The influence of skeletal muscle and fat on 28-day mortality in patients with pneumonia-induced sepsis

The influence of skeletal muscle and fat on 28-day mortality in patients with pneumonia-induced 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 Article The influence of skeletal muscle and fat on 28-day mortality in patients with pneumonia-induced sepsis Xiaomeng Liu, Huizhen Liu, Na Shang, xue mei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6875486/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 Objective To establish a predictive model for 28-day mortality in pneumonia-induced sepsis patients and evaluate its predictive efficacy. Methods The study included patients with pneumonia-induced sepsis admitted to the Emergency Department of Chaoyang Hospital, Capital Medical University, between January 1, 2024, and November 31, 2024. Patients were randomly divided into training and test cohort in a 7:3 ratio. The 28-day survival status of the patients was recorded. Univariate and multivariate Logistic regression analyses were conducted to screen the risk factors of 28-day mortality, and a nomogram was constructed based on these factors. The model was then verified in the test cohort. Results A total of 560 patients were enrolled in the study, with 392 in the training set and 168 in the internal validation set. Multivariate Logistic regression analysis revealed that the cross-sectional area of the erector spinal muscle at the T12 level ( OR =0.998, 95%CI : 0.997-0.999), SOFA score ( OR =1.173, 95%CI : 1.032-1.334), LAR( lactate-to-albumin ) ratio ( OR =44.174, 95%CI : 1.156- 1687.954) were independent predictors of 28-day mortality. There was a significant difference in the subcutaneous fat thickness at the umbilical level between the survival and death groups ( P = 0.02), but it was not an independent risk factor for 28-day mortality after being included in the Logistic regression analysis. A nomogram was established based on these independent risk factors, and evaluations using time-dependent area under the curve, calibration curves, and decision curve analysis demonstrated good calibration and discrimination of the model in both sets. Conclusion The cross-sectional area of the erector spinae muscle at the T12 level, SOFA score and LAR ratio were identified as independent factors for 28-day mortality in pneumonia-induced sepsis patients. Among these, the cross-sectional area of the erector spinae muscle at T12 served as a protective factor, while SOFA score and LAR ratio were identified as risk factors.However, subcutaneous fat thickness at the navel is not an independent risk factor. However, the thickness of subcutaneous fat at the umbilicus is not an independent risk factor. The nomogram constructed based on these risk factors exhibits good predictive performance and provides guidance for clinicians in the early assessment of pneumonia-induced sepsis patient prognosis. Health sciences/Diseases Health sciences/Risk factors Sepsis Cross-sectional area of the erector spinal muscle at the T12 level Lactate/albumin ratio Predictive model Nomogram‌ Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Sepsis, characterized by systemic inflammatory response, is a major global health issue that often leads to multiple organ dysfunction and is associated with extremely high mortality rates. According to foreign statistical data, sepsis patients visiting the emergency department have high hospitalization (70%) and mortality (10%) rates [1]. From 2017 to 2019 in China alone, the annual standardized incidence rate of sepsis increased from 328.25/100,000 to 421.85/100,000, with an in-hospital mortality rate of 28.4% [2]. Early identification of high-risk patients with poor prognosis can reduce hospitalization time and mortality. The prevalence of sarcopenia ranges between 5% and 10%. Muscle loss leads to declines in physical function and mobility, reduces resistance to disease, and increases mortality risk [3]. In recent years, the cross-sectional area of the erector spinae muscle has been shown to be associated with poor prognosis in various diseases [4–6]. While there have been studies on the impact of body mass index (BMI) on the prognosis of sepsis and COVID-19 [7,8], the conclusions remain inconsistent, and there is limited research on the influence of subcutaneous fat thickness on sepsis outcomes. This study aims to investigate the impact of the erector spinae muscle cross-sectional area at the T12 level and subcutaneous fat thickness at the umbilical level on the prognosis of pneumonia patients with sepsis. Additionally, a nomogram will be constructed to predict 28-day mortality, which will help identify high-risk patients with poor outcomes in a timely manner and provide a basis for future interventional research. Materials and Methods Study population A prospective cohort study was conducted, consecutively enrolling 560 patients with pneumonia-induced sepsis who visited the emergency department of Beijing Chaoyang Hospital from January 1, 2024, to November 31, 2024. These patients were randomly divided into a training set (70%, n = 392) and a test set (30%, n = 168). Inclusion criteria:Diagnosis of pneumonia based on the Diagnosis and Treatment of Adults with Community-Acquired Pneumonia(2019 Edition)[9] and the Sepsis-3.0 diagnostic criteria [10]. Exclusion criteria:Terminal-stage malignancy, severe liver disease, Missing clinical data. Research Methods The following data were collected:General information: sex, age, comorbidities (hypertension, diabetes, coronary heart disease, atrial fibrillation, cerebrovascular disease, history of malignancy), First vital signs upon admission: Mean arterial pressure (MAP). First laboratory results within 24 hours of admission:Complete blood count, High-sensitivity C-reactive protein (hs-CRP), Lactate, Albumin, D-dimer, N-terminal pro-B-type natriuretic peptide (NT-proBNP), Troponin I (TnI). Scoring systems: Glasgow Coma Scale (GCS) score, Sequential Organ Failure Assessment (SOFA) score, Acute Physiology and Chronic Health Evaluation II (APACHE II) score (assessed within 24 hours of admission). Muscle cross-sectional area measurement: Chest and abdominal CT scans were performed within 24 hours of admission. Using X-Section software, the erector spinae muscle cross-sectional area at the lower edge of the T12 vertebra was outlined, and the software automatically calculated:Total area, Muscle area within the region (referred to as T12 erector spinae cross-sectional area), Proportion of muscle area, Subcutaneous fat thickness was measured at the umbilical level. Statistical Analysis Data were analyzed using SPSS 27.0 and R 4.2.2. Non-normally distributed quantitative data were expressed as median (Q1, Q3) and analyzed using the Mann-Whitney U test.Normally distributed quantitative data were expressed as mean ± standard deviation (x̄ ± s) and compared using the independent samples t-test. Categorical data were expressed as frequency (%) and analyzed using the chi-square test. Multivariate logistic regression was used to identify independent risk factors for 28-day mortality, and a nomogram was constructed based on these factors. Calibration curves and time-dependent receiver operating characteristic (ROC) curve analysis (AUC) were used to evaluate the model's calibration and discrimination. Decision curve analysis (DCA) was performed to assess clinical utility. The nomogram prediction model was validated in the test set. A P-value < 0.05 was considered statistically significant. Results Baseline Clinical Characteristics of Enrolled Patients A total of 560 patients with pneumonia and sepsis were included, divided into a training set (n = 392) and an internal validation set (n = 168).Table 1 summarizes the demographic and clinical data of the study cohort. The variables in the training cohort and validation cohort were comparable with no statistically significant difference ( P < 0.05). Table 1 The baseline characteristics were comparable between the training and validation sets (Table 1 ). Variables Total ( n =560) Training set ( n =392) Internal validation set ( n =168) P Age(Years) 84 (74.3, 89) 84 (74, 88) 84 (77, 89) 0.828 Male 316 (56.4) 230 (58.7) 86 (51.2) 0.247 Comorbidity,n(%) Hypertension,n(%) 344(61.4) 242 (61.7) 102 (60.7) 0.872 Diabetes,n(%) 226 (40.4) 154 (39.3) 72 (42.9) 0.577 CHD,n(%) 188(33.6) 124 (31.6) 64 (38.1) 0.294 Atrial Fibrillation,n(%) 42 (7.5) 24 (6.1) 18 (10.7) 0.181 CVD,n(%) 158 (28.2) 110 (28.1) 48 (28.6) 0.931 History of MT,n(%) 72 (12.9) 50 (12.8) 22 (13.1) 0.938 MAP,(mmHg) a 71(60, 88.8) 70.35 (60, 89.75) 70.5 (62, 84) 0.567 Laboratory tests b WBC_max,(K/uL) 12.08(8.02, 17.96) 12.2(8.34, 18.23) 10.95(7.55, 15.85) 0.249 NEU_max,(K/uL) 9.95 (6.47, 15.98) 10.3 (6.82, 16.91) 9.15(5.98, 13.49) 0.184 LYM_max,(K/uL)) 0.72 0.42, 1.17) 0.72 (0.43, 1.18) 0.73 (0.42, 1.15) 0.493 NEU/LYM 12.79(7.08, 24.40) 13.04 (6.91, 27.27) 11.31 (7.19, 22.26) 0.799 hs-CRP_max(mg/L) 7.28 (2.63, 16.87) 6.99 (2.73, 16.62) 8.07 (2.09, 17.19) 0.691 Hemoglobin_min,(g/L) 121.6 ± 26.3 122.1 ± 24.97 120.3 ± 29.38 0.612 Hematocrit_min,(%) 36.7 (31.8, 42.2) 36.9 (32.93, 42.20) 36.15 (31.0, 41.73) 0.326 Lowest platelet level,(K/uL) 172.5 (118.8, 251) 168.5 (116.3, 260) 174(125.3, 250) 0.828 Lactate_max,(µmol/L) 2.3 (1.34, 4.3) 2.46 (1.29, 4.40) 2.01(1.50, 3.83) 0.501 Albumin_min,(g/L) 34.3 (30.7, 37.7) 34.1 (30.9, 37.7) 34.5 (30.0, 38.1) 0.973 LAR 0.068 (0.039, 0.127) 0.073(0.039, 0.130) 0.060 (0.043, 0.122) 0.548 D-dimer_max,(mg/L) 2.92 (1.4, 6.31) 2.68 (1.33, 6.63) 3.30 (1.52, 5.50) 0.506 NT-proBNP_max, (ng/L) 3084 (1260, 9543) 3245 (1228, 11325) 3056 (1315, 6775) 0.448 TnI_max,(ng/L) 0.039(0.012, 0.082) 0.038(.012, 0.087) 0.041(.012, 0.078) 0.891 T12 erector spinae CSA,(mm 2 ) 627 (460.8, 861) 620 (472, 868) 638.5(445.8, 842.3) 0.738 T12 erector spinae CSA,(%) 65.8 (58.7, 73.7) 66.1 (58.6, 73.8) 65.0 (58.8, 73.7) 0.780 subcutaneous fat thickness,(mm) 9 (6, 15) 9 (6, 15) 9 (6, 14.63) 0.813 Scores GCS 14(9, 15) 14.5 (9, 15) 13 (9, 15) 0.709 SOFA 6 (4, 8) 6 (4, 8) 6 (4, 7) 0.766 APACHEII 22 (17, 26) 21.5 (16.25, 27) 23 (17.25, 25.75) 0.995 28-day mortality,n(%) 246 (43.9) 164 (41.8) 82 (48.8) 0.281 Categorical data were presented as frequency (percentage), parametric continuous data were presented as mean±(standard deviation), whereas non-parametric continuous data were presented as median (interquartile ranges) CHD Coronary Heart Disease, CVD Cerebrovascular Disease, MT Malignant Tumor, LAR Lactate-to-Albumin ratio, CSA Cross-Sectional Area, GCS Glasgow Coma Scale, SOFA Sequential Organ Failure Assessment, APACHE II Acute Physiology and Chronic Health Evaluation II a Vital signs were calculated as mean value during the first 24 h since admission of each included patients b The laboratory tests recorded the worst value during the first 24 h since admission of each included patients Comparison of Clinical Data Between Survival and Non-Survival Groups in the Training Set A comparison between the survival and non-survival groups revealed statistically significant differences ( P < 0.05) in the following parameters: Lactate (Lac), D-dimer, NT-proBNP, troponin I (TNI), CSA(Cross-sectional area) of the erector spinae muscle at the lower border of the T12 vertebra, muscle area ratio, umbilical subcutaneous fat thickness, GCS, SOFA, APACHE II(Table 2 ). Table 2 Compare the clinical data between survival group and no-survival group Variables Survival in 28 day ( n =82) Death in 28 day ( n =114) P 值 Age(Years) 84 (73.8, 88.0) 84 (77.8, 89.2) 0.310 Male 128(56) 102 (62.2) 0.396 Comorbidity,n(%) Hypertension,n(%) 140 (61.4) 102 (62.2) 0.910 Diabetes,n(%) 90 (39.5) 64 (39) 0.959 CHD,n(%) 74 (32.5) 50 (30.5) 0.770 Atrial Fibrillation,n(%) 12 (5.3) 12 (7.3) 0.554 CVD,n(%) 60 (26.3) 50 (30.5) 0.521 History of MT,n(%) 26 (11.4) 24 (14.6) 0.504 MAP(mmHg) a 70.85 (62.75, 88.25) 70 (60, 96) 0.744 Laboratory tests b WBC_max,(K/uL) 12.2(8.55, 17.9) 12.13(7.03, 19.17) 0.843 NEU_max,(K/uL) 10.24 (7.12, 16.38) 10.41(6.49, 17.95) 0.878 LYM_max,(K/uL)) 0.71 (0.42, 1.10) 0.76 (0.45, 1.32) 0.625 NEU/LYM 12.79 (7.18, 26.57) 13.27 (6.17, 30.58) 0.599 hs-CRP_max(mg/L) 7.47 (2.21, 16.87) 6.51 (2.92, 16.88) 0.886 Hemoglobin_min,(g/L) 124.2 ± 23.18 119.15 ± 27.12 0.162 Hematocrit_min,(%) 37.85 (33.8, 42.13) 35.9 (31.5, 42.95) 0.129 Lowest platelet level,(K/uL) 166.5 (124, 242.3) 186.5(104.5, 272.3) 0.525 Lactate_max,(µmol/L) 2.18 (1.20, 3.86) 3.11(1.53, 5.51) 0.019 Albumin_min,(g/L) 35.95 (32.18, 38.7) 31.95 (28.95, 34.63) <0.001 LAR a 0.032(0.032, 0.110) 0.101 (0.045, 0.174) 0.001 D-dimer_max,(mg/L) 2.22 (1.20, 3.86) 4.65 (1.52, 8.27) 0.001 NT-proBNP_max, (ng/L) 2880 (757, 8443) 4500 (1825, 15900) 0.013 TnI_max,(ng/L) 0.031(0.010, 0.072) 0.050(.022, 0.130) 0.015 T12 erector spinae CSA,(mm 2 ) 749.5 (516.5, 985.8) 537.5 (409.5, 850.3) <0.001 T12 erector spinae CSA,(mm 2 ) 67.3 (60.6, 75.1) 62.7 (54.4, 72.7) 0.007 subcutaneous fat thickness,(mm) 10.5(6, 15) 7.5 (4.5, 12.38) 0.02 Scores GCS 15 (10.75, 15) 10 (5, 15) <0.001 SOFA 5 (4, 7) 7 (5, 9) <0.001 APACHE II 20 (15, 24) 25 (20, 30) <0.001 Categorical data were presented as frequency (percentage), parametric continuous data were presented as mean±(standard deviation), whereas non-parametric continuous data were presented as median (interquartile ranges) CHD Coronary Heart Disease, CVD Cerebrovascular Disease, MT Malignant Tumor, LAR Lactate-to-Albumin ratio, CSA Cross-Sectional Area, GCS Glasgow Coma Scale, SOFA Sequential Organ Failure Assessment, APACHE II Acute Physiology and Chronic Health Evaluation II a Vital signs were calculated as mean value during the first 24 h since admission of each included patients b The laboratory tests recorded the worst value during the first 24 h since admission of each included patients Identification of Independent Risk Factors for 28-Day Mortality in Pneumonia-induced sepsis Using Multivariable Logistic Regression Based on significant intergroup differences identified in clinical baseline characteristics, factors showing statistical significance ( P < 0.05) were included in a multivariable logistic regression analysis(Table 3 ). The results demonstrated the following independent predictors of 28-day mortality: Erector spinae CSA at T12 lower border (OR = 0.998, 95% CI: 0.997–0.999), SOFA score(OR = 1.173, 95% CI:1.032–1.334), LAR(OR = 44.174, 95% CI: 1.156-1687.954). A mortality risk prediction nomogram was constructed using these independent predictors (Fig. 1 ). The nomogram plot visually displays the score corresponding to each predictor. Summing the scores of all variables yields a total score, with the corresponding numerical value indicating the predicted probability of 28-day mortality for sepsis patients. The higher the score, the greater the probability of death. Table 3 Multivariate logistic regression analysis of risk factors associated with 28-day mortality in training cohort Variables β S.E . χ 2 P- value OR 95% CI T12 erector spinae CSA,(mm2) -0.002 0.001 10.836 <0.001 0.998 0.997 ~ 0.999 SOFA 0.160 0.066 5.959 0.015 1.173 1.032 ~ 1.334 LAR 3.788 1.859 4.153 0.042 44.174 1.156 ~ 1687.954 CSA Cross-Sectional Area, SOFA Sequential Organ Failure Assessmen, LAR Lactate-to-albumin ratio CSA Cross-Sectional Area, SOFA Sequential Organ Failure Assessmen, LAR Lactate-to-albumin ratio Evaluation and Validation of the Nomogram Prediction Model We evaluated the model's discriminative performance using the ROC curve and calculated the AUC (see Fig. 2 ). The model achieved an AUC of 0.78 (95% CI: 0.72–0.84). In the internal validation set, the AUC was 0.76 (95% CI: 0.66–0.86), indicating that the model has good predictive value. Goodness-of-fit was evaluated via the Hosmer-Lemeshow (HL) test ( P = 0.508 > 0.05), confirming adequate calibration with no significant deviation from the ideal model. The calibration curve (Fig. 3) further validated the model's accuracy and agreement between predicted and observed outcomes. Decision curve analysis (DCA) was performed with high-risk threshold probability on the x-axis and net benefit on the Y-axis (Fig. 4 ). The curves demonstrated clinical utility across threshold probabilities in both derivation and validation cohorts, confirming positive net benefit for clinical application. Dose-Response Relationship Between Umbilical Subcutaneous Fat Thickness and 28-Day Mortality in Pneumonia-induced Sepsis Using restricted cubic splines (RCS) analysis implemented in R Studio, we demonstrated a nonlinear association between umbilical subcutaneous fat thickness and 28-day mortality risk ( P -nonlinear < 0.001). Further RCS modeling characterized the exposure-response relationship between subcutaneous fat thickness and mortality risk (Fig. 5 ). The analysis revealed an inverse association: increasing subcutaneous fat thickness corresponded to progressively lower 28-day mortality rates. Conclusions Current scoring systems for predicting the prognosis of pneumonia complicated with sepsis show suboptimal efficacy [11]. Constructing an efficient predictive model is crucial for clinical stratification, early identification of high-risk populations, and targeted interventions to improve outcomes and reduce mortality. This study analyzed demographic data, admission vital signs, laboratory indices, clinical scores, cross-sectional area (CSA) of erector spinae at the T12 level, and subcutaneous fat thickness at the umbilical level. The results showed that: T12 erector spinae CSA was an independent protective factor for 28-day mortality in pneumonia with sepsis. SOFA score and LAR (lactate-to-albumin ratio) were independent risk factors. The established nomogram demonstrated high predictive value for 28-day mortality, with an AUC of 0.78 in the training set and 0.76 in the internal validation set. Calibration curves and decision curve analysis (DCA) further supported its clinical utility for risk stratification. LAR as a Marker of Tissue hypoxia and inflammation: LAR integrates lactate (a product of anaerobic metabolism reflecting tissue hypoperfusion) and albumin (a marker of nutrition and inflammation) [12–13]. Previous studies have shown that LAR outperforms lactate and albumin alone in predicting mortality in sepsis (AUC 0.869 vs. 0.816/0.812) [16], consistent with our finding that LAR is an independent risk factor for 28-day mortality. The SOFA score is used to assess the degree of organ failure in sepsis patients and is currently the main scoring system globally for evaluating organ failure. It is also associated with an increased mortality rate in sepsis patients [17]. Our results align with evidence that SOFA surpasses APACHE II in prognostic accuracy [18], reinforcing its role as an independent risk factor. In recent years, with the development of imaging technology, CT has become a widely used imaging method in muscle research, and there are more and more studies on evaluating acute muscle consumption through CT. As one of the primary anti-gravity muscles, it is highly stable and commonly used to assess muscle atrophy. The CSA of the erector spinae muscle serves as a protective factor for prognosis in numerous diseases [19,20]. A study by Murakawa et al. [21] demonstrated that the T12-level erector spinae muscle CSA is an independent factor influencing 30-day mortality in elderly patients with aspiration pneumonia, exhibiting an inverse correlation with mortality. Another study involving chronic obstructive pulmonary disease (COPD) patients indicated that the T12 erector spinae muscle CSA can predict in-hospital mortality and is associated with Activities of Daily Living (ADL) scores at discharge [22]. Our findings further establish that the T12-level erector spinae muscle CSA serves as an independent protective factor against 28-day mortality in patients with pneumonia-induced sepsis. Although subcutaneous fat thickness at the umbilical level differed between survival and death groups (P = 0.02), logistic regression analysis did not identify it as an independent risk factor. This may stem from the study’s limited sample of obese patients and a higher proportion of malnourished individuals, highlighting the need for multicenter studies with larger cohorts and subgroup analyses of body composition [23–24]. Declarations Acknowledgements None Authors’contributions XL was involved in study design, data collection, interpretation and writing of manuscript. HL and NS reviewed the manuscript. XM contributed to design, data interpretation and writing of manuscriptl. All authors read and approved the final manuscript. Funding This research received no external funding. Avaliablity of data and materials Data supporting the conclusions of this article are included in this article. Declarations Ethical approval and consent to participate The study was approved by the Medical Ethics Committee of Beijing Chaoyang Hospital, Capital Medical University (Ethics Approval No.: 2022-COP-430), in compliance with medical ethics standards. Written informed consent was obtained from all participants or their family members. The study was registered with the Chinese Clinical Trial Registry (Registration No.: ChiCTR 2300070377, 04/11/2023). All procedures followed declaration of Helsinki guidelines. Consent for publication Participants have given their consent that data from this study can be published in an anonymized form. Competing interests The authors declare no competing interests Author details 1 Emergency Medicine Clinical Research Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing 100020, China; 2 Department of Emergency Medicine, Beijing Bo'Ai Hospital, China Rehabilitation Research Center, Capital Medical University School of Rehabilitation Medicine, Beijing 100068, China References Kim HJ, Ko RE, Lim SY, et al. Sepsis alert systems, mortality, and adherence in emergency departments: a systematic review and meta-analysis[J]. JAMA Netw Open, 2024, 7(7): e2422823. DOI: 10.1001/jamanetworkopen. Weng L, Xu Y, Yin P, et al. National incidence and mortality of hospitalized sepsis in China [J]. Crit Care, 2023, 27(1): 84. DOI: 10.1186/s13054-023-04385-x. Sayer AA, Cooper R, Arai H, et al. 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DOI: 10.1080/15412 555. 2021.1897560. Murakawa Y, Tamaki A, Matsuzawa R, et al. Impact of the quantity and quality of erector spinae muscles on the short-term prognosis of elderly patients with aspiration pneumonia in Japan[J]. Respir Med, 2024, 232:107746. DOI: 10.1016/j.rmed.2024.107746. Murakami Y, Yasui H, Sato J, et al. Predictors of poor clinical outcomes including in-hospital death and low ability to perform activities of daily living at discharge in hospitalized patients with chronic obstructive pulmonary disease exacerbation[J]. Ther Adv Respir Dis, 2023, 17:17534666 231172924. DOI: 10.1177/17534666231172924. Pepper DJ, Demirkale CY, Sun J, et al. Does Obesity Protect Against Death in Sepsis? A Retrospective Cohort Study of 55,038 Adult Patients. Crit Care Med. 2019 May;47(5):643- 650. doi: 10.1097/CCM.0000000000003692. Rossi AP, Gottin L, Donadello K, et al. Obesity as a risk factor for unfavourable outcomes in critically ill patients affected by Covid 19. Nutr Metab Cardiovasc Dis. 2021 Mar 10;31(3):762-768. doi: 10.1016/j.numecd.2020. 11.012. 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-6875486","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":495376154,"identity":"6f2a0e14-0129-466f-a129-4f07a1c96e07","order_by":0,"name":"Xiaomeng Liu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaomeng","middleName":"","lastName":"Liu","suffix":""},{"id":495376155,"identity":"7f0cbe46-60a3-4616-bea5-377e5ef548b1","order_by":1,"name":"Huizhen Liu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huizhen","middleName":"","lastName":"Liu","suffix":""},{"id":495376156,"identity":"0b4854b2-b55a-47c6-b90c-94be18b0bb90","order_by":2,"name":"Na Shang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Shang","suffix":""},{"id":495376157,"identity":"6cb3de3a-84bb-48ea-a742-8da952dd4e80","order_by":3,"name":"xue mei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYFACxgYgYcPDL3+w8cEHAxs7YrWkyUnOYD5sOKMgLZlYqw4bG9xgSxPm+XAIbAJeoNve3CbNU8Gc2HC7x4zZxuAAMwP74aMb8GkxO3Ow2ZjnDFti45wzZo9zDO7wMfCkpd3Aq+VGYuNj3jaexGaGHHPjHINnzAwSPGb4tdx/2HCY959EYhtDjpm0hcFhxgaCWm4wAm1pMDDmkUhLk2YgSsuZxGbDOccS5CR4Dh827DFIS2Yj6Jfjx59JvKn5z2N/vLHxwY8/Nnb87IeP4dWCCdhIUz4KRsEoGAWjABsAAAfoThbntfZiAAAAAElFTkSuQmCC","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"xue","middleName":"","lastName":"mei","suffix":""}],"badges":[],"createdAt":"2025-06-12 01:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6875486/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6875486/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88502596,"identity":"9a2a569f-f58c-4120-8608-f77dcd8d2bcc","added_by":"auto","created_at":"2025-08-07 06:58:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64732,"visible":true,"origin":"","legend":"\u003cp\u003eThe nomogram of predicting 28-day mortality with sepsis\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCSA \u003c/em\u003eCross-Sectional Area, \u003cem\u003eSOFA \u003c/em\u003eSequential Organ Failure Assessmen, \u003cem\u003eLAR\u003c/em\u003e Lactate-to-albumin ratio\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6875486/v1/bd74a99a60945d42f2a9114f.png"},{"id":88500022,"identity":"3a26d947-dc22-4f3c-8c99-cea2d7a5f474","added_by":"auto","created_at":"2025-08-07 06:50:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67144,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6875486/v1/fca446f5ead264da539c41c9.png"},{"id":88500025,"identity":"404f3921-9d2f-4ebe-89f7-0c56295735d1","added_by":"auto","created_at":"2025-08-07 06:50:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56538,"visible":true,"origin":"","legend":"\u003cp\u003eThe calibration curves\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6875486/v1/f9e3ed68466a24970e4b69ef.png"},{"id":88500027,"identity":"eeeeb166-1cf2-4905-a22f-fb66e5144fbc","added_by":"auto","created_at":"2025-08-07 06:50:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":62402,"visible":true,"origin":"","legend":"\u003cp\u003eThe DCA of the nomogram in the training and validation cohorts\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6875486/v1/b7c90b86c977a94b96feaf6f.png"},{"id":88500028,"identity":"e8d81b8f-3d2d-432d-bf52-37f0a500b685","added_by":"auto","created_at":"2025-08-07 06:50:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":108294,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of subcutaneous fat thickness and 2-day mortality based on restricted cubic spline method\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6875486/v1/44cca71640444db9b227c8b3.png"},{"id":100777959,"identity":"886113cc-9cb7-40aa-988c-2d20861838b3","added_by":"auto","created_at":"2026-01-21 11:20:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1254370,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6875486/v1/7a39a6d6-f5b4-4f85-a47f-ec77970daef4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The influence of skeletal muscle and fat on 28-day mortality in patients with pneumonia-induced sepsis","fulltext":[{"header":"Background","content":"\u003cp\u003eSepsis, characterized by systemic inflammatory response, is a major global health issue that often leads to multiple organ dysfunction and is associated with extremely high mortality rates. According to foreign statistical data, sepsis patients visiting the emergency department have high hospitalization (70%) and mortality (10%) rates [1]. From 2017 to 2019 in China alone, the annual standardized incidence rate of sepsis increased from 328.25/100,000 to 421.85/100,000, with an in-hospital mortality rate of 28.4% [2]. Early identification of high-risk patients with poor prognosis can reduce hospitalization time and mortality. The prevalence of sarcopenia ranges between 5% and 10%. Muscle loss leads to declines in physical function and mobility, reduces resistance to disease, and increases mortality risk [3]. In recent years, the cross-sectional area of the erector spinae muscle has been shown to be associated with poor prognosis in various diseases [4\u0026ndash;6]. While there have been studies on the impact of body mass index (BMI) on the prognosis of sepsis and COVID-19 [7,8], the conclusions remain inconsistent, and there is limited research on the influence of subcutaneous fat thickness on sepsis outcomes.\u003c/p\u003e\u003cp\u003eThis study aims to investigate the impact of the erector spinae muscle cross-sectional area at the T12 level and subcutaneous fat thickness at the umbilical level on the prognosis of pneumonia patients with sepsis. Additionally, a nomogram will be constructed to predict 28-day mortality, which will help identify high-risk patients with poor outcomes in a timely manner and provide a basis for future interventional research.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eStudy population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA prospective cohort study was conducted, consecutively enrolling 560 patients with pneumonia-induced sepsis who visited the emergency department of Beijing Chaoyang Hospital from January 1, 2024, to November 31, 2024. These patients were randomly divided into a training set (70%, n\u0026thinsp;=\u0026thinsp;392) and a test set (30%, n\u0026thinsp;=\u0026thinsp;168). Inclusion criteria:Diagnosis of pneumonia based on the Diagnosis and Treatment of Adults with Community-Acquired Pneumonia(2019 Edition)[9] and the Sepsis-3.0 diagnostic criteria [10]. Exclusion criteria:Terminal-stage malignancy, severe liver disease, Missing clinical data.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch Methods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe following data were collected:General information: sex, age, comorbidities (hypertension, diabetes, coronary heart disease, atrial fibrillation, cerebrovascular disease, history of malignancy), First vital signs upon admission: Mean arterial pressure (MAP). First laboratory results within 24 hours of admission:Complete blood count, High-sensitivity C-reactive protein (hs-CRP), Lactate, Albumin, D-dimer, N-terminal pro-B-type natriuretic peptide (NT-proBNP), Troponin I (TnI). Scoring systems: Glasgow Coma Scale (GCS) score, Sequential Organ Failure Assessment (SOFA) score, Acute Physiology and Chronic Health Evaluation II (APACHE II) score (assessed within 24 hours of admission). Muscle cross-sectional area measurement: Chest and abdominal CT scans were performed within 24 hours of admission. Using X-Section software, the erector spinae muscle cross-sectional area at the lower edge of the T12 vertebra was outlined, and the software automatically calculated:Total area, Muscle area within the region (referred to as T12 erector spinae cross-sectional area), Proportion of muscle area, Subcutaneous fat thickness was measured at the umbilical level.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData were analyzed using SPSS 27.0 and R 4.2.2. Non-normally distributed quantitative data were expressed as median (Q1, Q3) and analyzed using the Mann-Whitney U test.Normally distributed quantitative data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̄ \u0026plusmn; s) and compared using the independent samples t-test. Categorical data were expressed as frequency (%) and analyzed using the chi-square test. Multivariate logistic regression was used to identify independent risk factors for 28-day mortality, and a nomogram was constructed based on these factors. Calibration curves and time-dependent receiver operating characteristic (ROC) curve analysis (AUC) were used to evaluate the model's calibration and discrimination. Decision curve analysis (DCA) was performed to assess clinical utility. The nomogram prediction model was validated in the test set. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eBaseline Clinical Characteristics of Enrolled Patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 560 patients with pneumonia and sepsis were included, divided into a training set (n\u0026thinsp;=\u0026thinsp;392) and an internal validation set (n\u0026thinsp;=\u0026thinsp;168).Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the demographic and clinical data of the study cohort. The variables in the training cohort and validation cohort were comparable with no statistically significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eThe baseline characteristics were comparable between the training and validation sets (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eTotal\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e=560)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTraining set\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e=392)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInternal validation set\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e=168)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\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\u003e84 (74.3, 89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84 (74, 88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84 (77, 89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.828\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\u003e316 (56.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e230 (58.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86 (51.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbidity,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e344(61.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e242 (61.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e102 (60.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.872\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e226 (40.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e154 (39.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72 (42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.577\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHD,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e188(33.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124 (31.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64 (38.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtrial Fibrillation,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e158 (28.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110 (28.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.931\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of MT,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72 (12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.938\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAP,(mmHg) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71(60, 88.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.35 (60, 89.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70.5 (62, 84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.567\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLaboratory tests\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC_max,(K/uL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.08(8.02, 17.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.2(8.34, 18.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.95(7.55, 15.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNEU_max,(K/uL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.95 (6.47, 15.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.3 (6.82, 16.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.15(5.98, 13.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLYM_max,(K/uL))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.72 0.42, 1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.72 (0.43, 1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.73 (0.42, 1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNEU/LYM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.79(7.08, 24.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.04 (6.91, 27.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.31 (7.19, 22.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.799\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehs-CRP_max(mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.28 (2.63, 16.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.99 (2.73, 16.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.07 (2.09, 17.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.691\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin_min,(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121.6\u0026thinsp;\u0026plusmn;\u0026thinsp;26.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e122.1\u0026thinsp;\u0026plusmn;\u0026thinsp;24.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e120.3\u0026thinsp;\u0026plusmn;\u0026thinsp;29.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematocrit_min,(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.7 (31.8, 42.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.9 (32.93, 42.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.15 (31.0, 41.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.326\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLowest platelet level,(K/uL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e172.5 (118.8, 251)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e168.5 (116.3, 260)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e174(125.3, 250)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLactate_max,(\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.3 (1.34, 4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.46 (1.29, 4.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.01(1.50, 3.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin_min,(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.3 (30.7, 37.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.1 (30.9, 37.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.5 (30.0, 38.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.973\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.068 (0.039, 0.127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.073(0.039, 0.130)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.060 (0.043, 0.122)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.548\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD-dimer_max,(mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.92 (1.4, 6.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.68 (1.33, 6.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.30 (1.52, 5.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNT-proBNP_max, (ng/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3084 (1260, 9543)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3245 (1228, 11325)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3056 (1315, 6775)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.448\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTnI_max,(ng/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.039(0.012, 0.082)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.038(.012, 0.087)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.041(.012, 0.078)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.891\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT12 erector spinae CSA,(mm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e627 (460.8, 861)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e620 (472, 868)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e638.5(445.8, 842.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.738\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT12 erector spinae CSA,(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.8 (58.7, 73.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.1 (58.6, 73.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.0 (58.8, 73.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.780\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esubcutaneous fat thickness,(mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (6, 15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (6, 15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (6, 14.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.813\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScores\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14(9, 15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.5 (9, 15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (9, 15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.709\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\u003e6 (4, 8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (4, 8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (4, 7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.766\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPACHEII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (17, 26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.5 (16.25, 27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (17.25, 25.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.995\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e28-day mortality,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e246 (43.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e164 (41.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 (48.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.281\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\u003eCategorical data were presented as frequency (percentage), parametric continuous data were presented as mean\u0026plusmn;(standard deviation), whereas non-parametric continuous data were presented as median (interquartile ranges)\u003c/p\u003e\u003cp\u003e\u003cem\u003eCHD\u003c/em\u003e Coronary Heart Disease, \u003cem\u003eCVD\u003c/em\u003e Cerebrovascular Disease, \u003cem\u003eMT\u003c/em\u003e Malignant Tumor, \u003cem\u003eLAR\u003c/em\u003e Lactate-to-Albumin ratio, \u003cem\u003eCSA\u003c/em\u003e Cross-Sectional Area, \u003cem\u003eGCS\u003c/em\u003e Glasgow Coma Scale, \u003cem\u003eSOFA\u003c/em\u003e Sequential Organ Failure Assessment, \u003cem\u003eAPACHE II\u003c/em\u003e Acute Physiology and Chronic Health Evaluation II\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eVital signs were calculated as mean value during the first 24 h since admission of each included patients\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eThe laboratory tests recorded the worst value during the first 24 h since admission of each included patients\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison of Clinical Data Between Survival and Non-Survival Groups in the Training Set\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA comparison between the survival and non-survival groups revealed statistically significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the following parameters: Lactate (Lac), D-dimer, NT-proBNP, troponin I (TNI), CSA(Cross-sectional area) of the erector spinae muscle at the lower border of the T12 vertebra, muscle area ratio, umbilical subcutaneous fat thickness, GCS, SOFA, APACHE II(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCompare the clinical data between survival group and no-survival group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurvival in 28 day\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e=82)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDeath in 28 day\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e=114)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e值\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\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\u003e84 (73.8, 88.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84 (77.8, 89.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.310\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\u003e128(56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102 (62.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.396\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbidity,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140 (61.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102 (62.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.910\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90 (39.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.959\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHD,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (32.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (30.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtrial Fibrillation,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.554\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60 (26.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (30.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.521\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of MT,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.504\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAP(mmHg) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.85 (62.75, 88.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70 (60, 96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.744\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLaboratory tests\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC_max,(K/uL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.2(8.55, 17.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.13(7.03, 19.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.843\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNEU_max,(K/uL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.24 (7.12, 16.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.41(6.49, 17.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.878\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLYM_max,(K/uL))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.71 (0.42, 1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.76 (0.45, 1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNEU/LYM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.79 (7.18, 26.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.27 (6.17, 30.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.599\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehs-CRP_max(mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.47 (2.21, 16.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.51 (2.92, 16.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.886\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin_min,(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124.2\u0026thinsp;\u0026plusmn;\u0026thinsp;23.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119.15\u0026thinsp;\u0026plusmn;\u0026thinsp;27.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematocrit_min,(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.85 (33.8, 42.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.9 (31.5, 42.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLowest platelet level,(K/uL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e166.5 (124, 242.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e186.5(104.5, 272.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.525\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLactate_max,(\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.18 (1.20, 3.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.11(1.53, 5.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin_min,(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.95 (32.18, 38.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.95 (28.95, 34.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAR\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.032(0.032, 0.110)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.101 (0.045, 0.174)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD-dimer_max,(mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.22 (1.20, 3.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.65 (1.52, 8.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNT-proBNP_max, (ng/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2880 (757, 8443)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4500 (1825, 15900)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTnI_max,(ng/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.031(0.010, 0.072)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.050(.022, 0.130)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT12 erector spinae CSA,(mm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e749.5 (516.5, 985.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e537.5 (409.5, 850.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT12 erector spinae CSA,(mm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67.3 (60.6, 75.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.7 (54.4, 72.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esubcutaneous fat thickness,(mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.5(6, 15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.5 (4.5, 12.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScores\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (10.75, 15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (5, 15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;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\u003e5 (4, 7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (5, 9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPACHE II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (15, 24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (20, 30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\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\u003eCategorical data were presented as frequency (percentage), parametric continuous data were presented as mean\u0026plusmn;(standard deviation), whereas non-parametric continuous data were presented as median (interquartile ranges)\u003c/p\u003e\u003cp\u003e\u003cem\u003eCHD\u003c/em\u003e Coronary Heart Disease, \u003cem\u003eCVD\u003c/em\u003e Cerebrovascular Disease, \u003cem\u003eMT\u003c/em\u003e Malignant Tumor, \u003cem\u003eLAR\u003c/em\u003e Lactate-to-Albumin ratio, \u003cem\u003eCSA\u003c/em\u003e Cross-Sectional Area, \u003cem\u003eGCS\u003c/em\u003e Glasgow Coma Scale, \u003cem\u003eSOFA\u003c/em\u003e Sequential Organ Failure Assessment, \u003cem\u003eAPACHE II\u003c/em\u003e Acute Physiology and Chronic Health Evaluation II\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eVital signs were calculated as mean value during the first 24 h since admission of each included patients\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eThe laboratory tests recorded the worst value during the first 24 h since admission of each included patients\u003c/p\u003e\u003cp\u003e\u003cb\u003eIdentification of Independent Risk Factors for 28-Day Mortality in Pneumonia-induced sepsis Using Multivariable Logistic Regression\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on significant intergroup differences identified in clinical baseline characteristics, factors showing statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were included in a multivariable logistic regression analysis(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results demonstrated the following independent predictors of 28-day mortality: Erector spinae CSA at T12 lower border (OR\u0026thinsp;=\u0026thinsp;0.998, 95% CI: 0.997\u0026ndash;0.999), SOFA score(OR\u0026thinsp;=\u0026thinsp;1.173, 95% CI:1.032\u0026ndash;1.334), LAR(OR\u0026thinsp;=\u0026thinsp;44.174, 95% CI: 1.156-1687.954). A mortality risk prediction nomogram was constructed using these independent predictors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The nomogram plot visually displays the score corresponding to each predictor. Summing the scores of all variables yields a total score, with the corresponding numerical value indicating the predicted probability of 28-day mortality for sepsis patients. The higher the score, the greater the probability of death.\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\u003eMultivariate logistic regression analysis of risk factors associated with 28-day mortality in training cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"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\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\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eS.E\u003c/em\u003e.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT12 erector spinae CSA,(mm2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.997\u0026thinsp;~\u0026thinsp;0.999\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\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.032\u0026thinsp;~\u0026thinsp;1.334\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e44.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.156\u0026thinsp;~\u0026thinsp;1687.954\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\u003cem\u003eCSA\u003c/em\u003e Cross-Sectional Area, \u003cem\u003eSOFA\u003c/em\u003e Sequential Organ Failure Assessmen, \u003cem\u003eLAR\u003c/em\u003e Lactate-to-albumin ratio\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eCSA\u003c/em\u003e Cross-Sectional Area, \u003cem\u003eSOFA\u003c/em\u003e Sequential Organ Failure Assessmen, \u003cem\u003eLAR\u003c/em\u003e Lactate-to-albumin ratio\u003c/p\u003e\u003cp\u003e\u003cb\u003eEvaluation and Validation of the Nomogram Prediction Model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe evaluated the model's discriminative performance using the ROC curve and calculated the AUC (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The model achieved an AUC of 0.78 (95% CI: 0.72\u0026ndash;0.84). In the internal validation set, the AUC was 0.76 (95% CI: 0.66\u0026ndash;0.86), indicating that the model has good predictive value. Goodness-of-fit was evaluated via the Hosmer-Lemeshow (HL) test (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.508\u0026thinsp;\u0026gt;\u0026thinsp;0.05), confirming adequate calibration with no significant deviation from the ideal model. The calibration curve (Fig.\u0026nbsp;3) further validated the model's accuracy and agreement between predicted and observed outcomes. Decision curve analysis (DCA) was performed with high-risk threshold probability on the x-axis and net benefit on the Y-axis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The curves demonstrated clinical utility across threshold probabilities in both derivation and validation cohorts, confirming positive net benefit for clinical application.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDose-Response Relationship Between Umbilical Subcutaneous Fat Thickness and 28-Day Mortality in Pneumonia-induced Sepsis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing restricted cubic splines (RCS) analysis implemented in R Studio, we demonstrated a nonlinear association between umbilical subcutaneous fat thickness and 28-day mortality risk (\u003cem\u003eP\u003c/em\u003e-nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Further RCS modeling characterized the exposure-response relationship between subcutaneous fat thickness and mortality risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The analysis revealed an inverse association: increasing subcutaneous fat thickness corresponded to progressively lower 28-day mortality rates.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eCurrent scoring systems for predicting the prognosis of pneumonia complicated with sepsis show suboptimal efficacy [11]. Constructing an efficient predictive model is crucial for clinical stratification, early identification of high-risk populations, and targeted interventions to improve outcomes and reduce mortality. This study analyzed demographic data, admission vital signs, laboratory indices, clinical scores, cross-sectional area (CSA) of erector spinae at the T12 level, and subcutaneous fat thickness at the umbilical level. The results showed that: T12 erector spinae CSA was an independent protective factor for 28-day mortality in pneumonia with sepsis. SOFA score and LAR (lactate-to-albumin ratio) were independent risk factors. The established nomogram demonstrated high predictive value for 28-day mortality, with an AUC of 0.78 in the training set and 0.76 in the internal validation set. Calibration curves and decision curve analysis (DCA) further supported its clinical utility for risk stratification.\u003c/p\u003e\u003cp\u003eLAR as a Marker of Tissue hypoxia and inflammation: LAR integrates lactate (a product of anaerobic metabolism reflecting tissue hypoperfusion) and albumin (a marker of nutrition and inflammation) [12\u0026ndash;13]. Previous studies have shown that LAR outperforms lactate and albumin alone in predicting mortality in sepsis (AUC 0.869 vs. 0.816/0.812) [16], consistent with our finding that LAR is an independent risk factor for 28-day mortality.\u003c/p\u003e\u003cp\u003eThe SOFA score is used to assess the degree of organ failure in sepsis patients and is currently the main scoring system globally for evaluating organ failure. It is also associated with an increased mortality rate in sepsis patients [17]. Our results align with evidence that SOFA surpasses APACHE II in prognostic accuracy [18], reinforcing its role as an independent risk factor.\u003c/p\u003e\u003cp\u003eIn recent years, with the development of imaging technology, CT has become a widely used imaging method in muscle research, and there are more and more studies on evaluating acute muscle consumption through CT. As one of the primary anti-gravity muscles, it is highly stable and commonly used to assess muscle atrophy. The CSA of the erector spinae muscle serves as a protective factor for prognosis in numerous diseases [19,20]. A study by Murakawa et al. [21] demonstrated that the T12-level erector spinae muscle CSA is an independent factor influencing 30-day mortality in elderly patients with aspiration pneumonia, exhibiting an inverse correlation with mortality. Another study involving chronic obstructive pulmonary disease (COPD) patients indicated that the T12 erector spinae muscle CSA can predict in-hospital mortality and is associated with Activities of Daily Living (ADL) scores at discharge [22]. Our findings further establish that the T12-level erector spinae muscle CSA serves as an independent protective factor against 28-day mortality in patients with pneumonia-induced sepsis.\u003c/p\u003e\u003cp\u003eAlthough subcutaneous fat thickness at the umbilical level differed between survival and death groups (P\u0026thinsp;=\u0026thinsp;0.02), logistic regression analysis did not identify it as an independent risk factor. This may stem from the study\u0026rsquo;s limited sample of obese patients and a higher proportion of malnourished individuals, highlighting the need for multicenter studies with larger cohorts and subgroup analyses of body composition [23\u0026ndash;24].\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\u003eAuthors\u0026rsquo;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXL was involved in study design, data collection, interpretation and writing of manuscript. HL and NS reviewed the manuscript. XM contributed to design, data interpretation and writing of manuscriptl. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvaliablity of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting the conclusions of this article are included in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Medical Ethics Committee of Beijing Chaoyang Hospital, Capital Medical University (Ethics Approval No.: 2022-COP-430), in compliance with medical ethics standards. Written informed consent was obtained from all participants or their family members. The study was registered with the Chinese Clinical Trial Registry (Registration No.: ChiCTR 2300070377, 04/11/2023). All procedures followed declaration of Helsinki guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants have given their consent that data from this study can be published in an anonymized form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eEmergency Medicine Clinical Research Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing 100020, China; \u003csup\u003e2\u003c/sup\u003eDepartment of Emergency Medicine, Beijing Bo\u0026apos;Ai Hospital, China Rehabilitation Research Center, Capital Medical University School of Rehabilitation Medicine, Beijing 100068, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKim HJ, Ko RE, Lim SY, et al. Sepsis alert systems, mortality, and adherence in emergency departments: a systematic review and meta-analysis[J]. JAMA Netw Open, 2024, 7(7): e2422823. DOI: 10.1001/jamanetworkopen.\u003c/li\u003e\n\u003cli\u003eWeng L, Xu Y, Yin P, et al. National incidence and mortality of hospitalized sepsis in China [J]. Crit Care, 2023, 27(1): 84. DOI: 10.1186/s13054-023-04385-x.\u003c/li\u003e\n\u003cli\u003eSayer AA, Cooper R, Arai H, et al. Sarcopenia[J]. Nat Rev Dis Primers, 2024, 10(1): 68. DOI: 10.1038/s41572- 024-00550-w. \u003c/li\u003e\n\u003cli\u003eLee GR, Ko SH, Choi HS, et al. Prognostic utility of paraspinal muscle index in elderly patients with community-acquired pneumonia[J]. Clin Exp Emerg Med, 2024, 11(2): 171-180. DOI: 10.15441/ ceem.23.142. \u003c/li\u003e\n\u003cli\u003eYang J, Jiang S, Fan Q, et al. Prevalence and effect on prognosis of sarcopenia in patients with primary biliary cholangitis[J]. Front Med (Lausanne), 2024, 11:1346165. DOI: 10.3389/ fmed. 2024.1346165. \u003c/li\u003e\n\u003cli\u003eSaito H, Matsue Y, Maeda D, et al. Sarcopenia prognosis using dual-energy X-ray absorptiometry and prediction model in older patients with heart failure[J]. ESC Heart Fail, 2024, 11(2):914-922. DOI: 10.1002/ehf2.14667. \u003c/li\u003e\n\u003cli\u003eDana R, Bannay A, Bourst P, et al. Obesity and mortality in critically ill COVID-19 patients with respiratory failure. Int J Obes (Lond). 2021 Sep;45(9):2028-2037. doi: 10.1038/s41366 -021- 00872-9.\u003c/li\u003e\n\u003cli\u003eAuld SC, Caridi-Scheible M, Blum JM, et al. ICU and ventilator mortality among critically Ill adults with Coronavirus disease 2019. Crit Care Med. 2020. https://doi.org/10.1097/ccm. 0000000000004457.\u003c/li\u003e\n\u003cli\u003eMetlay J P, Waterer G W, Long A C, et al. Diagnosis and treatment of adults with community- acquired pneumonia. An official clinical practice guideline of the american thoracic society and infectious diseases society of America[J]. Am J Respir Crit Care Med, 2019, 200(7):e45-e67.\u003c/li\u003e\n\u003cli\u003eger M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (sepsis-3)[J]. JAMA, 2016, 315(8): 801-810. DOI: 10.1001/jama.2016. 0287.\u003c/li\u003e\n\u003cli\u003eLv C, Chen Y, Shi W, et al. Comparison of different scoring systems for prediction of mortality and ICU admission in elderly CAP population[J]. Clin Interv Aging, 2021, 16: 1917-1929. DOI: 10.2147/CIA.S335315.\u003c/li\u003e\n\u003cli\u003eEmhoff CW, Messonnier LA. Concepts of lactate metablic clearance rate and lactate clamp for metabolic inquiry: a mini-review [J]. Nutrients, 2023, 15(14):3213. DOI: 10.3390/nu151 43213.\u003c/li\u003e\n\u003cli\u003eKlinkmann G, Waterstradt K, Klammt S, et al. Exploring albumin functionality assays: a pilot study on sepsis evaluation in intensive care medicine [J]. Int J Mol Sci, 2023, 24(16): 12551. DOI: 10.3390/ijms241612551.\u003c/li\u003e\n\u003cli\u003eSun H, Wang B, An G, et al. Association of lactate/albumin ratio with 3-month readmission risk in heart failure patients: a retrospective study[J]. ESC Heart Fail, 2024, 11(4):2182-2190. DOI: 10.1002/ehf2.14788.\u003c/li\u003e\n\u003cli\u003eXu W, Huo J, Hu Q, et al. Association between lactate dehydrogenase to albumin ratio and acute kidney injury in patients with sepsis: a retrospective cohort study[J]. Clin Exp Nephrol, 2024, 28(9):882-893. DOI: 10.1007/s10157-024-02500-y. \u003c/li\u003e\n\u003cli\u003eYoo KH, Choi SH, Suh GJ, et al. The usefulness of lactate/albumin ratio, C-reactive protein/albumin ratio, procalcitonin/albumin ratio, SOFA, and qSOFA in predicting the prognosis of patients with sepsis who presented to EDs[J]. Am J Emerg Med, 2024, 78:1-7. DOI: 10.1016/ j.ajem.2023.12.028. \u003c/li\u003e\n\u003cli\u003eLan L, Zhou M, Chen X, et al. Prognostic accuracy of SOFA, MEWS, and SIRS criteria in predicting the mortality rate of patients with sepsis: a meta-analysis[J]. Nurs Crit Care, 2024, 29(6):1623-1635. DOI: 10.1111/nicc.13016.\u003c/li\u003e\n\u003cli\u003eShahi S, Paneru H, Ojha R, et al. SOFA and APACHE II scoring systems for predicting outcome of neurological patients admitted in a tertiary hospital intensive care unit[J]. Ann Med Surg (Lond), 2024, 86(4):1895-1900. DOI: 10.1097/MS9.0000000000001734. \u003c/li\u003e\n\u003cli\u003eShimoda M, Yoshiyama T, Tanaka Y, et al. Relationship between the thickness of erector spinae muscles and mortality in patients with pulmonary tuberculosis[J]. Respir Investig, 2023, 61(4):511-519. DOI: 10.1016/j.resinv.2023.04.011.\u003c/li\u003e\n\u003cli\u003eAttaway AH, Welch N, Yadav R, et al. Quantitative computed tomography assessment of pectoralis and erector spinae muscle area and disease severity in chronic obstructive pulmonary disease referred for lung volume reduction[J]. COPD, 2021; 18(2):191-200. DOI: 10.1080/15412 555. 2021.1897560. \u003c/li\u003e\n\u003cli\u003eMurakawa Y, Tamaki A, Matsuzawa R, et al. Impact of the quantity and quality of erector spinae muscles on the short-term prognosis of elderly patients with aspiration pneumonia in Japan[J]. Respir Med, 2024, 232:107746. DOI: 10.1016/j.rmed.2024.107746. \u003c/li\u003e\n\u003cli\u003eMurakami Y, Yasui H, Sato J, et al. Predictors of poor clinical outcomes including in-hospital death and low ability to perform activities of daily living at discharge in hospitalized patients with chronic obstructive pulmonary disease exacerbation[J]. Ther Adv Respir Dis, 2023, 17:17534666 231172924. DOI: 10.1177/17534666231172924.\u003c/li\u003e\n\u003cli\u003ePepper DJ, Demirkale CY, Sun J, et al. Does Obesity Protect Against Death in Sepsis? A Retrospective Cohort Study of 55,038 Adult Patients. Crit Care Med. 2019 May;47(5):643- 650. doi: 10.1097/CCM.0000000000003692.\u003c/li\u003e\n\u003cli\u003eRossi AP, Gottin L, Donadello K, et al. Obesity as a risk factor for unfavourable outcomes in critically ill patients affected by Covid 19. Nutr Metab Cardiovasc Dis. 2021 Mar 10;31(3):762-768. doi: 10.1016/j.numecd.2020. 11.012.\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":"Sepsis, Cross-sectional area of the erector spinal muscle at the T12 level, Lactate/albumin ratio, Predictive model, Nomogram‌","lastPublishedDoi":"10.21203/rs.3.rs-6875486/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6875486/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective \u003c/strong\u003eTo establish a predictive model for 28-day mortality in pneumonia-induced sepsis patients and evaluate its predictive efficacy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e The study included patients with pneumonia-induced sepsis admitted to the Emergency Department of Chaoyang Hospital, Capital Medical University, between January 1, 2024, and November 31, 2024. Patients were randomly divided into training and test cohort in a 7:3 ratio. The 28-day survival status of the patients was recorded. Univariate and multivariate Logistic regression analyses were conducted to screen the risk factors of 28-day mortality, and a nomogram was constructed based on these factors. The model was then verified in the test cohort.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e A total of 560 patients were enrolled in the study, with 392 in the training set and 168 in the internal validation set. Multivariate Logistic regression analysis revealed that the cross-sectional area of the erector spinal muscle at the T12 level (\u003cem\u003eOR\u003c/em\u003e=0.998, \u003cem\u003e95%CI\u003c/em\u003e: 0.997-0.999), SOFA score (\u003cem\u003eOR\u003c/em\u003e=1.173, \u003cem\u003e95%CI\u003c/em\u003e: 1.032-1.334), LAR( lactate-to-albumin ) ratio (\u003cem\u003eOR\u003c/em\u003e =44.174, \u003cem\u003e95%CI\u003c/em\u003e: 1.156- 1687.954) were independent predictors of 28-day mortality. There was a significant difference in the subcutaneous fat thickness at the umbilical level between the survival and death groups (\u003cem\u003eP\u003c/em\u003e = 0.02), but it was not an independent risk factor for 28-day mortality after being included in the Logistic regression analysis. A nomogram was established based on these independent risk factors, and evaluations using time-dependent area under the curve, calibration curves, and decision curve analysis demonstrated good calibration and discrimination of the model in both sets.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e The cross-sectional area of the erector spinae muscle at the T12 level, SOFA score and LAR ratio were identified as independent factors for 28-day mortality in pneumonia-induced sepsis patients. Among these, the cross-sectional area of the erector spinae muscle at T12 served as a protective factor, while SOFA score and LAR ratio were identified as risk factors.However, subcutaneous fat thickness at the navel is not an independent risk factor. However, the thickness of subcutaneous fat at the umbilicus is not an independent risk factor. The nomogram constructed based on these risk factors exhibits good predictive performance and provides guidance for clinicians in the early assessment of pneumonia-induced sepsis patient prognosis.\u003c/p\u003e","manuscriptTitle":"The influence of skeletal muscle and fat on 28-day mortality in patients with pneumonia-induced sepsis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-07 06:49:55","doi":"10.21203/rs.3.rs-6875486/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":"31c887d2-dc38-4836-ae70-96d37388cedb","owner":[],"postedDate":"August 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52608959,"name":"Health sciences/Diseases"},{"id":52608960,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-01-21T09:56:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-07 06:49:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6875486","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6875486","identity":"rs-6875486","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}