Abstract:
Purpose: To identify risk factors for PICC-related bloodstream infections (PICC-CRBSI) in patients with hematologic malignancies and to develop and validate a predictive nomogram for clinical risk assessment.
Methods: This retrospective cohort study included 764 patients with hematologic malignancy who had PICC from a tertiary center (2021-2024). LASSO regression identified key predictors after addressing class imbalance via SMOTE-NC in the training set (n = 534). A multivariate logistic regression was used to construct a predictive nomogram, subsequently validated in a validation set (n = 230). Model performance was assessed by area under the receiver operating characteristic curve (AUC), calibration curves with bootstrap resampling, and decision curve analysis (DCA).
Results: In our cohort, the occurrence of PICC-CRBSI infections was 6.02% (46/764) and Gram-negative bacteria was the major causative pathogen. Multivariate regression analysis showed that History of diabetes, Age, Time of PICC placement, PICC insertion attempts, Catheterized Diameter, Catheterized vein, ANC, ALC and D-dimer were independent risk factors associated with PICC-CRBSI in hematologic malignancy patients. Our nomogram model demonstrated a good calibration and discrimination in both training and validation sets, with AUC values of 0.883 and 0.822, The DCA suggested potential clinical utility.
Conclusion: This validated nomogram integrates patient, catheter, and biomarker-specific factors to individualize PICC-CRBSI risk stratification in hematologic malignancies, potentially guiding targeted prevention strategies.
Reference:Guo Q, Liu Y, Zhu X, Gao Y, Zhou Y, Zhang X. Development and validation of a nomogram for predicting PICC catheter-related bloodstream infection among patients with hematologic malignancies. BMC Infect Dis. 2025 Oct 21;25(1):1370. doi: 10.1186/s12879-025-11815-4. PMID: 41120992.