Development of a Novel Prospective Model to Predict Unplanned 90-Day Readmissions After Total Hip Arthroplasty

For hospitals participating in bundled payment programs, unplanned readmissions after surgery are often termed "bundle busters." The aim of this study was to develop the framework for a prospective model to predict 90-day unplanned readmissions after elective primary total hip arthroplasty (THA) at a macroscopic hospital-based level.

A national, all-payer, inpatient claims and cost accounting database was used. A mixed-effect logistic regression model measuring the association of unplanned 90-day readmissions with a number of patient-level and hospital-level characteristics was constructed.

Using 427,809 unique inpatient THA encounters, 77 significant risk factors across 5 domains (ie, comorbidities, demographics, surgical history, active medications, and intraoperative factors) were identified. The highest frequency domain was comorbidities (64/100) with malignancies (odds ratio [OR] 2.26), disorders of the respiratory system (OR 1.75), epilepsy (OR 1.5), and psychotic disorders (OR 1.5), being the most predictive. Other notable risk factors identified by the model were the use of opioid analgesics (OR 7.3), Medicaid coverage (OR 1.8), antidepressants (OR 1.6), and blood-related medications (OR 1.6). The model produced an area under the curve of 0.715.

We developed a novel model to predict unplanned 90-day readmissions after elective primary THA. Fifteen percent of the risk factors are potentially modifiable such as use of tranexamic acid, spinal anesthesia, and opioid medications. Given the complexity of the factors involved, hospital systems with vested interest should consider incorporating some of the findings from this study in the form of electronic medical records predictive analytics tools to offer clinicians with real-time actionable data.