In-hospital Mortality Prognostication for Cancer Patients with Febrile Neutropenia: A Single Center Observational Study

Febrile neutropenia (FN) in cancer patients undergoing chemotherapy can result in life-threatening outcomes. Hence, an evaluation of associated risk factors can enable clinical surveillance as well as inform prophylactic measures. In this retrospective cohort study, we report a mortality prognostication model for chemotherapy-treated cancer patients upon a neutropenic episode.Clinical and diagnostic data of 137 febrile neutropenia patients (>18 years) was collected from a cancer hospital, with the primary endpoint of post-hospital admission mortality within 30 days. The data was integratively analyzed and machine learning techniques were applied to develop the predictive model which was then internally cross validated. Towards enabling personalized risk assessment, a nomogram was constructed and validated. Chemotherapy-treated cancer patients undergoing a neutropenic episode exhibited an overall mortality rate of 17.36%. Multivariate logistic analysis elucidated that shock, pneumonia, carboplatin, doxorubicin, antifungal and antiviral prophylaxis, and hemoglobin correctly classified cases with an overall accuracy of 92% and discriminated mortality with a specificity of 76%. Antiviral (odds ratio (OR): 0.669, p = 0.689), and antifungal prophylaxis (OR: 0.619, p = 0.5) demonstrated a protective effect. The receiver operating characteristic (ROC) curve of the nomogram exhibited an area under the curve of 0.878 (95% CI 0.778 - 0.977), Hosmer–Lemeshow test p-value = 0.635, and a high net benefit in the clinical decision curve. The proposed model offers insights into the role of clinical predictors as well as treatment characteristics that can ameliorate mortality risk in cancer patients with FN. The study highlights bacteremia-related surveillance, along with thrombocytopenia, linked to carboplatin, for reducing individualized mortality risk along with improved monitoring and informed treatment strategies.