Development of a Predictive Model for Emergency Department Utilization and Unanticipated Hospital Admission in Patients Receiving Cancer Treatment for Solid Tumor Malignancies.

Purpose: Unanticipated health care resource utilization, in the form of either emergency department utilization (EDU) or hospital admission (HA), may be an indicator of lower-quality cancer care. The objective of this study was to develop a predictive model for EDU and HAs within 14 days of receipt of systemic therapy for patients with solid tumors.

Methods: We abstracted electronic health data on oncology encounters from all patients receiving systemic therapy for solid tumors from March 1, 2015, to August 21, 2020, in the Duke University Health System. We defined a primary composite outcome of an EDU or HA within 14 days after the encounter and then developed a predictive model for the primary outcome using least absolute shrinkage and selection operator regression. To evaluate the model, we calculated the area under the receiver operator curve and the calibration slope.

Results: Twelve thousand eight hundred ninety unique patients with 134,641 oncology encounters were included. Five thousand one hundred fifty of these patients (40.0%) had at least one EDU or HA within 14 days of at least one treatment. Forty-six variables were incorporated into the final model. The top predictors, in order of absolute value of the predictive coefficients, were temperature, systolic blood pressure, cancer group, and marital status. The model's AUC was 0.73 (95% CI, 0.722 to 0.732), indicating good sensitivity and specificity to outcome.

Conclusion: The model developed in this study demonstrated good sensitivity in identifying patients with solid tumors who are at highest risk for EDU or HA and could be implemented in clinical practice to allow for preventive outpatient interventions.