Evidence map›Paper›PMID 38607513›Full record

ArticleSpine deformity2024

Which sagittal plane assessment method is most predictive of complications after adult spinal deformity surgery?

Javier Pizones, Jeffrey Hills, Michael Kelly, Caglar Yilgor, Lucía Moreno-Manzanaro, Francisco Javier Sánchez Perez-Grueso, Frank Kleinstück, Ibrahim Obeid, Ahmet Alanay, Ferran Pellisé and 1 more

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In one paragraph

Article in Spine deformity, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

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

Javier PizonesSpine Unit, Department of Orthopedic Surgery, Hospital Universitario La Paz, Paseo de la Castellana 261, 28046, Madrid, Spain. javierpizones@gmail.com.ORCID 0000-0001-7394-9637
Jeffrey HillsUniversity of Texas, San Antonio, TX, USA.
Michael KellyRady Children Hospital San Diego, University of California, San Diego, CA, USA.
Caglar YilgorDepartment of Orthopedics and Traumatology, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey.
Lucía Moreno-ManzanaroSpine Unit, Department of Orthopedic Surgery, Hospital Universitario La Paz, Paseo de la Castellana 261, 28046, Madrid, Spain.
Francisco Javier Sánchez Perez-GruesoSpine Unit, Department of Orthopedic Surgery, Hospital Universitario La Paz, Paseo de la Castellana 261, 28046, Madrid, Spain.
Frank KleinstückDepartment of Orthopedics, Schulthess Klinik, Zurich, Switzerland.
Ibrahim ObeidSpine Surgery Unit, Pellegrin University Hospital, Bordeaux, France.
Ahmet AlanayDepartment of Orthopedics and Traumatology, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey.
Ferran PelliséSpine Surgery Unit, Hospital Universitario Vall d'Hebron, Barcelona, Spain.
ESSG European Spine Study Group

Funding

CSRD VA 1Medtronic 2
6 · The paper itself

Abstract

purposeDifferent methods of sagittal alignment assessment compete for predicting adverse events after adult spinal deformity (ASD) surgery. We wanted to study which method provides greater benefit.

methodsRetrospective study of 391 patients operated for ASD, with > 6 instrumented levels, fused to the pelvis, and 2 years of follow-up. Three alignment methods were analyzed 6-week postoperatively: (1) Roussouly mismatch; (2) GAP score/GAP categories; (3) T4-L1-Hip axis. Binary logistic regression generated models that best predict the following adverse events: mechanical complications (MC): in general and isolated (PJK, PJF, rod breakage); reinterventions (in general and after MC); and readmissions. ROC/AUC analysis was also implemented. In a second regression round, we added different variables that were selected on univariate analysis-demographic, surgical, and radiographic-to complete the models.

resultsThe best predictor parameters in most models were T4-L1PA mismatch and GAP score; we could not prove a predictive ability of the Roussouly mismatch. The T4-L1PA mismatch best predicted general MC, PJK, PJK + PJF, and readmission, while the GAP score best predicted PJF and reinterventions (for MC and for any complication). However, the variance explained by these models was limited (Nagelkerke's R2 = 0.031-0.113), with odds ratios ranging from 1.070 to 1.456. ROC curves plotted an AUC between 0.57 and 0.70. Introducing additional variables (demographic, surgical, and radiographic) improved prediction in all the models (Nagelkerke's R2 = 0.082-0.329) and allowed predicting rod breakage.

conclusionThe T4-L1-Hip axis and GAP score show potential in predicting adverse events, surpassing the Roussouly method. Despite partial efficacy in complication anticipation, recognizing postoperative sagittal alignment as a key modifiable risk factor, the crucial need arises to integrate diverse variables, both modifiable and non-modifiable, for enhanced predictive accuracy. LEVEL OF EVIDENCE: Level IV.

Indexed as

Postoperative ComplicationsSpinal FusionAdultAgedFemaleFollow-Up StudiesHumansLumbar VertebraeMaleMiddle AgedRetrospective StudiesSpinal CurvaturesThoracic VertebraeAdult spinal deformityFused to pelvisMechanical complicationsPredictive modelsReinterventionsSagittal alignmentSpinal surgery

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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.