Advertisement

European Spine Journal

, Volume 28, Issue 1, pp 180–187 | Cite as

Predictive model for major complications 2 years after corrective spine surgery for adult spinal deformity

  • Mitsuru Yagi
  • Naobumi Hosogane
  • Nobuyuki Fujita
  • Eijiro Okada
  • Osahiko Tsuji
  • Narihito Nagoshi
  • Takashi Asazuma
  • Takashi Tsuji
  • Masaya Nakamura
  • Morio Matsumoto
  • Kota WatanabeEmail author
Original Article

Abstract

Purpose

ASD surgery improves a patient’s health-related quality of life, but it has a high complication rate. The aim of this study was to create a predictive model for complications after surgical treatment for adult spinal deformity (ASD), using spinal alignment, demographic data, and surgical invasiveness.

Methods

This study included 195 surgically treated ASD patients who were > 50 years old and had 2-year follow-up from multicenter database. Variables which included age, gender, BMI, BMD, frailty, fusion level, UIV and LIV, primary or revision surgery, pedicle subtraction osteotomy, spinal alignment, Schwab-SRS type, surgical time, and blood loss were recorded and analyzed at least 2 years after surgery. Decision-making trees for 2-year postoperative complications were constructed and validated by a 7:3 data split for training and testing. External validation was performed for 25 ASD patients who had surgery at a different hospital.

Results

Complications developed in 48% of the training samples. Almost half of the complications developed in late post-op period, and implant-related complications were the most common complication at 2 years after surgery. Univariate analyses showed that BMD, frailty, PSO, LIV, PI-LL, and EBL were risk factors for complications. Multivariate analysis showed that low BMD, PI-LL > 30°, and frailty were independent risk factors for complications. In the testing samples, our predictive model was 92% accurate with an area under the receiver operating characteristic curve of 0.963 and 84% accurate in the external validation.

Conclusion

A successful model was developed for predicting surgical complications. Our model could inform physicians about the risk of complications in ASD patients in the 2-year postoperative period.

Graphical abstract

These slides can be retrieved under Electronic Supplementary Material.

Keywords

Adult spinal deformity Complication Surgery Risk stratification Predictive model 

Notes

Acknowledgements

This study was approved by the appropriate institutional review board.

Compliance with ethical standards

Conflict of interest

The authors report no conflict of interest.

Supplementary material

586_2018_5816_MOESM1_ESM.pptx (21 mb)
Supplementary material 1 (PPTX 21467 kb)
586_2018_5816_MOESM2_ESM.pptx (147 kb)
Supplementary material 2 (PPTX 146 kb)

References

  1. 1.
    Schwab F, Ungar B, Blondel B et al (2012) Scoliosis Research Society—Schwab adult spinal deformity classification: a validation study. Spine 37:1077–1082CrossRefGoogle Scholar
  2. 2.
    Soroceanu A, Burton DC, Oren JH et al (2016) Medical complications after adult spinal deformity surgery: incidence, risk factors, and clinical impact. Spine 41:1718–1723CrossRefGoogle Scholar
  3. 3.
    Terran J, Schwab F, Shaffrey CI et al (2013) The SRS-Schwab adult spinal deformity classification: assessment and clinical correlations based on a prospective operative and nonoperative cohort. Neurosurgery 73:559–568CrossRefGoogle Scholar
  4. 4.
    Poorman GW, Passias PG, Buckland AJ et al (2017) Comparative analysis of peri-operative outcomes using nationally derived hospital discharge data relative to a prospective multi-center surgical database of adult spinal deformity surgery. Spine 42(15):1165–1171CrossRefGoogle Scholar
  5. 5.
    Smith JS, Lafage V, Shaffrey CI et al (2016) Outcomes of operative and nonoperative treatment for adult spinal deformity: a prospective, multicenter, propensity-matched cohort assessment with minimum 2-year follow-up. Neurosurgery 78(6):851–856CrossRefGoogle Scholar
  6. 6.
    DeWald CJ, Stanley T (2006) Instrumentation-related complications of multilevel fusions for adult spinal deformity patients over age 65: surgical considerations and treatment options in patients with poor bone quality. Spine 31:S144–S151CrossRefGoogle Scholar
  7. 7.
    Yagi M, Hosogane N, Okada E et al (2014) Factors affecting the post operative progression of thoracic kyphosis in surgically treated adult patient with lumbar degenerative scoliosis. Spine 39:E521–E528CrossRefGoogle Scholar
  8. 8.
    Yagi M, Rahm M, Gaines R et al (2014) Characterization and surgical outcomes of proximal junctional failure (PJF) in surgically treated adult spine deformity patients. Spine 39:E607–E614CrossRefGoogle Scholar
  9. 9.
    Yagi M, King AB, Boachie-Adjei O (2012) Incidence, risk factors, and natural course of proximal junctional kyphosis: surgical outcomes review of adult idiopathic scoliosis. Minimum 5 years of follow-up. Spine 37(17):1479–1489CrossRefGoogle Scholar
  10. 10.
    Cho SK, Bridwell KH, Lenke LG et al (2010) Major complication in revision adult deformity surgery: risk factors and clinical outcomes with 2- to 7-year follow-up. Spine 37:489–500CrossRefGoogle Scholar
  11. 11.
    Simon MJK, Halm HFH, Quante M (2018) Perioperative complications after surgical treatment in degenerative adult de novo scoliosis. BMC Musculoskelet Disord 19(1):10CrossRefGoogle Scholar
  12. 12.
    Schwab FJ, Lafage V, Farcy JP et al (2008) Predicting outcome and complications in the surgical treatment of adult scoliosis. Spine 33(20):2243–2247CrossRefGoogle Scholar
  13. 13.
    Lebude B, Yadla S, Albert T et al (2010) Defining “complications” in spine surgery: neurosurgery and orthopedic spine surgeons’ survey. J Spinal Disord Tech 23(8):493–500CrossRefGoogle Scholar
  14. 14.
    Yagi M, Cunningham E, King A et al (2013) Long term clinical and radiographic outcomes of pedicle subtraction osteotomy for fixed sagittal imbalance: Does level of proximal fusion affect the outcome?—minimum 5 years follow-up. Spine Deform 1:123–131CrossRefGoogle Scholar
  15. 15.
    Veeravagu A, Li A, Swinney C, Tian L et al (2017) Predicting complication risk in spine surgery: a prospective analysis of a novel risk assessment tool. J Neurosurg Spine 27(1):81–91CrossRefGoogle Scholar
  16. 16.
    Mirza SK, Deyo RA, Heagerty PJ et al (2006) Towards standardized measurement of adverse events in spine surgery: conceptual model and pilot evaluation. BMC Musculoskelet Disord 7:53CrossRefGoogle Scholar
  17. 17.
    Glassman SD, Bridwell K, Dimar JR et al (2005) The impact of positive sagittal balance in adult spinal deformity. Spine 30(18):2024–2029CrossRefGoogle Scholar
  18. 18.
    Leven DM, Lee NJ, Kothari P et al (2017) Frailty index is a significant predictor of complications and mortality after surgery for adult spinal deformity. Spine 41(23):E1394–E1401CrossRefGoogle Scholar
  19. 19.
    Cepeda MS, Boston R, Farrar JT et al (2003) Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders. Am J Epidemiol 158(3):280–287CrossRefGoogle Scholar
  20. 20.
    Charlson ME, Pompei P, Ales KL et al (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40(5):373–383CrossRefGoogle Scholar
  21. 21.
    Adams P, Ghanem T, Stachler R et al (2013) Frailty as a predictor of morbidity and mortality in inpatient head and neck surgery. JAMA Otolaryngol Head Neck Surg 139(8):783–789CrossRefGoogle Scholar
  22. 22.
    Karam J, Tsiouris A, Shepard A et al (2013) Simplified frailty index to predict adverse outcomes and mortality in vascular surgery patients. Ann Vasc Surg 27(7):904–908CrossRefGoogle Scholar
  23. 23.
    Scheer JK, Smith JS, Schwab F et al (2017) Development of a preoperative predictive model for major complications following adult spinal deformity surgery. J Neurosurg Spine 26(6):736–743CrossRefGoogle Scholar
  24. 24.
    Abbott D (2014) Applied predictive analytics: principles and techniques for the professional data analyst, 1st edn. Wiley, IndianapolisGoogle Scholar
  25. 25.
    Yagi M, Fujita N, Okada E et al (2017) Fine-tuning the predictive model for proximal junctional failure in surgically treated patients with adult spinal deformity. Spine 43(11):767–773CrossRefGoogle Scholar
  26. 26.
    Diebo BG, Gammal I, Ha Y et al (2018) Role of ethnicity in alignment compensation: propensity matched analysis of differential compensatory mechanism recruitment patterns for sagittal malalignment in 288 ASD patients from Japan, Korea, and United States. Spine 42(4):E234–E240CrossRefGoogle Scholar
  27. 27.
    Glassman SD, Hamill CL, Bridwell KH et al (2007) The impact of perioperative complications on clinical outcome in adult deformity surgery. Spine 32:2764–2770CrossRefGoogle Scholar
  28. 28.
    Pellisé F, Vila-Casademunt A, Núñez-Pereira S et al (2018) The Adult Deformity Surgery Complexity Index (ADSCI): a valid tool to quantify the complexity of posterior adult spinal deformity surgery and predict postoperative complications. Spine J 18(2):216–225CrossRefGoogle Scholar
  29. 29.
    Buchlak QD, Yanamadala V, Leveque JC et al (2017) The Seattle spine score: predicting 30-day complication risk in adult spinal deformity surgery. J Clin Neurosci 43:247–255CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Mitsuru Yagi
    • 1
    • 2
    • 3
  • Naobumi Hosogane
    • 3
    • 4
  • Nobuyuki Fujita
    • 1
    • 3
  • Eijiro Okada
    • 1
    • 3
  • Osahiko Tsuji
    • 1
    • 3
  • Narihito Nagoshi
    • 1
    • 3
  • Takashi Asazuma
    • 2
  • Takashi Tsuji
    • 3
    • 5
  • Masaya Nakamura
    • 1
    • 3
  • Morio Matsumoto
    • 1
    • 3
  • Kota Watanabe
    • 1
    • 3
    Email author
  1. 1.Department of Orthopedic SurgeryKeio University School of MedicineTokyoJapan
  2. 2.Department of Orthopedic SurgeryNational Hospital Organization Murayama Medical CenterTokyoJapan
  3. 3.Keio Spine Research GroupTokyoJapan
  4. 4.Department of Orthopedic SurgeryNational Defense Medical CollegeTokorozawaJapan
  5. 5.Department of Orthopedic SurgeryFujita Health UniversityToyoakeJapan

Personalised recommendations