Predictive Models of Fever, ICU Transfer, and Mortality in Hospitalized Patients With Neutropenia

Abstract

Objectives: Neutropenia is a common side effect of myelosuppressive chemotherapy and is associated with adverse outcomes. Early Warning Scores are used to identify at-risk patients and facilitate rapid clinical interventions. Since few Early Warning Scores have been validated in patients with neutropenia, we aimed to create predictive models and nomograms of fever, ICU transfer, and mortality in hospitalized neutropenic patients. Design: Development of statistical prediction models and nomograms using data from a retrospective cohort study of hospitalized patients with neutropenia. Setting: University of Virginia Medical Center, a tertiary-care academic medical center in Charlottesville, VA. Patients: The derivation and validation cohorts included hospitalized adult patients with neutropenia who were admitted to the inpatient wards between October 2010 and January 2015, and April 2017 and April 2020, respectively. We defined neutropenia as an absolute neutrophil count of less than 500 cells/mm3. Interventions: None. Measurements and Main Results: The derivation cohort included 1,531 hospital admissions in patients with neutropenia. Fever, ICU transfer, and in-hospital mortality occurred in 955 admissions (62%), 297 admissions (19%), and 147 admissions (10%), respectively. In the derivation cohort, the internally validated area under the curves with 95% CI for the fever, ICU transfer, and mortality models were HYPERLINK "callto:0.74%20(0.67-0.84),%200.77"0.74 (0.67-0.84), 0.77 (0.67-0.86), and HYPERLINK "callto:0.95%20(0.0.87-1.0"0.95 (0.0.87-1.0), respectively. The validation cohort included 1,250 admissions in patients with neutropenia. In the validation cohort, the area under the curve (95% CI) for the fever, ICU transfer, and mortality models were HYPERLINK "callto:0.70%20(0.67-0.73),%200.78"0.70 (0.67-0.73), 0.78 (0.72-0.84), and HYPERLINK "callto:0.91%20(0.88-0.94"0.91 (0.88-0.94), respectively. Using these models, we developed clinically applicable nomograms which detected adverse events a median of 4.0-11.4 hours prior to onset. Conclusions: We created predictive models and nomograms for fever, ICU transfer, and mortality in patients with neutropenia. These models could be prospectively validated to detect high-risk patients and facilitate early clinical intervention to improve patient outcomes.