Wang R, Ru N, Liu Q, Zhang F, Wu Y, Guo C, Liang J. Risk factors analysis and predictive model of degree I degenerative lumbar spondylolisthesis.
J Orthop Surg Res 2024;
19:831. [PMID:
39695800 DOI:
10.1186/s13018-024-05346-y]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 12/06/2024] [Indexed: 12/20/2024] Open
Abstract
STUDY DESIGN
Retrospective Case-Control Study.
BACKGROUND
There have been some previous studies on the risk factors associated with lumbar spondylolisthesis, but there are few studies on the risk factors for disease progression in mild degenerative lumbar spondylolisthesis (DLS). To analyze the risk factors associated with aggravation of spondylolisthesis in patients with grade I degenerative spondylolisthesis and construct a prediction model.
METHODS
We conducted a retrospective analysis of 220 patients diagnosed with DLS who were admitted to our hospital between January 2019 and January 2023. Data collected included gender, age, body mass index (BMI), diabetes, hypertension, occupation, and imaging parameters.
RESULTS
A total of 220 patients were included in this study, including 111 males and 109 females; 178 patients with no aggravation of lumbar spondylolisthesis (group A) and 42 patients with aggravation of lumbar spondylolisthesis (group B). Progression of grade I DLS was associated with single factors such as age, BMI, Occupation, vertebral CT value, facet joint angle (FJA), Modic change (MC), Pfirrmann grade of intervertebral disc (PG), Facet joint effusion (FJE), osteophyte formation, and Percentage of the Fat Infiltration (FIA%) of multifidus muscle (MM). BMI, FJA, PG, and FI% of MM had a significant impact on disease progression in lumbar spondylolisthesis.
CONCLUSION
BMI, FJA, PG, and FIA% of MM were independent risk factors for the progression of degenerative spondylolisthesis. The risk prediction model was established by including the above four variables and nomograms were drawn. The internal validation proved that the model had good discrimination, calibration, and clinical practicability.
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