Predictive Models for Clinical Outcomes in Total Knee Arthroplasty: A Systematic Analysis

Area of Science:

• Orthopedic surgery
• Clinical outcomes research
• Predictive analytics

Background:

• Predictive modeling can enhance understanding of factors influencing patient satisfaction post-total knee arthroplasty (TKA).
• Systematic review of literature focused on predictive models for clinical outcomes in TKA.
• Identifying successful predictor strategies for TKA outcomes.

Purpose of the Study:

• To systematically review literature on predictive models for clinical outcomes after TKA.
• To identify predictor strategies with high success rates in predicting TKA outcomes.
• To inform decision-making regarding TKA indications and surgical strategies.

Main Methods:

• Systematic review adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol.
• Searched MEDLINE, EMBASE, and PubMed for studies from 1996-2020 on predictive models or features for TKA outcomes.
• Evaluated study quality and risk of bias using the ROBINS-I tool.

Main Results:

• 48 out of 75 identified studies met inclusion criteria.
• Preoperative knee pain, knee-specific Patient-Reported Outcome Measure (PROM) scores, and mental health scores strongly predicted postoperative outcomes.
• Improvement in PROM scores, pain reduction, and patient satisfaction were the most correlated outcome measures with predictive models.

Conclusions:

• Algorithms based on PROM improvement, patient satisfaction, and pain can guide TKA decision-making.
• Preoperative functional factors like pain and PROM scores are highly predictive of TKA clinical outcomes.
• Demographic data and knee alignment showed weaker correlations with TKA outcomes.