Improving Orthopedic Patient Outcomes: A Model to Predict 30-Day and 90-Day Readmission Rates Following Total Joint...

Area of Science:

• Orthopedic Surgery
• Health Services Research
• Predictive Analytics

Background:

• Rising rates of total joint arthroplasty (TJA) necessitate cost-effective, high-quality procedures.
• Increased demand for quality TJA due to rising costs and evolving reimbursement models.
• Need for predictive tools to manage post-discharge readmissions for TJA patients.

Purpose of the Study:

• To develop predictive models for estimating 30-day and 90-day readmission rates in TJA patients.
• Identify key patient risk factors associated with readmission after TJA.
• Inform strategies for reducing readmission rates and improving patient outcomes.

Main Methods:

• Retrospective review of 5732 patients undergoing TJA over 56 months.
• Utilized binary logistic regression to build predictive models.
• Analyzed patient demographics, medical history, and discharge information.

Main Results:

• Identified 237 (30-day) and 547 (90-day) readmissions.
• Factors associated with 30-day readmissions: age, BMI, gender, discharge disposition, cardiac issues, psychiatric diagnoses, medication count.
• Factors for 90-day readmissions: similar to 30-day, with additions of IV drug abuse, narcotic use, and prior TJA.

Conclusions:

• Patient-specific risk factors can predict readmission likelihood after TJA.
• Models can guide interventions to mitigate readmission risks.
• Understanding these factors is crucial for optimizing post-operative care and reducing healthcare costs.