Prediction Model and Risk Factor Analysis of Adjacent Segment Disease After L4-5 Transforaminal Lumbar Interbody Fusion Through Preoperative Radiographic Features

Author:

Zhang Zi-Jian1ORCID,Chen Lu2,Zhu Lei2,Shi Hang2ORCID,Zhang Fu-Yu1,Jiang Zan-Li2,Wu Xiao-Tao2

Affiliation:

1. School of Medicine, Southeast University, Nanjing, Jiangsu, China

2. Department of Spine Surgery, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China

Abstract

Study Design A retrospective study. Objective To investigate the risk of adjacent segment disease (ASD) after L4-5 transforaminal lumbar interbody fusion (TLIF) in patients diagnosed with lumbar spinal stenosis (LSS), a prediction model for ASD is established and validated. Methods A retrospective study was carried out on a sample of 290 patients who underwent L4-5 TLIF at Zhongda Hospital, Southeast University, from January 2015 to January 2021. The study collected baseline data and preoperative radiographic features of L3-4 and L5-S1. The determination of the outcome variable was based on X-ray results spanning over 24 months and JOA scores. Multivariate logistic regression was used to identify the risk factors in constructing a nomogram. Results Independent risk factors for L3-4 degeneration after TLIF included osteoarthritis of L3-4 facet joints, L3-4 foraminal stenosis, L4 upper endplate osteochondritis, L3-4 local lordosis angle, and L3-4 spinal stenosis. Independent risk factors for L5-S1 degeneration after TLIF included osteoarthritis of L5-S1 facet joints, L5-S1 intervertebral disc degeneration, L5-S1 spinal stenosis, L5-S1 coronal imbalance, and S1 upper endplate osteochondritis. A predictive model was developed. The AUC for the prediction models at L3-4 and L5-S1 were .945 and .956. The calibration curve demonstrated good consistency between the predicted and actual probabilities. The DCA curve indicated the clinical benefit and practical value of this predictive model. Conclusion This study established nomograms for postoperative degeneration at L3-4 and L5-S1 based on selected preoperative radiographic features. These models provide a valuable auxiliary decision-making system for clinicians and aid in early surgical decisions.

Funder

Jiangsu Provincial Medical Key Discipline(Laboratory) Cultivation Unit

Publisher

SAGE Publications

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