Michael M Safaee1, Justin K Scheer2, Tamir Ailon3, Justin S Smith4, Robert A Hart5, Douglas C Burton6, Shay Bess7, Brian J Neuman8, Peter G Passias9, Emily Miller8, Christopher I Shaffrey4, Frank Schwab10, Virginie Lafage10, Eric O Klineberg11, Christopher P Ames12. 1. Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA. Electronic address: Michael.Safaee@ucsf.edu. 2. Department of Neurosurgery, University of Illinois at Chicago, Chicago, Illinois, USA. 3. Department of Neurosurgery, The University of British Columbia, Vancouver, British Columbia, Canada. 4. Department of Neurosurgery, University of Virginia Health System, Charlottesville, Virginia, USA. 5. Department of Orthopedic Surgery, Oregon Health & Science University, Portland, Oregon, USA. 6. Department of Orthopedic Surgery, University of Kansas Medical Center, Kansas City, Kansas, USA. 7. Denver International Spine Clinic, Presbyterian St. Luke's Medical Center, Rocky Mountain Hospital for Children, Denver, Colorado, USA. 8. Department of Orthopedic Surgery, The Johns Hopkins University, Baltimore, Maryland, USA. 9. Department of Orthopedic Surgery, NYU Hospital for Joint Diseases, New York, New York, USA. 10. Department of Orthopedic Surgery, Hospital for Special Surgery, New York, New York, USA. 11. Department of Orthopedic Surgery, University of California Davis, Davis, California, USA. 12. Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA.
Abstract
BACKGROUND: Length of stay (LOS) after surgery for adult spinal deformity (ASD) is a critical period that allows for optimal recovery. Predictive models that estimate LOS allow for stratification of high-risk patients. METHODS: A prospectively acquired multicenter database of patients with ASD was used. Patients with staged surgery or LOS >30 days were excluded. Univariable predictor importance ≥0.90, redundancy, and collinearity testing were used to identify variables for model building. A generalized linear model was constructed using a training dataset developed from a bootstrap sample; patients not randomly selected for the bootstrap sample were selected to the training dataset. LOS predictions were compared with actual LOS to calculate an accuracy percentage. RESULTS: Inclusion criteria were met by 653 patients. The mean LOS was 7.9 ± 4.1 days (median 7 days; range, 1-28 days). Following bootstrapping, 893 patients were modeled (653 in the training model and 240 in the testing model). Linear correlations for the training and testing datasets were 0.632 and 0.507, respectively. The prediction accuracy within 2 days of actual LOS was 75.4%. CONCLUSIONS: Our model successfully predicted LOS after ASD surgery with an accuracy of 75% within 2 days. Factors relating to actual LOS, such as rehabilitation bed availability and social support resources, are not captured in large prospective datasets. Predictive analytics will play an increasing role in the future of ASD surgery, and future models will seek to improve the accuracy of these tools.
BACKGROUND: Length of stay (LOS) after surgery for adult spinal deformity (ASD) is a critical period that allows for optimal recovery. Predictive models that estimate LOS allow for stratification of high-risk patients. METHODS: A prospectively acquired multicenter database of patients with ASD was used. Patients with staged surgery or LOS >30 days were excluded. Univariable predictor importance ≥0.90, redundancy, and collinearity testing were used to identify variables for model building. A generalized linear model was constructed using a training dataset developed from a bootstrap sample; patients not randomly selected for the bootstrap sample were selected to the training dataset. LOS predictions were compared with actual LOS to calculate an accuracy percentage. RESULTS: Inclusion criteria were met by 653 patients. The mean LOS was 7.9 ± 4.1 days (median 7 days; range, 1-28 days). Following bootstrapping, 893 patients were modeled (653 in the training model and 240 in the testing model). Linear correlations for the training and testing datasets were 0.632 and 0.507, respectively. The prediction accuracy within 2 days of actual LOS was 75.4%. CONCLUSIONS: Our model successfully predicted LOS after ASD surgery with an accuracy of 75% within 2 days. Factors relating to actual LOS, such as rehabilitation bed availability and social support resources, are not captured in large prospective datasets. Predictive analytics will play an increasing role in the future of ASD surgery, and future models will seek to improve the accuracy of these tools.
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