold of 234 pg/ml. Moreover, at higher levels, plasma IL-8 concentration exhibits a strong correlation with the frequency of septic events. Moreover, IL-8 holds potential as a valuable biomarker for predicting infections and septic incidents in patients suffering from burn injuries(Kraft, Herndon et al. 2015).\u003c/p\u003e \u003cp\u003eIn another experiment conducted by Liu XW, patients with IL-8 levels exceeding normal were found to be more susceptible to acute lung injury(Liu, Ma et al. 2019). Further, it has been suggested that the initial levels of IL-8 can be one of the most prognostic factors for mortality in sepsis patients(Zhou, Cheng et al. 2015). In a study by Wong et al.(Liou, Chang et al. 2014) involving pediatric patients with serum IL-8 levels of 220 pg/ml or lower obtained within 24 hours of admission, the data demonstrated the effectiveness of IL-8 in predicting a significant probability of survival in children with septic shock. Therefore, one may infer that IL-8 serves as a suitable stratification tool for interventional trials related to pediatric septic shock.\u003c/p\u003e \u003cp\u003eThe present study aimed to verify the association between IL-8 levels and the prognosis of sepsis in elderly patients. In the experimental trials, findings were made that IL-8 in the non-survivor group was higher than in the survivor group [10.85(5.13,31.68) vs. 25.1(11.29-116.42), P\u0026thinsp;=\u0026thinsp;0:00] and IL-8 emerged as a significant independent prognostic indicator for the 28-day mortality of elderly individuals, an outcome confirmed by logistic analysis [OR 1.011(1.005\u0026ndash;1.017)), P\u0026thinsp;=\u0026thinsp;0:001]. The sensitivity and specificity of IL-8 in predicting sepsis were 74.3% and 63.4% respectively, and the area under the ROC curve was 70.1%. Compared with the levels of blood lactic acid, there was no statistical significance (Z\u0026thinsp;=\u0026thinsp;0.1337, P\u0026thinsp;=\u0026thinsp;0.894) between IL-8 and lactic acid.\u003c/p\u003e \u003cp\u003eVarious related studies have evaluated lactate as an effective prognostic indicator for assessing sepsis patients(Gattinoni, Vasques et al. 2019, Grealish, Chiew et al. 2021). For instance, Grealish et al. investigated lactate values in critically ill patients admitted to the Emergency Department with or without diabetes and conducted a multivariable analysis with this patient sample. Their results demonstrated that lactate remained an independent predictor of ICU/in-hospital mortality in the non-diabetes group(Gattinoni, Vasques et al. 2019). In the present study, the sensitivity of IL-8 was 74.5%, while the cut-off value of IL-8 was 14.497pg/ml, nearly 76.5% for LAC. The specificity of IL-8 was 63.4%, which was slightly higher than that of lactic acid (56.3%). Both IL-8 and LAC were found to be independent risk factors for the prognosis of elderly patients with sepsis. Comparing their AUCs (Z\u0026thinsp;=\u0026thinsp;0.134, P\u0026thinsp;=\u0026thinsp;0.894), no statistically significant differences were found in the AUC values for IL-8 and LAC. Additionally, sensitivity and specificity analyses showed similar diagnostic value between IL-8 and LAC. In some respects, IL-8 may even be considered superior to lactate.\u003c/p\u003e \u003cp\u003eThe AUC of IL-8 in combination with the SOFA score was significantly higher when compared to that of IL-8 alone (Z\u0026thinsp;=\u0026thinsp;3.454, P\u0026thinsp;=\u0026thinsp;0.005). The AUC of IL-8 in combination with the SOFA score was slightly higher than that of SOFA (Z\u0026thinsp;=\u0026thinsp;0.463, P\u0026thinsp;=\u0026thinsp;0.644). The SOFA score was proposed by the European Society of Intensive Care Medicine (ESICM) in 1994 (Vincent, Moreno et al. 1996) and has since been associated with mortality rates in sepsis patients(Ferreira, Bota et al. 2001, Park, Lee et al. 2023). At present, it is widely utilized to assess the severity of sepsis and to perform prognostic evaluations(Oh, Roh et al. 2020). The present results are consistent with these claims and indicate that SOFA scores have the highest predictive value for sepsis patients. Compared with the SOFA score, the APACHE II score also demonstrated a high predictive value for 28-day mortality. While no single prediction biomarker stood out prominently, their predictive value increased significantly when combined with other prognostic indicators.\u003c/p\u003e \u003cp\u003eFurther findings were made that the utilization of plasma IL8 thresholds consistently aided in the identification of individuals at an elevated risk of mortality among elderly patients with sepsis. The results demonstrate that IL-8 could potentially serve as a prognostic factor for sepsis trials. After integrating it with the SOFA and APACHE II scoring systems, the prognostic value of IL-8 was significantly enhanced, providing valuable guidance for the clinical management and prognosis of sepsis. Utilizing these markers in the analysis of high-risk elderly patients could improve the efficacy and robustness of clinical trials while reducing the number of sepsis patients exposed to potentially harmful treatments unnecessarily.\u003c/p\u003e \u003cp\u003eIn the present literature review, several studies on IL-8 and its relationship with the prognosis of elderly patients with sepsis were discerned. The present study represents a pioneering effort in this field, and the conclusions hold significant importance for both the scholarly and medical communities. In future studies, the aim will be to further explore the risk stratification and predictive efficacy of IL-8 in the context of elderly septic patients to enhance the value of the present findings.\u003c/p\u003e \u003cp\u003eNonetheless, the present research has several limitations. First, the present study was limited to a single center and had a relatively small sample size. As such, large-scale, multi-center studies are needed to verify the results.\u003c/p\u003e \u003cp\u003eSecond, the dynamic correlation between IL-8 fluctuations and the prognosis of elderly sepsis was not actively monitored, necessitating further exploration in future studies.\u003c/p\u003e \u003cp\u003eLastly, while initial serological markers provide insight into the inflammatory response and disease severity at the disease's onset, they have limitations in predicting the ultimate prognosis comprehensively.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, IL-8, lactate (LAC), SOFA, and APACHE II scores each independently demonstrate significance in predicting mortality among elderly patients with sepsis. Particularly noteworthy is the high predictive value observed with the combination of IL-8 and SOFA scores. This underscores the importance of early and prompt evaluation for guiding therapeutic interventions and predicting outcomes in this vulnerable population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Funding:\u0026nbsp;\u003c/strong\u003eSupported by Beijing Municipal Science and Technology Commission China, No.\u0026nbsp; \u0026nbsp;Z211100002921061\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConflicts of interest:\u003c/strong\u003e All author reports no conflicts of interest in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEthics:\u003c/strong\u003e Ethics Committee of Beijing Chaoyang Hospital, which is affiliated with Capital Medical. University (approval number: 2021-Department 636). Informed consent was acquired from patients or their legal guardians\u0026nbsp;before\u0026nbsp;any treatments or tests, ensuring their comprehension and agreement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e:\u0026nbsp;The authors confirm that the data supporting the findings of this study are available in the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e: not available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e:\u0026nbsp;Written informed consent for publication was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e: Xiangqun Zhang have made substantial contributions to the conception and design of the work; and the acquisition, analysis, OR interpretation of data; OR the creation of new software used in the work; OR have drafted the work or substantively revised it Shubin Gun andJunyu Wang designed the work; Bing Wei, Ying Zhang, and YixuanLi helped to collect the data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBaggiolini, M., B. Dewald and B. Moser (1994). \u0026quot;Interleukin-8 and related chemotactic cytokines--CXC and CC chemokines.\u0026quot; \u003cu\u003eAdv Immunol\u003c/u\u003e \u003cstrong\u003e55\u003c/strong\u003e: 97-179.\u003c/li\u003e\n\u003cli\u003eBhattacharjee, P., D. P. Edelson and M. M. Churpek (2017). \u0026quot;Identifying Patients With Sepsis on the Hospital Wards.\u0026quot; \u003cu\u003eChest\u003c/u\u003e \u003cstrong\u003e151\u003c/strong\u003e(4): 898-907.\u003c/li\u003e\n\u003cli\u003eBorthwick, H. A., L. K. Brunt, K. L. Mitchem and C. Chaloner (2012). \u0026quot;Does lactate measurement performed on admission predict clinical outcome on the intensive care unit? A concise systematic review.\u0026quot; \u003cu\u003eAnn Clin Biochem\u003c/u\u003e \u003cstrong\u003e49\u003c/strong\u003e(Pt 4): 391-394.\u003c/li\u003e\n\u003cli\u003eCorre, I., D. Pineau and S. 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Williams (2008). \u0026quot;Interleukin-8 as a stratification tool for interventional trials involving pediatric septic shock.\u0026quot; \u003cu\u003eAm J Respir Crit Care Med\u003c/u\u003e \u003cstrong\u003e178\u003c/strong\u003e(3): 276-282.\u003c/li\u003e\n\u003cli\u003eZhou, M., S. Cheng, J. Yu and Q. Lu (2015). \u0026quot;Interleukin-8 for diagnosis of neonatal sepsis: a meta-analysis.\u0026quot; \u003cu\u003ePLoS One\u003c/u\u003e \u003cstrong\u003e10\u003c/strong\u003e(5): e0127170.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"IL-8, aged patient, sepsis, mortalities","lastPublishedDoi":"10.21203/rs.3.rs-4005892/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4005892/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThe present study aimed to explore the mortality risk of interleukin-8 (IL-8) among elderly patients diagnosed with sepsis upon admission to the emergency department.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 273 elderly patients with sepsis were admitted to the emergency department of Beijing Chaoyang Hospital from January 2022 to April 2023. In total, 220 patients were included in the present study. Serum samples were obtained within 1 hour of admission to assess serum IL-8, white blood cell (WBC), procalcitonin (PCT), C-reactive protein (CRP), and lactic acid (LAC) levels, along with other relevant parameters. The Sequential Organ Failure Score (SOFA) and Acute Physiological and Chronic Health Assessment II (APACHE II) were recorded. Logistic regression analysis was employed to identify independent predictors of mortality within 28 days for elderly patients diagnosed with sepsis. Further, the capacity of these factors to predict 28-day mortality within this patient cohort was evaluated using Receiver Operating Characteristic (ROC) curve analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe levels of lactic acid (LAC), interleukin-8 (IL-8), procalcitonin (PCT), as well as the severity scores of APACHE II and SOFA, and the albumin (ALB) score, demonstrated notable and statistically significant distinctions between the non-survivor and survivor cohorts (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Through logistic regression analysis, it was determined that the SOFA score, APACHE II score, LAC, and IL-8 were all significant independent predictors for 28-day mortality in elderly sepsis patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The AUC of the ROC curve for IL-8 was calculated to be 0.701, indicating a moderately predictive performance. Sensitivity and specificity were reported as 74.5% and 63.4%, respectively, with a cut-off value of 14.497. In comparison, the AUC for LAC was marginally higher at 0.708, accompanied by a sensitivity of 76.5% and a specificity of 56.3%, with a corresponding cut-off value of 1.15. Further, the AUC for APACHE II was 0.761, indicating good predictive performance. The sensitivity and specificity for APACHE II were found to be 55.0% and 91.5%, with a cut-off value of 21.5. Nevertheless, the results of the statistical analysis reveal no significant difference in the predictive value between IL-8 and LAC (Z\u0026thinsp;=\u0026thinsp;0.134, P\u0026thinsp;=\u0026thinsp;0.894). However, IL-8 demonstrated a slightly higher specificity (63.4%) compared to LAC (56.3%). Moreover, the present findings indicate that the combined assessment of IL-8 and SOFA score (AUC 0.862, sensitivity 71.1%, specificity 93.0%; Z\u0026thinsp;=\u0026thinsp;3.454, P\u0026thinsp;=\u0026thinsp;0.005) demonstrated superior predictive value for mortality compared to using IL-8 alone.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIL-8 LAC, APACHE II, and SOFA can be considered independent predictors factors for mortality of elderly sepsis patients. Utilizing the combination of IL-8 and SOFA demonstrates a heightened predictive capability compared to using any single index alone.\u003c/p\u003e","manuscriptTitle":"Predictive Value of IL-8 for Mortality Risk in elderly sepsis Patients of Emergency Department","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-01 19:25:46","doi":"10.21203/rs.3.rs-4005892/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d1fa4ea5-02da-4ccb-b03d-b956bb7d380b","owner":[],"postedDate":"April 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-30T06:54:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-01 19:25:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4005892","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4005892","identity":"rs-4005892","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}