Department of Infectious Diseases, Hospital Infantil de México Federico Gómez, Mexico City, Mexico.
Lucy Family Institute for Data and Society, University of Notre Dame, Notre Dame, Indiana, USA.
J Pediatric Infect Dis Soc. 2022 Dec 5;11(11):498-503. doi: 10.1093/jpids/piac080.
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.
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.
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.
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.
发热性中性粒细胞减少症(FN)是化疗后肿瘤患者感染的早期指标。我们旨在确定经历 FN 的儿科癌症患者发生感染性休克和/或菌血症的临床预测因素,并建立一种能够将患者分类为低风险的模型这些结果。
这是一项回顾性分析,纳入了 2015 年 7 月至 2017 年 9 月期间因 FN 住院的儿科肿瘤患者队列的临床数据。每位患者随机选择一次 FN 发作。统计分析包括分布分析、假设检验和多变量逻辑回归,以确定与结局相关的临床特征。
共有 429 例患者发生了 865 次 FN 发作。在分析的 404 个抽样发作中,有 20.8%出现了感染性休克和/或菌血症的结局。菌血症中革兰氏阴性菌的比例为 70%。具有统计学意义的预测这些结局的特征是血液恶性肿瘤(P <.001)、癌症复发(P =.011)、血小板计数(P =.004)和年龄(P =.023)。多元逻辑回归模型的 AUROC 为 0.66(95%CI 0.56-0.76)。最佳分类阈值的灵敏度为 0.96,特异性为 0.33,PPV 为 0.40,NPV 为 0.95。
该模型基于简单的临床变量,可以用于识别感染性休克和/或菌血症风险低的患者。该模型的 NPV 为 95%,满足优先避免因不良感染结局而将患者出院的要求。该模型需要在前瞻性人群中进一步验证。