Predictive modeling of length of hospital stay following adult spinal deformity correction: Analysis of 653 patients with an accuracy of 75% within 2 days.

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Predictive modeling of length of hospital stay following adult spinal deformity correction: Analysis of 653 patients with an accuracy of 75% within 2 days.

World Neurosurg. 2018 Apr 17;:

Authors: Safaee MM, Scheer JK, Ailon T, Smith JS, Hart RA, Burton DC, Bess S, Neuman BJ, Passias PG, Miller E, Shaffrey CI, Schwab F, Lafage V, Klineberg EO, Ames CP, International Spine Study Group

Abstract
BACKGROUND: Length of stay (LOS) following adult spinal deformity (ASD) surgery is a critical period that allows for optimal recovery. Predictive models that estimate length of stay allow for stratification of high risk patients.
METHODS: A prospectively acquired multi-center database of ASD patients was used. Those 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 bootstrapped sample; patients not randomly selected for the bootstrapped sample were selected to the training dataset. LOS predictions were compared to actual LOS to calculate an accuracy percentage.
RESULTS: A total of 653 patients met inclusion criteria. The mean LOS was 7.9±4.1 days (median: 7, range: 1-28). Following bootstrapping, a total of 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: We present a model that successfully predicts LOS following ASD surgery with an accuracy of 75% within 2 days. Factors relating to actual LOS are not captured in large prospective datasets such as rehabilitation bed availability and social support resources. Predictive analytics will play an increasing role in the future of ASD and future models will seek to improve the accuracy of these tools.

PMID: 29678702 [PubMed - as supplied by publisher]

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