Early differentiation of fungal from bacterial infections in children with hematologic malignancy: a single-center case-control study

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We evaluated the effectiveness of clinical indicators in differentiating IFI from bacteremia in this pediatric population. Methods: A case group of 50 patients with acute leukemia or lymphoma who developed probable or proven IFI following chemotherapy and had negative bacterial cultures was selected. A control group of 97 patients who developed bacteremia without IFI post-chemotherapy was also included. Results: Among 585 pediatric patients who were hospitalized 9,111 times, the IFI hospitalization rate of 1.02% (93/9,111). By contrast, 104 cases of bacterial infections were reported, with a hospitalization rate of 1.14% (104/9,111). Secondary fever, prolonged antibiotic use ≥ 7 days, history of glucocorticoid therapy, time since chemotherapy, neutropenia duration before diagnosis, fever duration before diagnosis, albumin level, and C-reactive protein (CRP) levels were significantly associated with IFI. Notably, prolonged antibiotic use ≥ 7 days (odds ratio [OR] = 10.879, 95% confidence interval [CI]: 2.033–58.218), time since chemotherapy (OR = 1.193, 95% CI: 1.064–1.336), and fever duration before diagnosis (OR = 2.821, 95% CI: 1.646–4.833) were identified as independent predictors of IFI. A predictive model incorporating these three factors demonstrated improved diagnostic performance, yielding an area under the curve of 0.938 (95% CI: 0.900–0.975), with a sensitivity of 85.1% and specificity of 87.5%. Conclusion: The combination of prolonged antibiotic use ≥ 7 days, time since chemotherapy, and fever duration before diagnosis might help distinguish IFI from bacteremia in pediatric patients with HM. Invasive fungal infections bacteremia hematological malignancies children Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Children with hematological malignancies (HM) undergoing chemotherapy are increasingly susceptible to infections caused by bacteria, fungi, viruses, and other pathogens [1, 2]. In this population, invasive fungal infections (IFI) rank among the primary contributors to morbidity and mortality [3-6]. Recent studies have estimated the prevalence of invasive fungal diseases (IFDs) among pediatric patients with cancer to range from 3.7% to 21.0% [5, 7-11], with associated mortality rates of approximately 4.2% to 14.0% [5, 8, 9, 11]. Prompt identification and diagnosis are essential for the effective management of IFI. However, the clinical manifestations are often nonspecific and may be subtle, particularly in children [2]. The primary diagnostic methods for IFI involve blood culture, analysis of sterile materials, and microscopic identification of fungal elements. These approaches frequently require invasive procedures, and blood cultures in particular exhibit limited sensitivity and are time-consuming, resulting in delays in initiating antifungal therapy [3]. Non-invasive diagnostic tests, such as galactomannan (GM) and ß-D-glucan (BDG) assays, offer alternative options [2, 3]. However, their positive predictive value remains limited in children with cancer, thereby hindering timely diagnosis [1]. Polymerase chain reaction (PCR)-based assays and other nucleic acid-based diagnostic methods have recently demonstrated high sensitivity for the early detection of IFI. Nevertheless, the availability of standardized commercial PCR assays remains limited, and many investigations still rely on in-house methods [2, 12]. Moreover, in children with HM presenting with persistent fever, granulocytopenia, and confirmed pulmonary IFD, computed tomography findings often lack diagnostic specificity [1, 3]. Differentiating IFI from other febrile illnesses remains clinically challenging, often delaying empirical antifungal therapy and increasing the risk of adverse outcomes [13]. The limited accessibility and diagnostic accuracy of antigen detection and PCR-based assays underscore the need for novel biomarkers capable of reliably distinguishing fungal infections from other causes of fever. Recent evidence suggests that combined evaluation of procalcitonin (PCT) and C-reactive protein (CRP) may help differentiate IFI from bacteremia in pediatric patients with HM [2, 13]. However, systematic evaluations of reliable diagnostic indicators for distinguishing post-chemotherapy fungal infections from bacteremia in this population remain scarce. To address this gap, we conducted a case-control study to investigate reliable biomarkers capable of differentiating IFI from bacteremia in children with HM. We analyzed clinical data from pediatric patients with acute leukemia and lymphoma treated at Shandong University Affiliated Children's Hospital between November 2019 and January 2025. Our primary objective was to identify diagnostic biomarkers that could facilitate the early detection of IFI in immunocompromised children with HM. Methods Study design and participants This single-center, retrospective, case-control study was conducted in our department. We analyzed clinical data from children aged ≤ 16 years who were hospitalized and diagnosed with acute leukemia or lymphoma between November 1, 2019, and January 31, 2025. The definitions of proven, probable, and possible IFI were based on criteria established by the European Organization for Research and Treatment of Cancer/IFI Cooperative Group and Mycoses Study Group (EORTC/MSG 2020) [12]. Bacteremia was defined as fever with a positive blood culture. Patients who developed proven or probable IFI with negative bacterial culture results following chemotherapy comprised the case cohort. The control group consisted of patients who developed bacteremia after chemotherapy without subsequent IFI. Patients with suspected fungal and bacterial coinfection or positive cultures for contaminants without clinical evidence of infection were excluded. The study protocol was approved by the Ethics Committee of the Children’s Hospital Affiliated with Shandong University in China (approval number: SPFE-IRB/P-2024034). Data collection We systematically collected clinical data from 50 patients with proven or probable IFI and 97 with bacterial bloodstream infections. Data were categorized into four domains: (1) demographic characteristics: gender, age at diagnosis, admission number, clinical diagnosis, and remission status; (2) clinical parameters: central nervous system involvement, peripherally inserted central catheter use, oral candidiasis, highest recorded body temperature, fever duration before diagnosis, secondary fever (defined as new-onset fever ≥48 hours after defervescence), and chemotherapy-induced neutropenia (absolute neutrophil count <500 cells/μL), including its presence and duration; (3) laboratory biomarkers: BDG and GM surveillance, inflammatory markers (CRP, PCT, interleukin-6 [IL-6], interleukin-10 [IL-10] ), and albumin levels; and (4) additional indicators: prolonged antibiotic use (≥ 7 days), history of glucocorticoid therapy (defined as glucocorticoid administration in the preceding chemotherapy cycle), and time since chemotherapy. Statistical analysis Continuous variables are expressed as medians along with their interquartile ranges. To compare groups, the Fisher exact test was conducted for categorical variables. Univariate logistic regression identified risk factors linked to IFI. Variables showing p < 0.05 in the univariate analysis were considered for inclusion in the multivariate logistic regression. In the multivariate model, variables with p < 0.05 contributed to the development of the diagnostic model. The performance of the model was evaluated using receiver operating characteristic curves. The reported metrics included sensitivity, specificity, positive and negative likelihood ratios, as well as positive and negative predictive values. All analyses were carried out using R version 4.2.1, and statistical significance was set at p < 0.05. Results General information Between 1 November 2019 and 31 January 2025, 585 consecutive pediatric patients with acute leukemia—acute lymphoblastic leukemia (n = 365) or acute myeloid leukemia (n = 56)—or lymphoma (n = 164) were screened for eligibility. These children underwent 9 111 hospital admissions, resulting in a hospitalization infection rate of 1.02 % (93 / 9 111). Nine IFI episodes were classified as proven, 45 as probable, and 39 as possible. Metagenomic next‑generation sequencing (mNGS) identified fungal pathogens in 28 children. Aspergillus spp. and Candida spp. accounted for 12 and 10 cases (32.4 % and 35.7 %), respectively. After exclusion of 7 fungal–bacterial co‑infections and the 39 possible IFI cases, 50 proven or probable IFI episodes formed the case group. Bacterial infection was documented in 104 episodes, yielding an admission‑based infection rate of 1.14 % (104 / 9 111). After removal of 7 fungal–bacterial co‑infections, 97 bacteremia episodes constituted the control group. In total, 147 children were enrolled (Fig. 1). Baseline characteristics are presented in Table 1. Bacteremia accounted for 97 episodes (63.9 %), whereas 50 episodes (36.1 %) met criteria for proven (9 / 50) or probable (41 / 50) IFI. Median age at diagnosis was 3.8 years in the IFI group and 6.0 years in the bacteremia group (interquartile ranges 2.2–6.0 and 2.5–8.0 years, respectively; P = 0.044). Table 1 . Patient demographic and baseline characteristics Characteristic Bacteremia Group (n = 97) IFI Group (n = 50) P Age (y), median (IQR) 6 (2.5, 8) 3.83 (2.2, 6) 0.044 Sex, n (%) 0.484 Female 62 (63.9%) 29 (58.0%) Male 35 (36.1%) 21 (42.0%) Clinical diagnoses, n (%) 0.135 ALL 55 (56.7%) 34 (68.0%) AML 14 (14.4%) 2 (4.0%) Lymphoma 28 (28.9%) 14 (28.0%) CNS, n (%) 0.880 CNS1 66 (68.0%) 32 (64.0%) CNS2 16 (16.5%) 9 (18.0%) CNS3 15 (15.5%) 9 (18.0%) Remission status, n (%) 97 50 0.707 No 59 (60.8%) 32 (64.0%) Yes 38 (39.2%) 18 (36.0%) PICC, n (%) 97 50 0.599 No 27 (27.8%) 16 (32.0%) 0.599 Yes 70 (72.2%) 34 (68.0%) IFI, invasive fungal infection; IQR, interquartile range; ALL, acute lymphoblastic leukemia; AML, acute myeloid leukemia; CNS, central nervous system; PICC, peripherally inserted central catheter. Pathogens causing IFI or bacteremia Figure 2 summarizes the distribution of pathogens. Among the 50 IFI episodes, 9 were proven infections. Candida spp. accounted for 6 of these 9 episodes (66.7 %), Mucorales for 2 (22.2 %), and Apiotrichum mycotoxinivorans for 1 (11.1 %). Five proven cases underwent mNGS of blood or cerebrospinal fluid; four yielded concordant fungal findings: Mucorales in 2 cases, Candida krusei in 1, and A. mycotoxinivorans in 1. Blood culture from the 97 bacteremic episodes yielded 104 bacterial isolates. Gram‑negative organisms comprised 52 isolates (50.0 %), dominated by Escherichia coli (16 isolates, 16.5 %) and Klebsiella pneumoniae (12 isolates, 12.4 %). Gram‑positive organisms also contributed 52 isolates (50.0 %); the most common were Streptococcus mitis (11 isolates, 11.3 %), Staphylococcus aureus (9 isolates, 9.3 %), and Staphylococcus hominis (9 isolates, 9.3 %). Treatment outcomes Fungal infection contributed to 4 deaths, resulting in a mortality rate of 4.3 % (4 / 93). All fatalities occurred in proven IFI and included 3 due to Candida infection and 1 due to A. mycotoxinivorans infection. No deaths related to bacteremia were recorded. Clinical and laboratory factors associated with IFI Univariate analysis (Table 2) identified secondary fever (P = 0.007), prolonged antibiotic use ≥ 7 days (P < 0.001), history of glucocorticoid therapy (P = 0.031), time since chemotherapy (P < 0.001), neutropenia duration before diagnosis (P = 0.047), fever duration before diagnosis (P < 0.001), albumin level (P = 0.038), and C‑reactive protein level (P = 0.019) as variables significantly associated with IFI. Multivariate logistic regression retained prolonged antibiotic use ≥ 7 days (P = 0.005), time since chemotherapy (P = 0.002), and fever duration before diagnosis (P < 0.001) as independent predictors of IFI (Table 2, Fig. 3). Table 2 . Univariate and Multiple logistic regression analys e s of biomarkers used to distinguish IFI from Bacteremia Variables Bacteremia Group IFI Group Total (N) Univariate analysis Multivariate analysis P OR (95% CI) P OR (95% CI) Oral candidiasis, n (%) 147 0.233 3.032（0.490–18.769） No 95 (97.9%) 47 (94.0%) Yes 2 (2.1%) 3 (6.0%) Secondary fever, n (%) 147 0.007 9.048（1.842–44.433） 0.462 0.264（0.008–9.155） No 95 (97.9%) 42 (84.0%) Yes 2 (2.1%) 8 (16.0%) Prolonged antibiotic use ≥ 7 days, n (%) 147 0.000 6.96（3.147–15.392） 0.005 10.879（2.033–58.218） No 83 (85.6%) 23 (46.0%) Yes 14 (14.4%) 27 (54.0%) History of glucocorticoid therapy, n (%) 147 0.031 2.318（1.080–4.977） 0.499 0.584（0.123–2.777） No 41 (27.9%) 12 (8.2%) Yes 56 (38.1%) 38 (25.9%) Prophylactic antifungal therapy, n (%) 147 0.22 1.563（0.766–3.191） No 68 (46.3%) 30 (20.4%) Yes 29 (19.7%) 20 (13.6%) Chemotherapy-induced neutropenia, n (%) 147 0.46 0.739（0.332–1.647） No 20 (20.6%) 13 (26.0%) Yes 77 (79.4%) 37 (74.0%) Age (y), median (IQR) 6 (2.5, 8) 3.83 (2.21, 6) 147 0.058 0.908（0.822–1.003） 0.083 0.833（0.678–1.024） Highest recorded body temperature (℃), median (IQR) 39 (38.6, 39.5) 39 (38.5, 39.65) 147 0.107 0.722（0.486–1.073） Time since chemotherapy （days）, median (IQR) 12 (9, 16.25) 17 (14.5, 27) 143 0.000 1.137（1.075–1.203） 0.002 1.193（1.064–1.336） Neutropenia duration before diagnosis (days), median (IQR) 3 (1, 6) 5 (0.25, 10) 147 0.047 1.053（1.001–1.108） 0.197 0.916（0.801–1.047） Fever duration before diagnosis (days), median (IQR) 1 (0, 1) 4.5 (2, 8) 147 0.000 2.165（1.642–2.855） 0.000 2.821（1.646–4.833） Albumin level (g/L), median (IQR) 34.9 (32.5, 38) 32.9 (28.9, 38) 138 0.038 0.924（0.858–0.995） 0.23 0.92（0.803–1.054） CRP level (mg/L), median (IQR) 12.3 (3.34, 35.168) 23.25 (6.98, 62.465) 139 0.019 1.01（1.002–1.019） 0.875 1.001（0.985–1.018） PCT level (ng/mL), median (IQR) 0.26 (0.137, 0.553) 0.282 (0.15175, 0.8225) 124 0.353 0.942（0.829–1.069） IL-6 level (pg/L), median (IQR) 168.86 (50.49, 603.92) 102.4 (24.445, 271.79) 129 0.11 0.999（0.999–1.000） IL-10 level (pg/L), median (IQR) 14.715 (6.85, 69.462) 8.76 (3.68, 15.675) 123 0.307 0.999（0.997–1.001） IFI, invasive fungal infection; OR, odds ratio; CI, confidence interval; IQR, interquartile range; CRP, C-reactive protein; PCT, procalcitonin, IL-6 interleukin-6, IL-10 interleukin-10 Diagnostic performance of individual markers and the composite model Receiver‑operating characteristic analysis (Fig. 4) yielded optimal cut‑off values of 14.5 days for time since chemotherapy and 1.5 days for fever duration before diagnosis. The areas under the curve (AUC) were 0.752 (95 % confidence interval [CI] 0.666–0.837; P < 0.001) for chemotherapy interval, 0.849 (95 % CI 0.773–0.925; P < 0.001) for fever duration before diagnosis, and 0.698 (95 % CI 0.620–0.775; P < 0.001) for prolonged antibiotic use ≥ 7 days. A composite model incorporating all three variables demonstrated superior discrimination (AUC = 0.938, 95 % CI 0.900–0.975; P < 0.001) (Table 3, Fig. 4). Table 3. Performance characteristics of days of chemotherapy, antibiotics ≥ 7days, fever, time, and their combination in diagnosing the IFI Biomarker AUC Cut-Off * Sensitivity (%) Specificity (%) PLR NLR PPV (%) NPV (%) Time since chemotherapy (days) 0.752(0.666–0.837) 14.5 74.5 65.6 2.2 0.4 51.5 84.0 Fever duration before diagnosis (days) 0.849(0.773–0.925） 1.5 78.0 87.6 6.3 0.3 76.5 88.5 Prolonged antibiotic use ≥ 7 days 0.698(0.620–0.776) - 54.0 85.6 3.7 0.5 65.9 78.3 model 0.938（0.900–0.975） - 85.1 87.5 6.8 0.2 76.9 92.3 IFI, invasive fungal infection; AUC, area under the curve; PLR, positive likelihood ratio; NLR, negative likelihood ratio; PPV, positive predictive value; NPV, negative predictive value; * cutoff values of days of chemotherapy and fever time were chosen based on the Youden index. Discussion Timely differentiation of IFI from other febrile conditions remains clinically challenging, often resulting in delayed empirical antifungal therapy and increased risk of adverse outcomes [13]. The accessibility and diagnostic performance of specific fungal antigens and PCR-based assays remain limited, underscoring the critical need for novel biomarkers capable of distinguishing fungal infections from other infectious processes. This study found that the combination of time since chemotherapy, fever duration before diagnosis, and prolonged antibiotic use ≥ 7 days effectively differentiated IFI from bacteremia in patients with HM. The combination's sensitivity and specificity were determined to be 85.1% and 87.5%, respectively, using cut-off values of 14.5 days for time since chemotherapy and 1.5 days for fever duration before diagnosis. These findings may support the timely initiation of prophylactic or therapeutic antifungal interventions in cases of suspected bloodstream infection, potentially reducing infection-associated mortality. Several studies have established risk factors for IFI or bacteremia in pediatric patients with HM. Risk factors associated with IFI include prolonged and profound neutropenia [9, 14-16], extended hospitalization, fever duration, neutropenia duration, and prolonged use of broad-spectrum antibiotics [8]. Prior research demonstrated that PCT effectively distinguishes bacteremia from non-bacteremia, whereas CRP lacks discriminatory power for bacteremia in febrile neutropenic episodes in patients with HM [17]. Furthermore, recent studies reported that in the initial fever episode, PCT and IL-6 levels—but not CRP—differed significantly between patients with sepsis and those without [18]. These results suggest that PCT and IL-6 may serve as useful adjunctive markers for diagnosing bacteremia. However, systematic and comprehensive evaluations of biomarkers for differentiating IFI from bacteremia in pediatric patients with HM remain limited. The three indicators identified in this study—time since chemotherapy, fever duration before diagnosis, and prolonged antibiotic use ≥ 7 days—contribute to addressing this gap. This finding differs from that reported by Stoma et al. [13], who proposed a composite biomarker for IFI in patients with hematological disorders comprising CRP > 120 mg/L combined with either PCT < 1.25 ng/mL or presepsin < 170 pg/mL. In contrast, it aligns with the findings of Liu [2], who showed that elevated CRP levels combined with low PCT levels could effectively differentiate IFI from bacteremia in immunocompromised children. While univariate analysis in Liu’s study revealed significantly higher CRP levels in patients with IFI than in those with bacterial bloodstream infections, multivariate analysis determined that neither CRP nor PCT reliably differentiated IFI from bacterial bloodstream infections in children undergoing chemotherapy for HM. The discrepancy across studies may reflect differences in the timing of biomarker measurement. Stoma et al. [13] evaluated biomarkers within the first 48 hours of fever onset, whereas Liu et al. [2] measured CRP and PCT in plasma collected 12–24 hours after fever onset. These temporal differences, along with variation in patient subgroups and underlying conditions, likely contributed to divergent results. Early detection of IFI using biomarkers such as GM, BDG, and PCR assays remains a key strategy for improving outcomes. These tests can serve as screening or diagnostic tools in patients with HM and suspected IFI [19, 20]. However, their use in pediatric populations remains controversial [21-24]. A systematic review by Lehrnbecher et al. [20] reported that GM, BDG, and PCR assays exhibit variable sensitivity, specificity, and predictive value in diagnosing IFI in pediatric oncology patients. Furthermore, their diagnostic performance in clinical practice is generally limited. The 2020 guidelines [12] supported the use of GM detection for diagnosing invasive aspergillosis in children but advised caution, as antifungal therapy may lead to false-negative results. BDG detection, due to its nonspecificity, is not recommended for IFI diagnosis. These challenges highlight the need for further investigation into fungal biomarker performance. This study contributes to this effort by exploring early indicators of IFI in pediatric patients, particularly in light of limitations associated with GM, BDG, PCR, and computed tomography-based approaches [1]. Prior studies identified Candida and Aspergillus species as the most common pathogens responsible for IFI in pediatric patients with HM [6]. Some studies reported that Aspergillus predominated in probable and proven IFI [5, 7, 9, 16, 21, 25], whereas others identified Candida as the leading fungal pathogen in clinical settings [26-28]. In this study, among the nine children diagnosed with IFI, Candida species were most prevalent, accounting for 66.7% (6/9). Notably, four deaths related to IFI occurred among children with proven IFI, corresponding to a mortality rate of 4.3% (4/93), and this was consistent with earlier findings [29, 30]. Next-generation sequencing (NGS) has recently emerged as a promising diagnostic tool for IFI [31, 32]. While Hormographiella aspergillata rarely causes IFI in immunocompromised hosts, NGS has demonstrated diagnostic utility for such rare pathogens [33, 34]. Compared to conventional blood, fluid, or tissue cultures, NGS enables pathogen identification 24–48 hours earlier and significantly improves detection rates. In four proven IFI cases in this study, NGS results were consistent with blood or cerebrospinal fluid cultures and identified Mortierella species (two cases), Candida krusei (one case), and A. mycotoxinivorans (one case) [35]. Overall, mNGS is a powerful and rapid diagnostic modality for IFI, although the current lack of standardization necessitates further validation. Limitations This research has two primary limitations. Firstly, being a retrospective analysis conducted at a single center, it could be influenced by selection bias. The patient population originates from a tertiary pediatric hospital, which may restrict the generalizability of our findings. The severity of the disease and the criteria for initiating prophylactic antifungal therapy may not reflect those of other institutions. Secondly, while our center consistently measured PCT and CRP levels at the onset of fever, these levels were not uniformly assessed 12-24 hours after fever onset, potentially leading to discrepancies with previous research findings. Therefore, future studies should implement a multicenter, prospective design with standardized clinical management and dynamic monitoring of infection-related indicators at multiple time points to improve comparability across different studies. Conclusion In conclusion, early diagnosis of IFI remains a significant clinical challenge. Prompt detection is essential for appropriate treatment and mortality reduction. Our results suggest that time since chemotherapy, fever duration before diagnosis, and prolonged antibiotic use ≥ 7 days might help distinguish IFI from bacteremia in patients undergoing chemotherapy for HM. These preliminary observations could provide pediatric clinicians with initial clues for early differentiation, but their clinical applicability requires rigorous validation in future, larger-scale studies. Abbreviations AUC Area under the curve BDG ß-D-glucan CI Confidence interval CRP C-reactive protein GM Galactomannan HM Hematological malignancies IFI Invasive fungal infection IFDs Invasive fungal diseases IL-6 Interleukin-6 IL-10 Interleukin-10 IQR Interquartile range mNGS Metagenomic next‑generation sequencing NLR Negative likelihood ratio NPV Negative predictive value OR Odds ratio PCR Polymerase chain reaction PCT Procalcitonin PLR Positive likelihood ratio PPV Positive predictive value Declarations Acknowledgments We sincerely thank the nurses and doctors in the Department of Hematology and Oncology at Children’s Hospital Affiliated to Shandong University (Jinan Children's Hospital) for their dedicated support, that was pivotal to the success of this study. Clinical trial number Not applicable. Author contributions DLA was responsible for the study design, data acquisition, analysis, and interpretation, and also drafted the manuscript. LF, YXM, and WSF contributed to the study concept and design, critically revised the article for significant intellectual content, and provided final approval for the version to be published. WRY, MX, and WYP also participated in the study concept and design and critically reviewed the manuscript for intellectual content. ZX, WLJ, and WWD contributed to data curation, formal analysis, investigation, and validation. All authors read and approved the final manuscript. Funding This study was supported by the Technology Development Program of Jinan Municipal Health Commission (2024304012) from FL. Data availability The data from this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate The study was conducted in accordance with the principles of the Declaration of Helsinki and approved by the Ethics Committee of Children’s Hospital Affiliated to Shandong University in China (Approval Number: SDFE-IRB/P-2024034). All patients included in this study are under 16 years of age. Due to the retrospective nature of this study, the consent from legal guardians of all patients was waived by the above ethics committee. Consent to publication Not applicable. Competing interests The authors have no conflict of interest to declare. References Groll AH, Pana D, Lanternier F, Mesini A, Ammann RA, Averbuch D, et al. 8th European Conference on Infections in Leukaemia: 2020 guidelines for the diagnosis, prevention, and treatment of invasive fungal diseases in paediatric patients with cancer or post-haematopoietic cell transplantation. Lancet Oncol. 2021;22:e254-e69. doi:10.1016/s1470-2045(20)30723-3. 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Guideline for the Management of Fever and Neutropenia in Pediatric Patients With Cancer and Hematopoietic Cell Transplantation Recipients: 2023 Update. J Clin Oncol. 2023;41:1774-85. doi:10.1200/jco.22.02224. Yeoh DK, Blyth CC, Clark JE, Abbotsford J, Corrente C, Cook S, et al. Invasive fungal disease and antifungal prophylaxis in children with acute leukaemia: a multicentre retrospective Australian cohort study. Lancet Reg Health West Pac. 2024;52:101201. doi:10.1016/j.lanwpc.2024.101201. Yang M, Choi SJ, Lee J, Lee DG, Kim YJ, Park YJ, et al. Serum procalcitonin as an independent diagnostic markers of bacteremia in febrile patients with hematologic malignancies. PLoS One. 2019;14:e0225765. doi:10.1371/journal.pone.0225765. Carcò D, Castorina P, Guardo P, Iachelli V, Pace T, Scirè P, et al. Combination of Interleukin-6, C-Reactive Protein and Procalcitonin Values as Predictive Index of Sepsis in Course of Fever Episode in Adult Haematological Patients: Observational and Statistical Study. J Clin Med. 2022;11. doi:10.3390/jcm11226800. Lamoth F, Nucci M, Fernandez-Cruz A, Azoulay E, Lanternier F, Bremerich J, et al. Performance of the beta-glucan test for the diagnosis of invasive fusariosis and scedosporiosis: a meta-analysis. Med Mycol. 2023;61. doi:10.1093/mmy/myad061. Lehrnbecher T, Robinson PD, Fisher BT, Castagnola E, Groll AH, Steinbach WJ, et al. Galactomannan, β-D-Glucan, and Polymerase Chain Reaction-Based Assays for the Diagnosis of Invasive Fungal Disease in Pediatric Cancer and Hematopoietic Stem Cell Transplantation: A Systematic Review and Meta-Analysis. Clin Infect Dis. 2016;63:1340-48. doi:10.1093/cid/ciw592. Das S, Capoor MR, Singh A, Agarwal Y. Diagnostic Utility of Galactomannan Enzyme Immunoassay in Invasive Aspergillosis in Pediatric patients with Hematological Malignancy. Mycopathologia. 2023;188:1055-63. doi:10.1007/s11046-023-00798-y. Fisher BT, Westling T, Boge CLK, Zaoutis TE, Dvorak CC, Nieder M, et al. Prospective Evaluation of Galactomannan and (1→3) β-d-Glucan Assays as Diagnostic Tools for Invasive Fungal Disease in Children, Adolescents, and Young Adults With Acute Myeloid Leukemia Receiving Fungal Prophylaxis. J Pediatric Infect Dis Soc. 2021;10:864-71. doi:10.1093/jpids/piab036. Hsu AJ, Tamma PD, Zhang SX. Challenges with Utilizing the 1,3-Beta-d-Glucan and Galactomannan Assays To Diagnose Invasive Mold Infections in Immunocompromised Children. J Clin Microbiol. 2021;59:e0327620. doi:10.1128/jcm.03276-20. Singh S, Singh M, Verma N, Sharma M, Pradhan P, Chauhan A, et al. Comparative accuracy of 1,3 beta-D glucan and galactomannan for diagnosis of invasive fungal infections in pediatric patients: a systematic review with meta-analysis. Med Mycol. 2021;59:139-48. doi:10.1093/mmy/myaa038. Wang SS, Kotecha RS, Bernard A, Blyth CC, McMullan BJ, Cann MP, et al. Invasive fungal infections in children with acute lymphoblastic leukaemia: Results from four Australian centres, 2003-2013. Pediatr Blood Cancer. 2019;66:e27915. doi:10.1002/pbc.27915. Cornely OA, Sprute R, Bassetti M, Chen SC, Groll AH, Kurzai O, et al. Global guideline for the diagnosis and management of candidiasis: an initiative of the ECMM in cooperation with ISHAM and ASM. Lancet Infect Dis. 2025;25:e280-e93. doi:10.1016/s1473-3099(24)00749-7. Ferreras-Antolín L, Irwin A, Atra A, Chapelle F, Drysdale SB, Emonts M, et al. Pediatric Antifungal Prescribing Patterns Identify Significant Opportunities to Rationalize Antifungal Use in Children. Pediatr Infect Dis J. 2022;41:e69-e74. doi:10.1097/inf.0000000000003402. Hon KLE, Chan VP, Leung AK, Leung KKY, Hui WF. Invasive fungal infections in critically ill children: epidemiology, risk factors and antifungal drugs. Drugs Context. 2024;13. doi:10.7573/dic.2023-9-2. Czyżewski K, Gałązka P, Frączkiewicz J, Salamonowicz M, Szmydki-Baran A, Zając-Spychała O, et al. Epidemiology and outcome of invasive fungal disease in children after hematopoietic cell transplantation or treated for malignancy: Impact of national programme of antifungal prophylaxis. Mycoses. 2019;62:990-98. doi:10.1111/myc.12990. Yeoh DK, Moore AS, Kotecha RS, Bartlett AW, Ryan AL, Cann MP, et al. Invasive fungal disease in children with acute myeloid leukaemia: An Australian multicentre 10-year review. Pediatr Blood Cancer. 2021;68:e29275. doi:10.1002/pbc.29275. Wang J, Liu L, Li J, Feng X, Yi H, Jiang E, et al. Clinical Characteristics, Prognosis Factors and Metagenomic Next-Generation Sequencing Diagnosis of Mucormycosis in patients With Hematologic Diseases. Mycopathologia. 2024;189:71. doi:10.1007/s11046-024-00875-w. Zhang X, Zhang L, Li Y, Wang N, Zhang Y. Clinical performance of metagenomic next-generation sequencing for diagnosis of invasive fungal disease after hematopoietic cell transplant. Front Cell Infect Microbiol. 2024;14:1210857. doi:10.3389/fcimb.2024.1210857. Wang Q, Song Y, Han D, Cai H, Yan Q, Liu W, et al. The first suspected disseminated Hormographiella aspergillata infection in China, diagnosed using metagenomic next-generation sequencing: a case report and literature review. Emerg Microbes Infect. 2023;12:2220581. doi:10.1080/22221751.2023.2220581. Wesdorp E, Rotte L, Chen LT, Jager M, Besselink N, Vermeulen C, et al. NGS-based Aspergillus detection in plasma and lung lavage of children with invasive pulmonary aspergillosis. NPJ Genom Med. 2025;10:24. doi:10.1038/s41525-025-00482-8. Li X, Wang D, Hao M, Li Z, Zhang C, Feng S, et al. The first report of Apiotrichum mycotoxinivorans isolation from human cerebrospinal fluid. Eur J Clin Microbiol Infect Dis. 2024;43:597-604. doi:10.1007/s10096-023-04736-0. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Apr, 2026 Reviews received at journal 19 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviews received at journal 11 Nov, 2025 Reviewers agreed at journal 01 Nov, 2025 Editor invited by journal 07 Oct, 2025 Reviewers invited by journal 30 Jul, 2025 Editor assigned by journal 24 Jul, 2025 Submission checks completed at journal 24 Jul, 2025 First submitted to journal 21 Jul, 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. We do this by developing innovative software and high quality services for the global research community. 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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-7070657","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":493090705,"identity":"118529fe-7390-4a8b-8267-19d2b5aae261","order_by":0,"name":"Li’an Du","email":"","orcid":"","institution":"Children's Hospital Affiliated to Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Li’an","middleName":"","lastName":"Du","suffix":""},{"id":493090706,"identity":"399bc94c-a139-4074-9c9a-d978cc05e315","order_by":1,"name":"Xiaomei Yang","email":"","orcid":"","institution":"Children's Hospital Affiliated to Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xiaomei","middleName":"","lastName":"Yang","suffix":""},{"id":493090707,"identity":"23bbeedf-d8b2-4ddf-9345-ad872d0ee12b","order_by":2,"name":"Ruoying Wei","email":"","orcid":"","institution":"Children's Hospital Affiliated to Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Ruoying","middleName":"","lastName":"Wei","suffix":""},{"id":493090708,"identity":"4c632a5d-9ef7-4615-867d-9dadbb01b68a","order_by":3,"name":"Xiao Mou","email":"","orcid":"","institution":"Children's Hospital Affiliated to Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Mou","suffix":""},{"id":493090709,"identity":"97ef057e-3e17-4784-8ed8-fdb6948c3240","order_by":4,"name":"Yaping Wang","email":"","orcid":"","institution":"Children's Hospital Affiliated to Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yaping","middleName":"","lastName":"Wang","suffix":""},{"id":493090710,"identity":"3bbe477e-2ffd-4474-baaa-850bb8eb86aa","order_by":5,"name":"Xiao Zhang","email":"","orcid":"","institution":"Children's Hospital Affiliated to Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Zhang","suffix":""},{"id":493090711,"identity":"ffb4efcc-93d0-405f-ba98-34585e7a8fca","order_by":6,"name":"Lijin Wang","email":"","orcid":"","institution":"Children's Hospital Affiliated to Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Lijin","middleName":"","lastName":"Wang","suffix":""},{"id":493090712,"identity":"0a6d86a8-41f4-4c2d-8039-bd35f8b3ed20","order_by":7,"name":"Weidong Wang","email":"","orcid":"","institution":"Sun Yat-Sen MemorialHospital of Sun Yat-Sen University","correspondingAuthor":false,"prefix":"","firstName":"Weidong","middleName":"","lastName":"Wang","suffix":""},{"id":493090713,"identity":"ae512447-7987-45a3-8202-095879dcb521","order_by":8,"name":"Shifu Wang","email":"","orcid":"","institution":"Children's Hospital Affiliated to Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Shifu","middleName":"","lastName":"Wang","suffix":""},{"id":493090714,"identity":"774c20ef-0c12-4e60-944c-5f415eacf3ef","order_by":9,"name":"Fu Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYBACPmbGBwwMbDY8/OwNRGphY2Y2AJJpMpI9B4jVwgDWctjG4IYDsVrYmdkkf5Sd52G4wcD44WMOcQ5jk+Y5d5uHcXYDs+TMbURp4T8mzdh2m4dZ5gAbMy9xWoAO+9l2jodNIoEELRK8bQd4eEjRwmzNcy6ZR4LnYDNxfuHnP8x480eZnb398eaDHz4SowUJMDaQpn4UjIJRMApGAW4AAGPfKL0ZvYVfAAAAAElFTkSuQmCC","orcid":"","institution":"Children's Hospital Affiliated to Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Fu","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-07-08 05:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7070657/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7070657/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88338254,"identity":"ae2f97fe-a941-4d14-8761-2ddbeae88830","added_by":"auto","created_at":"2025-08-05 12:23:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":225385,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of study inclusion and analysis.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7070657/v1/f1fe43a5977739bf90a4638c.png"},{"id":88339916,"identity":"47e6394c-bd68-439b-a9ca-16f74854c80b","added_by":"auto","created_at":"2025-08-05 12:31:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":317377,"visible":true,"origin":"","legend":"\u003cp\u003eInfection Types and Pathogen profiles.\u003c/p\u003e\n\u003cp\u003e(A) Among the 97 patients with bacteremia, 104 bacterial strains were isolated from blood cultures. The distribution of various bacterial colony proportions is shown. (B) Distribution of various colony proportions in different disease types. (C) Colony distribution among nine patients with proven IFI is presented.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7070657/v1/2e6f2aa2b0f878d6b6a8d188.png"},{"id":88338246,"identity":"30bd82e1-4e0e-4d6a-9120-c6bf4fdfad0d","added_by":"auto","created_at":"2025-08-05 12:23:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":199660,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of risk factors for IFI versus bacteremia based on multivariate logistic regression analysis.\u003c/p\u003e\n\u003cp\u003eIFI, invasive fungal infection; OR, odds ratio; CI, confidence interval; CRP, C-reactive protein.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7070657/v1/8eb43ce30e98620da247cbd9.png"},{"id":88338242,"identity":"a24c06dd-28af-434b-a86e-33d0dbe759c7","added_by":"auto","created_at":"2025-08-05 12:23:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":304019,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves for prolonged antibiotic use ≥ 7 days, time since chemotherapy, fever duration before diagnosis, and their combination in differentiating IFI from bacteremia.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7070657/v1/8aeaeffa2d4092ebe1422aaf.png"},{"id":88339917,"identity":"58206e12-9aa1-436b-b81f-4e63eb326fae","added_by":"auto","created_at":"2025-08-05 12:31:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1977158,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7070657/v1/d4aa8388-4103-4ce2-bcad-36c6ace0c953.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early differentiation of fungal from bacterial infections in children with hematologic malignancy: a single-center case-control study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChildren with hematological malignancies (HM) undergoing chemotherapy are increasingly susceptible to infections caused by bacteria, fungi, viruses, and other pathogens [1, 2]. In this population, invasive fungal infections (IFI) rank among the primary contributors to morbidity and mortality [3-6]. Recent studies have estimated the prevalence of invasive fungal diseases (IFDs) among pediatric patients with cancer to range from 3.7% to 21.0% [5, 7-11], with associated mortality rates of approximately 4.2% to 14.0% [5, 8, 9, 11]. Prompt identification and diagnosis are essential for the effective management of IFI. However, the clinical manifestations are often nonspecific and may be subtle, particularly in children [2]. The primary diagnostic methods for IFI involve blood culture, analysis of sterile materials, and microscopic identification of fungal elements. These approaches frequently require invasive procedures, and blood cultures in particular exhibit limited sensitivity and are time-consuming, resulting in delays in initiating antifungal therapy [3].\u003c/p\u003e\n\u003cp\u003eNon-invasive diagnostic tests, such as galactomannan (GM) and \u0026szlig;-D-glucan (BDG) assays, offer alternative options [2, 3]. However, their positive predictive value remains limited in children with cancer, thereby hindering timely diagnosis [1]. Polymerase chain reaction (PCR)-based assays and other nucleic acid-based diagnostic methods have recently demonstrated high sensitivity for the early detection of IFI. Nevertheless, the availability of standardized commercial PCR assays remains limited, and many investigations still rely on in-house methods [2, 12]. Moreover, in children with HM presenting with persistent fever, granulocytopenia, and confirmed pulmonary IFD, computed tomography findings often lack diagnostic specificity [1, 3].\u003c/p\u003e\n\u003cp\u003eDifferentiating IFI from other febrile illnesses remains clinically challenging, often delaying empirical antifungal therapy and increasing the risk of adverse outcomes [13]. The limited accessibility and diagnostic accuracy of antigen detection and PCR-based assays underscore the need for novel biomarkers capable of reliably distinguishing fungal infections from other causes of fever. Recent evidence suggests that combined evaluation of procalcitonin (PCT) and C-reactive protein (CRP) may help differentiate IFI from bacteremia in pediatric patients with HM [2, 13]. However, systematic evaluations of reliable diagnostic indicators for distinguishing post-chemotherapy fungal infections from bacteremia in this population remain scarce.\u003c/p\u003e\n\u003cp\u003eTo address this gap, we conducted a case-control study to investigate reliable biomarkers capable of differentiating IFI from bacteremia in children with HM. We analyzed clinical data from pediatric patients with acute leukemia and lymphoma treated at Shandong University Affiliated Children\u0026apos;s Hospital between November 2019 and January 2025. Our primary objective was to identify diagnostic biomarkers that could facilitate the early detection of IFI in immunocompromised children with HM.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis single-center, retrospective, case-control study was conducted in our department. We analyzed clinical data from children aged \u0026le; 16 years who were hospitalized and diagnosed with acute leukemia or lymphoma between November 1, 2019, and January 31, 2025. The definitions of proven, probable, and possible IFI were based on criteria established by the European Organization for Research and Treatment of Cancer/IFI Cooperative Group and Mycoses Study Group (EORTC/MSG 2020) [12]. Bacteremia was defined as fever with a positive blood culture. Patients who developed proven or probable IFI with negative bacterial culture results following chemotherapy comprised the case cohort. The control group consisted of patients who developed bacteremia after chemotherapy without subsequent IFI. Patients with suspected fungal and bacterial coinfection or positive cultures for contaminants without clinical evidence of infection were excluded. The study protocol was approved by the Ethics Committee of the Children\u0026rsquo;s Hospital Affiliated with Shandong University in China (approval number: SPFE-IRB/P-2024034).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe systematically collected clinical data from 50 patients with proven or probable IFI and 97 with bacterial bloodstream infections. Data were categorized into four domains: (1) demographic characteristics: gender, age at diagnosis, admission number, clinical diagnosis, and remission status; (2) clinical parameters: central nervous system involvement, peripherally inserted central catheter use, oral candidiasis, highest recorded body temperature, fever duration before diagnosis, secondary fever (defined as new-onset fever \u0026ge;48 hours after defervescence), and chemotherapy-induced neutropenia (absolute neutrophil count \u0026lt;500 cells/\u0026mu;L), including its presence and duration; (3) laboratory biomarkers: BDG and GM surveillance, inflammatory markers (CRP, PCT, interleukin-6 [IL-6], interleukin-10 [IL-10] ), and albumin levels; and (4) additional indicators: prolonged antibiotic use (\u0026ge; 7 days), history of glucocorticoid therapy (defined as glucocorticoid administration in the preceding chemotherapy cycle), and time since chemotherapy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous variables are expressed as medians along with their interquartile ranges. To compare groups, the Fisher exact test was conducted for categorical variables. Univariate logistic regression identified risk factors linked to IFI. Variables showing p \u0026lt; 0.05 in the univariate analysis were considered for inclusion in the multivariate logistic regression. In the multivariate model, variables with p \u0026lt; 0.05 contributed to the development of the diagnostic model. The performance of the model was evaluated using receiver operating characteristic curves. The reported metrics included sensitivity, specificity, positive and negative likelihood ratios, as well as positive and negative predictive values. All analyses were carried out using R version 4.2.1, and statistical significance was set at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGeneral information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween 1 November 2019 and 31 January 2025, 585 consecutive pediatric patients with acute leukemia\u0026mdash;acute lymphoblastic leukemia (n = 365) or acute myeloid leukemia (n = 56)\u0026mdash;or lymphoma (n = 164) were screened for eligibility. These children underwent 9 111 hospital admissions, resulting in a hospitalization infection rate of 1.02 % (93 / 9 111). Nine IFI episodes were classified as proven, 45 as probable, and 39 as possible. Metagenomic next‑generation sequencing (mNGS) identified fungal pathogens in 28 children. \u003cem\u003eAspergillus\u003c/em\u003e spp. and \u003cem\u003eCandida\u003c/em\u003e spp. accounted for 12 and 10 cases (32.4 % and 35.7 %), respectively. After exclusion of 7 fungal\u0026ndash;bacterial co‑infections and the 39 possible IFI cases, 50 proven or probable IFI episodes formed the case group. Bacterial infection was documented in 104 episodes, yielding an admission‑based infection rate of 1.14 % (104 / 9 111). After removal of 7 fungal\u0026ndash;bacterial co‑infections, 97 bacteremia episodes constituted the control group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn total, 147 children were enrolled (Fig. 1). Baseline characteristics are presented in Table 1. Bacteremia accounted for 97 episodes (63.9 %), whereas 50 episodes (36.1 %) met criteria for proven (9 / 50) or probable (41 / 50) IFI. Median age at diagnosis was 3.8 years in the IFI group and 6.0 years in the bacteremia group (interquartile ranges 2.2\u0026ndash;6.0 and 2.5\u0026ndash;8.0 years, respectively; P = 0.044).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003cstrong\u003e. Patient demographic\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and baseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"492\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eBacteremia Group (n = 97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003eIFI Group (n = 50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eAge (y), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e6 (2.5, 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e3.83 (2.2, 6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e62 (63.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e29 (58.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e35 (36.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e21 (42.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eClinical diagnoses, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eALL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e55 (56.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e34 (68.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eAML\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e14 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e2 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e28 (28.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e14 (28.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eCNS, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eCNS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e66 (68.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e32 (64.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eCNS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e16 (16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e9 (18.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eCNS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e15 (15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e9 (18.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eRemission status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e59 (60.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e32 (64.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e38 (39.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e18 (36.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ePICC, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e27 (27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e16 (32.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e70 (72.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e34 (68.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIFI, invasive fungal infection; IQR, interquartile range; ALL, acute lymphoblastic leukemia; AML, acute myeloid leukemia; CNS, central nervous system; PICC, peripherally inserted central catheter.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathogens causing IFI or bacteremia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2 summarizes the distribution of pathogens. Among the 50 IFI episodes, 9 were proven infections. \u003cem\u003eCandida\u003c/em\u003e spp. accounted for 6 of these 9 episodes (66.7 %), \u003cem\u003eMucorales\u003c/em\u003e for 2 (22.2 %), and \u003cem\u003eApiotrichum mycotoxinivorans\u003c/em\u003e for 1 (11.1 %). Five proven cases underwent mNGS of blood or cerebrospinal fluid; four yielded concordant fungal findings: \u003cem\u003eMucorales\u003c/em\u003e in 2 cases, \u003cem\u003eCandida krusei\u003c/em\u003e in 1, and \u003cem\u003eA. mycotoxinivorans\u003c/em\u003e in 1. Blood culture from the 97 bacteremic episodes yielded 104 bacterial isolates. Gram‑negative organisms comprised 52 isolates (50.0 %), dominated by \u003cem\u003eEscherichia coli\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(16 isolates, 16.5 %) and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(12 isolates, 12.4 %). Gram‑positive organisms also contributed 52 isolates (50.0 %); the most common were \u003cem\u003eStreptococcus mitis\u003c/em\u003e (11 isolates, 11.3 %), \u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(9 isolates, 9.3 %), and \u003cem\u003eStaphylococcus hominis\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(9 isolates, 9.3 %).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTreatment outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFungal infection contributed to 4 deaths, resulting in a mortality rate of 4.3 % (4 / 93). All fatalities occurred in proven IFI and included 3 due to Candida infection and 1 due to \u003cem\u003eA. mycotoxinivorans\u003c/em\u003e infection. No deaths related to bacteremia were recorded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical and laboratory factors associated with IFI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate analysis (Table 2) identified secondary fever (P = 0.007), prolonged antibiotic use \u0026ge; 7 days (P \u0026lt; 0.001), history of glucocorticoid therapy (P = 0.031), time since chemotherapy (P \u0026lt; 0.001), neutropenia duration before diagnosis (P = 0.047), fever duration before diagnosis (P \u0026lt; 0.001), albumin level (P = 0.038), and C‑reactive protein level (P = 0.019) as variables significantly associated with IFI. Multivariate logistic regression retained prolonged antibiotic use \u0026ge; 7 days (P = 0.005), time since chemotherapy (P = 0.002), and fever duration before diagnosis (P \u0026lt; 0.001) as independent predictors of IFI (Table 2, Fig. 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Univariate and Multiple logistic regression analys\u003c/strong\u003e\u003cstrong\u003ee\u003c/strong\u003e\u003cstrong\u003es of biomarkers used\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eto distinguish IFI from Bacteremia\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"650\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 24.9897%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 18.4706%;\"\u003e\n \u003cp\u003eBacteremia\u0026nbsp;Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.7099%;\"\u003e\n \u003cp\u003eIFI Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6.0534%;\"\u003e\n \u003cp\u003eTotal (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19.2467%;\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19.2467%;\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003eOral candidiasis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e3.032（0.490\u0026ndash;18.769）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e95 (97.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e47 (94.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e2 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e3 (6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003eSecondary fever, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e9.048（1.842\u0026ndash;44.433）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e0.264（0.008\u0026ndash;9.155）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e95 (97.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e42 (84.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e2 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e8 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003eProlonged antibiotic use \u0026ge; 7 days, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e6.96（3.147\u0026ndash;15.392）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e10.879（2.033\u0026ndash;58.218）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e83 (85.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e23 (46.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e14 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e27 (54.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003eHistory of glucocorticoid therapy, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e2.318（1.080\u0026ndash;4.977）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e0.584（0.123\u0026ndash;2.777）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e41 (27.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e12 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e56 (38.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e38 (25.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003eProphylactic antifungal therapy, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e1.563（0.766\u0026ndash;3.191）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e68 (46.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e30 (20.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e29 (19.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e20 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003eChemotherapy-induced neutropenia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e0.739（0.332\u0026ndash;1.647）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e20 (20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e13 (26.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e77 (79.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e37 (74.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003eAge (y), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e6 (2.5, 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e3.83 (2.21, 6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e0.908（0.822\u0026ndash;1.003）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e0.833（0.678\u0026ndash;1.024）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003eHighest recorded body temperature (℃), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e39 (38.6, 39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e39 (38.5, 39.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e0.722（0.486\u0026ndash;1.073）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003eTime since chemotherapy\u0026nbsp;（days）, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e12 (9, 16.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e17 (14.5, 27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e1.137（1.075\u0026ndash;1.203）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e1.193（1.064\u0026ndash;1.336）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003eNeutropenia duration before diagnosis (days), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e3 (1, 6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e5 (0.25, 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e1.053（1.001\u0026ndash;1.108）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e0.916（0.801\u0026ndash;1.047）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003eFever duration before diagnosis\u0026nbsp;(days), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e1 (0, 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e4.5 (2, 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e2.165（1.642\u0026ndash;2.855）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e2.821（1.646\u0026ndash;4.833）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003eAlbumin level (g/L), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e34.9 (32.5, 38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e32.9 (28.9, 38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e0.924（0.858\u0026ndash;0.995）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e0.92（0.803\u0026ndash;1.054）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003eCRP level (mg/L), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e12.3 (3.34, 35.168)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e23.25 (6.98, 62.465)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e1.01（1.002\u0026ndash;1.019）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e1.001（0.985\u0026ndash;1.018）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003ePCT level (ng/mL), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e0.26 (0.137, 0.553)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e0.282 (0.15175, 0.8225)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e0.942（0.829\u0026ndash;1.069）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003eIL-6 level (pg/L), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e168.86 (50.49, 603.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e102.4 (24.445, 271.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e0.999（0.999\u0026ndash;1.000）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9897%;\"\u003e\n \u003cp\u003eIL-10 level (pg/L), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4706%;\"\u003e\n \u003cp\u003e14.715 (6.85, 69.462)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7099%;\"\u003e\n \u003cp\u003e8.76 (3.68, 15.675)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0534%;\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.3638%;\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8829%;\"\u003e\n \u003cp\u003e0.999（0.997\u0026ndash;1.001）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.6743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIFI, invasive fungal infection; OR, odds ratio; CI, confidence interval; IQR, interquartile range; CRP, C-reactive protein; PCT, procalcitonin, IL-6 interleukin-6, IL-10 interleukin-10\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;performance of individual markers and the composite model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReceiver‑operating characteristic analysis (Fig. 4) yielded optimal cut‑off values of 14.5 days for time since chemotherapy and 1.5 days for fever duration before diagnosis. The areas under the curve (AUC) were 0.752 (95 % confidence interval [CI] 0.666\u0026ndash;0.837; P \u0026lt; 0.001) for chemotherapy interval, 0.849 (95 % CI 0.773\u0026ndash;0.925; P \u0026lt; 0.001) for fever duration before diagnosis, and 0.698 (95 % CI 0.620\u0026ndash;0.775; P \u0026lt; 0.001) for prolonged antibiotic use \u0026ge; 7 days. A composite model incorporating all three variables demonstrated superior discrimination (AUC = 0.938, 95 % CI 0.900\u0026ndash;0.975; P \u0026lt; 0.001) (Table 3, Fig. 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Performance characteristics of days of chemotherapy, antibiotics \u0026ge; 7days, fever, time, and their combination in diagnosing the IFI\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"651\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eBiomarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eCut-Off *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003ePPV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eNPV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eTime since chemotherapy (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e0.752(0.666\u0026ndash;0.837)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e74.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e65.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e51.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e84.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eFever duration before diagnosis (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e0.849(0.773\u0026ndash;0.925）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e78.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e87.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e6.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e76.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e88.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eProlonged antibiotic use \u0026ge; 7 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e0.698(0.620\u0026ndash;0.776)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e54.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e85.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e65.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e78.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003emodel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e0.938（0.900\u0026ndash;0.975）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e85.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e87.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e6.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e76.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e92.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIFI, invasive fungal infection; AUC, area under the curve; PLR, positive likelihood ratio; NLR, negative likelihood ratio; PPV, positive predictive value; NPV, negative predictive value; * cutoff values of days of chemotherapy and fever time were chosen based on the Youden index.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTimely differentiation of IFI from other febrile conditions remains clinically challenging, often resulting in delayed empirical antifungal therapy and increased risk of adverse outcomes [13]. The accessibility and diagnostic performance of specific fungal antigens and PCR-based assays remain limited, underscoring the critical need for novel biomarkers capable of distinguishing fungal infections from other infectious processes. This study found that the combination of time since chemotherapy, fever duration before diagnosis, and prolonged antibiotic use \u0026ge; 7 days effectively differentiated IFI from bacteremia in patients with HM. The combination\u0026apos;s sensitivity and specificity were determined to be 85.1% and 87.5%, respectively, using cut-off values of 14.5 days for time since chemotherapy and 1.5 days for fever duration before diagnosis. These findings may support the timely initiation of prophylactic or therapeutic antifungal interventions in cases of suspected bloodstream infection, potentially reducing infection-associated mortality.\u003c/p\u003e\n\u003cp\u003eSeveral studies have established risk factors for IFI or bacteremia in pediatric patients with HM. Risk factors associated with IFI include prolonged and profound neutropenia [9, 14-16], extended hospitalization, fever duration, neutropenia duration, and prolonged use of broad-spectrum antibiotics [8]. Prior research demonstrated that PCT effectively distinguishes bacteremia from non-bacteremia, whereas CRP lacks discriminatory power for bacteremia in febrile neutropenic episodes in patients with HM [17]. Furthermore, recent studies reported that in the initial fever episode, PCT and IL-6 levels\u0026mdash;but not CRP\u0026mdash;differed significantly between patients with sepsis and those without [18]. These results suggest that PCT and IL-6 may serve as useful adjunctive markers for diagnosing bacteremia. However, systematic and comprehensive evaluations of biomarkers for differentiating IFI from bacteremia in pediatric patients with HM remain limited. The three indicators identified in this study\u0026mdash;time since chemotherapy, fever duration before diagnosis, and prolonged antibiotic use \u0026ge; 7 days\u0026mdash;contribute to addressing this gap.\u003c/p\u003e\n\u003cp\u003eThis finding differs from that reported by Stoma et al. [13], who proposed a composite biomarker for IFI in patients with hematological disorders comprising CRP \u0026gt; 120 mg/L combined with either PCT \u0026lt; 1.25 ng/mL or presepsin \u0026lt; 170 pg/mL. In contrast, it aligns with the findings of Liu [2], who showed that elevated CRP levels combined with low PCT levels could effectively differentiate IFI from bacteremia in immunocompromised children. While univariate analysis in Liu\u0026rsquo;s study revealed significantly higher CRP levels in patients with IFI than in those with bacterial bloodstream infections, multivariate analysis determined that neither CRP nor PCT reliably differentiated IFI from bacterial bloodstream infections in children undergoing chemotherapy for HM. The discrepancy across studies may reflect differences in the timing of biomarker measurement. Stoma et al. [13] evaluated biomarkers within the first 48 hours of fever onset, whereas Liu et al. [2] measured CRP and PCT in plasma collected 12\u0026ndash;24 hours after fever onset. These temporal differences, along with variation in patient subgroups and underlying conditions, likely contributed to divergent results.\u003c/p\u003e\n\u003cp\u003eEarly detection of IFI using biomarkers such as GM, BDG, and PCR assays remains a key strategy for improving outcomes. These tests can serve as screening or diagnostic tools in patients with HM and suspected IFI [19, 20]. However, their use in pediatric populations remains controversial [21-24]. A systematic review by Lehrnbecher et al. [20] reported that GM, BDG, and PCR assays exhibit variable sensitivity, specificity, and predictive value in diagnosing IFI in pediatric oncology patients. Furthermore, their diagnostic performance in clinical practice is generally limited. The 2020 guidelines [12] supported the use of GM detection for diagnosing invasive aspergillosis in children but advised caution, as antifungal therapy may lead to false-negative results. BDG detection, due to its nonspecificity, is not recommended for IFI diagnosis. These challenges highlight the need for further investigation into fungal biomarker performance. This study contributes to this effort by exploring early indicators of IFI in pediatric patients, particularly in light of limitations associated with GM, BDG, PCR, and computed tomography-based approaches [1].\u003c/p\u003e\n\u003cp\u003ePrior studies identified \u003cem\u003eCandida\u003c/em\u003e and \u003cem\u003eAspergillus\u003c/em\u003e species as the most common pathogens responsible for IFI in pediatric patients with HM [6]. Some studies reported that \u003cem\u003eAspergillus\u003c/em\u003e predominated in probable and proven IFI [5, 7, 9, 16, 21, 25], whereas others identified \u003cem\u003eCandida\u003c/em\u003e as the leading fungal pathogen in clinical settings [26-28]. In this study, among the nine children diagnosed with IFI, \u003cem\u003eCandida\u003c/em\u003e species were most prevalent, accounting for 66.7% (6/9). Notably, four deaths related to IFI occurred among children with proven IFI, corresponding to a mortality rate of 4.3% (4/93), and this was consistent with earlier findings [29, 30]. Next-generation sequencing (NGS) has recently emerged as a promising diagnostic tool for IFI [31, 32]. While \u003cem\u003eHormographiella\u003c/em\u003e\u003cem\u003e\u0026nbsp;aspergillata\u003c/em\u003e rarely causes IFI in immunocompromised hosts, NGS has demonstrated diagnostic utility for such rare pathogens [33, 34]. Compared to conventional blood, fluid, or tissue cultures, NGS enables pathogen identification 24\u0026ndash;48 hours earlier and significantly improves detection rates. In four proven IFI cases in this study, NGS results were consistent with blood or cerebrospinal fluid cultures and identified \u003cem\u003eMortierella\u003c/em\u003e species (two cases), \u003cem\u003eCandida krusei\u003c/em\u003e (one case), and \u003cem\u003eA.\u003c/em\u003e\u003cem\u003e\u0026nbsp;mycotoxinivorans\u003c/em\u003e (one case) [35]. Overall, mNGS is a powerful and rapid diagnostic modality for IFI, although the current lack of standardization necessitates further validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research has two primary limitations. Firstly, being a retrospective analysis conducted at a single center, it could be influenced by selection bias. The patient population originates from a tertiary pediatric hospital, which may restrict the generalizability of our findings. The severity of the disease and the criteria for initiating prophylactic antifungal therapy may not reflect those of other institutions. Secondly, while our center consistently measured PCT and CRP levels at the onset of fever, these levels were not uniformly assessed 12-24 hours after fever onset, potentially leading to discrepancies with previous research findings. Therefore, future studies should implement a multicenter, prospective design with standardized clinical management and dynamic monitoring of infection-related indicators at multiple time points to improve comparability across different studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, early diagnosis of IFI remains a significant clinical challenge. Prompt detection is essential for appropriate treatment and mortality reduction. Our results suggest that time since chemotherapy, fever duration before diagnosis, and prolonged antibiotic use \u0026ge; 7 days might help distinguish IFI from bacteremia in patients undergoing chemotherapy for HM. These preliminary observations could provide pediatric clinicians with initial clues for early differentiation, but their clinical applicability requires rigorous validation in future, larger-scale studies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC \u0026nbsp; \u0026nbsp;Area under the curve\u003c/p\u003e\n\u003cp\u003eBDG \u0026nbsp; \u0026nbsp;\u0026szlig;-D-glucan\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp;Confidence interval\u003c/p\u003e\n\u003cp\u003eCRP \u0026nbsp; \u0026nbsp;C-reactive protein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGM \u0026nbsp; \u0026nbsp; Galactomannan\u003c/p\u003e\n\u003cp\u003eHM \u0026nbsp; \u0026nbsp; Hematological malignancies\u003c/p\u003e\n\u003cp\u003eIFI \u0026nbsp; \u0026nbsp; \u0026nbsp;Invasive fungal infection\u003c/p\u003e\n\u003cp\u003eIFDs \u0026nbsp; \u0026nbsp;Invasive fungal diseases\u003c/p\u003e\n\u003cp\u003eIL-6 \u0026nbsp; \u0026nbsp; Interleukin-6\u003c/p\u003e\n\u003cp\u003eIL-10 \u0026nbsp; \u0026nbsp;Interleukin-10\u003c/p\u003e\n\u003cp\u003eIQR \u0026nbsp; \u0026nbsp; Interquartile range\u003c/p\u003e\n\u003cp\u003emNGS \u0026nbsp; Metagenomic next‑generation sequencing\u003c/p\u003e\n\u003cp\u003eNLR \u0026nbsp; \u0026nbsp;Negative likelihood ratio\u003c/p\u003e\n\u003cp\u003eNPV \u0026nbsp; \u0026nbsp;Negative predictive value\u003c/p\u003e\n\u003cp\u003eOR \u0026nbsp; \u0026nbsp; \u0026nbsp;Odds ratio\u003c/p\u003e\n\u003cp\u003ePCR \u0026nbsp; \u0026nbsp; Polymerase chain reaction\u003c/p\u003e\n\u003cp\u003ePCT \u0026nbsp; \u0026nbsp; Procalcitonin\u003c/p\u003e\n\u003cp\u003ePLR \u0026nbsp; \u0026nbsp; Positive likelihood ratio\u003c/p\u003e\n\u003cp\u003ePPV \u0026nbsp; \u0026nbsp; Positive predictive value\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the nurses and doctors in the Department of Hematology and Oncology at Children\u0026rsquo;s Hospital Affiliated to Shandong University (Jinan Children\u0026apos;s Hospital) for their dedicated support, that was pivotal to the success of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDLA was responsible for the study design, data acquisition, analysis, and interpretation, and also drafted the manuscript. LF, YXM, and WSF contributed to the study concept and design, critically revised the article for significant intellectual content, and provided final approval for the version to be published. WRY, MX, and WYP also participated in the study concept and design and critically reviewed the manuscript for intellectual content. ZX, WLJ, and WWD contributed to data curation, formal analysis, investigation, and validation. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Technology Development Program of Jinan Municipal Health Commission (2024304012) from FL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data from this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the principles of the Declaration of Helsinki and approved by the Ethics Committee of Children\u0026rsquo;s Hospital Affiliated to Shandong University in China\u0026nbsp;(Approval Number:\u0026nbsp;SDFE-IRB/P-2024034). All patients included in this study are under 16 years of age. Due to the retrospective nature of this study, the consent from legal guardians of all patients was waived by the above ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interest to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGroll AH, Pana D, Lanternier F, Mesini A, Ammann RA, Averbuch D, et al. 8th European Conference on Infections in Leukaemia: 2020 guidelines for the diagnosis, prevention, and treatment of invasive fungal diseases in paediatric patients with cancer or post-haematopoietic cell transplantation. Lancet Oncol. 2021;22:e254-e69. doi:10.1016/s1470-2045(20)30723-3.\u003c/li\u003e\n\u003cli\u003eLiu Y, Zhang X, Yue T, Tang Y, Ke Z, Li Y, et al. Combination of C-Reactive Protein and Procalcitonin in Distinguishing Fungal from Bacterial Infections Early in Immunocompromised Children. Antibiotics (Basel). 2022;11. doi:10.3390/antibiotics11060730.\u003c/li\u003e\n\u003cli\u003eFerreras-Antolin L, Borman A, Diederichs A, Warris A, Lehrnbecher T. Serum Beta-D-Glucan in the Diagnosis of Invasive Fungal Disease in Neonates, Children and Adolescents: A Critical Analysis of Current Data. J Fungi (Basel). 2022;8. doi:10.3390/jof8121262.\u003c/li\u003e\n\u003cli\u003eGiannella M, Lanternier F, Delli\u0026egrave;re S, Groll AH, Mueller NJ, Alastruey-Izquierdo A, et al. Invasive fungal disease in the immunocompromised host: changing epidemiology, new antifungal therapies, and management challenges. Clin Microbiol Infect. 2025;31:29-36. doi:10.1016/j.cmi.2024.08.006.\u003c/li\u003e\n\u003cli\u003eLehrnbecher T, Groll AH, Cesaro S, Alten J, Attarbaschi A, Barbaric D, et al. Invasive fungal diseases impact on outcome of childhood ALL - an analysis of the international trial AIEOP-BFM ALL 2009. Leukemia. 2023;37:72-78. doi:10.1038/s41375-022-01768-x.\u003c/li\u003e\n\u003cli\u003eStemler J, Mellinghoff SC, Khodamoradi Y, Sprute R, Classen AY, Zapke SE, et al. Primary prophylaxis of invasive fungal diseases in patients with haematological malignancies: 2022 update of the recommendations of the Infectious Diseases Working Party (AGIHO) of the German Society for Haematology and Medical Oncology (DGHO). J Antimicrob Chemother. 2023;78:1813-26. doi:10.1093/jac/dkad143.\u003c/li\u003e\n\u003cli\u003eCalle-Miguel L, Garrido-Colino C, Santiago-Garc\u0026iacute;a B, Moreno Santos MP, Gonzalo Pascual H, Ponce Salas B, et al. 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Invasive fungal disease and antifungal prophylaxis in children with acute leukaemia: a multicentre retrospective Australian cohort study. Lancet Reg Health West Pac. 2024;52:101201. doi:10.1016/j.lanwpc.2024.101201.\u003c/li\u003e\n\u003cli\u003eYang M, Choi SJ, Lee J, Lee DG, Kim YJ, Park YJ, et al. Serum procalcitonin as an independent diagnostic markers of bacteremia in febrile patients with hematologic malignancies. PLoS One. 2019;14:e0225765. doi:10.1371/journal.pone.0225765.\u003c/li\u003e\n\u003cli\u003eCarc\u0026ograve; D, Castorina P, Guardo P, Iachelli V, Pace T, Scir\u0026egrave; P, et al. Combination of Interleukin-6, C-Reactive Protein and Procalcitonin Values as Predictive Index of Sepsis in Course of Fever Episode in Adult Haematological Patients: Observational and Statistical Study. J Clin Med. 2022;11. doi:10.3390/jcm11226800.\u003c/li\u003e\n\u003cli\u003eLamoth F, Nucci M, Fernandez-Cruz A, Azoulay E, Lanternier F, Bremerich J, et al. 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Prospective Evaluation of Galactomannan and (1\u0026rarr;3) \u0026beta;-d-Glucan Assays as Diagnostic Tools for Invasive Fungal Disease in Children, Adolescents, and Young Adults With Acute Myeloid Leukemia Receiving Fungal Prophylaxis. J Pediatric Infect Dis Soc. 2021;10:864-71. doi:10.1093/jpids/piab036.\u003c/li\u003e\n\u003cli\u003eHsu AJ, Tamma PD, Zhang SX. Challenges with Utilizing the 1,3-Beta-d-Glucan and Galactomannan Assays To Diagnose Invasive Mold Infections in Immunocompromised Children. J Clin Microbiol. 2021;59:e0327620. doi:10.1128/jcm.03276-20.\u003c/li\u003e\n\u003cli\u003eSingh S, Singh M, Verma N, Sharma M, Pradhan P, Chauhan A, et al. Comparative accuracy of 1,3 beta-D glucan and galactomannan for diagnosis of invasive fungal infections in pediatric patients: a systematic review with meta-analysis. Med Mycol. 2021;59:139-48. doi:10.1093/mmy/myaa038.\u003c/li\u003e\n\u003cli\u003eWang SS, Kotecha RS, Bernard A, Blyth CC, McMullan BJ, Cann MP, et al. 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Mycopathologia. 2024;189:71. doi:10.1007/s11046-024-00875-w.\u003c/li\u003e\n\u003cli\u003eZhang X, Zhang L, Li Y, Wang N, Zhang Y. Clinical performance of metagenomic next-generation sequencing for diagnosis of invasive fungal disease after hematopoietic cell transplant. Front Cell Infect Microbiol. 2024;14:1210857. doi:10.3389/fcimb.2024.1210857.\u003c/li\u003e\n\u003cli\u003eWang Q, Song Y, Han D, Cai H, Yan Q, Liu W, et al. The first suspected disseminated Hormographiella aspergillata infection in China, diagnosed using metagenomic next-generation sequencing: a case report and literature review. Emerg Microbes Infect. 2023;12:2220581. doi:10.1080/22221751.2023.2220581.\u003c/li\u003e\n\u003cli\u003eWesdorp E, Rotte L, Chen LT, Jager M, Besselink N, Vermeulen C, et al. NGS-based Aspergillus detection in plasma and lung lavage of children with invasive pulmonary aspergillosis. NPJ Genom Med. 2025;10:24. doi:10.1038/s41525-025-00482-8.\u003c/li\u003e\n\u003cli\u003eLi X, Wang D, Hao M, Li Z, Zhang C, Feng S, et al. The first report of Apiotrichum mycotoxinivorans isolation from human cerebrospinal fluid. Eur J Clin Microbiol Infect Dis. 2024;43:597-604. doi:10.1007/s10096-023-04736-0.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Invasive fungal infections, bacteremia, hematological malignancies, children","lastPublishedDoi":"10.21203/rs.3.rs-7070657/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7070657/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Traditional diagnostic approaches may delay the identification of invasive fungal infections (IFI) in children with hematological malignancies (HM) due to inherent limitations, thereby posing life-threatening risks. We evaluated the effectiveness of clinical indicators in differentiating IFI from bacteremia in this pediatric population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A case group of 50 patients with acute leukemia or lymphoma who developed probable or proven IFI following chemotherapy and had negative bacterial cultures was selected. A control group of 97 patients who developed bacteremia without IFI post-chemotherapy was also included.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among 585 pediatric patients who were hospitalized 9,111 times, the IFI hospitalization rate of 1.02% (93/9,111). By contrast, 104 cases of bacterial infections were reported, with a hospitalization rate of 1.14% (104/9,111). Secondary fever, prolonged antibiotic use ≥ 7 days, history of glucocorticoid therapy, time since chemotherapy, neutropenia duration before diagnosis, fever duration before diagnosis, albumin level, and C-reactive protein (CRP) levels were significantly associated with IFI. Notably, prolonged antibiotic use ≥ 7 days (odds ratio [OR] = 10.879, 95% confidence interval [CI]: 2.033–58.218), time since chemotherapy (OR = 1.193, 95% CI: 1.064–1.336), and fever duration before diagnosis (OR = 2.821, 95% CI: 1.646–4.833) were identified as independent predictors of IFI. A predictive model incorporating these three factors demonstrated improved diagnostic performance, yielding an area under the curve of 0.938 (95% CI: 0.900–0.975), with a sensitivity of 85.1% and specificity of 87.5%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The combination of prolonged antibiotic use ≥ 7 days, time since chemotherapy, and fever duration before diagnosis might help distinguish IFI from bacteremia in pediatric patients with HM.\u003c/p\u003e","manuscriptTitle":"Early differentiation of fungal from bacterial infections in children with hematologic malignancy: a single-center case-control study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-05 12:23:07","doi":"10.21203/rs.3.rs-7070657/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-13T06:21:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-20T02:11:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108307681803274560441888881605726733465","date":"2025-11-12T23:54:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-11T08:14:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309636031818968320587082564468583253366","date":"2025-11-01T14:31:06+00:00","index":"hide","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-07T08:06:42+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-30T09:53:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-24T14:46:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-24T14:39:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2025-07-21T08:17:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4750349b-0d47-4a36-bd17-21fabb9fe4fc","owner":[],"postedDate":"August 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T10:53:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-05 12:23:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7070657","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7070657","identity":"rs-7070657","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}