Hip fractures in especially older adults cause severe clinical and functional impacts. Despite improved surgical care, one year mortality remains 14-30%, and fewer than half of the survivors regain their pre-fracture functional status. After a hip fracture, patients are primarily concerned with what they will be able to do in daily life. Wihout accurate predictions of mobility and Activities of Daily Life (ADL) independence, it is difficult to set realistic expectations and make appropriate decisions regarding treatment and rehabilitation. While there have been advancements in developing predictive models for mortality following hip fractures, there is a notable gap in models focused on predicting functional recovery. This study aims to develop and validate a machine learning-based model that can predict mobility and ADL independence three months after obtaining a hip fracture.

Inclusion Criteria:

• Patient has been treated for a hip fracture in OLVG hospital betweek 01-01-2016 and 31-12-2025
• Patient was 18 years or older at the time of the injury

Exclusion Criteria:

• The patient does not grant permission for the use of their data for studies

The study population will involve patients who have obtained a hip fracture between 01-01-2016 and 31-12-2025 and who were 18 years or older at the time of the injury. To be included in the initial dataset it is necessary for the patient to have a value for baseline (pre-fracture) ADL independence and/or mobility, and to have an outcome at three months for ADL independence and/or mobility.