Individual differences in postoperative recovery trajectories for adult symptomatic lumbar scoliosis

View More View Less
  • 1 Departments of Neurological Surgery and
  • | 2 Orthopaedic Surgery,
  • | 3 Center for Population Health Informatics, Institute for Informatics,
  • | 4 Division of Computational and Data Sciences, Washington University School of Medicine in St. Louis, St. Louis, Missouri;
  • | 5 Paediatric and Adult Spine Surgery, Rocky Mountain Hospital for Children, Presbyterian St. Luke’s Medical Center, Denver, Colorado;
  • | 6 Department of Neurological Surgery, University of Virginia, Charlottesville, Virginia;
  • | 7 Department of Orthopedic Surgery, Columbia University, New York, New York; and
  • | 8 Department of Neurosurgery and Orthopaedic Surgery, Duke University, Durham, North Carolina
Restricted access

Purchase Now

USD  $45.00

Spine - 1 year subscription bundle (Individuals Only)

USD  $376.00

JNS + Pediatrics + Spine - 1 year subscription bundle (Individuals Only)

USD  $612.00
USD  $45.00
USD  $376.00
USD  $612.00
Print or Print + Online Sign in

OBJECTIVE

The Adult Symptomatic Lumbar Scoliosis–1 (ASLS-1) trial demonstrated the benefit of adult symptomatic lumbar scoliosis (ASLS) surgery. However, the extent to which individuals differ in their postoperative recovery trajectories is unknown. This study’s objective was to evaluate variability in and factors moderating recovery trajectories after ASLS surgery.

METHODS

The authors used longitudinal, multilevel models to analyze postoperative recovery trajectories following ASLS surgery. Study outcomes included the Oswestry Disability Index (ODI) score and Scoliosis Research Society–22 (SRS-22) subscore, which were measured every 3 months until 2 years postoperatively. The authors evaluated the influence of preoperative disability level, along with other potential trajectory moderators, including radiographic, comorbidity, pain/function, demographic, and surgical factors. The impact of different parameters was measured using the R2, which represented the amount of variability in ODI/SRS-22 explained by each model. The R2 ranged from 0 (no variability explained) to 1 (100% of variability explained).

RESULTS

Among 178 patients, there was substantial variability in recovery trajectories. Applying the average trajectory to each patient explained only 15% of the variability in ODI and 21% of the variability in SRS-22 subscore. Differences in preoperative disability (ODI/SRS-22) had the strongest influence on recovery trajectories, with patients having moderate disability experiencing the greatest and most rapid improvement after surgery. Reflecting this impact, accounting for the preoperative ODI/SRS-22 level explained an additional 56%–57% of variability in recovery trajectory, while differences in the rate of postoperative change explained another 7%–9%. Among the effect moderators tested, pain/function variables—such as visual analog scale back pain score—had the biggest impact, explaining 21%–25% of variability in trajectories. Radiographic parameters were the least influential, explaining only 3%–6% more variance than models with time alone. The authors identified several significant trajectory moderators in the final model, such as significant adverse events and the number of levels fused.

CONCLUSIONS

ASLS patients have highly variable postoperative recovery trajectories, although most reach steady state at 12 months. Preoperative disability was the most important influence, although other factors, such as number of levels fused, also impacted recovery.

ABBREVIATIONS

ASLS = adult symptomatic lumbar scoliosis; ASLS-1 = Adult Symptomatic Lumbar Scoliosis–1; MCS = Mental Component Summary; ODI = Oswestry Disability Index; PCS = Physical Component Summary; SRS-22 = Scoliosis Research Society–22; VAS = visual analog scale.

Supplementary Materials

    • Supplemental Fig. 1 and Supplemental Table 1 (PDF 426 KB)

Illustration from Dibble et al. (pp 384–394). © Washington University Department of Neurosurgery, published with permission.

Spine - 1 year subscription bundle (Individuals Only)

USD  $376.00

JNS + Pediatrics + Spine - 1 year subscription bundle (Individuals Only)

USD  $612.00
USD  $376.00
USD  $612.00
  • 1

    Kebaish KM, Neubauer PR, Voros GD, Khoshnevisan MA, Skolasky RL. Scoliosis in adults aged forty years and older: prevalence and relationship to age, race, and gender. Spine (Phila Pa 1976). 2011;36(9):731736.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 2

    Schwab F, Dubey A, Gamez L, et al. Adult scoliosis: prevalence, SF-36, and nutritional parameters in an elderly volunteer population. Spine (Phila Pa 1976). 2005;30(9):10821085.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3

    Bess S, Line B, Fu KM, et al. The health impact of symptomatic adult spinal deformity: comparison of deformity types to United States population norms and chronic diseases. Spine (Phila Pa 1976). 2016;41(3):224233.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4

    Sing DC, Berven SH, Burch S, Metz LN. Increase in spinal deformity surgery in patients age 60 and older is not associated with increased complications. Spine J. 2017;17(5):627635.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 5

    Soroceanu A, Burton DC, Oren JH, et al. Medical complications after adult spinal deformity surgery: incidence, risk factors, and clinical impact. Spine (Phila Pa 1976). 2016;41(22):17181723.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6

    Pellisé F, Vila-Casademunt A, Núñez-Pereira S, et al. 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. 2018;18(2):216225.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7

    Daubs MD, Lenke LG, Cheh G, Stobbs G, Bridwell KH. Adult spinal deformity surgery: complications and outcomes in patients over age 60. Spine (Phila Pa 1976). 2007;32(20):22382244.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 8

    Paulus MC, Kalantar SB, Radcliff K. Cost and value of spinal deformity surgery. Spine (Phila Pa 1976). 2014;39(5):388393.

  • 9

    Raman T, Nayar SK, Liu S, Skolasky RL, Kebaish KM. Cost-effectiveness of primary and revision surgery for adult spinal deformity. Spine (Phila Pa 1976). 2018;43(11):791797.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10

    McCarthy I, Hostin R, O’Brien M, Saigal R, Ames CP. Health economic analysis of adult deformity surgery. Neurosurg Clin N Am. 2013;24(2):293304.

  • 11

    Smith JS, Lafage V, Shaffrey CI, et al. Outcomes of operative and nonoperative treatment for adult spinal deformity: a prospective, multicenter, propensity-matched cohort assessment with minimum 2-year follow-up. Neurosurgery. 2016;78(6):851861.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12

    Acaroglu E, Yavuz AC, Guler UO, et al. A decision analysis to identify the ideal treatment for adult spinal deformity: is surgery better than non-surgical treatment in improving health-related quality of life and decreasing the disease burden?. Eur Spine J. 2016;25(8):23902400.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13

    Kelly MP, Lurie JD, Yanik EL, et al. Operative versus nonoperative treatment for adult symptomatic lumbar scoliosis. J Bone Joint Surg Am. 2019;101(4):338352.

  • 14

    Smith JS, Kelly MP, Yanik EL, et al. Operative versus nonoperative treatment for adult symptomatic lumbar scoliosis at 5-year follow-up: durability of outcomes and impact of treatment-related serious adverse events. J Neurosurg Spine. 2021;35(1):6779.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15

    Yanik EL, Kelly MP, Lurie JD, et al. Effect modifiers for patient-reported outcomes in operatively and nonoperatively treated patients with adult symptomatic lumbar scoliosis: a combined analysis of randomized and observational cohorts. J Neurosurg Spine. 2020;33(1):1726.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16

    Bridwell KH, Cats-Baril W, Harrast J, et al. The validity of the SRS-22 instrument in an adult spinal deformity population compared with the Oswestry and SF-12: a study of response distribution, concurrent validity, internal consistency, and reliability. Spine (Phila Pa 1976). 2005;30(4):455461.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 17

    Fairbank JC, Couper J, Davies JB, O’Brien JP. The Oswestry low back pain disability questionnaire. Physiotherapy. 1980;66(8):271273.

  • 18

    Stekhoven DJ. missForest: Nonparametric missing value imputation using random forest. Accessed February 15, 2022. https://ui.adsabs.harvard.edu/abs/2015ascl.soft05011S/abstract

    • Search Google Scholar
    • Export Citation
  • 19

    Hoffman L. Longitudinal Analysis: Modeling Within-Person Fluctuation and Change. Routledge;2015.

  • 20

    Bürkner PC. Advanced Bayesian multilevel modeling with the R package brms. arXiv. Preprint posted online May 31, 2017.

  • 21

    Depaoli S, Rus HM, Clifton JP, van de Schoot R, Tiemensma J. An introduction to Bayesian statistics in health psychology. Health Psychol Rev. 2017;11(3):248264.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22

    Bürkner PC. Bayesian distributional non-linear multilevel modeling with the R package brms. arXiv. Preprint posted online May 31, 2017.

    • Search Google Scholar
    • Export Citation
  • 23

    van de Schoot R, Depaoli S, King R, et al. Bayesian statistics and modelling. Nat Rev Methods Primers. 2021;1(1):1.

  • 24

    Spiegelhalter DJ, Myles JP, Jones DR, Abrams KR. Methods in health service research. An introduction to bayesian methods in health technology assessment. BMJ. 1999;319(7208):508512.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25

    Vehtari A, Gelman A, Gabry J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat Comput. 2017;27(5):14131432.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 26

    Lüdecke D, Makowski D, Waggoner P, Patil I. Performance: assessment of regression models performance. R package version. 0.4.7. Accessed February 14, 2022. https://cran.r-project.org/web/packages/performance/

    • Search Google Scholar
    • Export Citation
  • 27

    Bürkner PC. brms: an R package for Bayesian multilevel models using Stan. J Stat Softw. 2017;80(1):128.

  • 28

    Kay M. tidybayes: tidy data and geoms for bayesian models. R package version 2.3.1. Accessed February 14, 2022. https://cran.r-project.org/web/packages/tidybayes/

    • Search Google Scholar
    • Export Citation
  • 29

    Glassman SD, Carreon LY, Shaffrey CI, et al. Cost-effectiveness of adult lumbar scoliosis surgery: an as-treated analysis from the adult symptomatic scoliosis surgery trial with 5-year follow-up. Spine Deform. 2020;8(6):13331339.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30

    Hamburg MA, Collins FS. The path to personalized medicine. N Engl J Med. 2010;363(4):301304.

  • 31

    Ames CP, Smith JS, Pellisé F, et al. Development of predictive models for all individual questions of SRS-22R after adult spinal deformity surgery: a step toward individualized medicine. Eur Spine J. 2019;28(9):19982011.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32

    Scheer JK, Osorio JA, Smith JS, et al. Development of a preoperative predictive model for reaching the Oswestry Disability Index minimal clinically important difference for adult spinal deformity patients. Spine Deform. 2018;6(5):593599.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33

    Khor S, Lavallee D, Cizik AM, et al. Development and validation of a prediction model for pain and functional outcomes after lumbar spine surgery. JAMA Surg. 2018;153(7):634642.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34

    Passias PG, Jalai CM, Lafage V, et al. Recovery kinetics of radiographic and implant-related revision patients following adult spinal deformity surgery. Neurosurgery. 2018;83(4):700708.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35

    Passias PG, Segreto FA, Lafage R, et al. Recovery kinetics following spinal deformity correction: a comparison of isolated cervical, thoracolumbar, and combined deformity morphometries. Spine J. 2019;19(8):14221433.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36

    Yang J, Lafage R, Gum JL, et al. Group-based trajectory modeling: a novel approach to classifying discriminative functional status following adult spinal deformity surgery: study of a 3-year follow-up group. Spine (Phila Pa 1976). 2020;45(13):903910.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 37

    Lafage R, Ang B, Schwab F, et al. Depression symptoms are associated with poor functional status among operative spinal deformity patients. Spine (Phila Pa 1976). 2021;46(7):447456.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 38

    Theologis AA, Ailon T, Scheer JK, et al. Impact of preoperative depression on 2-year clinical outcomes following adult spinal deformity surgery: the importance of risk stratification based on type of psychological distress. J Neurosurg Spine. 2016;25(4):477485.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 39

    Paulsen RT, Bouknaitir JB, Fruensgaard S, Carreon L, Andersen M. Prognostic factors for satisfaction after decompression surgery for lumbar spinal stenosis. Neurosurgery. 2018;82(5):645651.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40

    Crawford CH III, Carreon LY, Bydon M, Asher AL, Glassman SD. Impact of preoperative diagnosis on patient satisfaction following lumbar spine surgery. J Neurosurg Spine. 2017;26(6):709715.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation

Metrics

All Time Past Year Past 30 Days
Abstract Views 1331 1331 283
Full Text Views 199 199 116
PDF Downloads 170 170 87
EPUB Downloads 0 0 0