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.

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.

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.

The study population consists of patients who underwent surgery for a hip fracture at OLVG between January 1, 2016, and June 6, 2024, including procedures with hemiarthroplasty or internal fixation. The average age of the study population is approximately 80 years. The cohort is inclusive due to the broad inclusion and minimal exclusion criteria, and data collection is based on an anonymous search strategy. This real-world population includes individuals who might not typically participate in randomized clinical trials, enhancing the generalizability of the results.

In addition, the study identifies patients admitted with a hip fracture who did not undergo surgery during the same period, allowing for a comparison of demographic and clinical characteristics between operated and non-operated patients.

Retrospective study using patient records without explicit consent for IPD sharing.