Comparison of extreme lateral approach with posterior approach in the treatment of lumbar degenerative diseases: A meta-analysis of clinical and imaging findings.

OBJECTIVE

To compare clinical and imaging findings between extreme lateral lumbar interbody fusion (XLIF) and posterior fusion (PF) via meta-analysis for the treatment of lumbar degenerative diseases.

METHODS

English papers reporting clinical and imaging findings for the treatment of lumbar degenerative diseases with XLIF and PF published electronically in the PubMed, Embase, Cochrane Library, and Web of Science databases from January 2006 to August 2019 were retrieved. Two authors independently extracted data and evaluated the quality of the included literature. Meta-analysis of outcome measures was performed using Stata 14 and RevMan 5.3 software.

RESULTS

This meta-analysis included 744 patients from nine studies, two of which were prospective studies, while the others were retrospective studies. The quality of each study was determined to be high. The meta-analysis showed no significant differences in the operative time, length of hospital stay, clinical effectiveness, and improvement in postoperative global sagittal alignment between two approaches (P > 0.05). However, XLIF was significantly better than PF in reducing intraoperative blood loss and recovery of local sagittal alignment (P < 0.05). Moreover, the high incidence of postoperative complications were detected in XLIF group (P < 0.05).

CONCLUSIONS

Both surgical approaches have equally promising clinical effectiveness for the treatment of lumbar degenerative diseases. Although XLIF can reduce intraoperative blood loss and obtain better postoperative local sagittal alignment than PF, the high incidence of postoperative complications should prompt us to consider why XLIF procedure is still being offered to our patients and how we can reduce these complications. In addition, any conclusions should be taken with caution because of the mix of prospective and retrospective studies, and the high heterogeneity and bias.