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Predictive models for identifying risk of readmission after index hospitalization for hip arthroplasty: A systematic
Satish M Mahajan1, Amey Mahajan2, Chantal Nguyen3
1Veterans Affairs Palo Alto Health Care System, Building 100, Office C3-125, 3801 Miranda Ave, Palo Alto, CA, 94304, USA.
Insights
Predicting 30-day readmissions after Total Hip Arthroplasty (THA) requires validated models. Key risk factors include bleeding disorders, higher ASA status, discharge disposition, and functional status for improved patient outcomes.
Area of Science:
- Orthopedic Surgery
- Healthcare Management
- Biostatistics
Background:
- Aging population increases demand for Total Hip Arthroplasty (THA).
- Healthcare policies incentivize reduced readmissions for THA patients.
- Predictive models for 30-day THA readmissions are actively researched.
Purpose of the Study:
- Assess validated statistical models for predicting 30-day THA readmissions.
- Identify evidence-based risk factors consistently associated with THA readmissions.
Main Methods:
- Systematic literature search of five electronic databases.
- Application of PRISMA and TRIPOD criteria for study assessment.
- Analysis of multivariate models correlating risk factors with THA readmissions.
Main Results:
- 26 studies identified, with two offering validated predictive models.
- Significant risk factors include bleeding disorder, higher ASA status, discharge disposition, and functional status.
- These factors showed broad and significant support across multiple studies.
Conclusions:
- Current reporting of predictive models for THA readmissions needs improvement.
- Further research should focus on model calibration, external validation, and EHR integration.
- Identified risk factors are crucial for clinical examination and future predictive modeling.
Background:
An aging United States population profoundly impacts healthcare from both a medical and financial standpoint, especially with an increase in related procedures such as Total Hip Arthroplasty (THA). The Hospital Readmission Reduction Program and Comprehensive Care for Joint Replacement Program incentivize hospitals to decrease post-operative readmissions by correlating reimbursements with smoother care transitions, thereby decreasing hospital burden and improving quantifiable patient outcomes. Many studies have proposed predictive models built upon risk factors for predicting 30-day THA readmissions.
Questions:
(1) Are there validated statistical models that predict 30-day readmissions for THA patients when appraised with a standards-based, reliable assessment tool?. (2) Which evidence-based factors are significant and have support across models for predicting risk of 30-day readmissions post-THA?
Methods:
Five major electronic databases were searched to identify studies that examined correlations between post-THA readmission and risk factors using multivariate models. We rigorously applied the PRISMA methodology and TRIPOD criteria for assessment of the prognostic studies.
Results:
We found 26 studies that offered predictive models, of which two presented models tested with validation cohorts. In addition to the many factors grouped into demographic, administrative, and clinical categories, bleeding disorder, higher ASA status, discharge disposition, and functional status appeared to have broad and significant support across the studies.
Conclusions:
Reporting of recent predictive models establishing risk factors for 30-day THA readmissions against the current standard could be improved. Aside from building better performing models, more work is needed to follow the thorough process of undergoing calibration, external validation, and integration with existing EHR systems for pursuing their use in clinical settings. 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.
