Which 4 Elements Can Predict Hip Patient Readmission Risk?

Total hip arthroplasties have become a common procedure as the American population continues to age.

The Hospital Readmission Reduction Program and Comprehensive Care for Joint Replacement Program incentivizes hospitals to decrease hospital burden and improve patient outcomes by decreasing post-operative readmissions.

But what are the risks for readmission? Researchers have developed predictive models to better understand what factors lead to 30-day readmission. But how accurate are they? And do they point to specific risk factors for hip patient readmission?

One study, “Predictive models for identifying risk of readmission after index hospitalization for hip arthroplasty: A systematic review,” reports that better performing models are needed.

The study which was published online in the November-December 2020 issue of the Journal of Orthopaedics asked two questions:

• Are there validated statistical models that predict 30-day readmission for total hip arthroplasty patients when appraised with a standards-based, reliable assessment tool?
• Which evidence-based factors are significant and have support across models for predicting risk of 30-day readmissions post total hip arthroplasty?

To find answers, the authors of the study identified 26 studies that offered predictive models, 2 of which were tested with validation cohorts. PRISMA methodology and TRIPOD criteria were both used to assess the studies.

The researchers also found that four specific items, bleeding disorder, higher American Society of Anesthesiologists physical status, discharge disposition, and functional status, appeared to be predictive of high risk of readmission.

“Reporting of recent predictive models establishing risk factors for 30-day total hip arthroplasty readmissions against the current standard could be improved,” the authors wrote.

“Aside from building better performing models, more work is needed to follow the thorough process of undergoing calibration, external validation, and integration with existing electronic health record systems for pursuing their use in clinical settings,” they added.

“There are several risk factors that are significant in multiple models; these factors should be closely examined clinically and leveraged in future risk modeling efforts.”