Literature DB >> 31252385

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

Ferran Pellisé1, Miquel Serra-Burriel2, Justin S Smith3, Sleiman Haddad1, Michael P Kelly4, Alba Vila-Casademunt5, Francisco Javier Sánchez Pérez-Grueso6, Shay Bess7, Jeffrey L Gum8, Douglas C Burton9, Emre Acaroğlu10, Frank Kleinstück11, Virginie Lafage12, Ibrahim Obeid13, Frank Schwab12, Christopher I Shaffrey3, Ahmet Alanay14, Christopher Ames15.   

Abstract

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.

Entities:  

Keywords:  ACS NSQIP = American College of Surgeons National Surgical Quality Improvement Program; ASD = adult spinal deformity; EBL = estimated blood loss; LIV = lowest instrumented vertebra; MC = major complication; ODI = Oswestry Disability Index; OOB = out of bag; PROM = patient-reported outcome measure; RA = readmission; RO = reoperation; SF-36 = SF-36v2 Health Survey; SRS-22r = Scoliosis Research Society 22-item patient outcome questionnaire; adult spinal deformity surgery; prognostic models; risk stratification

Year:  2019        PMID: 31252385     DOI: 10.3171/2019.3.SPINE181452

Source DB:  PubMed          Journal:  J Neurosurg Spine        ISSN: 1547-5646


  8 in total

1.  External Validation of the European Spine Study Group-International Spine Study Group Calculator Utilizing a Single Institutional Experience for Adult Spinal Deformity Corrective Surgery.

Authors:  Peter G Passias; Sara Naessig; Ashok Para; Katherine Pierce; Waleed Ahmad; Bassel G Diebo; Renaud Lafage; Virginie Lafage; Justin S Smith; Burhan Janjua
Journal:  Int J Spine Surg       Date:  2022-07-31

2.  Surgeons' risk perception in ASD surgery: The value of objective risk assessment on decision making and patient counselling.

Authors:  Ferran Pellisé; Alba Vila-Casademunt; Susana Núñez-Pereira; Sleiman Haddad; Justin S Smith; Michael P Kelly; Ahmet Alanay; Christopher Shaffrey; Javier Pizones; Çaglar Yilgor; Ibrahim Obeid; Douglas Burton; Frank Kleinstück; Tamas Fekete; Shay Bess; Munish Gupta; Markus Loibl; Eric O Klineberg; Francisco J Sánchez Pérez-Grueso; Miquel Serra-Burriel; Christopher P Ames
Journal:  Eur Spine J       Date:  2022-03-28       Impact factor: 2.721

Review 3.  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 4.  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

5.  Artificial Intelligence for Adult Spinal Deformity.

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

6.  Artificial Intelligence and the Future of Spine Surgery.

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

7.  Quantifying the collective influence of social determinants of health using conditional and cluster modeling.

Authors:  Zachary D Rethorn; Alessandra N Garcia; Chad E Cook; Oren N Gottfried
Journal:  PLoS One       Date:  2020-11-05       Impact factor: 3.240

8.  Spine Surgery and Preoperative Hemoglobin, Hematocrit, and Hemoglobin A1c: A Systematic Review.

Authors:  Krishna V Suresh; Kevin Wang; Ishaan Sethi; Bo Zhang; Adam Margalit; Varun Puvanesarajah; Amit Jain
Journal:  Global Spine J       Date:  2021-01-21
  8 in total

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