Literature DB >> 31325052

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

Christopher P Ames1, Justin S Smith2, Ferran Pellisé3, Michael Kelly4, Jeffrey L Gum5, Ahmet Alanay6, Emre Acaroğlu7, Francisco Javier Sánchez Pérez-Grueso8, Frank S Kleinstück9, Ibrahim Obeid10, Alba Vila-Casademunt11, Christopher I Shaffrey2, Douglas C Burton12, Virginie Lafage13, Frank J Schwab13, Christopher I Shaffrey2, Shay Bess14, Miquel Serra-Burriel15.   

Abstract

PURPOSE: Health-related quality of life (HRQL) instruments are essential in value-driven health care, but patients often have more specific, personal priorities when seeking surgical care. The Scoliosis Research Society-22R (SRS-22R), an HRQL instrument for spinal deformity, provides summary scores spanning several health domains, but these may be difficult for patients to utilize in planning their specific care goals. Our objective was to create preoperative predictive models for responses to individual SRS-22R questions at 1 and 2 years after adult spinal deformity (ASD) surgery to facilitate precision surgical care.
METHODS: Two prospective observational cohorts were queried for ASD patients with SRS-22R data at baseline and 1 and 2 years after surgery. In total, 150 covariates were used in training machine learning models, including demographics, surgical data and perioperative complications. Validation was accomplished via an 80%/20% data split for training and testing, respectively. Goodness of fit was measured using area under receiver operating characteristic (AUROC) curves.
RESULTS: In total, 561 patients met inclusion criteria. The AUROC ranged from 56.5 to 86.9%, reflecting successful fits for most questions. SRS-22R questions regarding pain, disability and social and labor function were the most accurately predicted. Models were less sensitive to questions regarding general satisfaction, depression/anxiety and appearance.
CONCLUSIONS: To the best of our knowledge, this is the first study to explicitly model the prediction of individual answers to the SRS-22R questionnaire at 1 and 2 years after deformity surgery. The ability to predict individual question responses may prove useful in preoperative counseling in the age of individualized medicine. These slides can be retrieved under Electronic Supplementary Material.

Entities:  

Keywords:  Adult spinal deformity; Individualized medicine; Outcomes; Predictive analytics; Scoliosis Research Society-22R (SRS-22R) questionnaire; Surgery

Year:  2019        PMID: 31325052     DOI: 10.1007/s00586-019-06079-x

Source DB:  PubMed          Journal:  Eur Spine J        ISSN: 0940-6719            Impact factor:   3.134


  25 in total

1.  Perioperative complications of posterior lumbar decompression and arthrodesis in older adults.

Authors:  Leah Y Carreon; Rolando M Puno; John R Dimar; Steven D Glassman; John R Johnson
Journal:  J Bone Joint Surg Am       Date:  2003-11       Impact factor: 5.284

2.  Validation of new clinical quantitative analysis software applicable in spine orthopaedic studies.

Authors:  S Champain; K Benchikh; A Nogier; C Mazel; J De Guise; W Skalli
Journal:  Eur Spine J       Date:  2005-06-17       Impact factor: 3.134

3.  Surgical treatment of pathological loss of lumbar lordosis (flatback) in patients with normal sagittal vertical axis achieves similar clinical improvement as surgical treatment of elevated sagittal vertical axis: clinical article.

Authors:  Justin S Smith; Manish Singh; Eric Klineberg; Christopher I Shaffrey; Virginie Lafage; Frank J Schwab; Themistocles Protopsaltis; David Ibrahimi; Justin K Scheer; Gregory Mundis; Munish C Gupta; Richard Hostin; Vedat Deviren; Khaled Kebaish; Robert Hart; Douglas C Burton; Shay Bess; Christopher P Ames
Journal:  J Neurosurg Spine       Date:  2014-04-25

4.  Risk-benefit assessment of surgery for adult scoliosis: an analysis based on patient age.

Authors:  Justin S Smith; Christopher I Shaffrey; Steven D Glassman; Sigurd H Berven; Frank J Schwab; Christopher L Hamill; William C Horton; Stephen L Ondra; Charles A Sansur; Keith H Bridwell
Journal:  Spine (Phila Pa 1976)       Date:  2011-05-01       Impact factor: 3.468

5.  Refinement of the SRS-22 Health-Related Quality of Life questionnaire Function domain.

Authors:  Marc A Asher; Sue Min Lai; R Chris Glattes; Douglas C Burton; Ahmet Alanay; Juan Bago
Journal:  Spine (Phila Pa 1976)       Date:  2006-03-01       Impact factor: 3.468

6.  Operative versus nonoperative treatment of leg pain in adults with scoliosis: a retrospective review of a prospective multicenter database with two-year follow-up.

Authors:  Justin S Smith; Christopher I Shaffrey; Sigurd Berven; Steven Glassman; Christopher Hamill; William Horton; Stephen Ondra; Frank Schwab; Michael Shainline; Kai-Ming G Fu; Keith Bridwell
Journal:  Spine (Phila Pa 1976)       Date:  2009-07-15       Impact factor: 3.468

7.  [Validation of a tool to measure pelvic and spinal parameters of sagittal balance].

Authors:  L Rillardon; N Levassor; P Guigui; P Wodecki; L Cardinne; A Templier; W Skalli
Journal:  Rev Chir Orthop Reparatrice Appar Mot       Date:  2003-05

8.  Improvement of back pain with operative and nonoperative treatment in adults with scoliosis.

Authors:  Justin S Smith; Christopher I Shaffrey; Sigurd Berven; Steven Glassman; Christopher Hamill; William Horton; Stephen Ondra; Frank Schwab; Michael Shainline; Kai-Ming Fu; Keith Bridwell
Journal:  Neurosurgery       Date:  2009-07       Impact factor: 4.654

9.  Does treatment (nonoperative and operative) improve the two-year quality of life in patients with adult symptomatic lumbar scoliosis: a prospective multicenter evidence-based medicine study.

Authors:  Keith H Bridwell; Steven Glassman; William Horton; Christopher Shaffrey; Frank Schwab; Lukas P Zebala; Lawrence G Lenke; Joan F Hilton; Michael Shainline; Christine Baldus; David Wootten
Journal:  Spine (Phila Pa 1976)       Date:  2009-09-15       Impact factor: 3.468

10.  Random generalized linear model: a highly accurate and interpretable ensemble predictor.

Authors:  Lin Song; Peter Langfelder; Steve Horvath
Journal:  BMC Bioinformatics       Date:  2013-01-16       Impact factor: 3.169

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  11 in total

Review 1.  State-of-the-art: outcome assessment in adult spinal deformity.

Authors:  Jeffrey L Gum; Leah Y Carreon; Steven D Glassman
Journal:  Spine Deform       Date:  2020-10-09

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

Review 3.  Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review.

Authors:  Mark E Stephens; Christen M O'Neal; Alison M Westrup; Fauziyya Y Muhammad; Daniel M McKenzie; Andrew H Fagg; Zachary A Smith
Journal:  Neurosurg Rev       Date:  2021-09-07       Impact factor: 3.042

4.  Validation of the ACS-NSQIP Risk Calculator: A Machine-Learning Risk Tool for Predicting Complications and Mortality Following Adult Spinal Deformity Corrective Surgery.

Authors:  Katherine E Pierce; Bhaveen H Kapadia; Sara Naessig; Waleed Ahmad; Shaleen Vira; Carl Paulino; Michael Gerling; Peter G Passias
Journal:  Int J Spine Surg       Date:  2021-12

5.  SMART on FHIR in spine: integrating clinical prediction models into electronic health records for precision medicine at the point of care.

Authors:  Aditya V Karhade; Joseph H Schwab; Guilherme Del Fiol; Kensaku Kawamoto
Journal:  Spine J       Date:  2020-06-26       Impact factor: 4.297

6.  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 7.  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 8.  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

9.  Artificial Intelligence for Adult Spinal Deformity.

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

10.  The Influence of Baseline Clinical Status and Surgical Strategy on Early Good to Excellent Result in Spinal Lumbar Arthrodesis: A Machine Learning Approach.

Authors:  Pedro Berjano; Francesco Langella; Luca Ventriglia; Domenico Compagnone; Paolo Barletta; David Huber; Francesca Mangili; Ginevra Licandro; Fabio Galbusera; Andrea Cina; Tito Bassani; Claudio Lamartina; Laura Scaramuzzo; Roberto Bassani; Marco Brayda-Bruno; Jorge Hugo Villafañe; Lorenzo Monti; Laura Azzimonti
Journal:  J Pers Med       Date:  2021-12-16
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