Prognostic Factors for Short-term Recovery of Independence in a Multistate Model for Patients With a Hip Fracture.

This study investigates the transitions of community-dwelling patients with a proximal femoral fracture towards recovery of independence using multistate modeling. The prognostic value of factors affecting the short-term rate of recovery of independence in activities of daily living was assessed for the resilient portion of the population. An inception cohort was recruited between 2016 and 2019. Only community-dwelling older patients admitted with a proximal femoral fracture were included. Follow-up was performed at 6 weeks and 3 months, when the patients' living situation and level of independence were recorded. Multistate modeling was used to study the transition rates of the population through prespecified states of the recovery process. Using this model, prognostic factors for the recovery of independence were identified for resilient patients (defined as those patients who managed to return home at any point in the follow-up after discharge). A total of 558 patients were included, and 218 (40.9%) recovered to prefracture levels of independence. Of the resilient patients, 20.7% were discharged home directly, and 79.3% via a rehabilitation home. In this patient group, a more favorable American Society of Anesthesiologists classification, better prefracture mobility, and the absence of a prefracture fear of falling were statistically significantly associated with a successful recovery. A low level of prefracture independence was inversely associated, meaning that patients with a low level of prefracture independence had a higher chance of successful recovery. This study identified 4 factors with an independent prognostic value for the recovery of independence in resilient patients after a proximal femoral fracture. These factors could be used to construct clinical profiles that contribute to the assessment of the patient's post-acute care needs and recovery capacity. In addition, multistate modeling has been shown to be an effective and versatile tool in the study of recovery prognostics. [ABSTRACT FROM AUTHOR]