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Erschienen in:
21.02.2017 | Original Paper
Predicting the post-operative length of stay for the orthopaedic trauma patient
Erschienen in: International Orthopaedics | Ausgabe 5/2017
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Purpose
Length of stay (LOS) is a major driver of cost and quality of care. A bundled payment system makes it essential for orthopaedic surgeons to understand factors that increase a patient’s LOS. Yet, minimal data regarding predictors of LOS currently exist. Using the ACS-NSQIP database, this is the first study to identify risk factors for increased LOS for orthopaedic trauma patients and create a personalized LOS calculator.
Methods
All orthopaedic trauma surgery between 2006 and 2013 were identified from the ACS-NSQIP database using CPT codes. Patient demographics, pre-operative comorbidities, anatomic location of injury, and post-operative in-hospital complications were collected. To control for individual patient comorbidities, a negative binomial regression model evaluated hospital LOS after surgery. Betas (β), were determined for each pre-operative patient characteristic. We selected significant predictors of LOS (p < 0.05) using backwards stepwise elimination.
Results
49,778 orthopaedic trauma patients were included in the analysis. Deep incisional surgical site infections and superficial surgical site infections were associated with the greatest percent change in predicted LOS (β = 1.2760 and 1.2473, respectively; p < 0.0001 for both). A post-operative LOS risk calculator was developed based on the formula: \( {\mathbf{e}}^{\left(\boldsymbol{intercept}+{\boldsymbol{\beta}}_1{\boldsymbol{X}}_1 + {\boldsymbol{\beta}}_2{\boldsymbol{X}}_2+\dots \right)} \).
Conclusions
Utilizing a large prospective cohort of orthopaedic trauma patients, we created the first personalized LOS calculator based on pre-operative comorbidities, post-operative complications and location of surgery. Future work may assess the use of this calculator and attempt to validate its utility as an accurate model. To improve the quality measures of hospitals, orthopaedists must employ such predictive tools to optimize care and better manage resources.