Better outcomes after mini-subvastus approach for primary total knee arthroplasty: a Bayesian network meta-analysis - Migliorini, Filippo (Eur J Orthop Surg Traumatol, 2020)
INTRODUCTION: Alternatives to the classical medial parapatellar (MPP) approach for total knee arthroplasty (TKA) include the mini-medial parapatellar (MMPP), mini-subvastus (MSV), mini-midvastus (MMV) and quadriceps-sparing (QS) approaches. The best approach has been not fully clarified. The purpose of the present study was to conduct a Bayesian network meta-analysis comparing these approaches. MATERIALS AND METHODS: The present analysis was carried out according to the PRISMA extension statement for reporting systematic reviews incorporating network meta-analyses of healthcare interventions. The databases search was performed in October 2019. All clinical trials comparing two or more approaches for primary TKA were considered for inclusion. The baseline comparability was evaluated through the analysis of variance (ANOVA) test. The statistical analysis was performed through the STATA software/MP. A Bayesian hierarchical random-effects model analysis was adopted in all the comparisons. RESULTS: Data from 52 articles (4533 patients) were collected. The mean follow-up was 20.38 months. With regard to diagnosis, gender, age and BMI, adequate baseline comparability was detected. The MSV approach ranked better concerning clinical scores (the lowest visual analogic scale, the higher KSS and KSFS) and functional outcomes (the shortest straight leg raise, the greatest degree of flexion and range of motion). Concerning perioperative data, the MSV evidenced the shortest hospital stay, while the MPP the shortest surgical duration and lowest estimated blood loss. CONCLUSION: According to the main findings of the present study, the mini-subvastus approach for total knee arthroplasty demonstrated superior overall compared to the other approaches. Orthopaedic surgeons should consider this approach in the light of the evidence and limitations of this Bayesian network meta-analysis.