Predictors of inpatient admission for pediatric cancer patients visiting the emergency department.
e22019 Background: Pediatric cancer patients represent a vulnerable cohort at risk of adverse outcomes after presenting to the emergency department (ED). Given the severity of cancer-related complications and uniqueness of this population, approaches to better risk stratify this cohort could potentially help define future interventions geared towards improving outcomes. We used a high-dimensional machine learning approach to help define the risk of hospitalization after an ED visit among pediatric patients with cancer. Methods: A cohort of cancer patients under 18 was identified from the Nationwide Emergency Department Sample (NEDS) between 2016-2018. We used a lasso regression model to predict inpatient admission after an ED visit. Model covariates included patient demographics, hospital characteristics, and International Classification of Diseases, version 10 (ICD-10) diagnosis codes. The data were split 75%/25% into training/testing data. The model was constructed with training data, and performance assessed on the test data using the area under the curve (AUC), with an AUC of 1.0 indicating perfect prediction. Results: We identified 129,631 pediatric cancer patients who visited the ED, of which 54.5% were subsequently admitted. The final predictive model included 150 variables, including 9 demographic, 6 hospital, and 135 unique ICD-10 codes. The model demonstrated excellent ability to predict hospitalization with an AUC of 0.96. The top 5 most important variables associated with inpatient admission were diagnoses of paralytic ileus/intestinal obstruction, neutropenia, sepsis, aplastic anemia/bone marrow failure, and bacterial infection. Conclusions: Pediatric cancer patients frequently present to the ED with complications of their cancer or their treatment, and over half of these patients are admitted. This study demonstrates the capacity of high-dimensional prediction models to help identify pediatric patients at risk of hospitalization. Additional research is needed to determine how to implement these predictive models in clinical practice.
Lee, TC; Qiao, EM; Qian, AS; Nalawade, V; Voora, RS; Kotha, NV; Dameff, C; Coyne, CJ; Murphy, JD
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