Peripheral blood inflammatory markers are considered useful for predicting the risk of bloodstream infections and evaluating infection severity in patients with hematological malignancies receiving chemotherapy who develop neutropenia

Peripheral blood inflammatory markers are considered useful for predicting the risk of bloodstream infections and evaluating infection severity in patients with hematological malignancies receiving chemotherapy who develop neutropenia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Peripheral blood inflammatory markers are considered useful for predicting the risk of bloodstream infections and evaluating infection severity in patients with hematological malignancies receiving chemotherapy who develop neutropenia Yabin Chen, Jing Jing, Dayan Zhang, Tingjin Zheng, Chengyong Huang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7399935/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Object Chemotherapy-induced neutropenia (CIN) and febrile neutropenia (FN) significantly increase the risk of invasive infections, which can progress to septic shock or sepsis and may ultimately result in death. our purpose of this study is to construct a predictive model based on peripheral blood inflammatory markers to enable timely detection of bloodstream infections(BSI) and evaluation of infection severity in this population. Methods A total of 112 patients with chemotherapy-induced neutropenia (CIN) who developed BSI and 145 patients without BSI (NBSI) were included. Inflammatory marker levels were compared across subgroups. The Logistic regression was performed to identify risk factors for BSI within the training cohort. Model performance was assessed by receiver operating characteristic curves (ROC), decision curve analysis (DCA) and diagnostic calibration curves. Results Patients with BSI had longer disease duration, a higher proportion of acute lymphoblastic leukemia, and significantly lower levels of complete blood count and inflammatory markers compared to those without BSI. Univariate analysis identified prolonged disease duration, hematologic diagnosis category, and reduced levels of NLR, MLR, PLR, SIRI, and SII as risk factors for BSI. A nomogram model was constructed incorporating disease duration, hematologic diagnosis category, MLR, PLR, and SII. ROC and DCA indicated a broad threshold range for clinical applicability and a high net benefit of predictive model, supporting its clinical utility. Diagnostic calibration curves showed good agreement between predicted and observed outcomes, with a Hosmer-Lemeshow test P-value of 0.3289. Conclusion A nomogram model incorporating disease duration, hematologic diagnosis category, MLR, PLR, and SII was constructed and shown to provide a practical and effective tool for the assessment and early identification of BSI in patients with CIN. Health sciences/Biomarkers Biological sciences/Cancer Health sciences/Diseases Health sciences/Medical research Health sciences/Oncology Health sciences/Risk factors Inflammatory markers Hematological malignancies Chemotherapy-induced neutropenia Bloodstream infection Predictive model Figures Figure 1 Figure 2 Figure 3 1. Introduction Neutropenia is the most common hematologic adverse event and a major dose-limiting toxicity induced by chemotherapy. Chemotherapy-induced neutropenia (CIN) and febrile neutropenia (FN) substantially increase the risk of invasive infections, which may progress to sepsis and septic shock, ultimately resulting in death [ 1 , 2 ] 。Therefore, early identification and evaluation of infection risk in patients with CIN are of critical importance. Bloodstream infections (BSI), the leading cause of infection-related complications in this population, are a primary clinical concern during the diagnosis and management of FN, often progressing to sepsis or septic shock [ 3 ] . Blood culture remains the gold standard for the diagnosis of BSI [ 4 ] ; however, its clinical utility is limited by a low positivity rate, long turnaround time, large blood volume required, and a high risk of contamination, posing significant challenges to early clinical decision-making [ 5 – 7 ] . Therefore, identifying appropriate biomarkers to screen for BSI in patients with CIN may offer a more effective alternative. Previous studies have shown that peripheral blood inflammatory markers can effectively detect BSI at an early stage and assess infection severity, prognosis, and clinical outcomes in non-CIN populations, including those in intensive care units and on hemodialysis [ 8 – 10 ] . However, the aforementioned inflammatory indices are derived from calculations involving neutrophil (N), lymphocyte (L), monocyte (M), and platelet (PLT) counts. In patients with CIN, the marked reduction in neutrophil levels results in distinct peripheral blood leukocyte profiles compared to those in non-chemotherapy populations. Consequently, it remains unclear whether inflammatory marker trends observed during BSI in CIN patients mirror those seen in non-CIN individuals. Moreover, studies specifically examining the inflammatory marker patterns associated with BSI in CIN patients are currently limited. To address this gap, we conducted a retrospective analysis of clinical data from 112 CIN patients with confirmed BSI, with the aim of evaluating whether peripheral blood inflammatory markers could serve as rapid and effective tools for BSI screening and infection severity assessment in this population. We further developed a predictive model based on these markers and assessed its performance. 2. Materials and Methods 2.1 Study population This retrospective study included patients with hematological malignancies and CIN who were diagnosed with BSI via blood culture between April 2020 and March 2025 at the Department of Hematology, Quanzhou First Hospital Affiliated to Fujian Medical University. A total of 112 patients were enrolled, comprising 57 males and 55 females, aged 18 to 85 years, with a median age of 47 years. As disease controls, 145 patients without BSI (NBSI)—matched by sex and age—were selected during the same period. These control patients were diagnosed by blood culture and final clinical evaluation as having non-bloodstream infections. The NBSI group included 81 males and 64 females, aged 16 to 72 years, with a median age of 54 years. For patients with multiple hospitalizations or repeated testing, only data from the initial clinical presentation were included. All protocols were approved by Ethics Committee of Quanzhou First Hospital (China), the protocol number is [2025] K218. 2.2 Inclusion and exclusion criteria Inclusion criteria Patients were eligible for inclusion if they met all of the following criteria:①Presence of clinical signs suggestive of infection, including fever, nausea and vomiting, abdominal pain or diarrhea, chest tightness or dyspnea, urinary frequency, or other relevant symptoms;②Underwent both blood culture and complete blood count testing during the same clinical episode;③Had a documented diagnosis of a primary hematological malignancy, such as acute leukemia, lymphoma, or myelodysplastic syndrome;④patients were undergoing chemotherapy at the time of blood culture collection, with CIN confirmed by an absolute neutrophil count (ANC) of < 2.0 × 10⁹/L [ 11 ] . Exclusion criteria Patients were excluded if they met any of the following criteria:①CIN resulting from chemotherapy for non-hematological malignancies;②Secondary hematologic disorders caused by drugs or immune-related conditions;③ANC < 2.0 × 10⁹/L without clear evidence that neutropenia was induced by chemotherapy;④Incomplete clinical or laboratory data;⑤Blood cultures in which only one of multiple bottles tested positive and the isolated organism was suspected to be a contaminant, making it impossible to distinguish between true BSI and contamination. 2.3 Sample Preparation Peripheral venous blood (2 mL) was collected from each patient into an EDTA-K₂ anticoagulant tube. After gentle inversion for mixing, samples were processed using an automated hematology analyzer (Sysmex XN9000, Sysmex Corporation, Japan) in accordance with the manufacturer’s instructions. The following parameters were recorded: white blood cell (WBC), N, L, M, and PLT counts. Inflammatory indices were calculated as follows: NLR, monocyte-to-lymphocyte ratio (MLR), PLR, SIRI (SIRI = N × M / L), and systemic immune-inflammation index (SII = N × PLT / L). 2.4 Blood culture and microbial identification Venous blood (8–10 mL) was aseptically collected from each patient by clinical staff and inoculated into blood culture bottles (Becton, Dickinson and Company [BD], USA) for microbiological testing. Cultures were processed using an automated blood culture system (BACTEC FX, BD, USA). Blood culture results were interpreted based on instrument reports as follows:①Negative: No positive signal reported within 120 hours;②Positive: A positive signal reported within 120 hours, after which the culture bottle was removed and subcultured onto blood agar and China blue agar plates (Guangzhou Dijing Microbial Technology Co., Ltd, China). Bacterial colonies were inspected the following day, and species identification along with antimicrobial susceptibility testing was performed using an automated system (Phoenix 100, BD, USA). 2.5 Clinical data collection Clinical information was obtained from the electronic medical record system of Quanzhou first hospital. Data collected included sex, age, history of hypertension, history of diabetes, hematologic diagnosis, duration of illness, presence of infections other than BSI, presence of chronic internal medical conditions (e.g., hepatitis, coronary artery disease, chronic kidney disease, chronic obstructive pulmonary disease), and infection severity in patients with BSI. 2.6 Subgroup classification within the BSI cohort Based on clinical data, the 112 patients with BSI were stratified into subgroups according to the following criteria: ①Co-infection status : Patients were categorized into the simple BSI (si-BSI, n = 44), who had BSIs without other concurrent infections, and the BSI with co-infections (co-BSI, n = 68), who had concurrent infections such as pulmonary infections, urinary tract infections, or peritonitis.② Infection severity : Patients were classified as having general BSI (ge-BSI, n = 80) if they showed clinical improvement and were discharged following blood culture collection and treatment, or serious BSI (se-BSI, n = 32) if they experienced shock, cardiopulmonary arrest, multiorgan failure, or required emergency resuscitation.③ Microbial classification : According to pathogen type, patients were grouped into Gram-negative BSI (G-BSI, n = 81) and Gram-positive BSI (G + BSI, n = 20). Patients with fungal (n = 4) or mixed infections (n = 7) were not included in subgroup comparisons due to their small numbers. 2.7 Cohort division for model development and validation The BSI and NBSI cohorts were randomly divided into training and validation sets in an 8:2 ratio. The training cohort included 87 patients with BSI and 115 patients with NBSI, while the validation cohort included 25 patients with BSI and 30 with NBSI. Data from the training cohort were used to develop the predictive model, and data from the validation cohort were used to evaluate its performance. 3. Statistical analysis Statistical analyses were performed using SPSS version 21.0 and R version 4.2.1. Mean ± standard deviation (SD ± X) represents continuous variables with normally distributed, such as age, were expressed as and compared between groups using independent samples t-tests. Disease duration, WBC, N, L, M, PLT, NLR, MLR, PLR, SIRI, and SII were non-normally distributed variables, presented as median (interquartile range, IQR) and compared using the Mann–Whitney U test. Categorical variables were reported as percentages: sex was expressed as the proportion of male patients; history of hypertension, diabetes, presence of co-infections, and chronic internal diseases were reported as positive rates; hematologic diagnoses were expressed as the distribution of disease types. Group comparisons for categorical variables were conducted using the chi-square (χ²) test. Binary logistic regression was used to identify risk factors for BSI in the training cohort. Spearman correlation analysis was applied to assess correlations among inflammatory indices. Variance inflation factor (VIF) analysis was performed to diagnose collinearity and select the most informative predictors for construction of a nomogram predictive model. The diagnostic performance of individual predictors and the predictive model was assessed using receiver operating characteristic curves (ROC). Decision curve analysis (DCA) and diagnostic calibration curves were used to validate and evaluate the predictive model. A two-tailed P value < 0.05 was considered statistically significant. 4. Results 4.1Pathogen distribution in the BSI cohort Among the 112 patients with BSI, a total of 119 pathogenic strains were isolated, including 7 cases of mixed infections involving two pathogens (6.25%). Of the isolates, 81 (72.32%) were Gram-negative (G−) bacteria, 20 (17.86%) were Gram-positive (G+) bacteria, and 4 (3.57%) were fungi. The three most frequently isolated pathogens—all Gram-negative—were Escherichia coli (25.00%), Klebsiella pneumoniae subsp. pneumoniae (24.11%), and Pseudomonas aeruginosa (16.96%). Notably, among the seven cases of mixed infection, five involved K. pneumoniae subsp. pneumoniae combined with other pathogens (Table 1 ). Table 1 Distribution of BSI pathogenic bacteria in CIN patients Pathogen name Cases (n) Proportion (%) Gram Escherichia coli 28 25.00 Klebsiella pneumoniae subsp. pneumoniae 27 24.11 Pseudomonas aeruginosa 19 16.96 Klebsiella oxytoca 2 1.79 Enterobacter cloacae 2 1.79 Aeromonas hydrophila 1 0.89 Stenotrophomonas maltophilia 1 0.89 Acinetobacter baumannii 1 0.89 Subtotal 81 72.32 G+ Staphylococcus aureus 8 7.14 Bacillus cereus 3 2.68 Streptococcus mitis 2 1.79 Streptococcus pneumoniae 2 1.79 Enterococcus faecium 2 1.79 Streptococcus anginosus 1 0.89 Granulicatella adiacens 1 0.89 Staphylococcus hominis 1 0.89 Subtotal 20 17.86 Fungi Candida tropicalis 3 2.68 Candida krusei 1 0.89 Subtotal 4 3.57 Mixed infections K. pneumoniae / A. hydrophila 2 1.79 E. coli / P. aeruginosa 1 0.89 P. aeruginosa / Staphylococcus cohnii 1 0.89 K. pneumoniae / S. aureus 1 0.89 K. pneumoniae / Streptococcus oralis 1 0.89 K. pneumoniae / E. cloacae 1 0.89 Subtotal 7 6.25 Total 112 100.00 4.2 Comparison of inflammatory marker levels among BSI subgroups Comparison of inflammatory index levels between different subgroups within the BSI group. The results showed that SII and NLR can be used to distinguish whether there is co infection and evaluate the severity of infection (Fig. 1 ). Among patients with BSI, those in the co-BSI group had significantly higher SII levels compared to those in the si-BSI group, with a statistically significant difference. The NLR level in the se-BSI group was significantly higher than that in the ge-BSI group, and the difference was statistically significant. There was no statistically significant difference in the levels of all inflammatory indicators between the G-BSI group and the G + BSI group. 4.3 Development of a nomogram predictive model for BSI prediction Comparison of clinical characteristics between the BSI and NBSI groups Compared with the NBSI group, patients in the BSI group had a significantly longer disease duration and a higher proportion of acute lymphoblastic leukemia. In addition, complete blood count parameters and inflammatory marker levels were significantly lower in the BSI group, with all differences reaching statistical significance (Table 2 ). Table 2 Comparison of baseline clinical characteristics between the BSI and NBSI groups in the training cohort BSI group NBSI group statistical value P case(n) 87 115 — — male(%) 54.02 57.39 0.228 0.633 age(y) 47.57 ± 17.06 51.22 ± 14.00 1.580 0.116 course of disease (m) 4.0(1.0 ~ 12.0) 2.0(1.0 ~ 5.0) -3.169 0.002 History of hypertension (%) 17.24 23.48 1.170 0.279 History of diabetes (%) 16.09 15.65 0.007 0.932 Merge chronic internal medicine diseases (%) 13.79 11.30 0.283 0.595 Diagnosis of Hematological Diseases 39.419 < 0.001 acute myeloid leukemia (%) 44.83 45.22 acute lymphoblastic leukemi a (%) 36.78 6.96 Mature B lymphoma (%) 8.05 31.30 Myelodysplastic syndrome (%) 10.34 11.30 Other blood diseases (%)* 0.00 5.22 Blood routine indicators WBC(×10 9 /L) 0.24(0.14 ~ 0.33) 0.72(0.40 ~ 1.22) -8.013 < 0.001 N(×10 9 /L) 0.01(0.01 ~ 0.04) 0.14(0.03 ~ 0.48) -7.237 < 0.001 L(×10 9 /L) 0.20(0.10 ~ 0.29) 0.38(0.23 ~ 0.54) -6.078 < 0.001 M(×10 9 /L) 0.01(0.00 ~ 0.03) 0.05(0.02 ~ 0.19) -6.370 < 0.001 PLT(×10 9 /L) 9.00(4.00 ~ 22.00) 31.00(14.00 ~ 66.00) -6.421 < 0.001 Inflammatory indicators NLR 0.077(0.036 ~ 0.250) 0.389(0.079 ~ 1.143) -4.849 < 0.001 MLR 0.048(0.000 ~ 0.133) 0.138(0.046 ~ 0.518) -4.390 < 0.001 PLR 48.000(22.222 ~ 119.799) 84.091(33.333 ~ 237.958) -2.614 0.009 SIRI 0.000(0.000 ~ 0.004) 0.027(0.001 ~ 0.150) -6.468 < 0.001 SII 0.622(0.195 ~ 2.561) 16.000(1.579 ~ 53.719) -6.689 < 0.001 Note : *Other hematologic diseases refer to conditions that could not be classified into the predefined categories in this study, including mixed-phenotype acute leukemia, acute undifferentiated leukemia, and T-lymphoblastic leukemia. 4.4 Logistic regression analysis of BSI risk factors Univariate logistic regression analysis identified prolonged disease duration, hematologic diagnosis category (primarily acute leukemia), and decreased levels of inflammatory markers as individual risk factors for the occurrence of BSI. However, multivariate analysis revealed that only prolonged disease duration was an independent predictor of BSI (Tables 3 ). Table 3 A. Univariate analysis of risk factors for BSI independent variable β P OR 95% CI of OR Lower limits Upper limits Course of disease (m) 0.026 0.045 1.027 1.001 1.053 Diagnosis of Hematological Diseases -0.310 0.016 0.733 0.570 0.944 NLR -0.570 0.002 0.565 0.394 0.811 MLR -1.183 0.004 0.306 0.137 0.684 PLR -0.002 0.014 0.998 0.996 1.000 SIRI -8.825 0.002 0.000 0.000 0.038 SII -0.041 < 0.001 0.959 0.940 0.979 Table 3 B. Multivariate analysis of independent risk factors for BSI independent variable β P OR 95% CI of OR Lower limits Upper limits Course of disease (m) 0.056 0.004 1.057 1.018 1.098 Diagnosis of Hematological Diseases -0.165 0.269 0.848 0.633 1.136 NLR 0.407 0.287 1.502 0.710 3.179 MLR 0.161 0.807 1.174 0.323 4.273 PLR 0.001 0.253 1.001 0.999 1.004 SIRI 0.879 0.768 2.410 0.007 829.143 SII -0.039 0.078 0.961 0.920 1.004 4.5 ROC analysis of individual inflammatory markers ROC analysis was performed to evaluate the diagnostic value of NLR, MLR, PLR, SIRI, and SII for identifying BSI. All markers demonstrated diagnostic utility, with areas under the curve (AUC) ranging from 0.607 to 0.793. Among them, SII exhibited the highest diagnostic performance, with an AUC of 0.775. The corresponding diagnostic metrics for each marker are summarized in Table 4 . Table 4 Diagnostic performance of individual inflammatory markers for screening BSI Inflammatory indicators cut off Youden index AUC (95% CI ) Sensitivity (%) specificity (%) PPV(%) NPV(%) NLR 0.366 0.341 0.699(0.627 ~ 0.772) 82.76 51.30 56.25 79.73 MLR 0.092 0.287 0.680(0.606 ~ 0.754) 67.82 60.87 56.73 71.43 PLR 100.530 0.182 0.607(0.529 ~ 0.686) 71.26 46.96 50.41 68.35 SIRI 0.002 0.455 0.763(0.699 ~ 0.828) 72.42 73.04 67.02 77.78 SII 1.950 0.449 0.775(0.711 ~ 0.839) 73.56 71.31 65.98 78.10 4.6 Correlation analysis of inflammatory markers As all inflammatory indices were derived from N, L, M, and PLT counts, potential collinearity was expected. Spearman correlation analysis was conducted to assess the relationships among these markers. Strong correlations (r > 0.7) were observed for the following pairs: NLR vs. SIRI (r = 0.715), NLR vs. SII (r = 0.868), MLR vs. SIRI (r = 0.856), and SIRI vs. SII (r = 0.713). Based on both correlation patterns and AUC performance, MLR, PLR, and SII were selected as candidate variables for nomogram predictive model construction. 4.7 Collinearity diagnostics VIF were calculated for the candidate variables included in the predictive model: disease duration (VIF = 1.052), hematologic diagnosis (VIF = 1.084), MLR (VIF = 1.215), PLR (VIF = 1.622), and SII (VIF = 1.713). All VIF values were below 2, indicating weak collinearity among variables. 4.8 Nomogram predictive model construction Disease duration, hematologic diagnosis, MLR, PLR, and SII—were incorporated into the construction of a nomogram for predicting bloodstream infections (Fig. 2 ). • ‘Disease duration (m)’ refers to the actual duration of illness in months. ‘Diagnosis’ is numerically coded as follows: 1 = acute myeloid leukemia, 2 = acute lymphoblastic leukemia, 3 = mature B-cell lymphoma, 4 = myelodysplastic syndrome, 5 = other hematologic diseases. ‘MLR’, ‘PLR’, and ‘log (SII + 1)’ values represent the actual calculated indices. The SII variable was transformed using log (SII + 1) to mitigate issues of complete or quasi-complete separation during model construction, as direct inclusion of raw SII values led to excessive predicted probabilities of 0 or 1. 4.9 Model validation ROC analysis ROC analysis was conducted to assess the discriminative ability of the predictive model for identifying BSI in both the training and validation cohorts. In the training cohort, the model demonstrated an AUC of 0.817 (95% CI: 0.768–0.876), with a sensitivity of 81.62%, specificity of 76.52%, positive predictive value (PPV) of 72.45%, and negative predictive value (NPV) of 84.62% (Fig. 3 A). In the validation cohort, the model yielded an AUC of 0.781 (95% CI: 0.658–0.905), with a sensitivity of 92.00%, specificity of 63.33%, PPV of 67.65%, and NPV of 90.48% (Fig. 3 B). DCA The decision curve of the predictive model was markedly distant from the “None” and “All” extremes (Fig. 3 C), indicating a wide threshold range and a relatively high net clinical benefit. These results suggest that the model may offer substantial value in guiding clinical decision-making. Diagnostic calibration curve Diagnostic calibration curve of the predictive model in the validation cohorts, Hosmer Leme show test result P = 0.3289, indicating good model fit (Fig. 3 D). 5. Discussion This study first investigated the distribution of pathogens isolated from 112 patients with BSI, providing preliminary insight into the predominant causative organisms of BSI in CIN patients within the local region. Subsequent comparisons of inflammatory marker levels across BSI subgroups revealed that the SII and NLR may be useful in evaluating the presence of co-infections and infection severity, respectively. A nomogram predictive model based on routine hematological inflammatory markers was then developed using training and validation cohorts to predict the risk of BSI in CIN patients. The predictive efficacy of this model was confirmed by ROC analysis, DCA, and diagnostic calibration curve assessment, demonstrating its potential to serve as a valuable tool in guiding clinical decision-making. Among the 112 patients with BSI, a total of 119 pathogenic strains were isolated, including 7 cases involving mixed infections with two pathogens. The three most frequently isolated organisms were all G − bacteria: Escherichia coli , Klebsiella pneumoniae subsp. pneumoniae , and Pseudomonas aeruginosa . These findings indicate that BSI in hematologic patients with CIN at our institution are predominantly caused by G − bacteria and are characterized by a diverse spectrum of pathogens. Notably, in 5 of the 7 cases of mixed infection, K. pneumoniae subsp. pneumoniae was co-isolated with other pathogens, suggesting a potential association between K. pneumoniae infection and increased susceptibility to co-infection. This pattern is consistent with previously reported pathogen profiles among hematologic patients with BSI [ 12 – 14 ] . However, compared with similar studies on hematologic patients with CIN, researchers such as Piukovics, Liu, and Guo have reported that Staphylococcus aureus and coagulase-negative staphylococci —both G + organisms—were among the top three pathogens isolated in cases of BSI [ 15 – 17 ] . This discrepancy may be attributable to differences in inclusion criteria: the aforementioned studies focused on patients with severe neutropenia (ANC < 0.5 × 10⁹/L), whereas the present study included patients with neutropenia defined as ANC < 2.0 × 10⁹/L. Another possible explanation lies in the study design. Our retrospective analysis was limited to CIN patients who underwent both blood culture and complete blood count testing simultaneously, and was conducted at a single center. These factors introduce notable limitations and may not fully capture the comprehensive pathogen spectrum of BSI in hematologic patients with CIN. In the future studies, a large number of specimens and multicenter participation are warranted to further characterize the microbiological profiles in this population. To further explore the clinical relevance of inflammatory markers, the BSI group was stratified by infection characteristics, and intergroup comparisons were conducted. The results indicate that SII levels were significantly higher in the co-BSI group compared to the si-BSI group, while NLR levels were significantly elevated in the se-BSI group relative to the ge-BSI group. These results indicate that inflammatory markers may reflect different infection profiles in patients with BSI, especially SII and NLR. Specifically, elevated SII levels may indicate the presence of additional infections beyond BSI, whereas an increase in NLR may signal a more severe infection burden. The role of NLR observed in this study aligns with findings by Roldgaard in patients with sepsis not related to CIN [ 10 ] . However, no prior studies have been identified that specifically examine the relationship between SII and infection characteristics in BSI, making direct comparisons unavailable. Additionally, comparisons between the G − BSI and G + BSI groups revealed no significant differences in any of the inflammatory marker levels. This suggests that such markers may not be suitable for assessing the type of infecting organisms in CIN patients with BSI. Nonetheless, the findings regarding NLR and SII may support clinical assessment of infection severity and inform timely adjustments in therapeutic strategies. In this study, random sampling was used to divide both the BSI and NBSI groups in an 8:2 ratio. Data from the training cohort were used to construct the predictive model, while the validation cohort was used to evaluate its performance. Comparative analysis of clinical characteristics and inflammatory markers between the BSI and NBSI groups in the training cohort revealed that BSI patients had significantly longer disease durations and a higher proportion of acute lymphoblastic leukemia. Furthermore, routine hematologic and inflammatory marker levels were markedly lower in the BSI group. These findings are consistent with results obtained in our preliminary studies [ 18 ] . These results indicate that prolonged disease duration and increased chemotherapy exposure elevate the risk of developing BSI in patients with hematologic malignancies, specifically acute myeloid leukemia and acute lymphoblastic leukemia—being the most susceptible. In addition, the lower the WBC and differential counts during episodes of CIN, the higher the likelihood of BSI occurrence. This conclusion was supported by the subsequent logistic regression analysis and is consistent with current expert consensus, which recognizes CIN as a significant risk factor for invasive infections. On the other hand, inflammatory marker levels in the BSI group were significantly decreased, in stark contrast to their typically elevated levels observed in BSI patients without CIN [ 8 , 9 , 19 ] . The suggests that in hematologic patients with CIN, a lower level of systemic immune response is related to a higher risk of developing BSI. Therefore, in clinical practice, declining inflammatory marker levels in suspected cases of BSI among CIN patients should be closely monitored and not overlooked. Given the narrow numerical range of WBC and its differential counts, particularly N, which typically fall within 0 to 2 × 10⁹/L in CIN patients. This study opted to use composite inflammatory markers instead of absolute WBC or subtype counts for constructing the nomogram. These markers, which represent ratios of different leukocyte subtypes, provide more representative and scalable metrics. Considering the overlapping formulas used to derive these indices, we first conducted a correlation analysis. Strong correlations were observed among NLR, SIRI, and SII, as well as between MLR and SIRI. Based on their respective AUCs for BSI prediction and the distribution characteristics shown in Table 2 , SII and MLR were selected as model variables. After collinearity diagnosis using VIFs, five variables(disease duration, hematologic diagnosis, MLR, PLR, and SII)were ultimately included in the final nomogram. Notably, direct inclusion of SII led to a high frequency of predicted probabilities clustering at 0 or 1, suggesting the presence of complete or quasi-complete separation. However, given the superior diagnostic performance of SII as a single predictor (AUC = 0.775, 95% CI: 0.711–0.839), it was retained in the model after logarithmic transformation as log (SII + 1), thereby preserving its predictive utility. Additionally, PLR exhibited a pattern in the predictive model whereby higher values corresponded to higher scores, which contradicts the earlier findings of this study. This inconsistency may be attributed to underlying correlations among the inflammatory markers included in the model—namely, MLR, PLR, and log (SII + 1). The nomogram predictive model developed in this study was validated using ROC analysis, DCA, and diagnostic calibration curve assessment. ROC analysis demonstrated an AUC of 0.817 (95% CI: 0.768–0.876) in the training cohort and 0.781 (95% CI: 0.658–0.905) in the validation cohort, indicating favorable diagnostic performance for BSI screening in both cohorts. Notably, the model outperformed any individual inflammatory marker in the training cohort, suggesting that incorporating disease duration and hematologic diagnosis significantly enhanced predictive accuracy. In the DCA, the model’s decision curve was well-separated from the “None” and “All” extremes, indicating a wide threshold range and high net clinical benefit. This highlights the model’s potential utility in guiding clinical decision-making. The diagnostic calibration curve showed a Hosmer–Lemes how P-value of 0.3289, suggesting good agreement between predicted and observed risks. However, the calibration curve slightly deviated above the ideal line within a certain probability range, indicating that the model may underestimate the actual risk of BSI in CIN patients to some extent. Consequently, for CIN patients with suspected BSI, clinicians should consider empirical prophylactic antibiotic therapy based on individual clinical assessment. Finally, this study was a retrospective analysis rather than a prospective investigation. Therefore, when comparing the BSI and NBSI groups, CIN patients matched by age and sex were selected to reduce baseline differences. However, due to substantial missing data on body mass index (BMI) among study participants, comparative analysis involving age, sex, and BMI could not be conducted. However, previous studies have suggested that increasing age may be associated with a higher risk of infection in patients with CIN [ 20 ] ,Moreover, a lower baseline BMI has been associated with poorer infection-related outcomes [ 21 ] ,Therefore, future studies will adopt a prospective design and incorporate additional variables such as age, sex, and BMI to further refine and improve the predictive model. This study identified several characteristic features of BSI in hematologic patients with CIN, including prolonged disease duration, predominance of acute leukemia, and significantly reduced levels of routine hematologic inflammatory markers. Among these markers, the SII and NLR demonstrated some utility in reflecting infection severity. A nomogram incorporating disease duration, hematologic diagnosis, MLR, PLR, and SII was developed and validated, offering a straightforward and effective tool for BSI risk assessment and screening in clinical settings. However, this was a single-center retrospective study with inherent limitations. Future prospective multicenter studies and external validation are warranted to further refine and validate the predictive model. Abbreviations CIN chemotherapy-induced neutropenia BSIs bloodstream infections NLR neutrophil-to-lymphocyte ratio MLR monocyte-to-lymphocyte ratio PLR platelet-to-lymphocyte ratio SIRI systemic inflammatory response index SII systemic immune inflammatory index Declarations Ethics approval and consent to participate The study was approved by Ethics Committee of Quanzhou First Hospital (China), the protocol number is [2025] K218. All participants provided written informed consent. And all methods were performed in accordance with Declaration of Helsinki. Consent for publication Not applicable Availability of data and materials All data generated or analyzed during this study are included in this published article. Competing interests The authors declare no competing interests. Funding NO. Authors’ contributions Yabin Chen wrote the manuscript and collected data; Jing Jing wrote the mian manuscript and revised the paper; Dayan Zhang collected clinical data and performed the experiments; Tingjin Zheng and Chengyong Huang analysed data; Zhishan Zhang designed the experiments; Huidan Chen revised the paper. Yabin Chen, Jing Jing and Dayan Zhang contributed equally to this work and share first authorship. Zhishan Zhang and Huidan Chen shared co corresponding authors. All authors read and approved the final manuscript. Acknowledgements NO. References Ba, Y. et al. Current management of chemotherapy-induced neutropenia in adults: key points and new challenges: Committee of Neoplastic Supportive-Care (CONS), China Anti-Cancer Association Committee of Clinical Chemotherapy, China Anti-Cancer Association. Cancer Biol. Med. 17 (4), 896–909 (2020). China Anti-Cancer Association Tumor Clinical Chemotherapy Professional Committee, China Anti-Cancer Association Tumor Support Therapy Professional Committee. Consensus on clinical diagnosis, treatment, and prevention of chemotherapy-induced neutropenia in China(2023 edition). Chin. J. Oncol. 45 (07), 575–583 (2023). Hansen, B. A. et al. Febrile Neutropenia in Acute Leukemia. Epidemiology, Etiology, Pathophysiology and Treatment. Mediterr. J. Hematol. Infect. Dis. 12 (1), e2020009 (2020). Society of Clinical Microbiology and Infection of China International Exchange and Promotion Association for Medical and Healthcare, Clinical Microbiology Group of the Laboratory Medicine Society of the Chinese Medical Association, Clinical Microbiology Group of the Microbiology and Immunology Society of the Chinese Medical Association. Chinese expert consensus on the clinical practice of blood culture in the diagnosis of bloodstream infection. Chin. J. Lab. Med. 45 (02), 105–121 (2022). Zboromyrska, Y. et al. Evaluation of the Magicplex™ Sepsis Real-Time Test for the Rapid Diagnosis of Bloodstream Infections in Adults. Front. Cell. Infect. Microbiol. 9 , 56 (2019). Peri, A. M., Harris, P. & Paterson, D. L. Culture-independent detection systems for bloodstream infection. Clin. Microbiol. Infect. 28 (2), 195–201 (2022). Ignak, S., Unay-Demirel, O. & Yuksel, M. Early Prediction of Bloodstream Infection with Complete Blood Count Parameters: an Ex-Vivo Human Whole Blood Model. Clin. Lab. 70 (4). (2024). Li, Y. et al. Pathogenic bacteria features of central line-associated bloodstream infections in ICU patients: focus on the early predictive value of neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios. Front. Cell. Infect. Microbiol. 15 , 1525758 (2025). Yang, J. et al. Diagnostic Value of Systemic Inflammatory Response Index for Catheter-Related Bloodstream Infection in Patients Undergoing Haemodialysis. J Immunol Res. 2022: 7453354. (2022). Roldgaard, M., Benfield, T. & Tingsgård, S. Blood neutrophil to lymphocyte ratio is associated with 90-day mortality and 60-day readmission in Gram negative bacteremia: a multi-center cohort study. BMC Infect. Dis. 24 (1), 255 (2024). Freites-Martinez, A. et al. Using the Common Terminology Criteria for Adverse Events (CTCAE - Version 5.0) to Evaluate the Severity of Adverse Events of Anticancer Therapies. Actas Dermosifiliogr (Engl Ed). 112 (1): 90–92. (2021). Li, Q. et al. Machine learning-based prognostic model for bloodstream infections in hematological malignancies using Th1/Th2 cytokines. BMC Infect. Dis. 25 (1), 415 (2025). Wu, H. et al. Pathogenic spectrum and drug resistance of bloodstream infection in patients with acute myeloid leukaemia: a single centre retrospective study. Front. Cell. Infect. Microbiol. 14 , 1390053 (2024). Che, M. T. et al. A single-center analysis of pathogenic bacteria distribution and drug resistance in bacterial bloodstream infections among patients with hematological diseases. Zhonghua Xue Ye Xue Za Zhi . 45 (10), 937–943 (2024). Piukovics, K. et al. Evaluation of Bloodstream Infections During Chemotherapy-Induced Febrile Neutropenia in Patients with Malignant Hematological Diseases: Single Center Experience. Eur. J. Microbiol. Immunol. (Bp) . 5 (3), 199–204 (2015). Liu, C. G. & Liao, A. J. Feature analysis of different neutrophil levels on the distribution of pathogens in bloodstream infection in patients with hematologic malignancy. Zhonghua Xue Ye Xue Za Zhi . 44 (10), 857–860 (2023). Guo, Q. et al. Analysis of Pathogenic Bacterial Spectrum, Drug Resistance and Risk Factors for Mortality of Bloodstream Infection in Patients with Hematologic Diseases. Zhongguo Shi Yan Xue Ye Xue Za Zhi . 31 (5), 1556–1562 (2023). Zhang, D. et al. The correlation between inflammatory indicators related to blood routine and bloodstream infections in blood disease patients with neutropenia. J. Clin. Hematol(China) . 38 (06), 472–476 (2025). Xie, W. et al. Levels of PCT, NLR, SAA and PLR of newborns with bloodstream infection and their values in early diagnosis. Chin. J. Nosocomiol (12): 1841–1845. (2025). Ossenkoppele, G. & Löwenberg, B. How I treat the older patient with acute myeloid leukemia. Blood 125 (5), 767–774 (2015). Baumgartner, A. et al. Association of Nutritional Parameters with Clinical Outcomes in Patients with Acute Myeloid Leukemia Undergoing Haematopoietic Stem Cell Transplantation. Ann. Nutr. Metab. 69 (2), 89–98 (2016). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 15 Sep, 2025 Reviews received at journal 15 Sep, 2025 Reviews received at journal 13 Sep, 2025 Reviewers agreed at journal 11 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers agreed at journal 06 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers invited by journal 29 Aug, 2025 Editor assigned by journal 29 Aug, 2025 Editor invited by journal 26 Aug, 2025 Submission checks completed at journal 23 Aug, 2025 First submitted to journal 23 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7399935","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":510282440,"identity":"bc1dc0c5-a873-4ee4-b4ec-edda4937d556","order_by":0,"name":"Yabin Chen","email":"","orcid":"","institution":"Quanzhou First Hospital Affiliated to Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yabin","middleName":"","lastName":"Chen","suffix":""},{"id":510282442,"identity":"90cb4674-66fe-4c09-b21c-f5f2e502cf84","order_by":1,"name":"Jing Jing","email":"","orcid":"","institution":"Fourth Military Medical University (Air Force Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Jing","suffix":""},{"id":510282444,"identity":"d0658dc5-8d7e-449d-8ab1-4f2f4d7a420e","order_by":2,"name":"Dayan Zhang","email":"","orcid":"","institution":"Quanzhou First Hospital Affiliated to Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dayan","middleName":"","lastName":"Zhang","suffix":""},{"id":510282446,"identity":"239be546-47a1-44cf-8caa-56419dc7c908","order_by":3,"name":"Tingjin Zheng","email":"","orcid":"","institution":"Quanzhou First Hospital Affiliated to Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tingjin","middleName":"","lastName":"Zheng","suffix":""},{"id":510282449,"identity":"5b5e3fd9-b4e2-4ecd-aeae-891b75fb2946","order_by":4,"name":"Chengyong Huang","email":"","orcid":"","institution":"Fujian Provincial Hospital Affiliated to Fuzhou University, Provincial Clinical College of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chengyong","middleName":"","lastName":"Huang","suffix":""},{"id":510282450,"identity":"ad71ad83-ce5d-42ee-b4b7-fa9362385d6b","order_by":5,"name":"Zhishan zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBAC+/kHEh98qPjPY3+8gUgtBhIMjw1nnGGWYzhzgGgtjM+keduYjRluJBCpxVy6OdlwBhtbYuPMxxtvMNTYRBPUYjnnGNAvPDyJzdJpxRYMx9JyGwjqOZADtEVCIrFNOsdMgrHhMDFa8r9J8xgYJPZIniFSi8GNhDRpnoQEYwkJHiK1SPYcADrswAE5Ax6gXxKI8Qs/e0Pig4//DvAYsB/eeONDjQ0RfkF2pEQCKcohWkjVMQpGwSgYBSMDAADrCENJTBLS6AAAAABJRU5ErkJggg==","orcid":"","institution":"Quanzhou First Hospital Affiliated to Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhishan","middleName":"","lastName":"zhang","suffix":""},{"id":510282452,"identity":"6e20b10e-4bab-4407-9637-8801cb6d7d4f","order_by":6,"name":"Huidan Chen","email":"","orcid":"","institution":"Fujian Provincial Hospital Affiliated to Fuzhou University, Provincial Clinical College of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huidan","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-08-18 12:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7399935/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7399935/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-23770-w","type":"published","date":"2025-11-14T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90812323,"identity":"1a328ef1-82d5-4e87-9ed5-0ff5d1ecfcf8","added_by":"auto","created_at":"2025-09-08 12:18:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":222384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eComparison of inflammatory marker levels between the co-BSI group and the si-BSI group. \u003cstrong\u003eB\u003c/strong\u003e. Comparison of inflammatory marker levels between the ge-BSI group and the se-BSI group. \u003cstrong\u003eC.\u003c/strong\u003e Comparison of inflammatory marker levels between the G-BSI group and the G+BSI group.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7399935/v1/3972ba8ca1a4938d502cc744.png"},{"id":90812324,"identity":"01e1b4c2-3d67-4a04-b5ff-4162ba84d4bb","added_by":"auto","created_at":"2025-09-08 12:18:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29099,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram predictive model for predicting BSI.evaluate the probability of developing BSI in patients with CIN hematological disorders by assigning scores to different indicators and calculating the total score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e‘Disease \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;duration (m)’ refers to the actual duration of illness in months.\u003c/li\u003e\n \u003cli\u003e‘Diagnosis’ \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;is numerically coded as follows: 1 = acute myeloid leukemia, 2 = acute \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;lymphoblastic leukemia, 3 = mature B-cell lymphoma, 4 = myelodysplastic \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;syndrome, 5 = other hematologic diseases.\u003c/li\u003e\n \u003cli\u003e‘MLR’, \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;‘PLR’, and ‘log (SII+1)’ values represent the actual calculated indices.\u003c/li\u003e\n \u003cli\u003eThe \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;SII variable was transformed using log (SII+1) to mitigate issues of \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;complete or quasi-complete separation during model construction, as direct \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;inclusion of raw SII values led to excessive predicted probabilities of 0 \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;or 1.\u003c/li\u003e\n\u003c/ul\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7399935/v1/c35247c2585500c10051c8bf.png"},{"id":90812328,"identity":"ed97aec3-d544-46f4-9b95-31297bdf1ba8","added_by":"auto","created_at":"2025-09-08 12:18:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72419,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of predictive model.\u003cstrong\u003e A. \u003c/strong\u003eThe ROC of the predictive model screening training cohort BSI. \u003cstrong\u003eB\u003c/strong\u003e. ROC of the predictive model screening validation cohort BSI.\u003cstrong\u003e C\u003c/strong\u003e.DCA of the predictive model for BSI in the validation cohort. \u003cstrong\u003eD. \u003c/strong\u003eDiagnostic calibration curve of the predictive model for BSI in the validation cohort.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7399935/v1/3ad33104395727289082155e.png"},{"id":96105041,"identity":"478e1733-a809-488d-a24d-b8cff25c614d","added_by":"auto","created_at":"2025-11-17 16:07:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1756711,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7399935/v1/f219f0d8-0e09-4918-a708-7ac7487665e5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Peripheral blood inflammatory markers are considered useful for predicting the risk of bloodstream infections and evaluating infection severity in patients with hematological malignancies receiving chemotherapy who develop neutropenia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNeutropenia is the most common hematologic adverse event and a major dose-limiting toxicity induced by chemotherapy. Chemotherapy-induced neutropenia (CIN) and febrile neutropenia (FN) substantially increase the risk of invasive infections, which may progress to sepsis and septic shock, ultimately resulting in death\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e。Therefore, early identification and evaluation of infection risk in patients with CIN are of critical importance. Bloodstream infections (BSI), the leading cause of infection-related complications in this population, are a primary clinical concern during the diagnosis and management of FN, often progressing to sepsis or septic shock\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Blood culture remains the gold standard for the diagnosis of BSI\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e; however, its clinical utility is limited by a low positivity rate, long turnaround time, large blood volume required, and a high risk of contamination, posing significant challenges to early clinical decision-making\u003csup\u003e[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Therefore, identifying appropriate biomarkers to screen for BSI in patients with CIN may offer a more effective alternative. Previous studies have shown that peripheral blood inflammatory markers can effectively detect BSI at an early stage and assess infection severity, prognosis, and clinical outcomes in non-CIN populations, including those in intensive care units and on hemodialysis\u003csup\u003e[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHowever, the aforementioned inflammatory indices are derived from calculations involving neutrophil (N), lymphocyte (L), monocyte (M), and platelet (PLT) counts. In patients with CIN, the marked reduction in neutrophil levels results in distinct peripheral blood leukocyte profiles compared to those in non-chemotherapy populations. Consequently, it remains unclear whether inflammatory marker trends observed during BSI in CIN patients mirror those seen in non-CIN individuals. Moreover, studies specifically examining the inflammatory marker patterns associated with BSI in CIN patients are currently limited. To address this gap, we conducted a retrospective analysis of clinical data from 112 CIN patients with confirmed BSI, with the aim of evaluating whether peripheral blood inflammatory markers could serve as rapid and effective tools for BSI screening and infection severity assessment in this population. We further developed a predictive model based on these markers and assessed its performance.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study population\u003c/h2\u003e\u003cp\u003eThis retrospective study included patients with hematological malignancies and CIN who were diagnosed with BSI via blood culture between April 2020 and March 2025 at the Department of Hematology, Quanzhou First Hospital Affiliated to Fujian Medical University. A total of 112 patients were enrolled, comprising 57 males and 55 females, aged 18 to 85 years, with a median age of 47 years. As disease controls, 145 patients without BSI (NBSI)\u0026mdash;matched by sex and age\u0026mdash;were selected during the same period. These control patients were diagnosed by blood culture and final clinical evaluation as having non-bloodstream infections. The NBSI group included 81 males and 64 females, aged 16 to 72 years, with a median age of 54 years. For patients with multiple hospitalizations or repeated testing, only data from the initial clinical presentation were included. All protocols were approved by Ethics Committee of Quanzhou First Hospital (China), the protocol number is [2025] K218.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Inclusion and exclusion criteria\u003c/h2\u003e\u003cp\u003e\u003cb\u003eInclusion criteria\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatients were eligible for inclusion if they met all of the following criteria:①Presence of clinical signs suggestive of infection, including fever, nausea and vomiting, abdominal pain or diarrhea, chest tightness or dyspnea, urinary frequency, or other relevant symptoms;②Underwent both blood culture and complete blood count testing during the same clinical episode;③Had a documented diagnosis of a primary hematological malignancy, such as acute leukemia, lymphoma, or myelodysplastic syndrome;④patients were undergoing chemotherapy at the time of blood culture collection, with CIN confirmed by an absolute neutrophil count (ANC) of \u0026lt;\u0026thinsp;2.0 \u0026times; 10⁹/L\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExclusion criteria\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatients were excluded if they met any of the following criteria:①CIN resulting from chemotherapy for non-hematological malignancies;②Secondary hematologic disorders caused by drugs or immune-related conditions;③ANC\u0026thinsp;\u0026lt;\u0026thinsp;2.0 \u0026times; 10⁹/L without clear evidence that neutropenia was induced by chemotherapy;④Incomplete clinical or laboratory data;⑤Blood cultures in which only one of multiple bottles tested positive and the isolated organism was suspected to be a contaminant, making it impossible to distinguish between true BSI and contamination.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Sample Preparation\u003c/h2\u003e\u003cp\u003ePeripheral venous blood (2 mL) was collected from each patient into an EDTA-K₂ anticoagulant tube. After gentle inversion for mixing, samples were processed using an automated hematology analyzer (Sysmex XN9000, Sysmex Corporation, Japan) in accordance with the manufacturer\u0026rsquo;s instructions. The following parameters were recorded: white blood cell (WBC), N, L, M, and PLT counts. Inflammatory indices were calculated as follows: NLR, monocyte-to-lymphocyte ratio (MLR), PLR, SIRI (SIRI\u0026thinsp;=\u0026thinsp;N \u0026times; M / L), and systemic immune-inflammation index (SII\u0026thinsp;=\u0026thinsp;N \u0026times; PLT / L).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Blood culture and microbial identification\u003c/h2\u003e\u003cp\u003eVenous blood (8\u0026ndash;10 mL) was aseptically collected from each patient by clinical staff and inoculated into blood culture bottles (Becton, Dickinson and Company [BD], USA) for microbiological testing. Cultures were processed using an automated blood culture system (BACTEC FX, BD, USA). Blood culture results were interpreted based on instrument reports as follows:①Negative: No positive signal reported within 120 hours;②Positive: A positive signal reported within 120 hours, after which the culture bottle was removed and subcultured onto blood agar and China blue agar plates (Guangzhou Dijing Microbial Technology Co., Ltd, China). Bacterial colonies were inspected the following day, and species identification along with antimicrobial susceptibility testing was performed using an automated system (Phoenix 100, BD, USA).\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.5 Clinical data collection\u003c/b\u003e Clinical information was obtained from the electronic medical record system of Quanzhou first hospital. Data collected included sex, age, history of hypertension, history of diabetes, hematologic diagnosis, duration of illness, presence of infections other than BSI, presence of chronic internal medical conditions (e.g., hepatitis, coronary artery disease, chronic kidney disease, chronic obstructive pulmonary disease), and infection severity in patients with BSI.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.6 Subgroup classification within the BSI cohort\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eBased on clinical data, the 112 patients with BSI were stratified into subgroups according to the following criteria:\u003c/p\u003e\u003cp\u003e\u003cb\u003e①Co-infection status\u003c/b\u003e: Patients were categorized into the simple BSI (si-BSI, n\u0026thinsp;=\u0026thinsp;44), who had BSIs without other concurrent infections, and the BSI with co-infections (co-BSI, n\u0026thinsp;=\u0026thinsp;68), who had concurrent infections such as pulmonary infections, urinary tract infections, or peritonitis.②\u003cb\u003eInfection severity\u003c/b\u003e: Patients were classified as having general BSI (ge-BSI, n\u0026thinsp;=\u0026thinsp;80) if they showed clinical improvement and were discharged following blood culture collection and treatment, or serious BSI (se-BSI, n\u0026thinsp;=\u0026thinsp;32) if they experienced shock, cardiopulmonary arrest, multiorgan failure, or required emergency resuscitation.③\u003cb\u003eMicrobial classification\u003c/b\u003e: According to pathogen type, patients were grouped into Gram-negative BSI (G-BSI, n\u0026thinsp;=\u0026thinsp;81) and Gram-positive BSI (G\u0026thinsp;+\u0026thinsp;BSI, n\u0026thinsp;=\u0026thinsp;20). Patients with fungal (n\u0026thinsp;=\u0026thinsp;4) or mixed infections (n\u0026thinsp;=\u0026thinsp;7) were not included in subgroup comparisons due to their small numbers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Cohort division for model development and validation\u003c/h2\u003e\u003cp\u003eThe BSI and NBSI cohorts were randomly divided into training and validation sets in an 8:2 ratio. The training cohort included 87 patients with BSI and 115 patients with NBSI, while the validation cohort included 25 patients with BSI and 30 with NBSI. Data from the training cohort were used to develop the predictive model, and data from the validation cohort were used to evaluate its performance.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Statistical analysis","content":"\u003cp\u003eStatistical analyses were performed using SPSS version 21.0 and R version 4.2.1. Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;X) represents continuous variables with normally distributed, such as age, were expressed as and compared between groups using independent samples t-tests. Disease duration, WBC, N, L, M, PLT, NLR, MLR, PLR, SIRI, and SII were non-normally distributed variables, presented as median (interquartile range, IQR) and compared using the Mann\u0026ndash;Whitney U test. Categorical variables were reported as percentages: sex was expressed as the proportion of male patients; history of hypertension, diabetes, presence of co-infections, and chronic internal diseases were reported as positive rates; hematologic diagnoses were expressed as the distribution of disease types. Group comparisons for categorical variables were conducted using the chi-square (χ\u0026sup2;) test.\u003c/p\u003e\u003cp\u003eBinary logistic regression was used to identify risk factors for BSI in the training cohort. Spearman correlation analysis was applied to assess correlations among inflammatory indices. Variance inflation factor (VIF) analysis was performed to diagnose collinearity and select the most informative predictors for construction of a nomogram predictive model. The diagnostic performance of individual predictors and the predictive model was assessed using receiver operating characteristic curves (ROC). Decision curve analysis (DCA) and diagnostic calibration curves were used to validate and evaluate the predictive model. A two-tailed P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1Pathogen distribution in the BSI cohort\u003c/h2\u003e\u003cp\u003eAmong the 112 patients with BSI, a total of 119 pathogenic strains were isolated, including 7 cases of mixed infections involving two pathogens (6.25%). Of the isolates, 81 (72.32%) were Gram-negative (G\u0026minus;) bacteria, 20 (17.86%) were Gram-positive (G+) bacteria, and 4 (3.57%) were fungi. The three most frequently isolated pathogens\u0026mdash;all Gram-negative\u0026mdash;were \u003cem\u003eEscherichia coli\u003c/em\u003e (25.00%), \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e subsp. \u003cem\u003epneumoniae\u003c/em\u003e (24.11%), and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (16.96%). Notably, among the seven cases of mixed infection, five involved \u003cem\u003eK. pneumoniae\u003c/em\u003e subsp. \u003cem\u003epneumoniae\u003c/em\u003e combined with other pathogens (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of BSI pathogenic bacteria in CIN patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePathogen name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCases (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProportion (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGram\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e subsp. \u003cem\u003epneumoniae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eKlebsiella oxytoca\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEnterobacter cloacae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAeromonas hydrophila\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSubtotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e81\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e72.32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG+\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBacillus cereus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStreptococcus mitis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEnterococcus faecium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStreptococcus anginosus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGranulicatella adiacens\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStaphylococcus hominis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSubtotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e17.86\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFungi\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCandida tropicalis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCandida krusei\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSubtotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e3.57\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMixed infections\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eK. pneumoniae\u003c/em\u003e / \u003cem\u003eA. hydrophila\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e / \u003cem\u003eP. aeruginosa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. aeruginosa\u003c/em\u003e / \u003cem\u003eStaphylococcus cohnii\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eK. pneumoniae\u003c/em\u003e / \u003cem\u003eS. aureus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eK. pneumoniae\u003c/em\u003e / \u003cem\u003eStreptococcus oralis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eK. pneumoniae\u003c/em\u003e / \u003cem\u003eE. cloacae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSubtotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e6.25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e112\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e100.00\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\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Comparison of inflammatory marker levels among BSI subgroups\u003c/h2\u003e\u003cp\u003eComparison of inflammatory index levels between different subgroups within the BSI group. The results showed that SII and NLR can be used to distinguish whether there is co infection and evaluate the severity of infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among patients with BSI, those in the co-BSI group had significantly higher SII levels compared to those in the si-BSI group, with a statistically significant difference. The NLR level in the se-BSI group was significantly higher than that in the ge-BSI group, and the difference was statistically significant. There was no statistically significant difference in the levels of all inflammatory indicators between the G-BSI group and the G\u0026thinsp;+\u0026thinsp;BSI group.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Development of a nomogram predictive model for BSI prediction\u003c/h2\u003e\u003cp\u003e\u003cb\u003eComparison of clinical characteristics between the BSI and NBSI groups\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCompared with the NBSI group, patients in the BSI group had a significantly longer disease duration and a higher proportion of acute lymphoblastic leukemia. In addition, complete blood count parameters and inflammatory marker levels were significantly lower in the BSI group, with all differences reaching statistical significance (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of baseline clinical characteristics between the BSI and NBSI groups in the training cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eBSI group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNBSI group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003estatistical value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecase(n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e54.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.633\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eage(y)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e47.57\u0026thinsp;\u0026plusmn;\u0026thinsp;17.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.22\u0026thinsp;\u0026plusmn;\u0026thinsp;14.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecourse of disease (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e4.0(1.0\u0026thinsp;~\u0026thinsp;12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0(1.0\u0026thinsp;~\u0026thinsp;5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.169\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of hypertension (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e17.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of diabetes (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e16.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.932\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMerge chronic internal medicine diseases (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis of Hematological Diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e39.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eacute myeloid leukemia (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e44.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eacute lymphoblastic leukemi\u003cb\u003ea\u003c/b\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e36.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMature B lymphoma (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e8.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMyelodysplastic syndrome (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e10.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther blood diseases (%)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBlood routine indicators\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.24(0.14\u0026thinsp;~\u0026thinsp;0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.72(0.40\u0026thinsp;~\u0026thinsp;1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-8.013\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.01(0.01\u0026thinsp;~\u0026thinsp;0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14(0.03\u0026thinsp;~\u0026thinsp;0.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-7.237\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.20(0.10\u0026thinsp;~\u0026thinsp;0.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.38(0.23\u0026thinsp;~\u0026thinsp;0.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6.078\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.01(0.00\u0026thinsp;~\u0026thinsp;0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05(0.02\u0026thinsp;~\u0026thinsp;0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6.370\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e9.00(4.00\u0026thinsp;~\u0026thinsp;22.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.00(14.00\u0026thinsp;~\u0026thinsp;66.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6.421\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInflammatory indicators\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\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\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.077(0.036\u0026thinsp;~\u0026thinsp;0.250)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.389(0.079\u0026thinsp;~\u0026thinsp;1.143)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.849\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.048(0.000\u0026thinsp;~\u0026thinsp;0.133)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.138(0.046\u0026thinsp;~\u0026thinsp;0.518)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.390\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e48.000(22.222\u0026thinsp;~\u0026thinsp;119.799)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84.091(33.333\u0026thinsp;~\u0026thinsp;237.958)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.614\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.000(0.000\u0026thinsp;~\u0026thinsp;0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.027(0.001\u0026thinsp;~\u0026thinsp;0.150)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6.468\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.622(0.195\u0026thinsp;~\u0026thinsp;2.561)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.000(1.579\u0026thinsp;~\u0026thinsp;53.719)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6.689\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote\u003c/b\u003e: *Other hematologic diseases refer to conditions that could not be classified into the predefined categories in this study, including mixed-phenotype acute leukemia, acute undifferentiated leukemia, and T-lymphoblastic leukemia.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Logistic regression analysis of BSI risk factors\u003c/h2\u003e\u003cp\u003eUnivariate logistic regression analysis identified prolonged disease duration, hematologic diagnosis category (primarily acute leukemia), and decreased levels of inflammatory markers as individual risk factors for the occurrence of BSI. However, multivariate analysis revealed that only prolonged disease duration was an independent predictor of BSI (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" 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\u003eA. Univariate analysis of risk factors for BSI\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eindependent variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e of OR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLower limits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003cp\u003elimits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCourse of disease (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.053\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis of Hematological Diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.944\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.811\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.684\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-8.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.979\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eB. Multivariate analysis of independent risk factors for BSI\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eindependent variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e of OR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLower limits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003cp\u003elimits\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCourse of disease (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.098\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis of Hematological Diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.136\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.179\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e829.143\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.5 ROC analysis of individual inflammatory markers\u003c/h2\u003e\u003cp\u003eROC analysis was performed to evaluate the diagnostic value of NLR, MLR, PLR, SIRI, and SII for identifying BSI. All markers demonstrated diagnostic utility, with areas under the curve (AUC) ranging from 0.607 to 0.793. Among them, SII exhibited the highest diagnostic performance, with an AUC of 0.775. The corresponding diagnostic metrics for each marker are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiagnostic performance of individual inflammatory markers for screening BSI\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInflammatory indicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecut off\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYouden index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003cp\u003e(95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003especificity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePPV(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNPV(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.366\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.699(0.627\u0026thinsp;~\u0026thinsp;0.772)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e56.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e79.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.680(0.606\u0026thinsp;~\u0026thinsp;0.754)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e56.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e71.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100.530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.607(0.529\u0026thinsp;~\u0026thinsp;0.686)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e46.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e50.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e68.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.763(0.699\u0026thinsp;~\u0026thinsp;0.828)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e73.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e67.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e77.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.775(0.711\u0026thinsp;~\u0026thinsp;0.839)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e71.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e65.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e78.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Correlation analysis of inflammatory markers\u003c/h2\u003e\u003cp\u003eAs all inflammatory indices were derived from N, L, M, and PLT counts, potential collinearity was expected. Spearman correlation analysis was conducted to assess the relationships among these markers. Strong correlations (r\u0026thinsp;\u0026gt;\u0026thinsp;0.7) were observed for the following pairs: NLR vs. SIRI (r\u0026thinsp;=\u0026thinsp;0.715), NLR vs. SII (r\u0026thinsp;=\u0026thinsp;0.868), MLR vs. SIRI (r\u0026thinsp;=\u0026thinsp;0.856), and SIRI vs. SII (r\u0026thinsp;=\u0026thinsp;0.713). Based on both correlation patterns and AUC performance, MLR, PLR, and SII were selected as candidate variables for nomogram predictive model construction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.7 Collinearity diagnostics\u003c/h2\u003e\u003cp\u003eVIF were calculated for the candidate variables included in the predictive model: disease duration (VIF\u0026thinsp;=\u0026thinsp;1.052), hematologic diagnosis (VIF\u0026thinsp;=\u0026thinsp;1.084), MLR (VIF\u0026thinsp;=\u0026thinsp;1.215), PLR (VIF\u0026thinsp;=\u0026thinsp;1.622), and SII (VIF\u0026thinsp;=\u0026thinsp;1.713). All VIF values were below 2, indicating weak collinearity among variables.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.8 Nomogram predictive model construction\u003c/h2\u003e\u003cp\u003eDisease duration, hematologic diagnosis, MLR, PLR, and SII\u0026mdash;were incorporated into the construction of a nomogram for predicting bloodstream infections (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u0026bull; \u0026lsquo;Disease duration (m)\u0026rsquo; refers to the actual duration of illness in months.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026lsquo;Diagnosis\u0026rsquo; is numerically coded as follows: 1\u0026thinsp;=\u0026thinsp;acute myeloid leukemia, 2\u0026thinsp;=\u0026thinsp;acute lymphoblastic leukemia, 3\u0026thinsp;=\u0026thinsp;mature B-cell lymphoma, 4\u0026thinsp;=\u0026thinsp;myelodysplastic syndrome, 5\u0026thinsp;=\u0026thinsp;other hematologic diseases.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026lsquo;MLR\u0026rsquo;, \u0026lsquo;PLR\u0026rsquo;, and \u0026lsquo;log (SII\u0026thinsp;+\u0026thinsp;1)\u0026rsquo; values represent the actual calculated indices.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe SII variable was transformed using log (SII\u0026thinsp;+\u0026thinsp;1) to mitigate issues of complete or quasi-complete separation during model construction, as direct inclusion of raw SII values led to excessive predicted probabilities of 0 or 1.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.9 Model validation\u003c/h2\u003e\u003cp\u003e\u003cb\u003eROC analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eROC analysis was conducted to assess the discriminative ability of the predictive model for identifying BSI in both the training and validation cohorts. In the training cohort, the model demonstrated an AUC of 0.817 (95% CI: 0.768\u0026ndash;0.876), with a sensitivity of 81.62%, specificity of 76.52%, positive predictive value (PPV) of 72.45%, and negative predictive value (NPV) of 84.62% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In the validation cohort, the model yielded an AUC of 0.781 (95% CI: 0.658\u0026ndash;0.905), with a sensitivity of 92.00%, specificity of 63.33%, PPV of 67.65%, and NPV of 90.48% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDCA\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe decision curve of the predictive model was markedly distant from the \u0026ldquo;None\u0026rdquo; and \u0026ldquo;All\u0026rdquo; extremes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), indicating a wide threshold range and a relatively high net clinical benefit. These results suggest that the model may offer substantial value in guiding clinical decision-making.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiagnostic calibration curve\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDiagnostic calibration curve of the predictive model in the validation cohorts, Hosmer Leme show test result \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3289, indicating good model fit (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study first investigated the distribution of pathogens isolated from 112 patients with BSI, providing preliminary insight into the predominant causative organisms of BSI in CIN patients within the local region. Subsequent comparisons of inflammatory marker levels across BSI subgroups revealed that the SII and NLR may be useful in evaluating the presence of co-infections and infection severity, respectively. A nomogram predictive model based on routine hematological inflammatory markers was then developed using training and validation cohorts to predict the risk of BSI in CIN patients. The predictive efficacy of this model was confirmed by ROC analysis, DCA, and diagnostic calibration curve assessment, demonstrating its potential to serve as a valuable tool in guiding clinical decision-making.\u003c/p\u003e\u003cp\u003eAmong the 112 patients with BSI, a total of 119 pathogenic strains were isolated, including 7 cases involving mixed infections with two pathogens. The three most frequently isolated organisms were all G\u0026thinsp;\u0026minus;\u0026thinsp;bacteria: \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e subsp. \u003cem\u003epneumoniae\u003c/em\u003e, and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e. These findings indicate that BSI in hematologic patients with CIN at our institution are predominantly caused by G\u0026thinsp;\u0026minus;\u0026thinsp;bacteria and are characterized by a diverse spectrum of pathogens. Notably, in 5 of the 7 cases of mixed infection, \u003cem\u003eK. pneumoniae\u003c/em\u003e subsp. \u003cem\u003epneumoniae\u003c/em\u003e was co-isolated with other pathogens, suggesting a potential association between \u003cem\u003eK. pneumoniae\u003c/em\u003e infection and increased susceptibility to co-infection. This pattern is consistent with previously reported pathogen profiles among hematologic patients with BSI\u003csup\u003e[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. However, compared with similar studies on hematologic patients with CIN, researchers such as Piukovics, Liu, and Guo have reported that \u003cem\u003eStaphylococcus aureus\u003c/em\u003e and \u003cem\u003ecoagulase-negative staphylococci\u003c/em\u003e\u0026mdash;both G\u0026thinsp;+\u0026thinsp;organisms\u0026mdash;were among the top three pathogens isolated in cases of BSI\u003csup\u003e[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. This discrepancy may be attributable to differences in inclusion criteria: the aforementioned studies focused on patients with severe neutropenia (ANC\u0026thinsp;\u0026lt;\u0026thinsp;0.5 \u0026times; 10⁹/L), whereas the present study included patients with neutropenia defined as ANC\u0026thinsp;\u0026lt;\u0026thinsp;2.0 \u0026times; 10⁹/L. Another possible explanation lies in the study design. Our retrospective analysis was limited to CIN patients who underwent both blood culture and complete blood count testing simultaneously, and was conducted at a single center. These factors introduce notable limitations and may not fully capture the comprehensive pathogen spectrum of BSI in hematologic patients with CIN. In the future studies, a large number of specimens and multicenter participation are warranted to further characterize the microbiological profiles in this population.\u003c/p\u003e\u003cp\u003eTo further explore the clinical relevance of inflammatory markers, the BSI group was stratified by infection characteristics, and intergroup comparisons were conducted. The results indicate that SII levels were significantly higher in the co-BSI group compared to the si-BSI group, while NLR levels were significantly elevated in the se-BSI group relative to the ge-BSI group. These results indicate that inflammatory markers may reflect different infection profiles in patients with BSI, especially SII and NLR. Specifically, elevated SII levels may indicate the presence of additional infections beyond BSI, whereas an increase in NLR may signal a more severe infection burden. The role of NLR observed in this study aligns with findings by Roldgaard in patients with sepsis not related to CIN\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. However, no prior studies have been identified that specifically examine the relationship between SII and infection characteristics in BSI, making direct comparisons unavailable. Additionally, comparisons between the G\u0026thinsp;\u0026minus;\u0026thinsp;BSI and G\u0026thinsp;+\u0026thinsp;BSI groups revealed no significant differences in any of the inflammatory marker levels. This suggests that such markers may not be suitable for assessing the type of infecting organisms in CIN patients with BSI. Nonetheless, the findings regarding NLR and SII may support clinical assessment of infection severity and inform timely adjustments in therapeutic strategies.\u003c/p\u003e\u003cp\u003eIn this study, random sampling was used to divide both the BSI and NBSI groups in an 8:2 ratio. Data from the training cohort were used to construct the predictive model, while the validation cohort was used to evaluate its performance. Comparative analysis of clinical characteristics and inflammatory markers between the BSI and NBSI groups in the training cohort revealed that BSI patients had significantly longer disease durations and a higher proportion of acute lymphoblastic leukemia. Furthermore, routine hematologic and inflammatory marker levels were markedly lower in the BSI group. These findings are consistent with results obtained in our preliminary studies\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. These results indicate that prolonged disease duration and increased chemotherapy exposure elevate the risk of developing BSI in patients with hematologic malignancies, specifically acute myeloid leukemia and acute lymphoblastic leukemia\u0026mdash;being the most susceptible. In addition, the lower the WBC and differential counts during episodes of CIN, the higher the likelihood of BSI occurrence. This conclusion was supported by the subsequent logistic regression analysis and is consistent with current expert consensus, which recognizes CIN as a significant risk factor for invasive infections. On the other hand, inflammatory marker levels in the BSI group were significantly decreased, in stark contrast to their typically elevated levels observed in BSI patients without CIN\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. The suggests that in hematologic patients with CIN, a lower level of systemic immune response is related to a higher risk of developing BSI. Therefore, in clinical practice, declining inflammatory marker levels in suspected cases of BSI among CIN patients should be closely monitored and not overlooked.\u003c/p\u003e\u003cp\u003eGiven the narrow numerical range of WBC and its differential counts, particularly N, which typically fall within 0 to 2 \u0026times; 10⁹/L in CIN patients. This study opted to use composite inflammatory markers instead of absolute WBC or subtype counts for constructing the nomogram. These markers, which represent ratios of different leukocyte subtypes, provide more representative and scalable metrics. Considering the overlapping formulas used to derive these indices, we first conducted a correlation analysis. Strong correlations were observed among NLR, SIRI, and SII, as well as between MLR and SIRI. Based on their respective AUCs for BSI prediction and the distribution characteristics shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, SII and MLR were selected as model variables. After collinearity diagnosis using VIFs, five variables(disease duration, hematologic diagnosis, MLR, PLR, and SII)were ultimately included in the final nomogram. Notably, direct inclusion of SII led to a high frequency of predicted probabilities clustering at 0 or 1, suggesting the presence of complete or quasi-complete separation. However, given the superior diagnostic performance of SII as a single predictor (AUC\u0026thinsp;=\u0026thinsp;0.775, 95% CI: 0.711\u0026ndash;0.839), it was retained in the model after logarithmic transformation as log (SII\u0026thinsp;+\u0026thinsp;1), thereby preserving its predictive utility. Additionally, PLR exhibited a pattern in the predictive model whereby higher values corresponded to higher scores, which contradicts the earlier findings of this study. This inconsistency may be attributed to underlying correlations among the inflammatory markers included in the model\u0026mdash;namely, MLR, PLR, and log (SII\u0026thinsp;+\u0026thinsp;1).\u003c/p\u003e\u003cp\u003eThe nomogram predictive model developed in this study was validated using ROC analysis, DCA, and diagnostic calibration curve assessment. ROC analysis demonstrated an AUC of 0.817 (95% CI: 0.768\u0026ndash;0.876) in the training cohort and 0.781 (95% CI: 0.658\u0026ndash;0.905) in the validation cohort, indicating favorable diagnostic performance for BSI screening in both cohorts. Notably, the model outperformed any individual inflammatory marker in the training cohort, suggesting that incorporating disease duration and hematologic diagnosis significantly enhanced predictive accuracy. In the DCA, the model\u0026rsquo;s decision curve was well-separated from the \u0026ldquo;None\u0026rdquo; and \u0026ldquo;All\u0026rdquo; extremes, indicating a wide threshold range and high net clinical benefit. This highlights the model\u0026rsquo;s potential utility in guiding clinical decision-making. The diagnostic calibration curve showed a Hosmer\u0026ndash;Lemes how P-value of 0.3289, suggesting good agreement between predicted and observed risks. However, the calibration curve slightly deviated above the ideal line within a certain probability range, indicating that the model may underestimate the actual risk of BSI in CIN patients to some extent. Consequently, for CIN patients with suspected BSI, clinicians should consider empirical prophylactic antibiotic therapy based on individual clinical assessment.\u003c/p\u003e\u003cp\u003eFinally, this study was a retrospective analysis rather than a prospective investigation. Therefore, when comparing the BSI and NBSI groups, CIN patients matched by age and sex were selected to reduce baseline differences. However, due to substantial missing data on body mass index (BMI) among study participants, comparative analysis involving age, sex, and BMI could not be conducted. However, previous studies have suggested that increasing age may be associated with a higher risk of infection in patients with CIN\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e,Moreover, a lower baseline BMI has been associated with poorer infection-related outcomes\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e,Therefore, future studies will adopt a prospective design and incorporate additional variables such as age, sex, and BMI to further refine and improve the predictive model.\u003c/p\u003e\u003cp\u003eThis study identified several characteristic features of BSI in hematologic patients with CIN, including prolonged disease duration, predominance of acute leukemia, and significantly reduced levels of routine hematologic inflammatory markers. Among these markers, the SII and NLR demonstrated some utility in reflecting infection severity. A nomogram incorporating disease duration, hematologic diagnosis, MLR, PLR, and SII was developed and validated, offering a straightforward and effective tool for BSI risk assessment and screening in clinical settings. However, this was a single-center retrospective study with inherent limitations. Future prospective multicenter studies and external validation are warranted to further refine and validate the predictive model.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCIN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003echemotherapy-induced neutropenia\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBSIs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebloodstream infections\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eneutrophil-to-lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emonocyte-to-lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eplatelet-to-lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSIRI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esystemic inflammatory response index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSII\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esystemic immune inflammatory index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by Ethics Committee of Quanzhou First Hospital (China), the protocol number is [2025] K218. All participants provided written informed consent. And all methods were performed in accordance with Declaration of Helsinki.\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNO.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYabin Chen wrote the manuscript and collected data; Jing Jing wrote the mian manuscript and revised the paper; Dayan Zhang collected clinical data and performed the experiments; Tingjin Zheng and Chengyong Huang analysed data; Zhishan Zhang designed the experiments; Huidan Chen revised the paper. Yabin Chen, Jing Jing and Dayan Zhang contributed equally to this work and share first authorship. \u0026nbsp;Zhishan Zhang and Huidan Chen shared co corresponding authors. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNO.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBa, Y. et al. Current management of chemotherapy-induced neutropenia in adults: key points and new challenges: Committee of Neoplastic Supportive-Care (CONS), China Anti-Cancer Association Committee of Clinical Chemotherapy, China Anti-Cancer Association. \u003cem\u003eCancer Biol. 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A single-center analysis of pathogenic bacteria distribution and drug resistance in bacterial bloodstream infections among patients with hematological diseases. \u003cem\u003eZhonghua Xue Ye Xue Za Zhi\u003c/em\u003e. \u003cb\u003e45\u003c/b\u003e (10), 937\u0026ndash;943 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePiukovics, K. et al. Evaluation of Bloodstream Infections During Chemotherapy-Induced Febrile Neutropenia in Patients with Malignant Hematological Diseases: Single Center Experience. \u003cem\u003eEur. J. Microbiol. Immunol. (Bp)\u003c/em\u003e. \u003cb\u003e5\u003c/b\u003e (3), 199\u0026ndash;204 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, C. G. \u0026amp; Liao, A. J. 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Hematol(China)\u003c/em\u003e. \u003cb\u003e38\u003c/b\u003e (06), 472\u0026ndash;476 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXie, W. et al. Levels of PCT, NLR, SAA and PLR of newborns with bloodstream infection and their values in early diagnosis. \u003cem\u003eChin. J. Nosocomiol\u003c/em\u003e (12): 1841\u0026ndash;1845. (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOssenkoppele, G. \u0026amp; L\u0026ouml;wenberg, B. How I treat the older patient with acute myeloid leukemia. \u003cem\u003eBlood\u003c/em\u003e \u003cb\u003e125\u003c/b\u003e (5), 767\u0026ndash;774 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaumgartner, A. et al. Association of Nutritional Parameters with Clinical Outcomes in Patients with Acute Myeloid Leukemia Undergoing Haematopoietic Stem Cell Transplantation. \u003cem\u003eAnn. Nutr. Metab.\u003c/em\u003e \u003cb\u003e69\u003c/b\u003e (2), 89\u0026ndash;98 (2016).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Inflammatory markers, Hematological malignancies, Chemotherapy-induced neutropenia, Bloodstream infection, Predictive model","lastPublishedDoi":"10.21203/rs.3.rs-7399935/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7399935/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObject\u003c/h2\u003e\u003cp\u003eChemotherapy-induced neutropenia (CIN) and febrile neutropenia (FN) significantly increase the risk of invasive infections, which can progress to septic shock or sepsis and may ultimately result in death. our purpose of this study is to construct a predictive model based on peripheral blood inflammatory markers to enable timely detection of bloodstream infections(BSI) and evaluation of infection severity in this population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA total of 112 patients with chemotherapy-induced neutropenia (CIN) who developed BSI and 145 patients without BSI (NBSI) were included. Inflammatory marker levels were compared across subgroups. The Logistic regression was performed to identify risk factors for BSI within the training cohort. Model performance was assessed by receiver operating characteristic curves (ROC), decision curve analysis (DCA) and diagnostic calibration curves.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePatients with BSI had longer disease duration, a higher proportion of acute lymphoblastic leukemia, and significantly lower levels of complete blood count and inflammatory markers compared to those without BSI. Univariate analysis identified prolonged disease duration, hematologic diagnosis category, and reduced levels of NLR, MLR, PLR, SIRI, and SII as risk factors for BSI. A nomogram model was constructed incorporating disease duration, hematologic diagnosis category, MLR, PLR, and SII. ROC and DCA indicated a broad threshold range for clinical applicability and a high net benefit of predictive model, supporting its clinical utility. Diagnostic calibration curves showed good agreement between predicted and observed outcomes, with a Hosmer-Lemeshow test P-value of 0.3289.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eA nomogram model incorporating disease duration, hematologic diagnosis category, MLR, PLR, and SII was constructed and shown to provide a practical and effective tool for the assessment and early identification of BSI in patients with CIN.\u003c/p\u003e","manuscriptTitle":"Peripheral blood inflammatory markers are considered useful for predicting the risk of bloodstream infections and evaluating infection severity in patients with hematological malignancies receiving chemotherapy who develop neutropenia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-08 12:18:27","doi":"10.21203/rs.3.rs-7399935/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-15T11:46:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-15T09:35:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-13T09:11:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279625398098456367689661609405184157925","date":"2025-09-11T17:41:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102547488926515379164870670390021064746","date":"2025-09-08T14:00:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277431208531522272499252258424897269567","date":"2025-09-06T18:39:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36353860450494354191661741942617756302","date":"2025-09-03T07:35:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-29T07:20:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-29T07:19:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-26T17:20:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-23T11:45:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-23T11:42:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"429018bf-ba40-444e-9d83-7fc1b219bdde","owner":[],"postedDate":"September 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":54201810,"name":"Health sciences/Biomarkers"},{"id":54201811,"name":"Biological sciences/Cancer"},{"id":54201812,"name":"Health sciences/Diseases"},{"id":54201813,"name":"Health sciences/Medical research"},{"id":54201814,"name":"Health sciences/Oncology"},{"id":54201815,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-11-17T16:01:07+00:00","versionOfRecord":{"articleIdentity":"rs-7399935","link":"https://doi.org/10.1038/s41598-025-23770-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-14 15:57:44","publishedOnDateReadable":"November 14th, 2025"},"versionCreatedAt":"2025-09-08 12:18:27","video":"","vorDoi":"10.1038/s41598-025-23770-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-23770-w","workflowStages":[]},"version":"v1","identity":"rs-7399935","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7399935","identity":"rs-7399935","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}