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INTRODUCTION: Typically, a healthcare intervention is evaluated by comparing data before and after its implementation using statistical tests. Comparing group means can miss underlying trends and lead to erroneous conclusions. Segmented linear regression can be used to reveal secular trends but is susceptible to outliers. We described a novel method using segmented robust regression techniques to evaluate the effect of introducing a dedicated hip fracture unit (HFU). METHODS: We retrospectively analysed patient outcomes from a total of 2777 patients sustaining proximal femoral fragility fractures over a 6-year period at a Level 1 Major Trauma Centre. We compared time to surgical intervention and length of hospital stay before and after the implementation of the HFU using group comparison tests, segmented ordinary regression and robust regression techniques to evaluate the effect of the intervention. RESULTS: Group comparison tests did not identify a significant difference in time to surgery pre and post- HFU. Segmented regression revealed that there was a significant reduction in time to surgery but that this predated the introduction of the HFU. Group comparison tests did not identify a significant difference in length of stay pre and post-HFU. Ordinary segmented regression demonstrated that there was a constant reduction in length of stay, which accelerated after the introduction of the HFU. Robust regression identified that this change occurred prior to the HFU. DISCUSSION: There was a significant decrease in time to surgical intervention during the study period that occurred long before the introduction of the HFU, and that cannot be attributed to the HFU itself. Length of stay started dropping early in the study period and was unrelated to the HFU. However, with robust regression we concluded that the HFU was effective in reducing relatively long hospital stays (outliers). Several explanatory factors that may have affected the observed trends in time to surgery and length of stay were identified. CONCLUSION: Robust regression is a useful adjunct to ordinary segmented linear regression techniques in modelling retrospective time-series and dealing with outliers. The changes observed in hip fracture patient outcomes over a 6-year period was likely multifactorial.

Original publication

DOI

10.1016/j.injury.2020.03.056

Type

Journal article

Journal

Injury

Publication Date

11/05/2020

Keywords

Hip Fractures, Linear Models, Quality Improvement, Regression Analysis