Factors associated with SIRS negativity at the early stage of sepsis among nonsurviving sepsis patients in ICU: Targeting “silent sepsis”

Factors associated with SIRS negativity at the early stage of sepsis among nonsurviving sepsis patients in ICU: Targeting “silent sepsis” | 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 Factors associated with SIRS negativity at the early stage of sepsis among nonsurviving sepsis patients in ICU: Targeting “silent sepsis” Taotao Liu, Jingchao Luo, Xiaogang Wang, Yuan Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4458847/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background Despite the very high sensitivity of the Systemic Inflammatory Response Syndrome (SIRS) score for identifying sepsis, there remains a subset of septic patients who exhibit negative SIRS scores, and unfortunately, many of these patients experience poor outcomes. This study aims to investigate the factors associated with SIRS negativity during the early stage of sepsis in deceased septic patients. Methods Adult septic patients were included from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database between 2008 and 2019. Sepsis was determined based on the Sepsis 3.0 criteria. Patients who did not survive after 28 days were assigned to the SIRS-negative or SIRS-positive group according to whether the SIRS score was less than two points within 24 hours of intensive care unit (ICU) admission. The baseline data of patients in the SIRS-negative and SIRS-positive groups were collected and compared. The factors associated with SIRS negativity in septic patients were analysed by logistic regression. The dose-response relationships of SIRS negativity with SOFA score and age were determined with a restricted cubic spline model. Results A total of 53,150 patients were screened in the MIMIC-IV database, and 2706 sepsis nonsurvivors were ultimately included, 101 of whom were negative for SIRS. There were significant differences in SOFA scores between groups (8.18 ± 3.58 vs. 9.75 ± 4.28, P < 0.001). In addition, differences in several other parameters nearly reached statistical significance, including age (76 [61 to 86] vs. 72 [60 to 82], P = 0.053), body mass index (BMI) (26 [22 to 31] vs. 27 [24 to 32], P = 0.056), and the Charlson comorbidity index (8 [6 to 9] vs. 7 [5 to 9], P = 0.052). Logistic regression analysis indicated that both SOFA score (OR = 0.93 [95% CI = 0.87-1.00], P = 0.046) and age (OR = 1.04 [95% CI = 0.88–1.15], P = 0.012) were independent factors related to SIRS negativity in septic patients. Analysis with a restricted cubic spline model showed that the odds ratio (OR) of SIRS negativity continued to increase with age, particularly for those over 80 years old (p for nonlinearity = 0.024). The odds ratio of SIRS negativity was more than 1 when the SOFA score was less than 4 (p for nonlinearity = 0.261). Conclusions For sepsis patients with poor prognoses, elderly individuals (over 80 years) are more likely to be SIRS negative when they have mild organ dysfunction damage (less than 4 SOFA scores) in the early stage of sepsis. This warranted an opportunity to provide early diagnosis for elderly population with negative SIRS score, in order to prevent poor outcomes. sepsis SIRS negative SOFA MIMIC database Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Systemic inflammatory response syndrome (SIRS) score is an early diagnostic criterion for sepsis that has been used worldwide for twenty years[ 1 ]. Moreover, SIRS has still been widely used by clinicians for sepsis screening since the 2016 International Consensus of Sepsis 3.0[ 2 ]. Although SIRS score is highly sensitive, it also may result in mis-diagnosis[ 3 , 4 ]. In the context of highly contagious infectious diseases such as novel coronavirus pneumonia (COVID-19), the prevalence of SIRS-negative cases can be high due to the vast number of affected individuals. These patients have mild signs and symptoms and are prone to mis-diagnosis[ 5 ]. Even though mortality is generally lower in patients who are SIRS negative, there may still be a poor prognosis for some of these patients[ 6 ]. Since the SIRS score is composed of four scoring criteria, namely, heart rate, respiratory rate, temperature, and white blood cell count, a negative SIRS score indicates that the patient's clinical symptoms are mild[ 7 , 8 ]. There are two possible explanations for this: first, the actual condition of sepsis is mild, and the prognosis is good, so the clinical manifestations are not prominent; second, the actual condition is severe, and the clinical manifestations are not typical. The latter group of patients are prone to delayed treatment in clinical practice due to mild early symptoms. We named these patients the “silent sepsis” group. Clinicians may miss the opportunity to diagnose sepsis during early screening and delay treatment. Thus, it is necessary to explore the factors related to SIRS negativity in septic patients with poor prognosis. This study aimed to analyse the data of patients who were admitted to the intensive care unit (ICU) and diagnosed with sepsis within 24 hours in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database[ 9 ], to identify factors related to SIRS negativity during the early stage of sepsis in deceased septic patients. Methods Data source and ethics approval This retrospective cohort study was conducted using data from the MIMIC-IV database; it included information on inpatients admitted to the ICU of the Beth Israel Deaconess Medical Center from 2008 to 2019 and had preexisting institutional review board approval. The patients’ information was anonymous, and the study was exempt from the need for informed consent. The study was approved by the Ethics Committee of Beijing Hospital (2021BJYYEC-255-01), and the clinical study registration number was ChiCTR2200060095 ( http://www.chictr.org.cn/ ). Study design Patients admitted to the ICU between 2008 and 2019 in the MIMIC-IV database were included based on below inclusion criteria: older than 18 years, diagnosed with infection and have a sequential organ failure assessment (SOFA) score of two or more within the first 24 hours of ICU admission[ 2 ]. The exclusion criteria were as follows: patients who received vasopressors, mechanical ventilation, or intra-aortic balloon pump (IABP) therapy within the first 24 hours of ICU admission and patients for whom SIRS or SOFA scores could not be collected due to missing data. Patients who deceased within 28 days were divided into a SIRS-negative group and a SIRS-positive group according to whether they had SIRS scores less than two within the first 24 hours of ICU admission. The baseline data, including age, sex, body mass index (BMI), Charlson Comorbidity Index (CCI), lactate levels at ICU admission and SOFA score, were recorded. Patient outcomes, including septic shock incidence, mechanical ventilation, length of stay in ICU and length of stay in hospital, were also collected. Statistical analyses The characteristics of the enrolled subjects were described using descriptive statistics. Variables with normal distributions are presented as the means (standard deviations (SDs)) and were compared with an independent sample t test. Nonnormally distributed variables are presented as the medians (interquartile ranges, IQRs), and comparison were analysed with the Mann‒Whitney U test. Categorical variables are presented as percentages and were compared using the chi-square test or Fisher's exact test, when appropriate. The baseline data of the SIRS-negative and SIRS-positive groups were compared, and variables with significant differences according to univariate analysis were visualized in two-dimensional histograms with LOWESS regression curves for SIRS-negative patients. Variables with significant differences according to univariate analysis were included in the logistic regression. The proportions of SIRS-negative patients were analysed according to SOFA score or age, and the variables that were significantly different according to logistic regression were identified. Restricted cubic spline curves were also generated to explore the nonlinear associations between SIRS negativity and age or SOFA score. All the statistical analyses were performed with SPSS 25.0, and the figures were generated with Prism 9.3 and R 4.2.2. Restricted cubic splines were used with four knots to model the nonlinear associations of SOFA score with SIRS negativity and age with SIRS negativity. A p value less than 0.05 was considered to indicate statistical significance. Results Characteristics of SIRS-negative and SIRS-positive nonsurviving sepsis patients A total of 20,136 patients were diagnosed with sepsis within 24 hours of ICU admission, and 1,691 of these patients (8.40%) were SIRS negative. There were 101 (3.73%) patients who were negative for SIRS with 24 hours of ICU admission but dead at 28 days. The study flow chart is shown in Fig. 1 . There were significant differences in SOFA scores between patients who were SIRS negative and SIRS positive (8.18 ± 3.58 vs. 9.75 ± 4.28, P < 0.001). In addition, differences in several other parameters, such as age (76 [61 to 86] vs. 72 [60 to 82], P = 0.053), body mass index (26 [22 to 31] vs. 27 [24 to 32], P = 0.056) and Charlson comorbidity index (8 [6 to 9] vs. 7 [5 to 9], P = 0.052], approached statistical significance. The age, SOFA score distribution and percentage of non-survived patients of the SIRS-negative and SIRS-positive groups are shown in supplementary files. The distributions of SOFA scores according to age in SIRS-negative and SIRS-positive patients are displayed on two-dimensional histograms with LOWESS regression curves, which show that for older patients, the SOFA score tended to decrease with increasing age (Fig. 2 ). There might be nonlinear relationships between these variables and SIRS negativity. Factors related to SIRS negativity Logistic regression analysis indicated that both SOFA score (adjusted odds ratio (OR) = 0.93 (95% CI = 0.87 to 1.00), P = 0.046) and age (adjusted OR = 1.04 (95% CI = 0.88 to 1.15), P = 0.012) were independent factors related to SIRS negativity in septic patients (Table 2 ). The results of the restricted cubic spline model showed that OR of SIRS negativity continued to increase with age, particularly for those over 80 years old (p for nonlinearity = 0.024) (Fig. 3 ). The OR of SIRS negativity was more than 1 when the SOFA score was less than 4 (p for nonlinearity = 0.261) (Fig. 4 ). Table 1 Characteristics and outcomes of SIRS-negative or SIRS-positive nonsurviving sepsis patients Overall SIRS positive SIRS negative P value Baseline data within 24 hours of ICU admission N 2706 2605 101 - Male, % 1488 (55.0) 1424 (54.7) 64 (63.4) 0.105 Age, years 73 [60 to 82] 72 [60 to 82] 76 [61 to 86] 0.053 BMI, kg/m 2 27 [23 to 32] 27 [24 to 32] 26 [22 to 31] 0.056 CCI 7 [5 to 9] 7 [5 to 9] 8 [6 to 9] 0.052 Congestive heart failure, % 953 (35.2) 904 (34.7) 49 (48.5) 0.006 Renal disease, % 729 (26.9) 692 (26.6) 37 (36.6) 0.034 Lactate upon ICU admission, mmol/L 2.5 [1.6 to 4.7] 2.6 [1.6 to 4.8] 1.7 [1.3 to 2.1] < 0.001 SIRS 3 [3 to 4] 3 [3 to 4] 1 [1 to 1] < 0.001 Temperature, % 1337 (51.5) 1332 (53.2) 5 (5.4) < 0.001 Heart rate, % 2229 (82.4) 2220 (85.2) 9 (8.9) < 0.001 Respiratory rate, % 2629 (97.2) 2555 (98.1) 74 (74.0) < 0.001 White blood cell, % 2081 (77.2) 2073 (79.9) 8 (7.9) < 0.001 APS-3 79 [60 to 101] 79 [60 to 102] 63 [49 to 78] < 0.001 SOFA 9 [6 to 13] 9 [6 to 13] 8 [5 to 10] < 0.001 Respiration score 3 [2 to 4] 3 [2 to 4] 2 [2 to 3.75] 0.227 Coagulation score 0 [0 to 2] 0 [0 to 2] 1 [0 to 2] 0.322 Liver score 0 [0 to 2] 0 [0 to 2] 0 [0 to 2] 0.716 Cardiovascular score 1 [1 to 4] 3 [1 to 4] 1 [1 to 3] < 0.001 Central nervous score 2 [0 to 4] 2 [0 to 4] 2 [1 to 3] 0.581 Renal score 2 [0 to 3] 2 [0 to 3] 1 [0 to 2] 0.030 Clinical Outcomes Ventilation, % 1747 (64.6) 1689 (64.8) 58 (57.4) 0.155 Septic shock, % 1191 (44.0) 1170 (44.9) 21 (20.8) < 0.001 48-h mortality, % 704 (26.0) 694 (26.6) 10 (9.9) < 0.001 7-day mortality, % 1686 (62.3) 1638 (62.9) 48 (47.5) 0.003 Time from ICU to death, h 116 [46 to 243] 114 [45 to 241] 175 [97 to 291] < 0.001 LOS in ICU, days 3 [2 to 7] 3 [2 to7] 4 [2 to 8] 0.036 LOS in hospital, days 6 [2 to 12] 6 [2 to 12] 8 [5 to 13] 0.001 APS: acute physiology score; BMI: body mass index; CCI: Charlson comorbidity index; ICU: intensive care unit; LOS: length of stay; SIRS: systemic inflammatory response syndrome; SOFA: sequential organ failure assessment Table 2 Associations of different variables with SIRS-negative status Crude OR P value Adjusted OR P value P for nonlinear Male, % 1.43 (0.95–- 2.17) 0.086 1.86 (1.05–- 3.27) 0.032 - Age, years 1.02 (1.00–- 1.03) 0.029 1.04 (0.88–- 1.15) 0.012 0.024 BMI, kg/m 2 0.98 (0.94–- 1.01) 0.202 0.98 (0.95–- 1.02) 0.338 - CCI 1.07 (1.00–- 1.14) 0.052 1.01 (00.92–- 1.11) 0.784 0.737 Congestive heart failure, % 1.77 (1.19–- 2.64) 0.005 1.38 (0.77–- 2.45) 0.276 - Renal disease, % 1.60 (1.06–- 2.42) 0.026 1.19 (0.66–- 2.15) 0.564 - Lactate upon ICU admission, mmol/L 0.71 (0.59–- 0.85) < 0.001 0.75 (0.62–- 0.90) 0.002 - APS-3 0.97 (0.97–0.98) < 0.001 0.97 (0.97–0.98) < 0.001 0.300 SOFA 0.91 (0.87–0.96) < 0.001 0.93 (0.87-1.00) 0.046 0.261 Respiration score 0.89 (0.73–1.09) 0.254 1.15 (0.86–1.55) 0.355 - Coagulation score 1.03 (0.87–1.22) 0.758 1.23 (0.89–1.70) 0.355 - Liver score 1.05 (0.89 − 0.547) 0.547 0.73 (0.48–1.11) 0.145 - Cardiovascular score 0.72 (0.62–0.83) < 0.001 0.73 (0.56–0.93) 0.012 - Central nervous score 1.04 (0.91–1.18) 0.553 0.01 (0.81–1.25) 0.931 - Renal score 0.85 (0.74–0.98) 0.026 0.92 (0.71–1.19) 0.528 - Discussion In this study, 8.40% of patients were SIRS negative in the early stage of sepsis, and the 28-days mortality rate of patients who were SIRS negative was approximately 6%. Previous general research data indicate that the mortality rate of patients with sepsis can reach 20–30% or more[ 10 ]。Reports on the mortality rate for SIRS-negative sepsis patients are relatively scarce, approximately 9–28%[ 7 ]. Based on the results of this study, it can be inferred that septic patients with SIRS-negative in the early stages and died within 28 days account for approximately 5‰ of all septic patients. More importantly, this study demonstrated that the main factors associated with the early presentation of sepsis patients as SIRS-negative are age and SOFA score. Additionally, there is a nonlinear dose-response relationship between age and SIRS negativity. The patients who were SIRS negative were predominantly concentrated in the those who were older than 80 years, which was clearly visible in the two-dimensional histograms and mortality distribution charts generated in our study. The RCS model revealed a nonlinear relationship between age and SIRS negativity. When age exceeded the inflection point of 80 years, the OR of SIRS negativity increased significantly. When sepsis occurs, increases in heart rate and respiratory rate occur as a result of physiological compensation for a decrease in oxygen supply[ 11 , 12 ]. However, elderly people may lose the ability to compensate, which could result in mild symptoms and signs, even when serious organ function damage occurs[ 13 , 14 ]. Therefore, severity and prognosis cannot be evaluated by physical signs alone, especially for elderly sepsis patients with a mild to moderate degree of organ function damage. The SOFA score was the most important factor related to SIRS negativity according to multivariate logistic regression. There was a clear linear relationship between the SOFA score and SIRS negativity. When the SOFA score was less than 4, the OR of SIRS negativity was greater than 1. Patients with high SOFA scores usually have severe organ function impairment, generally accompanied by obvious symptoms, changes in physical parameters, and SIRS positivity[ 15 , 16 ]. In contrast, patients with sepsis and mild to moderate organ function damage still may have a poor prognosis despite mild clinical signs and SIRS negativity. Septic patients with more comorbidities could have higher mortality than those with less comorbidities[ 17 – 19 ], but the correlation between SIRS negativity and comorbidities is unclear. We compared the CCI between the two groups and found no significant difference. We also compared the individual comorbidities between the two patient groups. The results revealed that only congestive heart failure and renal disease were significantly different. However, the adjusted ORs showed that there was no correlation between any comorbidity and SIRS negativity. Therefore, we cannot yet conclude that comorbidities, including those generally considered to potentially lead to an immunosuppressed state, causes the SIRS score negativity in sepsis patients. We excluded patients who received mechanical ventilation, IABP or vasopressors at ICU admission because even if these patients were SIRS negative, clinicians may also have thought that they were in crisis and paid more attention to them. Additionally, effective noninvasive ventilation and high-flow oxygen can also change physiological variables such as the heart rate and respiratory rate[ 20 – 22 ]. This study has several limitations. First, this study lacked data on the patients’ infection sites and aetiologies, and infections at different sites can result in differences in patient clinical manifestations and varying degrees of organ damage, which may affect SIRS and SOFA scores. Second, the missing BMI of some patients may have led to bias. However, the average BMI 27 (IQR 23 to 32 ) kg/m 2 indicates that very few patients may have experienced extreme emaciation and cachexia. Therefore, assuming that BMI is not related to SIRS negativity might be reasonable. In addition, the research data from one central source may not fully reflect the incidence rate of sepsis with a negative SIRS score in the real world, and further epidemiological surveys are needed. Conclusions For sepsis patients with poor prognoses, elderly individuals (over 80 years) are more likely to be SIRS negative when they have mild organ dysfunction damage (less than 4 SOFA scores) in the early stage of sepsis. This warranted an opportunity to provide early diagnosis for elderly population with negative SIRS score, in order to prevent poor outcomes. Abbreviations APS: acute physiology score; BMI: body mass index; CCI: Charlson comorbidity index; IABP: intra-aortic balloon pump; IQR: interquartile range; MIMIC: Medical Information Mart for Intensive Care; RCS: restricted cubic spline; SD: standard deviation; SOFA: sequential organ failure assessment; SIRS: systemic inflammatory response syndrome; ICU: intensive care unit; OR: odds ratio Declarations Acknowledgements None. Funding This study was supported by National High-Level Hospital Clinical Research Funding (BJ-2023-173). Availability of data and materials The MIMIC-IV data are available on the project website at https://mimic-iv.mit.edu/. Ethics approval and consent to participate This study was approved by the ethics committee of Beijing Hospital (2021BJYYEC-255-01). Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Authors' contributions Taotao Liu conceived the idea. Taotao Liu and Jingchao Luo performed the analysis, interpreted the results, and drafted the manuscript. Yuan Xu and Xiaogang Wang helped perform the analysis and draft the manuscript. All the authors read and approved the final manuscript. References Bone RC, Balk RA, Cerra FB, Dellinger RP, Fein AM, Knaus WA, Schein RM, Sibbald WJ: Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine . Chest 1992, 101 (6):1644-1655. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM et al : The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) . Jama 2016, 315 (8):801-810. Vincent JL, Opal SM, Marshall JC, Tracey KJ: Sepsis definitions: time for change . Lancet 2013, 381 (9868):774-775. Williams JM, Greenslade JH, McKenzie JV, Chu K, Brown AFT, Lipman J: Systemic Inflammatory Response Syndrome, Quick Sequential Organ Function Assessment, and Organ Dysfunction: Insights From a Prospective Database of ED Patients With Infection . Chest 2017, 151 (3):586-596. Bhimraj A, Morgan RL, Shumaker AH, Baden L, Cheng VCC, Edwards KM, Gallagher JC, Gandhi RT, Muller WJ, Nakamura MM et al : Infectious Diseases Society of America Guidelines on the Treatment and Management of Patients with COVID-19 . Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2022. Schlapbach LJ, Straney L, Bellomo R, MacLaren G, Pilcher D: Prognostic accuracy of age-adapted SOFA, SIRS, PELOD-2, and qSOFA for in-hospital mortality among children with suspected infection admitted to the intensive care unit . Intensive care medicine 2018, 44 (2):179-188. Kaukonen KM, Bailey M, Pilcher D, Cooper DJ, Bellomo R: Systemic inflammatory response syndrome criteria in defining severe sepsis . The New England journal of medicine 2015, 372 (17):1629-1638. Zhang W, Zheng Y, Feng X, Chen M, Kang Y: Systemic inflammatory response syndrome in Sepsis-3: a retrospective study . BMC infectious diseases 2019, 19 (1):139. Johnson A, Bulgarelli, L., Pollard, T., Horng, S., Celi, L. A., & Mark, R. : MIMIC-IV (version 1.0). PhysioNet 2021. Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, Colombara DV, Ikuta KS, Kissoon N, Finfer S et al : Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study . Lancet 2020, 395 (10219):200-211. Rudiger A, Singer M: The heart in sepsis: from basic mechanisms to clinical management . Current vascular pharmacology 2013, 11 (2):187-195. Mikkelsen ME, Shah CV, Meyer NJ, Gaieski DF, Lyon S, Miltiades AN, Goyal M, Fuchs BD, Bellamy SL, Christie JD: The epidemiology of acute respiratory distress syndrome in patients presenting to the emergency department with severe sepsis . Shock (Augusta, Ga) 2013, 40 (5):375-381. Shimazui T, Nakada TA, Walley KR, Oshima T, Abe T, Ogura H, Shiraishi A, Kushimoto S, Saitoh D, Fujishima S et al : Significance of body temperature in elderly patients with sepsis . Critical care (London, England) 2020, 24 (1):387. Rowe TA, McKoy JM: Sepsis in Older Adults . Infectious disease clinics of North America 2017, 31 (4):731-742. Raith EP, Udy AA, Bailey M, McGloughlin S, MacIsaac C, Bellomo R, Pilcher DV: 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 2017, 317 (3):290-300. Vincent JL, de Mendonça A, Cantraine F, Moreno R, Takala J, Suter PM, Sprung CL, Colardyn F, Blecher S: Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Working group on "sepsis-related problems" of the European Society of Intensive Care Medicine . Crit Care Med 1998, 26 (11):1793-1800. Dąbrowska AM, Słotwiński R: The immune response to surgery and infection . Central-European journal of immunology 2014, 39 (4):532-537. Stenberg H, Li X, Pello-Esso W, Larsson Lönn S, Thønnings S, Khoshnood A, Knudsen JD, Sundquist K, Jansåker F: The effects of sociodemographic factors and comorbidities on sepsis: A nationwide Swedish cohort study . Preventive medicine reports 2023, 35 :102326. Cajander S, Kox M, Scicluna BP, Weigand MA, Mora RA, Flohé SB, Martin-Loeches I, Lachmann G, Girardis M, Garcia-Salido A et al : Profiling the dysregulated immune response in sepsis: overcoming challenges to achieve the goal of precision medicine . The Lancet Respiratory medicine 2023. Gandhi ZJ, Amgai B, Karca A, Mangaroliya V, Arkless PA, Naik S: PREDICTORS OF INTUBATION AND MECHANICAL VENTILATION OF SEPSIS ADMISSIONS ON NON-INVASIVE VENTILATION: A NATIONAL 5-YEAR ANALYSIS . Chest 2023, 164 (4, Supplement):A1626-A1627. Lewis SR, Baker PE, Parker R, Smith AF: High-flow nasal cannulae for respiratory support in adult intensive care patients . The Cochrane database of systematic reviews 2021, 3 (3):Cd010172. Mauri T, Spinelli E, Pavlovsky B, Grieco DL, Ottaviani I, Basile MC, Dalla Corte F, Pintaudi G, Garofalo E, Rundo A et al : Respiratory Drive in Patients with Sepsis and Septic Shock: Modulation by High-flow Nasal Cannula . Anesthesiology 2021, 135 (6):1066-1075. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Submission checks completed at journal 27 May, 2024 Editor assigned by journal 27 May, 2024 First submitted to journal 22 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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. 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jingchao","middleName":"","lastName":"Luo","suffix":""},{"id":307095906,"identity":"641cae9f-3efe-4921-a725-05fbbdd9b285","order_by":2,"name":"Xiaogang Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Xiaogang","middleName":"","lastName":"Wang","suffix":""},{"id":307095908,"identity":"f31de3d8-e12f-4a85-889f-abb5ccd018a9","order_by":3,"name":"Yuan Xu","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-05-22 07:11:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4458847/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4458847/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58153157,"identity":"65cc309f-0524-44f9-ba4b-6dbfe7319c84","added_by":"auto","created_at":"2024-06-11 20:26:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":546105,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flow chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4458847/v1/de3c070ae6d533db897d9ce7.png"},{"id":58153160,"identity":"7def1f1e-2a6d-4437-89a8-fbd1c1b534b3","added_by":"auto","created_at":"2024-06-11 20:26:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":681243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTwo-dimensional histograms with LOWESS regression curves\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4458847/v1/88e91d517ce0642e2c0feb01.png"},{"id":58153772,"identity":"4fa40956-5307-44df-b633-fdc6ca00a805","added_by":"auto","created_at":"2024-06-11 20:34:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":180655,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between SIRS negativity and age according to a restricted cubic spline regression model.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4458847/v1/3ddad8a9f086fbda11ed1584.png"},{"id":58153159,"identity":"306d5ffe-ad63-4e3a-a501-d818ade69572","added_by":"auto","created_at":"2024-06-11 20:26:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":218185,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between SIRS negativity and SOFA score according to a restricted cubic spline regression model.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4458847/v1/4b1a691d9ee81fa49264c3e1.png"},{"id":58154943,"identity":"0b8d15f7-24f8-49e1-964e-2f89a771022d","added_by":"auto","created_at":"2024-06-11 20:42:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3213772,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4458847/v1/e122c556-f6b1-4b26-8f8a-51e8607a04c4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors associated with SIRS negativity at the early stage of sepsis among nonsurviving sepsis patients in ICU: Targeting “silent sepsis”","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSystemic inflammatory response syndrome (SIRS) score is an early diagnostic criterion for sepsis that has been used worldwide for twenty years[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Moreover, SIRS has still been widely used by clinicians for sepsis screening since the 2016 International Consensus of Sepsis 3.0[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although SIRS score is highly sensitive, it also may result in mis-diagnosis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In the context of highly contagious infectious diseases such as novel coronavirus pneumonia (COVID-19), the prevalence of SIRS-negative cases can be high due to the vast number of affected individuals. These patients have mild signs and symptoms and are prone to mis-diagnosis[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Even though mortality is generally lower in patients who are SIRS negative, there may still be a poor prognosis for some of these patients[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince the SIRS score is composed of four scoring criteria, namely, heart rate, respiratory rate, temperature, and white blood cell count, a negative SIRS score indicates that the patient's clinical symptoms are mild[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. There are two possible explanations for this: first, the actual condition of sepsis is mild, and the prognosis is good, so the clinical manifestations are not prominent; second, the actual condition is severe, and the clinical manifestations are not typical. The latter group of patients are prone to delayed treatment in clinical practice due to mild early symptoms. We named these patients the \u0026ldquo;silent sepsis\u0026rdquo; group. Clinicians may miss the opportunity to diagnose sepsis during early screening and delay treatment. Thus, it is necessary to explore the factors related to SIRS negativity in septic patients with poor prognosis.\u003c/p\u003e \u003cp\u003eThis study aimed to analyse the data of patients who were admitted to the intensive care unit (ICU) and diagnosed with sepsis within 24 hours in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], to identify factors related to SIRS negativity during the early stage of sepsis in deceased septic patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eData source and ethics approval\u003c/p\u003e \u003cp\u003e This retrospective cohort study was conducted using data from the MIMIC-IV database; it included information on inpatients admitted to the ICU of the Beth Israel Deaconess Medical Center from 2008 to 2019 and had preexisting institutional review board approval. The patients\u0026rsquo; information was anonymous, and the study was exempt from the need for informed consent. The study was approved by the Ethics Committee of Beijing Hospital (2021BJYYEC-255-01), and the clinical study registration number was ChiCTR2200060095 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.chictr.org.cn/\u003c/span\u003e\u003cspan address=\"http://www.chictr.org.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003ePatients admitted to the ICU between 2008 and 2019 in the MIMIC-IV database were included based on below inclusion criteria: older than 18 years, diagnosed with infection and have a sequential organ failure assessment (SOFA) score of two or more within the first 24 hours of ICU admission[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The exclusion criteria were as follows: patients who received vasopressors, mechanical ventilation, or intra-aortic balloon pump (IABP) therapy within the first 24 hours of ICU admission and patients for whom SIRS or SOFA scores could not be collected due to missing data. Patients who deceased within 28 days were divided into a SIRS-negative group and a SIRS-positive group according to whether they had SIRS scores less than two within the first 24 hours of ICU admission. The baseline data, including age, sex, body mass index (BMI), Charlson Comorbidity Index (CCI), lactate levels at ICU admission and SOFA score, were recorded. Patient outcomes, including septic shock incidence, mechanical ventilation, length of stay in ICU and length of stay in hospital, were also collected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eThe characteristics of the enrolled subjects were described using descriptive statistics. Variables with normal distributions are presented as the means (standard deviations (SDs)) and were compared with an independent sample t test. Nonnormally distributed variables are presented as the medians (interquartile ranges, IQRs), and comparison were analysed with the Mann‒Whitney U test. Categorical variables are presented as percentages and were compared using the chi-square test or Fisher's exact test, when appropriate. The baseline data of the SIRS-negative and SIRS-positive groups were compared, and variables with significant differences according to univariate analysis were visualized in two-dimensional histograms with LOWESS regression curves for SIRS-negative patients. Variables with significant differences according to univariate analysis were included in the logistic regression. The proportions of SIRS-negative patients were analysed according to SOFA score or age, and the variables that were significantly different according to logistic regression were identified. Restricted cubic spline curves were also generated to explore the nonlinear associations between SIRS negativity and age or SOFA score. All the statistical analyses were performed with SPSS 25.0, and the figures were generated with Prism 9.3 and R 4.2.2. Restricted cubic splines were used with four knots to model the nonlinear associations of SOFA score with SIRS negativity and age with SIRS negativity. A p value less than 0.05 was considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of SIRS-negative and SIRS-positive nonsurviving sepsis patients\u003c/h2\u003e \u003cp\u003eA total of 20,136 patients were diagnosed with sepsis within 24 hours of ICU admission, and 1,691 of these patients (8.40%) were SIRS negative. There were 101 (3.73%) patients who were negative for SIRS with 24 hours of ICU admission but dead at 28 days. The study flow chart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were significant differences in SOFA scores between patients who were SIRS negative and SIRS positive (8.18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.58 vs. 9.75\u0026thinsp;\u0026plusmn;\u0026thinsp;4.28, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, differences in several other parameters, such as age (76 [61 to 86] vs. 72 [60 to 82], P\u0026thinsp;=\u0026thinsp;0.053), body mass index (26 [22 to 31] vs. 27 [24 to 32], P\u0026thinsp;=\u0026thinsp;0.056) and Charlson comorbidity index (8 [6 to 9] vs. 7 [5 to 9], P\u0026thinsp;=\u0026thinsp;0.052], approached statistical significance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe age, SOFA score distribution and percentage of non-survived patients of the SIRS-negative and SIRS-positive groups are shown in supplementary files. The distributions of SOFA scores according to age in SIRS-negative and SIRS-positive patients are displayed on two-dimensional histograms with LOWESS regression curves, which show that for older patients, the SOFA score tended to decrease with increasing age (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There might be nonlinear relationships between these variables and SIRS negativity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFactors related to SIRS negativity\u003c/h2\u003e \u003cp\u003eLogistic regression analysis indicated that both SOFA score (adjusted odds ratio (OR)\u0026thinsp;=\u0026thinsp;0.93 (95% CI\u0026thinsp;=\u0026thinsp;0.87 to 1.00), P\u0026thinsp;=\u0026thinsp;0.046) and age (adjusted OR\u0026thinsp;=\u0026thinsp;1.04 (95% CI\u0026thinsp;=\u0026thinsp;0.88 to 1.15), P\u0026thinsp;=\u0026thinsp;0.012) were independent factors related to SIRS negativity in septic patients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results of the restricted cubic spline model showed that OR of SIRS negativity continued to increase with age, particularly for those over 80 years old (p for nonlinearity\u0026thinsp;=\u0026thinsp;0.024) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The OR of SIRS negativity was more than 1 when the SOFA score was less than 4 (p for nonlinearity\u0026thinsp;=\u0026thinsp;0.261) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\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\u003eCharacteristics and outcomes of SIRS-negative or SIRS-positive nonsurviving sepsis patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSIRS positive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSIRS negative\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eBaseline data within 24 hours of ICU admission\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1488 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1424 (54.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (63.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 [60 to 82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 [60 to 82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76 [61 to 86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 [23 to 32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 [24 to 32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 [22 to 31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 [5 to 9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 [5 to 9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 [6 to 9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e953 (35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e904 (34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal disease, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e729 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e692 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate upon ICU admission, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5 [1.6 to 4.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6 [1.6 to 4.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7 [1.3 to 2.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 [3 to 4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 [3 to 4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [1 to 1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1337 (51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1332 (53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2229 (82.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2220 (85.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory rate, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2629 (97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2555 (98.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (74.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2081 (77.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2073 (79.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPS-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 [60 to 101]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 [60 to 102]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 [49 to 78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 [6 to 13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 [6 to 13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 [5 to 10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiration score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 [2 to 4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 [2 to 4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 [2 to 3.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoagulation score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 [0 to 2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 [0 to 2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [0 to 2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 [0 to 2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 [0 to 2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 [0 to 2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [1 to 4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 [1 to 4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [1 to 3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral nervous score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 [0 to 4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 [0 to 4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 [1 to 3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 [0 to 3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 [0 to 3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [0 to 2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eClinical Outcomes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentilation, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1747 (64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1689 (64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (57.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptic shock, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1191 (44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1170 (44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48-h mortality, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e704 (26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e694 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7-day mortality, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1686 (62.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1638 (62.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from ICU to death, h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116 [46 to 243]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 [45 to 241]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e175 [97 to 291]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOS in ICU, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 [2 to 7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 [2 to7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 [2 to 8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOS in hospital, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 [2 to 12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 [2 to 12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 [5 to 13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAPS: acute physiology score; BMI: body mass index; CCI: Charlson comorbidity index; ICU: intensive care unit; LOS: length of stay; SIRS: systemic inflammatory response syndrome; SOFA: sequential organ failure assessment\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of different variables with SIRS-negative status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP for nonlinear\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.43 (0.95\u0026ndash;- 2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.86 (1.05\u0026ndash;- 3.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.00\u0026ndash;- 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.88\u0026ndash;- 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.94\u0026ndash;- 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.95\u0026ndash;- 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07 (1.00\u0026ndash;- 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (00.92\u0026ndash;- 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.77 (1.19\u0026ndash;- 2.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38 (0.77\u0026ndash;- 2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal disease, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60 (1.06\u0026ndash;- 2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19 (0.66\u0026ndash;- 2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate upon ICU admission, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71 (0.59\u0026ndash;- 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75 (0.62\u0026ndash;- 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPS-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.97\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.97\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.300\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.87\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93 (0.87-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiration score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89 (0.73\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15 (0.86\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoagulation score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.87\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23 (0.89\u0026ndash;1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 (0.89\u0026thinsp;\u0026minus;\u0026thinsp;0.547)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73 (0.48\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.62\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73 (0.56\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral nervous score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.91\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01 (0.81\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.74\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92 (0.71\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\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\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, 8.40% of patients were SIRS negative in the early stage of sepsis, and the 28-days mortality rate of patients who were SIRS negative was approximately 6%. Previous general research data indicate that the mortality rate of patients with sepsis can reach 20\u0026ndash;30% or more[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]。Reports on the mortality rate for SIRS-negative sepsis patients are relatively scarce, approximately 9\u0026ndash;28%[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Based on the results of this study, it can be inferred that septic patients with SIRS-negative in the early stages and died within 28 days account for approximately 5\u0026permil; of all septic patients. More importantly, this study demonstrated that the main factors associated with the early presentation of sepsis patients as SIRS-negative are age and SOFA score. Additionally, there is a nonlinear dose-response relationship between age and SIRS negativity.\u003c/p\u003e \u003cp\u003eThe patients who were SIRS negative were predominantly concentrated in the those who were older than 80 years, which was clearly visible in the two-dimensional histograms and mortality distribution charts generated in our study. The RCS model revealed a nonlinear relationship between age and SIRS negativity. When age exceeded the inflection point of 80 years, the OR of SIRS negativity increased significantly. When sepsis occurs, increases in heart rate and respiratory rate occur as a result of physiological compensation for a decrease in oxygen supply[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, elderly people may lose the ability to compensate, which could result in mild symptoms and signs, even when serious organ function damage occurs[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, severity and prognosis cannot be evaluated by physical signs alone, especially for elderly sepsis patients with a mild to moderate degree of organ function damage.\u003c/p\u003e \u003cp\u003e The SOFA score was the most important factor related to SIRS negativity according to multivariate logistic regression. There was a clear linear relationship between the SOFA score and SIRS negativity. When the SOFA score was less than 4, the OR of SIRS negativity was greater than 1. Patients with high SOFA scores usually have severe organ function impairment, generally accompanied by obvious symptoms, changes in physical parameters, and SIRS positivity[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In contrast, patients with sepsis and mild to moderate organ function damage still may have a poor prognosis despite mild clinical signs and SIRS negativity.\u003c/p\u003e \u003cp\u003eSeptic patients with more comorbidities could have higher mortality than those with less comorbidities[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], but the correlation between SIRS negativity and comorbidities is unclear. We compared the CCI between the two groups and found no significant difference. We also compared the individual comorbidities between the two patient groups. The results revealed that only congestive heart failure and renal disease were significantly different. However, the adjusted ORs showed that there was no correlation between any comorbidity and SIRS negativity. Therefore, we cannot yet conclude that comorbidities, including those generally considered to potentially lead to an immunosuppressed state, causes the SIRS score negativity in sepsis patients.\u003c/p\u003e \u003cp\u003eWe excluded patients who received mechanical ventilation, IABP or vasopressors at ICU admission because even if these patients were SIRS negative, clinicians may also have thought that they were in crisis and paid more attention to them. Additionally, effective noninvasive ventilation and high-flow oxygen can also change physiological variables such as the heart rate and respiratory rate[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, this study lacked data on the patients\u0026rsquo; infection sites and aetiologies, and infections at different sites can result in differences in patient clinical manifestations and varying degrees of organ damage, which may affect SIRS and SOFA scores. Second, the missing BMI of some patients may have led to bias. However, the average BMI 27 (IQR 23 to 32 ) kg/m\u003csup\u003e2\u003c/sup\u003e indicates that very few patients may have experienced extreme emaciation and cachexia. Therefore, assuming that BMI is not related to SIRS negativity might be reasonable. In addition, the research data from one central source may not fully reflect the incidence rate of sepsis with a negative SIRS score in the real world, and further epidemiological surveys are needed.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eFor sepsis patients with poor prognoses, elderly individuals (over 80 years) are more likely to be SIRS negative when they have mild organ dysfunction damage (less than 4 SOFA scores) in the early stage of sepsis. This warranted an opportunity to provide early diagnosis for elderly population with negative SIRS score, in order to prevent poor outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAPS: acute physiology score; BMI: body mass index; CCI: Charlson comorbidity index; IABP: intra-aortic balloon pump; IQR: interquartile range; MIMIC: Medical Information Mart for Intensive Care; RCS: restricted cubic spline; SD: standard deviation; SOFA: sequential organ failure assessment; SIRS: systemic inflammatory response syndrome; ICU: intensive care unit; OR: odds ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by National High-Level Hospital Clinical Research Funding (BJ-2023-173).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MIMIC-IV data are available on the project website at https://mimic-iv.mit.edu/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the ethics committee of Beijing Hospital (2021BJYYEC-255-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTaotao Liu conceived the idea. Taotao Liu and Jingchao Luo performed the analysis, interpreted the results, and drafted the manuscript. Yuan Xu and Xiaogang Wang helped perform the analysis and draft the manuscript. All the authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBone RC, Balk RA, Cerra FB, Dellinger RP, Fein AM, Knaus WA, Schein RM, Sibbald WJ: \u003cstrong\u003eDefinitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine\u003c/strong\u003e. \u003cem\u003eChest \u003c/em\u003e1992, \u003cstrong\u003e101\u003c/strong\u003e(6):1644-1655.\u003c/li\u003e\n\u003cli\u003eSinger M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)\u003c/strong\u003e. \u003cem\u003eJama \u003c/em\u003e2016, \u003cstrong\u003e315\u003c/strong\u003e(8):801-810.\u003c/li\u003e\n\u003cli\u003eVincent JL, Opal SM, Marshall JC, Tracey KJ: \u003cstrong\u003eSepsis definitions: time for change\u003c/strong\u003e. \u003cem\u003eLancet \u003c/em\u003e2013, \u003cstrong\u003e381\u003c/strong\u003e(9868):774-775.\u003c/li\u003e\n\u003cli\u003eWilliams JM, Greenslade JH, McKenzie JV, Chu K, Brown AFT, Lipman J: \u003cstrong\u003eSystemic Inflammatory Response Syndrome, Quick Sequential Organ Function Assessment, and Organ Dysfunction: Insights From a Prospective Database of ED Patients With Infection\u003c/strong\u003e. \u003cem\u003eChest \u003c/em\u003e2017, \u003cstrong\u003e151\u003c/strong\u003e(3):586-596.\u003c/li\u003e\n\u003cli\u003eBhimraj A, Morgan RL, Shumaker AH, Baden L, Cheng VCC, Edwards KM, Gallagher JC, Gandhi RT, Muller WJ, Nakamura MM\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eInfectious Diseases Society of America Guidelines on the Treatment and Management of Patients with COVID-19\u003c/strong\u003e. \u003cem\u003eClinical infectious diseases : an official publication of the Infectious Diseases Society of America \u003c/em\u003e2022.\u003c/li\u003e\n\u003cli\u003eSchlapbach LJ, Straney L, Bellomo R, MacLaren G, Pilcher D: \u003cstrong\u003ePrognostic accuracy of age-adapted SOFA, SIRS, PELOD-2, and qSOFA for in-hospital mortality among children with suspected infection admitted to the intensive care unit\u003c/strong\u003e. \u003cem\u003eIntensive care medicine \u003c/em\u003e2018, \u003cstrong\u003e44\u003c/strong\u003e(2):179-188.\u003c/li\u003e\n\u003cli\u003eKaukonen KM, Bailey M, Pilcher D, Cooper DJ, Bellomo R: \u003cstrong\u003eSystemic inflammatory response syndrome criteria in defining severe sepsis\u003c/strong\u003e. \u003cem\u003eThe New England journal of medicine \u003c/em\u003e2015, \u003cstrong\u003e372\u003c/strong\u003e(17):1629-1638.\u003c/li\u003e\n\u003cli\u003eZhang W, Zheng Y, Feng X, Chen M, Kang Y: \u003cstrong\u003eSystemic inflammatory response syndrome in Sepsis-3: a retrospective study\u003c/strong\u003e. \u003cem\u003eBMC infectious diseases \u003c/em\u003e2019, \u003cstrong\u003e19\u003c/strong\u003e(1):139.\u003c/li\u003e\n\u003cli\u003eJohnson A, Bulgarelli, L., Pollard, T., Horng, S., Celi, L. A., \u0026amp; Mark, R. : \u003cstrong\u003eMIMIC-IV (version 1.0).\u003c/strong\u003e \u003cem\u003ePhysioNet \u003c/em\u003e2021.\u003c/li\u003e\n\u003cli\u003eRudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, Colombara DV, Ikuta KS, Kissoon N, Finfer S\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGlobal, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study\u003c/strong\u003e. \u003cem\u003eLancet \u003c/em\u003e2020, \u003cstrong\u003e395\u003c/strong\u003e(10219):200-211.\u003c/li\u003e\n\u003cli\u003eRudiger A, Singer M: \u003cstrong\u003eThe heart in sepsis: from basic mechanisms to clinical management\u003c/strong\u003e. \u003cem\u003eCurrent vascular pharmacology \u003c/em\u003e2013, \u003cstrong\u003e11\u003c/strong\u003e(2):187-195.\u003c/li\u003e\n\u003cli\u003eMikkelsen ME, Shah CV, Meyer NJ, Gaieski DF, Lyon S, Miltiades AN, Goyal M, Fuchs BD, Bellamy SL, Christie JD: \u003cstrong\u003eThe epidemiology of acute respiratory distress syndrome in patients presenting to the emergency department with severe sepsis\u003c/strong\u003e. \u003cem\u003eShock (Augusta, Ga) \u003c/em\u003e2013, \u003cstrong\u003e40\u003c/strong\u003e(5):375-381.\u003c/li\u003e\n\u003cli\u003eShimazui T, Nakada TA, Walley KR, Oshima T, Abe T, Ogura H, Shiraishi A, Kushimoto S, Saitoh D, Fujishima S\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eSignificance of body temperature in elderly patients with sepsis\u003c/strong\u003e. \u003cem\u003eCritical care (London, England) \u003c/em\u003e2020, \u003cstrong\u003e24\u003c/strong\u003e(1):387.\u003c/li\u003e\n\u003cli\u003eRowe TA, McKoy JM: \u003cstrong\u003eSepsis in Older Adults\u003c/strong\u003e. \u003cem\u003eInfectious disease clinics of North America \u003c/em\u003e2017, \u003cstrong\u003e31\u003c/strong\u003e(4):731-742.\u003c/li\u003e\n\u003cli\u003eRaith EP, Udy AA, Bailey M, McGloughlin S, MacIsaac C, Bellomo R, Pilcher DV: \u003cstrong\u003ePrognostic 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\u003c/strong\u003e. \u003cem\u003eJama \u003c/em\u003e2017, \u003cstrong\u003e317\u003c/strong\u003e(3):290-300.\u003c/li\u003e\n\u003cli\u003eVincent JL, de Mendon\u0026ccedil;a A, Cantraine F, Moreno R, Takala J, Suter PM, Sprung CL, Colardyn F, Blecher S: \u003cstrong\u003eUse of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Working group on \u0026quot;sepsis-related problems\u0026quot; of the European Society of Intensive Care Medicine\u003c/strong\u003e. \u003cem\u003eCrit Care Med \u003c/em\u003e1998, \u003cstrong\u003e26\u003c/strong\u003e(11):1793-1800.\u003c/li\u003e\n\u003cli\u003eDąbrowska AM, Słotwiński R: \u003cstrong\u003eThe immune response to surgery and infection\u003c/strong\u003e. \u003cem\u003eCentral-European journal of immunology \u003c/em\u003e2014, \u003cstrong\u003e39\u003c/strong\u003e(4):532-537.\u003c/li\u003e\n\u003cli\u003eStenberg H, Li X, Pello-Esso W, Larsson L\u0026ouml;nn S, Th\u0026oslash;nnings S, Khoshnood A, Knudsen JD, Sundquist K, Jans\u0026aring;ker F: \u003cstrong\u003eThe effects of sociodemographic factors and comorbidities on sepsis: A nationwide Swedish cohort study\u003c/strong\u003e. \u003cem\u003ePreventive medicine reports \u003c/em\u003e2023, \u003cstrong\u003e35\u003c/strong\u003e:102326.\u003c/li\u003e\n\u003cli\u003eCajander S, Kox M, Scicluna BP, Weigand MA, Mora RA, Floh\u0026eacute; SB, Martin-Loeches I, Lachmann G, Girardis M, Garcia-Salido A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eProfiling the dysregulated immune response in sepsis: overcoming challenges to achieve the goal of precision medicine\u003c/strong\u003e. \u003cem\u003eThe Lancet Respiratory medicine \u003c/em\u003e2023.\u003c/li\u003e\n\u003cli\u003eGandhi ZJ, Amgai B, Karca A, Mangaroliya V, Arkless PA, Naik S: \u003cstrong\u003ePREDICTORS OF INTUBATION AND MECHANICAL VENTILATION OF SEPSIS ADMISSIONS ON NON-INVASIVE VENTILATION: A NATIONAL 5-YEAR ANALYSIS\u003c/strong\u003e. \u003cem\u003eChest \u003c/em\u003e2023, \u003cstrong\u003e164\u003c/strong\u003e(4, Supplement):A1626-A1627.\u003c/li\u003e\n\u003cli\u003eLewis SR, Baker PE, Parker R, Smith AF: \u003cstrong\u003eHigh-flow nasal cannulae for respiratory support in adult intensive care patients\u003c/strong\u003e. \u003cem\u003eThe Cochrane database of systematic reviews \u003c/em\u003e2021, \u003cstrong\u003e3\u003c/strong\u003e(3):Cd010172.\u003c/li\u003e\n\u003cli\u003eMauri T, Spinelli E, Pavlovsky B, Grieco DL, Ottaviani I, Basile MC, Dalla Corte F, Pintaudi G, Garofalo E, Rundo A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eRespiratory Drive in Patients with Sepsis and Septic Shock: Modulation by High-flow Nasal Cannula\u003c/strong\u003e. \u003cem\u003eAnesthesiology \u003c/em\u003e2021, \u003cstrong\u003e135\u003c/strong\u003e(6):1066-1075.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"sepsis, SIRS negative, SOFA, MIMIC database","lastPublishedDoi":"10.21203/rs.3.rs-4458847/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4458847/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDespite the very high sensitivity of the Systemic Inflammatory Response Syndrome (SIRS) score for identifying sepsis, there remains a subset of septic patients who exhibit negative SIRS scores, and unfortunately, many of these patients experience poor outcomes. This study aims to investigate the factors associated with SIRS negativity during the early stage of sepsis in deceased septic patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAdult septic patients were included from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database between 2008 and 2019. Sepsis was determined based on the Sepsis 3.0 criteria. Patients who did not survive after 28 days were assigned to the SIRS-negative or SIRS-positive group according to whether the SIRS score was less than two points within 24 hours of intensive care unit (ICU) admission. The baseline data of patients in the SIRS-negative and SIRS-positive groups were collected and compared. The factors associated with SIRS negativity in septic patients were analysed by logistic regression. The dose-response relationships of SIRS negativity with SOFA score and age were determined with a restricted cubic spline model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 53,150 patients were screened in the MIMIC-IV database, and 2706 sepsis nonsurvivors were ultimately included, 101 of whom were negative for SIRS. There were significant differences in SOFA scores between groups (8.18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.58 vs. 9.75\u0026thinsp;\u0026plusmn;\u0026thinsp;4.28, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, differences in several other parameters nearly reached statistical significance, including age (76 [61 to 86] vs. 72 [60 to 82], P\u0026thinsp;=\u0026thinsp;0.053), body mass index (BMI) (26 [22 to 31] vs. 27 [24 to 32], P\u0026thinsp;=\u0026thinsp;0.056), and the Charlson comorbidity index (8 [6 to 9] vs. 7 [5 to 9], P\u0026thinsp;=\u0026thinsp;0.052). Logistic regression analysis indicated that both SOFA score (OR\u0026thinsp;=\u0026thinsp;0.93 [95% CI\u0026thinsp;=\u0026thinsp;0.87-1.00], P\u0026thinsp;=\u0026thinsp;0.046) and age (OR\u0026thinsp;=\u0026thinsp;1.04 [95% CI\u0026thinsp;=\u0026thinsp;0.88\u0026ndash;1.15], P\u0026thinsp;=\u0026thinsp;0.012) were independent factors related to SIRS negativity in septic patients. Analysis with a restricted cubic spline model showed that the odds ratio (OR) of SIRS negativity continued to increase with age, particularly for those over 80 years old (p for nonlinearity\u0026thinsp;=\u0026thinsp;0.024). The odds ratio of SIRS negativity was more than 1 when the SOFA score was less than 4 (p for nonlinearity\u0026thinsp;=\u0026thinsp;0.261).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFor sepsis patients with poor prognoses, elderly individuals (over 80 years) are more likely to be SIRS negative when they have mild organ dysfunction damage (less than 4 SOFA scores) in the early stage of sepsis. This warranted an opportunity to provide early diagnosis for elderly population with negative SIRS score, in order to prevent poor outcomes.\u003c/p\u003e","manuscriptTitle":"Factors associated with SIRS negativity at the early stage of sepsis among nonsurviving sepsis patients in ICU: Targeting “silent sepsis”","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-11 20:25:56","doi":"10.21203/rs.3.rs-4458847/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"checksComplete","content":"","date":"2024-05-27T06:50:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-27T06:50:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Emergency Medicine","date":"2024-05-22T07:10:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"26da382a-908d-49f0-864a-b9cc212fd9c2","owner":[],"postedDate":"June 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-11T20:25:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-11 20:25:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4458847","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4458847","identity":"rs-4458847","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}