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Published on: August 17, 2017
Prognostic factors and prediction model for 1-year mortality after proximal humeral fracture
Bastiaan Van Grootven1, Sigrid Janssens2, Laurence De Keyser2
1Department of Public Health and Primary Care, Academic Centre for Nursing and Midwifery, KU Leuven, Leuven, Belgium.
Insights
Six pre-fracture patient characteristics can predict 1-year mortality after proximal humeral fracture (PHF). This clinical model aids in making informed treatment decisions for PHF patients.
Area of Science:
- Gerontology
- Orthopedic Surgery
- Public Health
Background:
- Proximal humeral fractures (PHF) are common in older adults and associated with increased mortality risk.
- Identifying factors predicting post-fracture mortality is crucial for patient management.
Purpose of the Study:
- To investigate the utility of patient characteristics in predicting 1-year mortality following proximal humeral fracture (PHF).
- To develop and validate a clinical prediction model for post-PHF mortality.
Main Methods:
- Retrospective study of 261 patients aged 65+ treated for PHF.
- Collected baseline data including demographics, residency, and comorbidities.
- Developed a prediction model using LASSO regression and validated it with split-sample and bootstrapping methods.
Main Results:
- 10.3% of participants died within 1 year.
- Key predictors for survival included pre-fracture ambulation, living at home, younger age, higher BMI, female gender, and fewer comorbidities.
- A 6-predictor model (age, gender, comorbidity score, BMI, cognitive impairment, nursing home residency) showed good discrimination (0.891 training, 0.878 validation).
Conclusions:
- A clinical prediction model combining 6 pre-fracture characteristics effectively predicts 1-year mortality after PHF.
- These findings can inform treatment decisions for patients with proximal humeral fractures.
Abstract:
The goal was to investigate if patient characteristics can be used to predict 1-year post-fracture mortality after proximal humeral fracture (PHF). A clinical prediction model showed that the combination of 6 pre-fracture characteristics demonstrated good predictive properties for mortality within 1 year of PHF.
Introduction:
Proximal humeral fractures (PFH) are the third most common major non-vertebral osteoporotic fractures in older persons and result in an increased mortality risk. The aim of this study was to investigate if patient characteristics can be used to predict 1-year post-fracture mortality.
Methods:
Retrospective study with 261 patients aged 65 and older who were treated for a PHF in University Hospitals Leuven between 2016 and 2018. Baseline variables including demographics, residential status, and comorbidities were collected. The primary outcome was 1-year mortality. A clinical prediction model was developed using LASSO regression and validated using split sample and bootstrapping methods. The discrimination and calibration were evaluated.
Results:
Twenty-seven (10.3%) participants died within 1-year post-PHF. Pre-fracture independent ambulation (p < 0.001), living at home at time of fracture (p < 0.001), younger age (p = 0.006), higher BMI (p = 0.012), female gender (p = 0.014), and low number of comorbidities (p < 0.001) were predictors for 1-year survival. LASSO regression identified 6 stable predictors for a prediction model: age, gender, Charlson comorbidity score, BMI, cognitive impairment, and pre-fracture nursing home residency. The discrimination was 0.891 (95% CI, 0.833 to 0.949) in the training sample, 0.878 (0.792 to 0.963) in the validation sample and 0.756 (0.636 to 0.876) in the bootstrapping samples. A similar performance was observed for patients with and without surgery. The developed model demonstrated good calibration.
Conclusions:
The combination of 6 pre-fracture characteristics demonstrated good predictive properties for mortality within 1 year of PHF. These findings can guide PHF treatment decisions.
