C-Reactive protein-triglyceride glucose index and controlling nutritional status score for predicting severe sepsis or septic shock in diabetic patients with Enterobacteriaceae bloodstream infections

C-Reactive protein-triglyceride glucose index and controlling nutritional status score for predicting severe sepsis or septic shock in diabetic patients with Enterobacteriaceae bloodstream infections | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article C-Reactive protein-triglyceride glucose index and controlling nutritional status score for predicting severe sepsis or septic shock in diabetic patients with Enterobacteriaceae bloodstream infections Shixiao Li, Fenfen Xu, Peng Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8877570/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 investigate the value of the C-reactive protein-triglyceride glucose index (CTI) and the controlling nutritional status score (CONUT) in predicting the risk of progression to severe sepsis or septic shock in patients with diabetes complicated by Enterobacteriaceae bloodstream infections. Methods A total of 236 patients with diabetes complicated by Klebsiella pneumoniae or Escherichia coli bloodstream infections from January 2023 to December 2025 were enrolled. Patients were grouped based on CTI quartiles (Q1-Q4) and CONUT scores (normal-low risk, moderate risk, high risk). Demographic, clinical characteristics, and laboratory indicators were collected to analyze intergroup differences and their association with severe clinical outcomes (severe sepsis or septic shock). Multivariate logistic regression models adjusted for confounding factors, and ROC curves evaluated the predictive ability of combined CTI and CONUT indices. Results The study found that as the CTI quartile increased, the incidence of sepsis ( p < 0.001) and in-hospital mortality ( p = 0.009) significantly increased. The CONUT high-risk group exhibited a particularly high sepsis incidence of 89.8% ( p < 0.001), with more pronounced trends toward elevated HbA1c levels and decreased platelet counts ( p < 0.05). Significant correlations were observed between CTI and HbA1c levels. Within different CONUT score groups, the high-risk group demonstrated markedly higher HbA1c levels compared to the normal light exposure group. Multivariate regression analysis revealed that CTI Q4 (adjusted OR = 5.301, 95% CI: 2.054–13.679, p = 0.001) and moderate/high CONUT scores (adjusted OR = 3.379 and 5.099, respectively, p < 0.001) were independent predictors of severe outcomes. ROC analysis indicated that the area under the curve (AUC) for combined CTI and CONUT scores in predicting severe outcomes was 0.777 (95% CI: 0.716–0.837), outperforming either single indicator. Conclusion Both CTI and CONUT scores were effective indicators for predicting severe sepsis or septic shock in patients with diabetes complicated by Enterobacteriaceae bloodstream infections. C-reactive protein-triglyceride-glucose index Controlling nutritional status score Type 2 diabetes mellitus Bloodstream infections Figures Figure 1 Figure 2 Introduction Diabetes, as a globally prevalent chronic metabolic disorder, poses a significant disease burden and mortality risk due to its complications. Beyond cardiovascular, renal, and neuropathic complications, individuals with diabetes are more susceptible to infections, particularly bloodstream infections [ 1 ] . Reports indicate that the risk of bacterial-induced bloodstream infections is approximately twice as high in diabetic patients compared to non-diabetic individuals [ 2 ] . Hyperglycemia-induced immune dysfunction and microcirculatory disorders not only significantly increase the risk of pathogen invasion and dissemination but also cause the infection process to deteriorate rapidly. This often leads to progression into sepsis and septic shock, ultimately resulting in elevated mortality rates. The mortality rate for severe sepsis ranges from approximately 10% to 40% [ 3 , 4 ] . Therefore, early identification of such high-risk patients is crucial. The CTI serves as an emerging composite biomarker. By integrating the triglyceride glucose index (TyG), which reflects insulin resistance, with C-reactive protein (CRP), representing systemic inflammation levels, it enables simultaneous assessment of metabolic disorders and inflammatory states in the body [ 5 ] . CTI has demonstrated value in assessing the progression of various diseases, including cancer-related mortality, diabetes, stroke [ 6 – 8 ] . Recent large-scale cohort studies have confirmed that CTI is significantly associated with the risk of cardiovascular disease incidence and mortality in the general population and in specific metabolic subgroups [ 9 , 10 ] . CTI demonstrates superior risk stratification for mortality in patients with acute decompensated heart failure and concomitant diabetes mellitus [ 11 ] . At the same time, the critical role of nutritional and immune status in the onset and progression of infections cannot be overlooked. The CONUT score effectively assesses an individual's nutritional reserves and immune capacity using three simple indicators: serum albumin, total cholesterol, and lymphocyte count [ 12 ] . In patients with hepatocellular carcinoma, CONUT has been demonstrated to be a robust predictor of severe postoperative complications such as liver failure, highlighting its value in assessing stress tolerance and prognosis [ 13 ] . Studies indicated that elevated CONUT scores in patients with T2DM were closely associated with the presence and severity of chronic kidney disease and retinopathy [ 14 , 15 ] . However, among high-risk populations with diabetes-a condition characterized by both metabolic abnormalities and chronic inflammation-research demonstrating the effectiveness of integrating multidimensional information on metabolism, inflammation, and nutrition-immunity to predict the prognosis of bloodstream infections in diabetic patients remains extremely limited. Therefore, this study aims to explore the value of two composite indices-CTI and CONUT-which respectively approach the prediction from the integrated dimensions of metabolic inflammation and nutrition-immunity, in assessing the risk of developing septic shock following Enterobacteriaceae bloodstream infections in diabetic patients. Materials and methods Study population This study enrolled 236 diabetic patients diagnosed with K. pneumoniae or E. coli bloodstream infections admitted to Taizhou Hospital, Zhejiang Province, from January 2023 to December 2025. Inclusion criteria: (1) Patients must meet the diagnostic criteria for diabetes as outlined in the Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2020 Edition); (2) Patients must meet the diagnostic criteria for bloodstream infection and have corresponding pathogens detected in blood cultures. Exclusion criteria: (1) Age < 18 years; (2) Hemorrhagic or anaphylactic shock; (3) Incomplete medical records or transfer to another hospital during the study period. Sepsis and septic shock were defined according to internationally applicable guidelines during the study period. Severe sepsis was defined as sepsis with organ dysfunction. Septic shock was defined as the need for vasopressors to maintain mean arterial pressure ≥ 65 mmHg despite adequate resuscitation, with serum lactate > 2 mmol/L [ 16 ] . The study was approved by the Helsinki ethics committee at our institution. Microbiological methods The isolation and culture procedures for K. pneumoniae and E. coli followed the standardized protocols described in the 4th edition of the National Clinical Laboratory Procedures. Blood culture bottles were incubated in the BacT/Alert system (bioMérieux, France) for 5 days. Positive isolates were identified using the VITEK Mass Spectrometry System V3.2 (bioMérieux, France). Data Collection and calculation Collect patients' clinical data, including age, gender, underlying conditions, site of infection, diagnosis, length of hospital stay, blood culture results, diabetes complications, and clinical outcomes. Laboratory results encompassed all blood parameters measured concurrently with the first positive blood culture collected after admission, including WBC count, PLT count, HB, lymphocyte count (LY), HbA1c, CRP, triglycerides (TG), fasting blood glucose (FPG), total cholesterol (TC), and serum albumin levels. The calculation method for CTI was as follows [ 9 ] : CTI = 0.412 × Ln (CRP [mg/L]) + Ln (TG [mg/dl] × FPG [mg/dl])/2 The CONUT score was determined by calculations based on ALB, LY, and TC obtained from blood samples. Serum albumin concentrations ≥ 35, 30–34.9, 25–29.9, and < 25 g/L correspond to 0, 2, 4, and 6 points, respectively; Lymphocyte counts ≥ 1600, 1200–1599, 800–1199, and < 800/µL correspond to 0, 1, 2, and 3 points, respectively; Total cholesterol concentrations ≥ 180, 140–179, 100–139, and < 100 mg/dL correspond to 0, 1, 2, and 3 points, respectively. According to previously published criteria [ 13 ] , a CONUT score of 0–1 defined the low-risk group, a score of 2–4 defined the mild-risk group, a score of 5–8 defined the moderate-risk group, and a score of 9–12 defined the high-risk group. Statistical analysis Statistical analysis and graphing were performed using SPSS 23.0 (SPSS Inc., Chicago, IL, USA) and GraphPad Prism software (version 10.1.2). For baseline characteristics, descriptive data were summarized as counts and percentages for categorical variables, or median and interquartile range (IQR) for continuous variables. Categorical variables were analyzed using the chi-square test (χ²) or Fisher's exact test. Normally distributed continuous variables were analyzed using analysis of variance (ANOVA), while non-normally distributed data were analyzed using the Kruskal-Wallis test. Multivariate logistic stepwise regression analysis was employed to identify independent predictors of intergroup differences. Performance was assessed using receiver operating characteristic (ROC) curves and area under the curve (AUC). P values ≤ 0.05 were considered statistically significant. Results The study population was divided into four quartiles (Q1-Q4) based on CTI (Table 1 ). Significant differences existed in age distribution among the four groups (p = 0.036). The prevalence of fever differed notably (p = 0.002). Infection type showed a strong association ( p = 0.001), with community-acquired infections dominating across all groups, particularly in Q3 (100%) and Q4 (98.3%). Hypertension prevalence was highest in Q1 (73.3%, p = 0.004). Metabolic and hematologic markers including HbA1c ( p < 0.001), PLT ( p < 0.001), and serious sepsis and/ or septic shock incidence ( p < 0.001) varied significantly. In-hospital mortality was also significantly different ( p = 0.009), with Q4 reporting the highest mortality (10.2%). Table 1 Baseline characteristics of the participants, stratified according to CTI Characteristics Q1 (n = 60) Q2 (n = 58) Q3 (n = 59) Q4 (n = 59) p value Age (years) 70(59.3–77.8) 67.5(58-74.2) 61(54–69) 63(57–74) 0.036 Gender, n (%) 0.777 Male 27(45.0) 24(41.4) 27(45.8) 22(37.3) Female 33(55.0) 34(58.6) 32(54.2) 37(62.7) Smoking, n (%) 11(18.3) 8(13.8) 11(18.6) 7(11.9) 0.673 Drinking, n (%) 4(6.7) 4(6.9) 11(18.6) 4(6.8) 0.099 Clinical symptoms Fever 23(38.3) 39(67.2) 40(67.8) 38(64.4) 0.002 Nausea 2(3.3) 5(8.6) 3(5.1) 4(6.8) 0.643 Weakness 5(8.3) 5(8.6) 11(18.6) 7(11.9) 0.298 Abdominal.pain 10(16.7) 5(8.6) 6(10.2) 7(11.9) 0.572 Bacteria, n (%) 0.674 K. pneumoniae 32(53.3) 32(55.2) 33(55.9) 27(45.8) E. coli 28(46.7) 26(44.8) 26(44.1) 32(54.2) Source.of.infection, n (%) Respiratory system 9(15.0) 3(5.2) 3(5.1) 7(11.9) 0.154 Urinary.tract 20(33.3) 26(44.8) 28(47.5) 29(49.2) 0.288 Original.infection 18(30.0) 10(17.2) 12(20.3) 11(18.6) 0.339 Liver- gallbladder and pancreas 11(18.3) 18(31.0) 16(27.1) 11(18.6) 0.274 Others 2(3.3) 1(1.7) 0 0 0.211 Infection.type, n (%) 0.001 Community-acquired 51(85.0) 55(94.8) 59(100.0) 58(98.3) Hospital-acquired 9(15.0) 3(5.2) 0 1(1.7) Comorbidities, n(%) Hypertension 44(73.3) 29(50.0) 25(42.4) 31(52.5) 0.004 Tumor 14(23.3) 8(13.8) 6(10.2) 5(8.5) 0.101 Liver cirrhosis 6(10.0) 4(6.9) 6(10.2) 2(3.4) 0.416 Cardiovascular 26(43.3) 23(39.7) 24(40.7) 28(47.5) 0.832 Chronic pulmonary disease 4(6.7) 3(5.2) 1(1.7) 3(5.1) 0.558 HbA1c (%) 8.0(6.9–9.7) 8.6(7.5–10.2) 10.1(8.2–12.0) 9.9(8.5–11.8) < 0.001 WBC (10^9/L) 11.3(8.6–15.2) 11.9(8.2–15.6) 11.9(9.0-15.9) 13.9(9.5–19.5) 0.115 PLT (10^9/L) 190(126.5-256.8) 206(139-267.8) 161(122–220) 145(87–202) 0.001 HB (g/L) 115(93-128.8) 115(102.8-135.5) 123(107–134) 121(101–135) 0.359 Serious sepsis and/ or septic shock 31(51.7) 32(55.2) 28(47.5) 51(86.4) < 0.001 Complications.of.diabetes (%) Diabetic kidney disease 10(16.7) 5(8.6) 5(8.5) 8(13.6) 0.440 Diabetic cardiovascular diseases 8(13.3) 3(5.2) 6(10.2) 5(8.5) 0.474 Diabetic neuropathy 5(8.3) 5(8.6) 8(13.6) 4(6.8) 0.634 Diabetic ketoacidosis 8(13.3) 7(12.1) 13(22.0) 12(20.3) 0.372 Diabetic retinopathy 3(5.0) 4(6.9) 7(11.9) 4(6.8) 0.557 Diabetic.foot ulcers 1(1.7) 3(5.2) 0 1(1.7) 0.211 Mortality in hospital 5(8.3) 0 1(1.7) 6(10.2) 0.009 Abbreviations: WBC white blood cells, Hb hemoglobin, PLT platelets, HbA1c Glycated Hemoglobin A1c According to CONUT score, patients were categorized into three groups: normal-light, moderate, and high (Table 2 ). The prevalence of K. pneumoniae was significantly higher in the high-risk group ( p = 0.034). Tumor comorbidity was less common in the high-risk group ( p = 0.041). HbA1c levels increased with worsening nutritional status ( p = 0.004), while platelet counts decreased ( p = 0.002). Serious sepsis and/ or septic shock incidence rose markedly across categories, reaching 89.8% in the high-risk group ( p < 0.001). No significant differences were found for age, gender, smoking, drinking, clinical symptoms, infection source, or diabetes complications. Table 2 Baseline clinical characteristics of the participants, stratified according to CONUT score Characteristics Normal-light (n = 92) Moderate (n = 95) High (n = 49) p value Age (years) 65(57-73.8) 66(58–75) 64(57.5–74) 0.746 Gender, n (%) 0.395 Male 34(37.0) 43(45.3) 23(46.9) Female 58(63.0) 52(54.7) 26(53.1) Smoking, n (%) 14(15.2) 16(16.8) 7(14.3) 0.912 Drinking, n (%) 9(9.8) 12(12.6) 2(4.1) 0.215 Clinical symptoms Fever 53(57.6) 59(62.1) 28(57.1) 0.773 Nausea 5(5.4) 4(4.2) 5(10.2) 0.380 Weakness 7(7.6) 14(14.7) 7(14.3) 0.252 Abdominal.pain 9(9.8) 14(14.7) 5(10.2) 0.538 Bacteria, n (%) 0.034 K. pneumoniae 41(44.6) 50(52.6) 33(67.3) E. coli 51(55.4) 45(47.4) 16(32.7) Source.of.infection, n (%) Respiratory system 5(5.4) 11(11.6) 6(12.2) 0.235 Urinary.tract 43(46.7) 41(43.2) 19(38.8) 0.656 Original.infection 25(27.2) 16(16.8) 10(20.4) 0.225 Liver- gallbladder and pancreas 17(18.5) 26(27.4) 13(26.5) 0.307 Others 1(1.1) 1(1.1) 1(2.0) 0.878 Infection.type, n (%) 0.415 Community-acquired 86(93.5) 89(93.7) 48(98.0) Hospital-acquired 6(6.5) 6(6.3) 1(2.0) Comorbidities, n(%) Hypertension 57(62.0) 50(52.6) 22(44.9) 0.133 Tumor 16(17.4) 15(15.8) 2(4.1) 0.041 Liver cirrhosis 7(7.6) 6(6.3) 5(10.2) 0.717 Cardiovascular 43(46.7) 41(43.2) 17(34.7) 0.382 Chronic pulmonary disease 5(5.4) 4(4.2) 2(4.1) 0.904 HbA1c (%) 8.6(7.3–10.2) 9.0(7.6–11.4) 10.6(8.3–12.0) 0.004 WBC (10^9/L) 11.9(8.0–15.0) 12.3(9.4–17.0) 12.5(9.0-17.3) 0.293 PLT (10^9/L) 195.5(152.3-242.3) 154(102–243) 139(77.5–215) 0.002 HB (g/L) 123.5(108.3-134.8) 117(101–133) 109(97–131) 0.038 Serious sepsis and/ or septic shock 34(37.0) 64(67.4) 44(89.8) < 0.001 Complications.of.diabetes (%) Diabetic kidney disease 11(12.0) 13(13.7) 4(8.2) 0.606 Diabetic cardiovascular diseases 9(9.8) 10(10.5) 3(6.1) 0.655 Diabetic neuropathy 11(12.0) 8(8.4) 3(6.1) 0.482 Diabetic ketoacidosis 14(15.2) 15(15.8) 11(22.4) 0.530 Diabetic retinopathy 6(6.5) 6(6.3) 6(12.2) 0.431 Diabetic.foot ulcers 0 3(3.2) 2(4.1) 0.078 Mortality in hospital 2(2.2) 6(6.3) 4(8.2) 0.208 Figure 1 A revealed a significant positive correlation between the CTI and HbA1c levels (r = 0.322, p < 0.001). As CTI increased, there was a corresponding rise in HbA1c (%) and CTI, indicating that higher CTI values were associated with poorer glycemic control. Figure 1 B detailed the relationship between CONUT scores and HbA1c levels. Compared with the normal-light group, the HbA1c level of the high-risk group increased significantly. As the CONUT score increased, HbA1c (%) consistently rose within each group, indicating that poorer nutritional status was associated with worsening glycemic control. No significant differences were observed in age, gender, smoking, drinking, bacterial type, infection source, infection type, or comorbidities in the non-serious and serious groups. The use of hypoglycemic agents before admission was significantly lower in the serious group (61.3% vs. 76.6%, p = 0.021) (Table 3 ). Table 3 Demographics, clinical characteristics according to serious sepsis and/ or septic shock Characteristics Non- serious sepsis and/ or septic shock group (n = 94) Serious sepsis and/ or septic shock group (n = 142) p value Age (years) 66.5(57-81.5) 64(56.8–74) 0.244 Gender, n (%) 0.531 Male 37(39.4) 63(44.4) Female 57(60.6) 79(55.6) Smoking, n (%) 14(14.9) 23(16.2) 0.931 Drinking, n (%) 11(11.7) 12(8.5) 0.548 Bacteria, n (%) 0.300 K. pneumoniae 45(47.9) 79(55.6) E. coli 49(52.1) 63(44.4) Source.of.infection, n (%) Respiratory system 5(5.3) 17(12.0) 0.136 Urinary.tract 37(39.4) 66(46.5) 0.345 Original.infection 26(27.7) 25(17.6) 0.094 Liver- gallbladder and pancreas 24(25.5) 32(22.5) 0.709 Others 2(2.1) 1(0.7) 0.565 Infection.type, n (%) 0.693 Community-acquired 90(95.7) 133(93.7) Hospital-acquired 4(4.3) 9(6.3) Comorbidities, n(%) Hypertension 58(61.7) 71(50.0) 0.102 Tumor 18(19.1) 15(10.6) 0.095 Liver cirrhosis 8(8.5) 10(7.0) 0.868 Cardiovascular 34(36.2) 67(47.2) 0.124 Chronic pulmonary disease 5(5.3) 6(4.2) 0.940 Use of hypoglycemic agents before admission 72(76.6) 87(61.3) 0.021 In unadjusted analysis, Q4 of the CTI classification was strongly associated with severe outcomes (OR = 5.964, 95% CI: 2.422–14.684, p < 0.001). Similarly, moderate (OR = 3.522) and high (OR = 15.012) CONUT scores were significant predictors ( p < 0.001). After adjusting for comorbidities and infection source in multivariate analysis, Q4 remained a significant risk factor (OR = 5.301, 95% CI: 2.054–13.679, p = 0.001), as did moderate (OR = 3.379, 95% CI:1.797–6.353) and high (OR = 5.099, 95% CI:14.827–43.120) CONUT scores ( p < 0.001) (Table 4 ). In the ROC analysis, the AUC for CTI combined CONUT score was 0.777 (95%CI:0.716–0.837) (Fig. 2 ), demonstrating that CTI combined CONUT score has a good discriminative ability for identifying patients at higher risk of severe outcomes. Table 4 Univariable and multivariate analysis for diabetic patients with Enterobacteriaceae bloodstream infections with severe sepsis and/ or septic shock Model Cluster Unadjusted OR (95%CI) p value Adjusted OR (95%CI) * p value Model 1 CTI Q1 Reference Q2 1.151(0.558–2.375) 0.703 Q3 0.845(0.412–1.735) 0.646 Q4 5.964(2.422–14.684) < 0.001 5.301(2.054–13.679) 0.001 Model 2 CONUT score Normal-light Reference Moderate 3.522(1.928–6.434) < 0.001 3.379(1.797–6.353) < 0.001 High 15.012(5.428–41.517) < 0.001 5.099(14.827–43.120) < 0.001 * Comorbidities and source of infection were included in multivariate analysis. Discussion This study analyzed 236 patients with diabetes complicated by bloodstream infections caused by K. pneumoniae or E. coli to evaluate the predictive value of CTI and CONUT in identifying patients at risk of progressing to severe sepsis or septic shock. Results demonstrated that both CTI and CONUT effectively identified high-risk patients, with combined use exhibiting superior predictive performance (AUC = 0.777), providing robust evidence for early clinical intervention. CTI, as a composite indicator integrating metabolic and inflammatory information, demonstrated significant association with adverse outcomes in this study. With increasing CTI quartiles, patients exhibited markedly elevated risks of sepsis and in-hospital mortality, consistent with previous research [ 17 ] . Wang et al. found that critically ill patients with high CTI levels had a higher risk of all-cause mortality and longer hospital and ICU stays [ 18 ] . Additionally, CTI exhibited a positive correlation with HbA1c, further supporting its value in assessing the synergistic effects of glycemic control and systemic inflammation. Specifically, the combined presence of hyperglycemia, hypertriglyceridemia, and inflammatory states exacerbated immune dysfunction and microcirculatory disorders, thereby promoting the progression of infections. The CONUT score, as a key indicator of nutritional and immune status, also demonstrated strong risk stratification capabilities in this study. The high-risk CONUT group exhibited not only higher HbA1c levels and lower platelet counts but also a significantly increased incidence of sepsis (89.8%). It suggested that malnutrition and immune exhaustion may impair the body's ability to respond to infection, thereby elevating the risk of organ dysfunction. Studies indicated that malnutrition frequently occured in patients with acute and chronic illnesses, with particularly high prevalence among the elderly and critically ill patients. It may lead to increased mortality rates, prolonged hospital stays, and extended ICU admissions [ 19 , 20 ] . The subjects of this study were diabetic patients, who often presented with varying degrees of malnutrition and impaired immune function. This may significantly affect their ability to respond to bloodstream infections and their susceptibility to severe sepsis or septic shock. In the multivariate logistic regression analysis, CTI Q4 and moderate-to-high CONUT scores remained independent predictors of severe outcomes even after adjusting for potential confounders such as underlying diseases and source of infection. It suggested that in patients with diabetes complicated by bloodstream infections, metabolic inflammatory status and nutritional-immunological status represented two relatively independent yet potentially additive risk dimensions. Combined assessment of both dimensions provided a more comprehensive reflection of patients' pathophysiological burden and clinical vulnerability than either metric alone. This study also has several limitations. First, it was a single-center design with a limited sample size, and only two common Gram-negative bacterial infections were included. Second, both CTI and CONUT scores were based on a single laboratory test at admission, failing to dynamically reflect changes during the treatment process. Future studies should validate these findings in larger, multicenter cohorts and explore whether early intervention strategies based on these indicators—such as enhanced metabolic control, nutritional support, and immune modulation—can improve clinical outcomes in such high-risk patients. Conclusion In summary, CTI and CONUT demonstrate excellent discriminatory ability for identifying patients with diabetes and bloodstream infections who develop severe sepsis or septic shock. Clinically, these two simple, readily available composite indicators may be incorporated into risk assessment systems to aid in the early identification of high-risk patients, facilitate individualized treatment, and ultimately improve outcomes. Declarations Acknowledgements We would like to thank Clinical Microbiology Laboratory for supporting this study. Authors’ contributions SX L had roles in the study design, data analysis, literature search, and writing of the manuscript. P Z had roles in guiding research and clinical management. FF X had roles in data collection and interpretation. All authors have read and agreed with the final manuscript. Funding This work was supported by grants from Traditional Chinese Medicine Science and Technology Project of Zhejiang Province (2024ZL526). Availability of data and materials The datasets generated and analyzed during the current study are not publicly available due to privacy or ethical restrictions but are available from the corresponding author on reasonable request. Ethics approval and consent to participate The Institutional Medical Ethics Committee of Taizhou Hospital of Zhejiang Province granted approval for this retrospective study, with a waiver of informed consent because the medical records of the subjects were deidentified from the Medical Records and Statistics Room to ensure patient confidentiality. The use of the raw data and the study’s protocol was permitted by the Institutional Medical Ethics Committee of Taizhou Hospital of Zhejiang Province, and all methods were performed following the relevant guidelines and regulations. Consent for publication Not applicable. Competing interests The authors declare no competing interests. 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Association between controlling nutritional status score and chronic kidney disease in diabetic patients: a cross-sectional study based on the National Health and Nutrition Examination Survey. Int Urol Nephrol. 2024;56(2):795–804. Ruan Y, Zhang P, Jia X, Hua S, Yao D. Association between controlling nutritional status score and diabetic retinopathy: Data from National Health and Nutrition Examination Survey. Eur J Ophthalmol. 2025;35(3):1082–90. Singer M, Deutschman C, Seymour C, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard G, Chiche J, Coopersmith C, Hotchkiss R, Levy M, Marshall J, Martin G, Opal S, Rubenfeld G, van der Poll T, Vincent J, Angus DJJ. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801–10. Shuai WL, Liu J, Zhou T, Li JX, Tao X, Zou B. Prognostic value of C-reactive protein triglyceride glucose index versus triglyceride glucose index in critically ill patients: a machine learning-driven cohort analysis. Expert Rev Mol Diagn. 2026;26(1):85–95. Wang H, Hu L, Chen Y, He H, Huang Y, Tao R, Li S, Yang R. The C-reactive protein-triglyceride-glucose index (CTI) as a predictor of mortality and hospital stay in critically ill patients: a retrospective cohort study. Lipids Health Dis. 2025;24(1):299. Hiura G, Lebwohl B, Seres DS. Malnutrition Diagnosis in Critically Ill Patients Using 2012 Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition Standardized Diagnostic Characteristics Is Associated With Longer Hospital and Intensive Care Unit Length of Stay and Increased In-Hospital Mortality. JPEN J Parenter Enter Nutr. 2020;44(2):256–64. Stello BB, Zucatti KP, Freitas IM, Burgel CF, Milesi BM, Milanez DSJ, Silva FM. Concurrent and predictive validity of different tools for malnutrition diagnosis in adult critically ill patients: a systematic review with meta-analysis of observational studies. Am J Clin Nutr. 2026:101195. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8877570","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598408999,"identity":"22698aee-4493-43f3-afc6-b6b6c4bb777b","order_by":0,"name":"Shixiao Li","email":"","orcid":"","institution":"Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shixiao","middleName":"","lastName":"Li","suffix":""},{"id":598409000,"identity":"ccf157ca-22ba-4ec9-b7a9-a0a2c244d09f","order_by":1,"name":"Fenfen Xu","email":"","orcid":"","institution":"Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fenfen","middleName":"","lastName":"Xu","suffix":""},{"id":598409001,"identity":"dac18e3a-9326-4910-bc3f-b30cdbad24fa","order_by":2,"name":"Peng Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYDACCSjNxt584EBChQ0PP3sDkVr4eI4lHnhwJk1GsucAkVrkJHKMDz5sO2xjcMMBvw752c3HpAsq7ti1SaQlHEg4c56H4QYD44ePObi1MM45liY948yz5DaexyC/3OZhnN3ALDlzG24tzBI5ZtK8bYeT2djBttzmYZY5wMbMi0cLG1jLP6AWhhyDA4lt53jYJBLwa+EBa2k4bMfGAdZygIeHkBYJibRk6xnHDiew8RwDOSyZR4LnYDNev8jPSD54u6DmsL18e/Phjz8q7Oztjzcf/PARjxZwEABxYgOCz9iAQyGqFnuCqkbBKBgFo2DkAgAmu1ZpBLbJhAAAAABJRU5ErkJggg==","orcid":"","institution":"The Third Affiliated Hospital of Zhejiang Chinese Medical University","correspondingAuthor":true,"prefix":"","firstName":"Peng","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2026-02-14 06:39:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8877570/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8877570/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104169754,"identity":"6c5beb31-af61-4f25-a0c1-4cce95d4e777","added_by":"auto","created_at":"2026-03-08 14:40:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":187377,"visible":true,"origin":"","legend":"\u003cp\u003eA. Correlation between HbA1c (%) and CTI\u003c/p\u003e\n\u003cp\u003eB. Comparison of HbA1c (%) between different CONUT score groups\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8877570/v1/fe19b4f830015212f5ce599f.png"},{"id":104169718,"identity":"f423cd02-682c-4e1c-bebd-429e90792fb3","added_by":"auto","created_at":"2026-03-08 14:40:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":18403,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of CTI combination CONUT score for severe sepsis and/or septic shock following \u003cem\u003eEnterobacteriaceae\u003c/em\u003e bloodstream infection in diabetic patients\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8877570/v1/693f68acb469d9dc83e4e09c.png"},{"id":105886496,"identity":"87ee067a-f60f-4d9b-bf2b-ec05ff6fe9dc","added_by":"auto","created_at":"2026-04-01 07:29:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1245159,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8877570/v1/b09655ba-1bde-45bc-9045-5987842f82a3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"C-Reactive protein-triglyceride glucose index and controlling nutritional status score for predicting severe sepsis or septic shock in diabetic patients with Enterobacteriaceae bloodstream infections","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetes, as a globally prevalent chronic metabolic disorder, poses a significant disease burden and mortality risk due to its complications. Beyond cardiovascular, renal, and neuropathic complications, individuals with diabetes are more susceptible to infections, particularly bloodstream infections \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Reports indicate that the risk of bacterial-induced bloodstream infections is approximately twice as high in diabetic patients compared to non-diabetic individuals \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Hyperglycemia-induced immune dysfunction and microcirculatory disorders not only significantly increase the risk of pathogen invasion and dissemination but also cause the infection process to deteriorate rapidly. This often leads to progression into sepsis and septic shock, ultimately resulting in elevated mortality rates. The mortality rate for severe sepsis ranges from approximately 10% to 40% \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Therefore, early identification of such high-risk patients is crucial.\u003c/p\u003e \u003cp\u003eThe CTI serves as an emerging composite biomarker. By integrating the triglyceride glucose index (TyG), which reflects insulin resistance, with C-reactive protein (CRP), representing systemic inflammation levels, it enables simultaneous assessment of metabolic disorders and inflammatory states in the body \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. CTI has demonstrated value in assessing the progression of various diseases, including cancer-related mortality, diabetes, stroke \u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Recent large-scale cohort studies have confirmed that CTI is significantly associated with the risk of cardiovascular disease incidence and mortality in the general population and in specific metabolic subgroups \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. CTI demonstrates superior risk stratification for mortality in patients with acute decompensated heart failure and concomitant diabetes mellitus \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt the same time, the critical role of nutritional and immune status in the onset and progression of infections cannot be overlooked. The CONUT score effectively assesses an individual's nutritional reserves and immune capacity using three simple indicators: serum albumin, total cholesterol, and lymphocyte count \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In patients with hepatocellular carcinoma, CONUT has been demonstrated to be a robust predictor of severe postoperative complications such as liver failure, highlighting its value in assessing stress tolerance and prognosis \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Studies indicated that elevated CONUT scores in patients with T2DM were closely associated with the presence and severity of chronic kidney disease and retinopathy \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, among high-risk populations with diabetes-a condition characterized by both metabolic abnormalities and chronic inflammation-research demonstrating the effectiveness of integrating multidimensional information on metabolism, inflammation, and nutrition-immunity to predict the prognosis of bloodstream infections in diabetic patients remains extremely limited. Therefore, this study aims to explore the value of two composite indices-CTI and CONUT-which respectively approach the prediction from the integrated dimensions of metabolic inflammation and nutrition-immunity, in assessing the risk of developing septic shock following \u003cem\u003eEnterobacteriaceae\u003c/em\u003e bloodstream infections in diabetic patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis study enrolled 236 diabetic patients diagnosed with \u003cem\u003eK. pneumoniae\u003c/em\u003e or \u003cem\u003eE. coli\u003c/em\u003e bloodstream infections admitted to Taizhou Hospital, Zhejiang Province, from January 2023 to December 2025. Inclusion criteria: (1) Patients must meet the diagnostic criteria for diabetes as outlined in the Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2020 Edition); (2) Patients must meet the diagnostic criteria for bloodstream infection and have corresponding pathogens detected in blood cultures. Exclusion criteria: (1) Age\u0026thinsp;\u0026lt;\u0026thinsp;18 years; (2) Hemorrhagic or anaphylactic shock; (3) Incomplete medical records or transfer to another hospital during the study period. Sepsis and septic shock were defined according to internationally applicable guidelines during the study period. Severe sepsis was defined as sepsis with organ dysfunction. Septic shock was defined as the need for vasopressors to maintain mean arterial pressure\u0026thinsp;\u0026ge;\u0026thinsp;65 mmHg despite adequate resuscitation, with serum lactate\u0026thinsp;\u0026gt;\u0026thinsp;2 mmol/L \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e The study was approved by the Helsinki ethics committee at our institution.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMicrobiological methods\u003c/h3\u003e\n\u003cp\u003eThe isolation and culture procedures for \u003cem\u003eK. pneumoniae\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e followed the standardized protocols described in the 4th edition of the National Clinical Laboratory Procedures. Blood culture bottles were incubated in the BacT/Alert system (bioM\u0026eacute;rieux, France) for 5 days. Positive isolates were identified using the VITEK Mass Spectrometry System V3.2 (bioM\u0026eacute;rieux, France).\u003c/p\u003e\n\u003ch3\u003eData Collection and calculation\u003c/h3\u003e\n\u003cp\u003eCollect patients' clinical data, including age, gender, underlying conditions, site of infection, diagnosis, length of hospital stay, blood culture results, diabetes complications, and clinical outcomes. Laboratory results encompassed all blood parameters measured concurrently with the first positive blood culture collected after admission, including WBC count, PLT count, HB, lymphocyte count (LY), HbA1c, CRP, triglycerides (TG), fasting blood glucose (FPG), total cholesterol (TC), and serum albumin levels.\u003c/p\u003e \u003cp\u003eThe calculation method for CTI was as follows \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e: CTI\u0026thinsp;=\u0026thinsp;0.412 \u0026times; Ln (CRP [mg/L])\u0026thinsp;+\u0026thinsp;Ln (TG [mg/dl] \u0026times; FPG [mg/dl])/2\u003c/p\u003e \u003cp\u003eThe CONUT score was determined by calculations based on ALB, LY, and TC obtained from blood samples. Serum albumin concentrations\u0026thinsp;\u0026ge;\u0026thinsp;35, 30\u0026ndash;34.9, 25\u0026ndash;29.9, and \u0026lt;\u0026thinsp;25 g/L correspond to 0, 2, 4, and 6 points, respectively; Lymphocyte counts\u0026thinsp;\u0026ge;\u0026thinsp;1600, 1200\u0026ndash;1599, 800\u0026ndash;1199, and \u0026lt;\u0026thinsp;800/\u0026micro;L correspond to 0, 1, 2, and 3 points, respectively; Total cholesterol concentrations\u0026thinsp;\u0026ge;\u0026thinsp;180, 140\u0026ndash;179, 100\u0026ndash;139, and \u0026lt;\u0026thinsp;100 mg/dL correspond to 0, 1, 2, and 3 points, respectively.\u003c/p\u003e \u003cp\u003eAccording to previously published criteria \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, a CONUT score of 0\u0026ndash;1 defined the low-risk group, a score of 2\u0026ndash;4 defined the mild-risk group, a score of 5\u0026ndash;8 defined the moderate-risk group, and a score of 9\u0026ndash;12 defined the high-risk group.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis and graphing were performed using SPSS 23.0 (SPSS Inc., Chicago, IL, USA) and GraphPad Prism software (version 10.1.2). For baseline characteristics, descriptive data were summarized as counts and percentages for categorical variables, or median and interquartile range (IQR) for continuous variables. Categorical variables were analyzed using the chi-square test (χ\u0026sup2;) or Fisher's exact test. Normally distributed continuous variables were analyzed using analysis of variance (ANOVA), while non-normally distributed data were analyzed using the Kruskal-Wallis test. Multivariate logistic stepwise regression analysis was employed to identify independent predictors of intergroup differences. Performance was assessed using receiver operating characteristic (ROC) curves and area under the curve (AUC). P values\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe study population was divided into four quartiles (Q1-Q4) based on CTI (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Significant differences existed in age distribution among the four groups (p\u0026thinsp;=\u0026thinsp;0.036). The prevalence of fever differed notably (p\u0026thinsp;=\u0026thinsp;0.002). Infection type showed a strong association (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), with community-acquired infections dominating across all groups, particularly in Q3 (100%) and Q4 (98.3%). Hypertension prevalence was highest in Q1 (73.3%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). Metabolic and hematologic markers including HbA1c (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PLT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and serious sepsis and/ or septic shock incidence (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) varied significantly. In-hospital mortality was also significantly different (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), with Q4 reporting the highest mortality (10.2%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the participants, stratified according to CTI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1 (n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2 (n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3 (n\u0026thinsp;=\u0026thinsp;59)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4 (n\u0026thinsp;=\u0026thinsp;59)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70(59.3\u0026ndash;77.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.5(58-74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61(54\u0026ndash;69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63(57\u0026ndash;74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, 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 \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.777\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27(45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22(37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33(55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34(58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37(62.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11(18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4(6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical symptoms\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23(38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(67.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(67.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38(64.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNausea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2(3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeakness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5(8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal.pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10(16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria, 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 \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eK. pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32(53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27(45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28(46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26(44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32(54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource.of.infection, 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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9(15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary.tract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20(33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29(49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOriginal.infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18(30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11(18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver- gallbladder and pancreas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11(18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11(18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2(3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfection.type, 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 \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity-acquired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51(85.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55(94.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58(98.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital-acquired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9(15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities, 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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44(73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31(52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14(23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver cirrhosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6(10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26(43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24(40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28(47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4(6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.0(6.9\u0026ndash;9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6(7.5\u0026ndash;10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.1(8.2\u0026ndash;12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.9(8.5\u0026ndash;11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.3(8.6\u0026ndash;15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.9(8.2\u0026ndash;15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.9(9.0-15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.9(9.5\u0026ndash;19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e190(126.5-256.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206(139-267.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161(122\u0026ndash;220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e145(87\u0026ndash;202)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115(93-128.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115(102.8-135.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123(107\u0026ndash;134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121(101\u0026ndash;135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerious sepsis and/ or septic shock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31(51.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51(86.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplications.of.diabetes (%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10(16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8(13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic cardiovascular diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic neuropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5(8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic ketoacidosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12(20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic retinopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3(5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic.foot ulcers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1(1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality in hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5(8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: WBC white blood cells, Hb hemoglobin, PLT platelets, HbA1c Glycated Hemoglobin A1c\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to CONUT score, patients were categorized into three groups: normal-light, moderate, and high (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The prevalence of \u003cem\u003eK. pneumoniae\u003c/em\u003e was significantly higher in the high-risk group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034). Tumor comorbidity was less common in the high-risk group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041). HbA1c levels increased with worsening nutritional status (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), while platelet counts decreased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Serious sepsis and/ or septic shock incidence rose markedly across categories, reaching 89.8% in the high-risk group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant differences were found for age, gender, smoking, drinking, clinical symptoms, infection source, or diabetes complications.\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\u003eBaseline clinical characteristics of the participants, stratified according to CONUT score\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\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal-light (n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate (n\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\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\u003e65(57-73.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(58\u0026ndash;75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64(57.5\u0026ndash;74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, 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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.395\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\u003e34(37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(46.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58(63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52(54.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26(53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical symptoms\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\u003eFever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53(57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59(62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNausea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeakness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal.pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria, 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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eK. pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41(44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(67.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51(55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource.of.infection, 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\u003eRespiratory system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary.tract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43(46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOriginal.infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(27.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver- gallbladder and pancreas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfection.type, 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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity-acquired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86(93.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89(93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48(98.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital-acquired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities, 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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57(62.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver cirrhosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43(46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.6(7.3\u0026ndash;10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.0(7.6\u0026ndash;11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.6(8.3\u0026ndash;12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.9(8.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.3(9.4\u0026ndash;17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.5(9.0-17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195.5(152.3-242.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154(102\u0026ndash;243)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139(77.5\u0026ndash;215)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123.5(108.3-134.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117(101\u0026ndash;133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109(97\u0026ndash;131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerious sepsis and/ or septic shock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64(67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44(89.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplications.of.diabetes (%)\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\u003eDiabetic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic cardiovascular diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic neuropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic ketoacidosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic retinopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic.foot ulcers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality in hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.208\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\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA revealed a significant positive correlation between the CTI and HbA1c levels (r\u0026thinsp;=\u0026thinsp;0.322, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). As CTI increased, there was a corresponding rise in HbA1c (%) and CTI, indicating that higher CTI values were associated with poorer glycemic control. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB detailed the relationship between CONUT scores and HbA1c levels. Compared with the normal-light group, the HbA1c level of the high-risk group increased significantly. As the CONUT score increased, HbA1c (%) consistently rose within each group, indicating that poorer nutritional status was associated with worsening glycemic control.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNo significant differences were observed in age, gender, smoking, drinking, bacterial type, infection source, infection type, or comorbidities in the non-serious and serious groups. The use of hypoglycemic agents before admission was significantly lower in the serious group (61.3% vs. 76.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics, clinical characteristics according to serious sepsis and/ or septic shock\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon- serious sepsis and/ or septic shock group (n\u0026thinsp;=\u0026thinsp;94)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSerious sepsis and/ or septic shock group (n\u0026thinsp;=\u0026thinsp;142)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\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\u003e66.5(57-81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64(56.8\u0026ndash;74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, 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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.531\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\u003e37(39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63(44.4)\u003c/p\u003e \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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57(60.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79(55.6)\u003c/p\u003e \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\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23(16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12(8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria, 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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eK. pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45(47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79(55.6)\u003c/p\u003e \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\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49(52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63(44.4)\u003c/p\u003e \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\u003eSource.of.infection, 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\u003eRespiratory system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17(12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary.tract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66(46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOriginal.infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25(17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver- gallbladder and pancreas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32(22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfection.type, 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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity-acquired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(95.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133(93.7)\u003c/p\u003e \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\u003eHospital-acquired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9(6.3)\u003c/p\u003e \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\u003eComorbidities, 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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58(61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15(10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver cirrhosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10(7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67(47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6(4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of hypoglycemic agents before admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72(76.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87(61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn unadjusted analysis, Q4 of the CTI classification was strongly associated with severe outcomes (OR\u0026thinsp;=\u0026thinsp;5.964, 95% CI: 2.422\u0026ndash;14.684, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, moderate (OR\u0026thinsp;=\u0026thinsp;3.522) and high (OR\u0026thinsp;=\u0026thinsp;15.012) CONUT scores were significant predictors (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for comorbidities and infection source in multivariate analysis, Q4 remained a significant risk factor (OR\u0026thinsp;=\u0026thinsp;5.301, 95% CI: 2.054\u0026ndash;13.679, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), as did moderate (OR\u0026thinsp;=\u0026thinsp;3.379, 95% CI:1.797\u0026ndash;6.353) and high (OR\u0026thinsp;=\u0026thinsp;5.099, 95% CI:14.827\u0026ndash;43.120) CONUT scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the ROC analysis, the AUC for CTI combined CONUT score was 0.777 (95%CI:0.716\u0026ndash;0.837) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), demonstrating that CTI combined CONUT score has a good discriminative ability for identifying patients at higher risk of severe outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable and multivariate analysis for diabetic patients with \u003cem\u003eEnterobacteriaceae\u003c/em\u003e bloodstream infections with severe sepsis and/ or septic shock\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnadjusted OR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted OR (95%CI) *\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.151(0.558\u0026ndash;2.375)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.845(0.412\u0026ndash;1.735)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.964(2.422\u0026ndash;14.684)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.301(2.054\u0026ndash;13.679)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCONUT score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal-light\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.522(1.928\u0026ndash;6.434)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.379(1.797\u0026ndash;6.353)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.012(5.428\u0026ndash;41.517)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.099(14.827\u0026ndash;43.120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e* Comorbidities and source of infection were included in multivariate analysis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study analyzed 236 patients with diabetes complicated by bloodstream infections caused by \u003cem\u003eK. pneumoniae\u003c/em\u003e or \u003cem\u003eE. coli\u003c/em\u003e to evaluate the predictive value of CTI and CONUT in identifying patients at risk of progressing to severe sepsis or septic shock. Results demonstrated that both CTI and CONUT effectively identified high-risk patients, with combined use exhibiting superior predictive performance (AUC\u0026thinsp;=\u0026thinsp;0.777), providing robust evidence for early clinical intervention.\u003c/p\u003e \u003cp\u003eCTI, as a composite indicator integrating metabolic and inflammatory information, demonstrated significant association with adverse outcomes in this study. With increasing CTI quartiles, patients exhibited markedly elevated risks of sepsis and in-hospital mortality, consistent with previous research \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Wang et al. found that critically ill patients with high CTI levels had a higher risk of all-cause mortality and longer hospital and ICU stays \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Additionally, CTI exhibited a positive correlation with HbA1c, further supporting its value in assessing the synergistic effects of glycemic control and systemic inflammation. Specifically, the combined presence of hyperglycemia, hypertriglyceridemia, and inflammatory states exacerbated immune dysfunction and microcirculatory disorders, thereby promoting the progression of infections.\u003c/p\u003e \u003cp\u003eThe CONUT score, as a key indicator of nutritional and immune status, also demonstrated strong risk stratification capabilities in this study. The high-risk CONUT group exhibited not only higher HbA1c levels and lower platelet counts but also a significantly increased incidence of sepsis (89.8%). It suggested that malnutrition and immune exhaustion may impair the body's ability to respond to infection, thereby elevating the risk of organ dysfunction. Studies indicated that malnutrition frequently occured in patients with acute and chronic illnesses, with particularly high prevalence among the elderly and critically ill patients. It may lead to increased mortality rates, prolonged hospital stays, and extended ICU admissions \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. The subjects of this study were diabetic patients, who often presented with varying degrees of malnutrition and impaired immune function. This may significantly affect their ability to respond to bloodstream infections and their susceptibility to severe sepsis or septic shock.\u003c/p\u003e \u003cp\u003eIn the multivariate logistic regression analysis, CTI Q4 and moderate-to-high CONUT scores remained independent predictors of severe outcomes even after adjusting for potential confounders such as underlying diseases and source of infection. It suggested that in patients with diabetes complicated by bloodstream infections, metabolic inflammatory status and nutritional-immunological status represented two relatively independent yet potentially additive risk dimensions. Combined assessment of both dimensions provided a more comprehensive reflection of patients' pathophysiological burden and clinical vulnerability than either metric alone.\u003c/p\u003e \u003cp\u003eThis study also has several limitations. First, it was a single-center design with a limited sample size, and only two common Gram-negative bacterial infections were included. Second, both CTI and CONUT scores were based on a single laboratory test at admission, failing to dynamically reflect changes during the treatment process. Future studies should validate these findings in larger, multicenter cohorts and explore whether early intervention strategies based on these indicators\u0026mdash;such as enhanced metabolic control, nutritional support, and immune modulation\u0026mdash;can improve clinical outcomes in such high-risk patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, CTI and CONUT demonstrate excellent discriminatory ability for identifying patients with diabetes and bloodstream infections who develop severe sepsis or septic shock. Clinically, these two simple, readily available composite indicators may be incorporated into risk assessment systems to aid in the early identification of high-risk patients, facilitate individualized treatment, and ultimately improve outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Clinical Microbiology Laboratory for supporting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSX L had roles in the study design, data analysis, literature search, and writing of the manuscript. P Z had roles in guiding research and clinical management. FF X had roles in data collection and interpretation. All authors have read and agreed with the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from Traditional Chinese Medicine Science and Technology Project of Zhejiang Province (2024ZL526).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to privacy or ethical restrictions but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Institutional Medical Ethics Committee of Taizhou Hospital of Zhejiang Province granted approval for this retrospective study, with a waiver of informed consent because the medical records of the subjects were deidentified from the Medical Records and Statistics Room to ensure patient confidentiality. The use of the raw data and the study’s protocol was permitted by the Institutional Medical Ethics Committee of Taizhou Hospital of Zhejiang Province, and all methods were performed following the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNaveed F, Khattak IU, Hafeez A, Imtiaz A, Naz B, Sabir R, Jameel SAA, Kumar N, Hamid A, Kumar S, Nisa RU, Rizvi KA, Rani P. Temporal trends in diabetes mellitus type 2 and sepsis-related mortality among U.S. adults: a 22-year analysis. J Diabetes Metab Disord. 2025;24(2):289.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang CH, Chiu CH, Chen IW, Hung SY, Lin CW, Hsu BR, Huang YY. Antimicrobial resistance and outcomes of community-onset bacterial bloodstream infections in patients with type 2 diabetes. J global Antimicrob Resist 2018, 15:271\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrinsley JS, Egi M, Kiss A, Devendra AN, Schuetz P, Maurer PM, Schultz MJ, van Hooijdonk RT, Kiyoshi M, Mackenzie IM, Annane D, Stow P, Nasraway SA, Holewinski S, Holzinger U, Preiser JC, Vincent JL, Bellomo R. Diabetic status and the relation of the three domains of glycemic control to mortality in critically ill patients: an international multicenter cohort study. Crit Care. 2013;17(2):R37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAngriman F, Saoraya J, Lawler PR, Shah BR, Martin CM, Scales DC. Preexisting Diabetes Mellitus and All-Cause Mortality in Adult Patients With Sepsis: A Population-Based Cohort Study. Critical care explorations. 2024, 6(5):e1085.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang S, Wang H, Li K, Chen Y, Zheng Q, Meng J, Chen X. C-reactive protein-triglyceride glucose index predicts stroke incidence in a hypertensive population: a national cohort study. Diabetol Metab Syndr. 2024;16(1):277.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuan GT, Xie HL, Zhang HY, Liu CA, Ge YZ, Zhang Q, Wang ZW, Zhang X, Tang M, Song MM, Zhang XW, Yang M, Chen YB, Yu KY, Deng L, Gong YZ, Hu W, Wang KH, Cong MH, Shi HP. A Novel Inflammation and Insulin Resistance Related Indicator to Predict the Survival of Patients With Cancer. Front Endocrinol. 2022;13:905266.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuo G, Tang Y, Liu Z, Cao J, Yao Z, Zhou D. Association between C-reactive protein-triglyceride glucose index and stroke risk in different glycemic status: insights from the China Health and Retirement Longitudinal Study (CHARLS). Cardiovasc Diabetol. 2025;24(1):142.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou YF, Zeng QT, Zhang XT, Wang FX. Cumulative of c-reactive protein-triglyceride glucose index exposure and risk of diabetes in middle-aged and older Chinese adults: findings from a national longitudinal cohort. Diabetol Metab Syndr. 2025;18(1):33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Y, Guo Y, Ma S, Mao Z, Meng D, Xuan K, Lu R, Pan X, Zhu X. Association of C-reactive protein-triglyceride glucose index with the incidence and mortality of cardiovascular disease: a retrospective cohort study. Cardiovasc Diabetol. 2025;24(1):313.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Li S, Liu D, Gui J, Hu J, Wang Q, Mao W. The relationship between C-reactive protein-triglyceride-glucose index and cardiovascular disease: insights from the China health and retirement longitudinal study (CHARLS). Cardiovasc Diabetol. 2025;24(1):410.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang N, Xie L, Zhang S, Wang Q, Lu H, Xiong Z, Xie G, Sheng G, Yang H, He S, Liao T, Wang W, Zou Y. Inflammatory storm and metabolic disorders: unraveling heterogeneity in mortality risk for comorbid diabetes mellitus and heart failure via the C-reactive protein-triglyceride-glucose index. Front Endocrinol. 2025;16:1689238.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaruyama N, Nakayama M, Tanaka S, Tsuda S, Yoshitomi R, Fukui A, Tsuruya K, Kitazono T, Nakano T. Prognostic Value of Controlling Nutritional Status Score and Prognostic Nutritional Index for All-Cause Mortality in Patients with Chronic Kidney Disease: The Fukuoka Kidney Disease Registry Study. J Nutr. 2026;156(1):101231.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan J, Zhang RQ, Guo Q, Tuerganaili A, Shao YM. Controlling nutritional status score predicts posthepatectomy liver failure: an online interpretable machine learning prediction model. Eur J Gastroenterol Hepatol. 2025;37(7):875\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQu W, Liu S, Gu J, Wei X. Association between controlling nutritional status score and chronic kidney disease in diabetic patients: a cross-sectional study based on the National Health and Nutrition Examination Survey. Int Urol Nephrol. 2024;56(2):795\u0026ndash;804.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuan Y, Zhang P, Jia X, Hua S, Yao D. Association between controlling nutritional status score and diabetic retinopathy: Data from National Health and Nutrition Examination Survey. Eur J Ophthalmol. 2025;35(3):1082\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinger M, Deutschman C, Seymour C, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard G, Chiche J, Coopersmith C, Hotchkiss R, Levy M, Marshall J, Martin G, Opal S, Rubenfeld G, van der Poll T, Vincent J, Angus DJJ. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShuai WL, Liu J, Zhou T, Li JX, Tao X, Zou B. Prognostic value of C-reactive protein triglyceride glucose index versus triglyceride glucose index in critically ill patients: a machine learning-driven cohort analysis. Expert Rev Mol Diagn. 2026;26(1):85\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Hu L, Chen Y, He H, Huang Y, Tao R, Li S, Yang R. The C-reactive protein-triglyceride-glucose index (CTI) as a predictor of mortality and hospital stay in critically ill patients: a retrospective cohort study. Lipids Health Dis. 2025;24(1):299.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiura G, Lebwohl B, Seres DS. Malnutrition Diagnosis in Critically Ill Patients Using 2012 Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition Standardized Diagnostic Characteristics Is Associated With Longer Hospital and Intensive Care Unit Length of Stay and Increased In-Hospital Mortality. JPEN J Parenter Enter Nutr. 2020;44(2):256\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStello BB, Zucatti KP, Freitas IM, Burgel CF, Milesi BM, Milanez DSJ, Silva FM. Concurrent and predictive validity of different tools for malnutrition diagnosis in adult critically ill patients: a systematic review with meta-analysis of observational studies. Am J Clin Nutr. 2026:101195.\u003c/span\u003e\u003c/li\u003e\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":"C-reactive protein-triglyceride-glucose index, Controlling nutritional status score, Type 2 diabetes mellitus, Bloodstream infections","lastPublishedDoi":"10.21203/rs.3.rs-8877570/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8877570/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo investigate the value of the C-reactive protein-triglyceride glucose index (CTI) and the controlling nutritional status score (CONUT) in predicting the risk of progression to severe sepsis or septic shock in patients with diabetes complicated by \u003cem\u003eEnterobacteriaceae\u003c/em\u003e bloodstream infections.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 236 patients with diabetes complicated by \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e or \u003cem\u003eEscherichia coli\u003c/em\u003e bloodstream infections from January 2023 to December 2025 were enrolled. Patients were grouped based on CTI quartiles (Q1-Q4) and CONUT scores (normal-low risk, moderate risk, high risk). Demographic, clinical characteristics, and laboratory indicators were collected to analyze intergroup differences and their association with severe clinical outcomes (severe sepsis or septic shock). Multivariate logistic regression models adjusted for confounding factors, and ROC curves evaluated the predictive ability of combined CTI and CONUT indices.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study found that as the CTI quartile increased, the incidence of sepsis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and in-hospital mortality (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) significantly increased. The CONUT high-risk group exhibited a particularly high sepsis incidence of 89.8% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with more pronounced trends toward elevated HbA1c levels and decreased platelet counts (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Significant correlations were observed between CTI and HbA1c levels. Within different CONUT score groups, the high-risk group demonstrated markedly higher HbA1c levels compared to the normal light exposure group. Multivariate regression analysis revealed that CTI Q4 (adjusted OR\u0026thinsp;=\u0026thinsp;5.301, 95% CI: 2.054\u0026ndash;13.679, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and moderate/high CONUT scores (adjusted OR\u0026thinsp;=\u0026thinsp;3.379 and 5.099, respectively, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independent predictors of severe outcomes. ROC analysis indicated that the area under the curve (AUC) for combined CTI and CONUT scores in predicting severe outcomes was 0.777 (95% CI: 0.716\u0026ndash;0.837), outperforming either single indicator.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eBoth CTI and CONUT scores were effective indicators for predicting severe sepsis or septic shock in patients with diabetes complicated by \u003cem\u003eEnterobacteriaceae\u003c/em\u003e bloodstream infections.\u003c/p\u003e","manuscriptTitle":"C-Reactive protein-triglyceride glucose index and controlling nutritional status score for predicting severe sepsis or septic shock in diabetic patients with Enterobacteriaceae bloodstream infections","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 14:39:01","doi":"10.21203/rs.3.rs-8877570/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":"38fa4bda-80ef-4a3d-9538-af2186fdc171","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-01T07:28:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 14:39:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8877570","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8877570","identity":"rs-8877570","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}