Predicting 90-day return to the emergency department in orthopaedic trauma patients in the Southeastern USA: a machine-learning approach.

Abstract

Introduction: Return-to-acute-care metrics, such as early emergency department (ED) visits, are key indicators of healthcare quality, with ED returns following surgery often considered avoidable and costly events. Proactively identifying patients at high risk of ED return can support quality improvement efforts, allowing interventions to target vulnerable patients. With its predictive capabilities, machine learning (ML) has shown potential in forecasting various clinical outcomes but remains underutilised in orthopaedic trauma. This study uses a random forest model to predict 90-day ED return in orthopaedic trauma patients, aiming to identify high-risk individuals and elucidate risk factors associated with returns. This study hypothesised that a highly accurate model could be developed to predict patients at high risk of ED return within 90 days of surgery.

Purpose: To develop and validate an ML model that predicts 90-day ED returns after orthopaedic trauma surgery using input data readily available in the electronic health record.

Methods: This is a retrospective model development and validation study. The study used data from a registry that includes information on all orthopaedic surgeries conducted at a level 1 academic medical centre. Patients who underwent orthopaedic trauma between 1 January 2017 and 1 March 2023 were identified using common procedural terminology code. The model used demographic, comorbid and perioperative variables. Return to the ED was captured as a binary outcome. Model performance was evaluated using the area under the receiver operator curve (AUROC).

Results: A total of 12 069 patients met the inclusion criteria. Patients were predominantly female (53%) and white (70%), with a median age of 55. The 90-day ED return rate was 14% (table 1). The random forest model identified body mass index, distance from the patient's residence to the hospital, age, length of hospital stay and complexity of procedure (work relative value unit) as significant predictors of ED return, each accounting for greater than 10% of the total importance across all features in the model (table 2). Further, the model displayed strong discrimination of patients returning to the ED (AUROC=0.74) (figure 1).

Conclusions: The random forest model demonstrated predictive discrimination of 90-day ED returns. Critical predictors such as patient distance from the hospital suggest considering geographical and socioeconomic factors in postdischarge care planning. Operational factors such as length of stay or complexity of the procedure also predicted return to the ED. The study lays the groundwork for future predictive models in clinical decision-making and healthcare resource utilisation.

Level of evidence: Level III, retrospective model development and validation study.