Evidence map›Paper›PMID 41152502›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

Machine learning clustering reveals risk factors of mechanical complications requiring revision in adult spinal deformity surgery: a 2-year follow-up study.

Alice Baroncini, Daniel Larrieu, Anouar Bourghli, Javier Pizones, Ferran Pellisè, Frank Kleinstueck, Ahmet Alanay, Yann Philippe Charles, Cecile Roscop, Louis Boissiere and 1 more

Abstract read
PubMed Publisher
In one paragraph

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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Alice BaronciniCasa di Cura Humanitas San Pio X, Milan, Italy. alice.baroncini@gmail.com.
Daniel LarrieuELSAN, Polyclinique Jean Villar, Bruges, France.
Anouar BourghliKing Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia.
Javier PizonesHospital Universitario La Paz, Madrid, Spain.
Ferran PellisèVall d Hebron Hospital Universitari, Barcelona, Spain.
Frank KleinstueckSchulthess-Klinik, Zurich, Switzerland.
Ahmet AlanayAcibadem University School of Medicine, Istanbul, Turkey.
Yann Philippe CharlesHôpitaux Universitaires de Strasbourg, Strasbourg, France.
Cecile RoscopELSAN, Polyclinique Jean Villar, Bruges, France.
Louis BoissiereELSAN, Polyclinique Jean Villar, Bruges, France.
Ibrahim ObeidELSAN, Polyclinique Jean Villar, Bruges, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMechanical complications requiring revision represent one of the main issues after surgical management for adult spine deformity (ASD). The heterogeneity of the population and the multifactorial nature of mechanical complications have not yet allowed to identify patient-specific risk factors for this complication. The aim of this study was to utilize machine learning (ML) to create homogeneous patient clusters, which could then be analyzed with classical statistical methods to evaluate with granularity the type and rate of mechanical complications and the relative risk factors.

methodsAll patients who underwent surgical management for ASD and had a minimal follow-up of two years were clustered into three homogeneous groups. For each, the type and rate of mechanical complication requiring revision were analyzed and logistic regression and marginal effect study were used to evaluate possible risk factors.

resultsFor Cluster 1 (N = 192, younger subjects with coronal deformity), the revision rate for mechanical complication was 6% (33% pseudarthrosis, 13% screw loosening); the main risk factors were implant density, coronal balance and pelvic incidence at 6 weeks and presence of pelvic fixation. In Cluster 2 (N = 455, older patients with mild coronal and sagittal imbalance and mild disability) the revision rate was 19% (41% pseudarthrosis, 24% proximal junctional kyphosis - PJK); the main risk factors were thoracic kyphosis (TK) at 6 weeks, n. of instrumented levels, ODI, preoperative sacral obliquity, TK and pelvic tilt, and presence of pelvic fixation. In Cluster 3 (N = 172, older patients with severe imbalance and mild disability), the revision rate was 30% (58% pseudarthrosis, 14% screw loosening); the main risk factors were n. of instrumented levels and global tilt at 6 weeks.

conclusionML allows to cluster patients into homogeneous groups, which showed differences for mechanical complication rates requiring revision, with specific risk factors in each group. This ML approach might improve preoperative risk assessment.

Indexed as

Machine LearningPostoperative ComplicationsReoperationSpinal CurvaturesSpinal FusionAdultAgedCluster AnalysisFemaleFollow-Up StudiesHumansMaleMiddle AgedRisk FactorsYoung AdultAdult spine deformityArtificial intelligenceClusteringMechanical complicationsSpine deformity surgery

Identifiers

PMID41152502

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.