Prediction Model for Discharge Destination in Older Patients After Hip Fracture Surgery Using Early Predictors

Objectives: To identify early predictors of discharge destination in patients aged ≥70 years following hip fracture surgery, using the Dutch Hip Fracture Audit (DHFA) Indicator Taskforce database with the aim of developing and validating a prediction model. Methods: Patients living in a (residential) home prior to the hip fracture were categorized into three discharge groups: home (n = 1322), geriatric rehabilitation (n = 2181), and nursing home (n = 337). The dataset was randomly split into a training set (75%) and a test set (25%). Potential predictors of discharge destination were collected immediately after hip fracture surgery and first tested univariately in the training set. Variables showing p < 0.15 were entered into a multinomial regression model. The model was validated on the test set. Results: The training set consisted of 2878 patients (median age: 83 years (IQR 77–89 years), 2012 females (70%)). The final multinomial regression model included age, gender, PFMS, premorbid Katz-ADL, ASA score, polypharmacy, history of dementia, delirium during previous hospital admissions, fall in the past six months, surgical treatment, anaesthesia type, and co-treatment by a geriatrician. The strength and direction of the associations between these variables and discharge destination varied across the three pairwise comparisons. The validated model predicted discharge to geriatric rehabilitation, home, and a nursing home with sensitivities of 86.7%, 37.9%, and 9.6%, respectively. Precision ranged from 33.3% (nursing home) to 60.9% (home) and 66.7% (geriatric rehabilitation). Conclusions: Discharge destination could be predicted with relatively high sensitivity and moderate precision for geriatric rehabilitation, whereas sensitivity and precision for predicting discharge home and to a nursing home remained limited. This highlights the need to refine the model before clinical implementation can be considered.

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Publication Details

Journal
Geriatrics
Published
2026-09-11
DOI
https://doi.org/10.3390/geriatrics11050130
Primary Topic
Hip and Femur Fractures
Type
article
Field-Weighted Citation Impact
0.00
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article

Prediction Model for Discharge Destination in Older Patients After Hip Fracture Surgery Using Early Predictors

Miriam Vollenbroek-Hutten, Dieuwke van Dartel, Marloes Vermeer, Johannes H. Hegeman et al.
Geriatrics
Hip and Femur Fractures
article

Prediction Model for Discharge Destination in Older Patients After Hip Fracture Surgery Using Early Predictors

Miriam Vollenbroek-Hutten, Dieuwke van Dartel, Marloes Vermeer, Johannes H. Hegeman, Sanne Krakers
article en

Abstract

Objectives: To identify early predictors of discharge destination in patients aged ≥70 years following hip fracture surgery, using the Dutch Hip Fracture Audit (DHFA) Indicator Taskforce database with the aim of developing and validating a prediction model. Methods: Patients living in a (residential) home prior to the hip fracture were categorized into three discharge groups: home (n = 1322), geriatric rehabilitation (n = 2181), and nursing home (n = 337). The dataset was randomly split into a training set (75%) and a test set (25%). Potential predictors of discharge destination were collected immediately after hip fracture surgery and first tested univariately in the training set. Variables showing p < 0.15 were entered into a multinomial regression model. The model was validated on the test set. Results: The training set consisted of 2878 patients (median age: 83 years (IQR 77–89 years), 2012 females (70%)). The final multinomial regression model included age, gender, PFMS, premorbid Katz-ADL, ASA score, polypharmacy, history of dementia, delirium during previous hospital admissions, fall in the past six months, surgical treatment, anaesthesia type, and co-treatment by a geriatrician. The strength and direction of the associations between these variables and discharge destination varied across the three pairwise comparisons. The validated model predicted discharge to geriatric rehabilitation, home, and a nursing home with sensitivities of 86.7%, 37.9%, and 9.6%, respectively. Precision ranged from 33.3% (nursing home) to 60.9% (home) and 66.7% (geriatric rehabilitation). Conclusions: Discharge destination could be predicted with relatively high sensitivity and moderate precision for geriatric rehabilitation, whereas sensitivity and precision for predicting discharge home and to a nursing home remained limited. This highlights the need to refine the model before clinical implementation can be considered.

GeriatricsVol. 11(5)
Medisch Spectrum Twente (NL), Ziekenhuis Groep Twente (NL), University of Twente (NL)
Good health and well-being
Openalex Percentile: Top 8%
Hip and Femur Fractures
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