Dataset Information

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

ABSTRACT:

Background

Predictive modeling promises to improve our understanding of what variables influence patient satisfaction after total knee arthroplasty (TKA). The purpose of this article was to systematically review the relevant literature using predictive models of clinical outcomes after TKA. The aim was to identify the predictor strategies used for systematic data collection with the highest likelihood of success in predicting clinical outcomes.

Methods

A Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol systematic review was conducted using 3 databases (MEDLINE, EMBASE, and PubMed) to identify all clinical studies that had used predictive models or that assessed predictive features for outcomes after TKA between 1996 and 2020. The ROBINS-I tool was used to evaluate the quality of the studies and the risk of bias.

Results

A total of 75 studies were identified of which 48 met our inclusion criteria. Preoperative predictive factors strongly associated with postoperative clinical outcomes were knee pain, knee-specific Patient-Reported Outcome Measure (PROM) scores, and mental health scores. Demographic characteristics, pre-existing comorbidities, and knee alignment had an inconsistent association with outcomes. The outcome measures that correlated best with the predictive models were improvement of PROM scores, pain scores, and patient satisfaction.

Conclusions

Several algorithms, based on PROM improvement, patient satisfaction, or pain after TKA, have been developed to improve decision-making regarding both indications for surgery and surgical strategy. Functional features such as preoperative pain and PROM scores were highly predictive for clinical outcomes after TKA. Some variables such as demographics data or knee alignment were less strongly correlated with TKA outcomes.

Level of evidence

Systematic review - Level III.

SUBMITTER: Batailler C

PROVIDER: S-EPMC8099715 | biostudies-literature |

REPOSITORIES: biostudies-literature

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