Evidence map›Paper›PMID 42811138›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026

The S1-LIV angle is essential for fusion selection in thoracic adolescent idiopathic scoliosis - a dual-center machine learning study with external validation.

Søren Ohrt-Nissen, Jason Pui Yin Cheung, Prudence Wing Hang Cheung, Xihe Tony Kuang, Lærke Ragborg, Martin Heegaard, Thomas Borbjerg Andersen, Martin Gehrchen, Benny Dahl

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Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

9 authors.

Søren Ohrt-NissenDepartment of orthopedic surgery, Spine Unit, Rigshospitalet, Copenhagen, Denmark. ohrtnissen@gmail.com.ORCID https://orcid.org/0000-0002-8234-9948
Jason Pui Yin CheungDepartment of Orthopaedics and Traumatology, University of Hong Kong, Hong Kong, China.
Prudence Wing Hang CheungDepartment of Orthopaedics and Traumatology, University of Hong Kong, Hong Kong, China.
Xihe Tony KuangDepartment of Orthopaedics and Traumatology, University of Hong Kong, Hong Kong, China.ORCID https://orcid.org/0000-0002-4696-5284
Lærke RagborgDepartment of orthopedic surgery, Spine Unit, Rigshospitalet, Copenhagen, Denmark.
Martin HeegaardDepartment of orthopedic surgery, Spine Unit, Rigshospitalet, Copenhagen, Denmark.
Thomas Borbjerg AndersenDepartment of orthopedic surgery, Spine Unit, Rigshospitalet, Copenhagen, Denmark.
Martin GehrchenDepartment of orthopedic surgery, Spine Unit, Rigshospitalet, Copenhagen, Denmark.
Benny DahlDepartment of orthopedic surgery, Spine Unit, Rigshospitalet, Copenhagen, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWe aimed to develop a model, using preoperative radiographic parameters, to improve patient-specific selection of the lowest instrumented vertebra (LIV) and decrease the risk of distal adding-on in patients with thoracic adolescent idiopathic scoliosis (AIS).

methodsThis was a retrospective dual-center cohort study. The derivation cohort included 177 AIS patients (Lenke type 1 or 2, lumbar modifier A/B) treated with selective posterior fusion. Radiographic analysis included a wide variety of parameters from coronal, sagittal and bending x-rays. After identifying the five most predictive preoperative parameters using random forest modeling and ROC analysis, the algorithm was externally validated in a cohort of 289 patients where LIV selection followed a standardized protocol. The primary outcome was distal adding-on at minimum two-year follow-up.

resultsAdding-on was observed in 33% and 21% of patients in the derivation and validation cohorts, respectively. Of the variables tested, only the S1-LIV angle - a measure of vertebral deviation from the midline - remained a significant predictor in multivariate analysis. An S1-LIV angle above 9.5° was associated with fourfold increased odds of adding-on. Machine learning-selected axial and sagittal parameters did not improve prediction beyond the S1-LIV angle alone when validated externally. Notably, selection of the LIV based on the S1-LIV angle saved lumbar motion segments in approximately 20% of patients without increasing the risk of adding-on.

conclusionThe S1-LIV angle offers a quantitative tool for individualized LIV selection in AIS, and its use may facilitate level-sparing strategies in surgical planning without compromising patient outcomes.

Indexed as

Adolescent idiopathic scoliosis; Fusion selection; Machine learning; Lowest instrumented vertebra; S1-LIV angle; Thoracic scoliosis; Lenke 1; Lenke 2

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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 OpenQuestion graph.