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Impact of Dementia on Long-Term Hip Fracture Surgery Outcomes: An Electronic Health Record Analysis

Published:December 13, 2022DOI:https://doi.org/10.1016/j.jamda.2022.11.006

      Abstract

      Objective

      Older adults with dementia are at higher risk for sustaining hip fracture and their long-term health outcomes after surgery are usually worse than those without dementia. Widespread adoption of electronic health records (EHRs) may allow hospitals to better monitor long-term health outcomes in patients with dementia after hospitalization. This study aimed to (1) estimate how dementia influences discharge location, mortality, and readmission 180 days and 1 year after hip fracture surgery in older adults, and (2) demonstrate the feasibility of using selection-bias reduced EHR data for research and long-term health outcomes monitoring.

      Design

      Retrospective observational cohort study using EHRs.

      Setting and Participants

      A cohort of 1171 patients over age 65 years who had an initial hip fracture surgery between October 2015 and December 2018 was extracted from EHRs of one health system; 376 of these patients had dementia.

      Methods

      Logistic regression was applied to estimate influences of dementia on discharge disposition and Cox proportional hazards model for mortality. The Fine and Gray regression model was used to analyze readmission, accounting for the competing risk of death. To reduce selection bias in EHRs, inverse probability of treatment weighting using propensity scores was implemented before modeling.

      Results

      Dementia had significant impacts on all outcomes: being discharged to facilities [odds ratio (OR) = 2.11, 95% confidence interval (CI) 1.19–3.74], 180-day mortality [hazard ratio (HR) = 1.69, 95% CI 1.20–2.38], 1-year mortality (HR = 1.78, 95% CI 1.33–2.38), 180-day readmission (HR = 1.62, 95% CI 1.39–1.89), and 1 year readmission (HR = 1.39, 95% CI 1.21–1.58).

      Conclusions and Implications

      Dementia was a significant risk factor for worse long-term outcomes. The inverse probability of treatment weighting approach can be used to reduce selection bias in EHR data for research and monitoring long-term health outcomes in the target population. Such monitoring could foster collaborations with post-acute and long-term health care services to improve recovery outcomes in patients with dementia after hip fracture surgery.

      Keywords

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