Development and validation of risk stratification models for adult spinal deformity surgery.

Published online

Journal Article

OBJECTIVE: Adult spinal deformity (ASD) surgery has a high rate of major complications (MCs). Public information about adverse outcomes is currently limited to registry average estimates. The object of this study was to assess the incidence of adverse events after ASD surgery, and to develop and validate a prognostic tool for the time-to-event risk of MC, hospital readmission (RA), and unplanned reoperation (RO). METHODS: Two models per outcome, created with a random survival forest algorithm, were trained in an 80% random split and tested in the remaining 20%. Two independent prospective multicenter ASD databases, originating from the European continent and the United States, were queried, merged, and analyzed. ASD patients surgically treated by 57 surgeons at 23 sites in 5 countries in the period from 2008 to 2016 were included in the analysis. RESULTS: The final sample consisted of 1612 ASD patients: mean (standard deviation) age 56.7 (17.4) years, 76.6% women, 10.4 (4.3) fused vertebral levels, 55.1% of patients with pelvic fixation, 2047.9 observation-years. Kaplan-Meier estimates showed that 12.1% of patients had at least one MC at 10 days after surgery; 21.5%, at 90 days; and 36%, at 2 years. Discrimination, measured as the concordance statistic, was up to 71.7% (95% CI 68%-75%) in the development sample for the postoperative complications model. Surgical invasiveness, age, magnitude of deformity, and frailty were the strongest predictors of MCs. Individual cumulative risk estimates at 2 years ranged from 3.9% to 74.1% for MCs, from 3.17% to 44.2% for RAs, and from 2.67% to 51.9% for ROs. CONCLUSIONS: The creation of accurate prognostic models for the occurrence and timing of MCs, RAs, and ROs following ASD surgery is possible. The presented variability in patient risk profiles alongside the discrimination and calibration of the models highlights the potential benefits of obtaining time-to-event risk estimates for patients and clinicians.

Full Text

Duke Authors

Cited Authors

  • Pellisé, F; Serra-Burriel, M; Smith, JS; Haddad, S; Kelly, MP; Vila-Casademunt, A; Sánchez Pérez-Grueso, FJ; Bess, S; Gum, JL; Burton, DC; Acaro─člu, E; Kleinstück, F; Lafage, V; Obeid, I; Schwab, F; Shaffrey, CI; Alanay, A; Ames, C; International Spine Study Group, ; European Spine Study Group,

Published Date

  • June 28, 2019

Published In

Start / End Page

  • 1 - 13

PubMed ID

  • 31252385

Pubmed Central ID

  • 31252385

Electronic International Standard Serial Number (EISSN)

  • 1547-5646

Digital Object Identifier (DOI)

  • 10.3171/2019.3.SPINE181452

Language

  • eng

Conference Location

  • United States