Literature DB >> 30122396

Development of a Preoperative Predictive Model for Reaching the Oswestry Disability Index Minimal Clinically Important Difference for Adult Spinal Deformity Patients.

Justin K Scheer1, Joseph A Osorio2, Justin S Smith3, Frank Schwab4, Robert A Hart5, Richard Hostin6, Virginie Lafage4, Amit Jain7, Douglas C Burton8, Shay Bess9, Tamir Ailon10, Themistocles S Protopsaltis4, Eric O Klineberg11, Christopher I Shaffrey3, Christopher P Ames2.   

Abstract

STUDY
DESIGN: Retrospective review of prospective multicenter adult spinal deformity (ASD) database.
OBJECTIVE: To create a model based on baseline demographic, radiographic, health-related quality of life (HRQOL), and surgical factors that can predict patients meeting the Oswestry Disability Index (ODI) minimal clinically important difference (MCID) at the two-year postoperative follow-up. SUMMARY OF BACKGROUND DATA: Surgical correction of ASD can result in significant improvement in disability as measured by ODI, with the goal of reaching at least one MCID. However, a predictive model for reaching MCID following ASD correction does not exist.
METHODS: ASD patients ≥18 years and baseline ODI ≥ 30 were included. Initial training of the model comprised forty-three variables including demographic data, comorbidities, modifiable surgical variables, baseline HRQOL, and coronal/sagittal radiographic parameters. Patients were grouped by whether or not they reached at least one ODI MCID at two-year follow-up. Decision trees were constructed using the C5.0 algorithm with five different bootstrapped models. Internal validation was accomplished via a 70:30 data split for training and testing each model, respectively. Final predictions from the models were chosen by voting with random selection for tied votes. Overall accuracy, and the area under a receiver operating characteristic curve (AUC) were calculated.
RESULTS: 198 patients were included (MCID: 109, No-MCID: 89). Overall model accuracy was 86.0%, with an AUC of 0.94. The top 11 predictors of reaching MCID were gender, Scoliosis Research Society (SRS) activity subscore, back pain, sagittal vertical axis (SVA), pelvic incidence-lumbar lordosis mismatch (PI-LL), primary version revision, T1 spinopelvic inclination angle (T1SPI), American Society of Anesthesiologists (ASA) grade, T1 pelvic angle (T1PA), SRS pain, SRS total.
CONCLUSIONS: A successful model was built predicting ODI MCID. Most important predictors were not modifiable surgical parameters, indicating that baseline clinical and radiographic status is a critical factor for reaching ODI MCID. LEVEL OF EVIDENCE: Level II.
Copyright © 2018 Scoliosis Research Society. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Adult spinal deformity; Minimum clinically important difference; Oswestry Disability Index; Predictive modeling; Scoliosis

Mesh:

Year:  2018        PMID: 30122396     DOI: 10.1016/j.jspd.2018.02.010

Source DB:  PubMed          Journal:  Spine Deform        ISSN: 2212-134X


  8 in total

1.  A Simpler, Modified Frailty Index Weighted by Complication Occurrence Correlates to Pain and Disability for Adult Spinal Deformity Patients.

Authors:  Peter G Passias; Cole A Bortz; Katherine E Pierce; Haddy Alas; Avery Brown; Dennis Vasquez-Montes; Sara Naessig; Waleed Ahmad; Bassel G Diebo; Tina Raman; Themistocles S Protopsaltis; Aaron J Buckland; Michael C Gerling; Renaud Lafage; Virginie Lafage
Journal:  Int J Spine Surg       Date:  2020-12

2.  Artificial Intelligence in Adult Spinal Deformity.

Authors:  Pramod N Kamalapathy; Aditya V Karhade; Daniel Tobert; Joseph H Schwab
Journal:  Acta Neurochir Suppl       Date:  2022

3.  Estimating measurement error of the Oswestry Disability Index with missing data.

Authors:  Emmanuel L McNeely; Bo Zhang; Brian J Neuman; Richard L Skolasky
Journal:  Spine J       Date:  2022-02-01       Impact factor: 4.297

4.  Narrative Review of Predictive Analytics of Patient-Reported Outcomes in Adult Spinal Deformity Surgery.

Authors:  Kurt Lehner; Jeff Ehresman; Zach Pennington; A Karim Ahmed; Daniel Lubelski; Daniel M Sciubba
Journal:  Global Spine J       Date:  2020-10-09

Review 5.  A narrative review of machine learning as promising revolution in clinical practice of scoliosis.

Authors:  Kai Chen; Xiao Zhai; Kaiqiang Sun; Haojue Wang; Changwei Yang; Ming Li
Journal:  Ann Transl Med       Date:  2021-01

Review 6.  State-of-the-art reviews predictive modeling in adult spinal deformity: applications of advanced analytics.

Authors:  Rushikesh S Joshi; Darryl Lau; Justin K Scheer; Miquel Serra-Burriel; Alba Vila-Casademunt; Shay Bess; Justin S Smith; Ferran Pellise; Christopher P Ames
Journal:  Spine Deform       Date:  2021-05-18

7.  Development of predictive models for all individual questions of SRS-22R after adult spinal deformity surgery: a step toward individualized medicine.

Authors:  Christopher P Ames; Justin S Smith; Ferran Pellisé; Michael Kelly; Jeffrey L Gum; Ahmet Alanay; Emre Acaroğlu; Francisco Javier Sánchez Pérez-Grueso; Frank S Kleinstück; Ibrahim Obeid; Alba Vila-Casademunt; Christopher I Shaffrey; Douglas C Burton; Virginie Lafage; Frank J Schwab; Christopher I Shaffrey; Shay Bess; Miquel Serra-Burriel
Journal:  Eur Spine J       Date:  2019-07-19       Impact factor: 3.134

8.  Artificial Intelligence for Adult Spinal Deformity.

Authors:  Rushikesh S Joshi; Alexander F Haddad; Darryl Lau; Christopher P Ames
Journal:  Neurospine       Date:  2019-12-31
  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.