ArticleSpine2026
Patient Specific Finite Element Modeling Outputs Outperform Clinical Metrics in Predicting Fusion Cage Subsidence.
Article in Spine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
designFinite element (FE) analysis of retrospective clinical cohort.
objectiveTo determine whether preoperative CT-derived FE model outputs can improve subsidence prediction compared with conventional clinical measurements alone in patients undergoing transforaminal lumbar interbody fusion (TLIF). SUMMARY OF BACKGROUND DATA: Cage subsidence occurs in ∼20% of spinal fusion patients and can lead to complications requiring reoperation. While individual risk factors are known, no validated tool integrates patient anatomy, bone quality, and implant characteristics to predict subsidence. Finite element models have been hypothesized to predict subsidence but lack clinical validation. MATERIALS AND
methodsPatient-specific FE models were created from preoperative CT scans of 42 TLIF patients: N=22 severe subsidence (≥4 mm); N=20 nonsevere subsidence (<4 mm). Vertebral geometries were segmented, and bone material properties were assigned based on Hounsfield units (HU). Cage positions from postoperative scans were registered to preoperative anatomy. Endplate and trabecular stresses and strains from FE models were compared with clinical measures using receiver operating characteristic analysis.
resultsFifteen principal stresses and strains of the FE simulations showed significantly higher values in severely subsided patients compared with the nonsevere group. Average trabecular intermediate strain achieved the highest area under the curve score (AUC=0.809), outperforming all clinical metrics. Peak endplate minimum principal stress (AUC=0.775) was the second-best FE classifier. Traditional clinical measures showed lower discriminative ability: cage length (AUC=0.797), cage width (AUC=0.750), and cage height (AUC=0.698).
conclusionPatient-specific FE model outputs significantly correlate with clinical subsidence outcomes and outperform several traditional metrics in classifying severe subsidence. Both endplate and trabecular stresses and strains are important predictors, with average values showing comparable or superior performance to peak values. Integration of FE models into the clinical workflow could provide a comprehensive preoperative subsidence prediction tool.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.