How Are We Matching in ACL Reconstruction Research? A Systematic Review of Methods, Reporting, and Covariate...

Matching techniques such as direct covariate matching and propensity score matching (PSM) are increasingly used in anterior cruciate ligament reconstruction (ACLR) research to reduce bias in observational study designs. However, the rationale for covariate selection, consistency in methodological reporting, and patterns of matching practices remain unclear.

To systematically evaluate covariate matching practices in ACLR literature, including the types and number of covariates used, methodological transparency, and trends in matching strategies.

Systematic review.

A systematic literature search of the PubMed, EMBASE, and Cochrane databases was conducted to evaluate covariate matching practices in the ACLR literature. A comprehensive search identified 798 unique studies, of which 97 met eligibility criteria. Data were extracted on study design, matching technique, covariate inclusion, reporting practices, and matching ratios. Descriptive and comparative statistics were used to summarize trends.

The 97 included studies encompassed 91,165 ACLRs. Most studies were retrospective (90.7%) and cohort in design (92.8%). PSM was used in 41 studies (42.3%), while 56 (57.7%) used direct matching. A total of 60 unique covariates were used across 76 different combinations. PSM studies used significantly more covariates than direct matching studies (6.17 ± 2.79 vs 3.75 ± 1.63; P < .0001), and database studies used more covariates than single-center studies (6.33 ± 2.89 vs 4.08 ± 1.96; P < .0001). The most commonly used covariates were age (96.9%), sex (84.5%), and body mass index (41.2%). Only 6 studies (6.2%) provided justification for covariate selection. The most frequent matching ratio was 1:1 (73.2%). While most studies reported descriptive statistics after matching (95.9%), only 10.3% did so before matching, and 13.4% failed to report prematching sample size.

Matching practices in ACLR studies remain highly variable, with limited justification provided for covariate selection. PSM and database-based studies tend to incorporate a greater number of covariates, yet reporting of matching methodology is often inconsistent. To enhance the quality, reproducibility, and comparability of ACLR research, future studies should adopt standardized reporting practices for matching, including explicit descriptions of covariate selection, matching algorithms, balance diagnostics, and match ratios. These steps can serve as a foundation for a more unified research framework, enabling future studies to collectively generate higher quality, generalizable evidence for ACLR outcomes.