[Prognostic modeling of long-term outcomes after minimally invasive lumbar fusion based on complex clinical, radiological and neuroimaging analysis in employees of JSC «Russian Railways»].

Abstract

The incidence of unsatisfactory postoperative outcomes after treatment of degenerative lumbosacral spine diseases depends on surgical method. Their prediction is difficult due to heterogeneity of clinical and instrumental characteristics of patient groups. Development of predictive models of clinical outcomes may be useful for decision making and favorable postoperative outcomes.

Objective: To develop and validate the effectiveness of multivariate predictive model for assessing the risks and benefits of minimally invasive lumbar fusion in railway workers with degenerative lumbar spine diseases.

Material and methods: We retrospectively analyzed prospective database consisting of 457 patients. They underwent minimally invasive lumbar fusion between 2020 and 2023. Comprehensive clinical, radiological and neuroimaging assessments were performed before surgery and 24 months later. Regression analysis was used to create prognostic models of long-term clinical outcomes (y0 - ODI; y1 - SF-36 PCS (physical component score); y2 - SF-36 MCS (mental component score); y3 - Macnab scale).

Results: All clinical parameters (ODI, VAS, SF-36) significantly improved after surgery compared to preoperative values (p<0.01). Within 90 days after surgery, redo surgery rate was 5.7%, and the number of readmissions throughout the entire follow-up period - 8.3%. About 87.3% of railway workers returned to previous work. Clinical deterioration was observed in 51 (11.2%) patients. Preoperative variables predict long-term clinical outcomes. Their role and dominance depend on prognostic model.

Conclusion: Predictive models may be valuable to improve long-term outcomes when planning minimally invasive lumbar spine fusion.