Hip Fracture, Hip Fracture Surgeries
Conditions
Keywords
hip fracture, hip surgery, mortality, prediction models
Brief summary
This study aims to externally validate ten existing prediction models with a low risk of bias for 30-day mortality following hip fracture. Data will be collected from the Dutch Hip Fracture Audit (DHFA) and supplemented with structured and unstructured data extracted through text mining using CTcue. Approximately 35 clinical variables will be used, including factors consistently associated with short-term mortality. The primary outcome is all-cause mortality within 30 days after hip fracture. Predictive performance will be assessed through discrimination (AUC), explained variance (R²), and calibration analysis. Clinical usefulness will be evaluated using Net Benefit and Decision Curve Analysis. This study seeks to identify models with strong predictive performance and practical applicability to support shared decision-making between clinicians and patients.
Detailed description
Hip fractures are a major health concern, especially among older adults, and are associated with substantial morbidity, mortality, and healthcare costs. While surgical intervention is standard practice for most patients, a growing number of cases require careful consideration of operative versus non-operative management based on individual risk profiles and patient preferences. Several prediction models have been developed to estimate the risk of short-term mortality after hip fracture, but many have shown only moderate predictive performance or lacked clinical applicability. In 2024, a systematic review identified ten models with a low risk of bias, based on methodological criteria such as adequate sample size, proper handling of missing data, internal validation, and assessment of calibration. This study aims to externally validate these ten prediction models using data from the Dutch Hip Fracture Audit (DHFA) combined with additional structured and unstructured clinical information extracted through CTcue, a text-mining software tool. Approximately 35 variables, including key preoperative factors such as age, sex, ASA score, institutionalization, and metastatic cancer, will be analyzed. Missing data will be addressed through multiple imputation. The primary outcome is 30-day all-cause mortality following a hip fracture. Validation of the models will involve evaluation of predictive performance through discrimination (area under the curve \[AUC\]), explained variance (R²), and calibration curves. The DeLong test will be used to statistically compare model AUCs. Clinical usefulness will be assessed by calculating Net Benefit and conducting Decision Curve Analysis. By rigorously validating these models in a large, real-world cohort, the study aims to identify which models offer both strong predictive accuracy and practical feasibility for supporting shared decision-making between clinicians and patients.
Interventions
None listed
Sponsors
JointResearch
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Patients admitted to OLVG between January 1, 2016, and March 1, 2024, with a diagnosis of hip fracture; and/or. * Patients who underwent surgery at OLVG between January 1, 2016, and March 1, 2024, with an indication of hip fracture; and. * Availability of independent predictor variables for at least one of the selected prediction models in the medical record.
Exclusion criteria
* Patients with unknown 30-day follow-up mortality status. * Patients with periprosthetic fractures (fractures around an existing hip prosthesis) at the time of presentation. * Patients aged under 18 years at the time of hip fracture. * Patients who declined the use of their medical data for research purposes.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 30-day mortality | 30 days post-fracture | Occurrence of death from any cause within 30 days following hip fracture diagnosis or hospital admission. Mortality status (yes/no) will be determined based on hospital records and confirmed via the Dutch national population registry (Basisregistratie Personen, BRP). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Discriminative Ability (AUC) | 30 days post-fracture | Area Under the Receiver Operating Characteristic Curve (AUC) for each prediction model, measuring the ability to distinguish between patients who die and those who survive within 30 days post-fracture. |
Other
| Measure | Time frame | Description |
|---|---|---|
| Comparison of Model Discrimination (DeLong Test) | 30 days post-fracture | Statistical comparison of AUCs between prediction models using the DeLong test to evaluate significant differences in discriminative performance. |
| Explained Variance (R²) | 30 days post-fracture | Proportion of variance in 30-day mortality explained by each prediction model, assessed using pseudo R² statistics. |
| Calibration | 30 days post-fracture | Assessment of calibration for each model by plotting predicted versus observed 30-day mortality risks, including calibration curves and statistical calibration slopes/intercepts. |
| Clinical Usefulness (Net Benefit and Decision Curve Analysis) | 30 days post-fracture | Evaluation of the clinical usefulness of each model through Net Benefit calculation and Decision Curve Analysis, to assess potential impact on shared decision-making. |
Countries
Netherlands
Outcome results
None listed