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. 2019 Mar 15;27(6):e293-e300.
doi: 10.5435/JAAOS-D-17-00447.

Predictive Modeling for Geriatric Hip Fracture Patients: Early Surgery and Delirium Have the Largest Influence on Length of Stay

Garin Hecht  1 , Christina A Slee, Parker B Goodell, Sandra L Taylor, Philip R Wolinsky
Affiliations

Predictive Modeling for Geriatric Hip Fracture Patients: Early Surgery and Delirium Have the Largest Influence on Length of Stay

Garin Hecht et al. J Am Acad Orthop Surg. .

Abstract

Background: Averaging length of stay (LOS) ignores patient complexity and is a poor metric for quality control in geriatric hip fracture programs. We developed a predictive model of LOS that compares patient complexity to the logistic effects of our institution's hip fracture care pathway.

Methods: A retrospective analysis was performed on patients enrolled into a hip fracture co-management pathway at an academic level I trauma center from 2014 to 2015. Patient complexity was approximated using the Charlson Comorbidity Index and ASA score. A predictive model of LOS was developed from patient-specific and system-specific variables using a multivariate linear regression analysis; it was tested against a sample of patients from 2016.

Results: LOS averaged 5.95 days. Avoidance of delirium and reduced time to surgery were found to be notable predictors of reduced LOS. The Charlson Comorbidity Index was not a strong predictor of LOS, but the ASA score was. Our predictive LOS model worked well for 63% of patients from the 2016 group; for those it did not work well for, 80% had postoperative complications.

Discussion: Predictive LOS modeling accounting for patient complexity was effective for identifying (1) reasons for outliers to the expected LOS and (2) effective measures to target for improving our hip fracture program.

Level of evidence: III.

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Conflict of interest statement

Conflict of Interest: The authors declare that they have no conflict of interest.

Figures

Figure 1.
Figure 1.
Actual LOS of consecutive 2016 patients plotted against the predicted LOS value. The grey zone is ±1.5 days from the predicted LOS, chosen as a clinically relevant accuracy of our predicted modeling.
See this image and copyright information in PMC

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