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. 2022 Dec 5;11(11):498-503.
doi: 10.1093/jpids/piac080.

Predictors of Septic Shock or Bacteremia in Children Experiencing Febrile Neutropenia Post-Chemotherapy

Martha Avilés-Robles  1 , Jennifer J Schnur  2   3 , Elisa Dorantes-Acosta  4 , Horacio Márquez-González  5 , Luis A Ocampo-Ramírez  1 , Nitesh V Chawla  2   3
Affiliations

Predictors of Septic Shock or Bacteremia in Children Experiencing Febrile Neutropenia Post-Chemotherapy

Martha Avilés-Robles et al. J Pediatric Infect Dis Soc. .

Abstract

Background: Febrile neutropenia (FN) is an early indicator of infection in oncology patients post-chemotherapy. We aimed to determine clinical predictors of septic shock and/or bacteremia in pediatric cancer patients experiencing FN and to create a model that classifies patients as low-risk for these outcomes.

Methods: This is a retrospective analysis with clinical data of a cohort of pediatric oncology patients admitted during July 2015 to September 2017 with FN. One FN episode per patient was randomly selected. Statistical analyses include distribution analysis, hypothesis testing, and multivariate logistic regression to determine clinical feature association with outcomes.

Results: A total of 865 episodes of FN occurred in 429 subjects. In the 404 sampled episodes that were analyzed, 20.8% experienced outcomes of septic shock and/or bacteremia. Gram-negative bacteria count for 70% of bacteremias. Features with statistically significant influence in predicting these outcomes were hematological malignancy (P < .001), cancer relapse (P = .011), platelet count (P = .004), and age (P = .023). The multivariate logistic regression model achieves AUROC = 0.66 (95% CI 0.56-0.76). The optimal classification threshold achieves sensitivity = 0.96, specificity = 0.33, PPV = 0.40, and NPV = 0.95.

Conclusions: This model, based on simple clinical variables, can be used to identify patients at low-risk of septic shock and/or bacteremia. The model's NPV of 95% satisfies the priority to avoid discharging patients at high-risk for adverse infection outcomes. The model will require further validation on a prospective population.

Keywords: cancer; children; clinical decision rule; febrile neutropenia; risk prediction.

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Figures

Figure 1.
Figure 1.
Receiver operating characteristic (ROC) curve for occurrence of HOI on the testing set (n = 81 patient episodes). AUC = 0.66 (95% CI 0.56–0.76). Point A corresponds to the optimal classification threshold (model probability output < 0.076) per Youden’s J Index, where the model achieves sensitivity = 0.96, specificity = 0.33, PPV = 0.40, and NPV = 0.95.
See this image and copyright information in PMC

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