Inflammatory markers predict bloodstream infections risk in hematological malignancy patients with chemotherapy induced neutropenia

Chemotherapy-induced neutropenia (CIN) and febrile neutropenia (FN) significantly increase the risk of invasive infections, which can progress to septic shock or sepsis and may ultimately result in death. our purpose of this study is to construct a predictive model based on peripheral blood inflammatory markers to enable timely detection of bloodstream infections (BSI) and evaluation of infection severity in this population. A total of 112 patients with chemotherapy-induced neutropenia (CIN) who developed BSI and 145 patients without BSI (NBSI) were included. Inflammatory marker levels were compared across subgroups. The Logistic regression was performed to identify risk factors for BSI within the training cohort. Model performance was assessed by receiver operating characteristic curves (ROC), decision curve analysis (DCA) and diagnostic calibration curves. Patients with BSI had longer disease duration, a higher proportion of acute lymphoblastic leukemia, and significantly lower levels of complete blood count and inflammatory markers compared to those without BSI. Univariate analysis identified prolonged disease duration, hematologic diagnosis category, and reduced levels of NLR, MLR, PLR, SIRI, and SII as risk factors for BSI. A nomogram model was constructed incorporating disease duration, hematologic diagnosis category, MLR, PLR, and SII. ROC and DCA indicated a broad threshold range for clinical applicability and a high net benefit of predictive model, supporting its clinical utility. Diagnostic calibration curves showed good agreement between predicted and observed outcomes, with a Hosmer-Lemeshow test P-value of 0.3289. A nomogram model incorporating disease duration, hematologic diagnosis category, MLR, PLR, and SII was constructed and shown to provide a practical and effective tool for the assessment and early identification of BSI in patients with CIN.