Maximilian Michael Saller

Introduction: The measurement of physical frailty in elderly patients with orthopedic impairments remains a challenge due to its subjectivity, unreliability, time-consuming nature, and limited applicability to uninjured individuals. Our study aims to address this gap by developing objective, multifactorial machine models that do not rely on mobility data and subsequently validating their predictive capacity concerning the Timed-up-and-Go test (TUG test) in orthogeriatric patients. Methods: We utilized 67 multifactorial non-mobility parameters in a pre-processing phase, employing six feature selection algorithms. Subsequently, these parameters were used to train four distinct machine learning algorithms, including a generalized linear model, a support vector machine, a random forest algorithm, and an extreme gradient boost algorithm. The primary goal was to predict the time required for the TUG test without relying on mobility data. Results: The random forest algorithm yielded the most accurate estimations of the TUG test time. The best-performing algorithm demonstrated a mean absolute error of 2.7 s, while the worst-performing algorithm exhibited an error of 7.8 s. The methodology used for variable selection appeared to exert minimal influence on the overall performance. It is essential to highlight that all the employed algorithms tended to overestimate the time for quick patients and underestimate it for slower patients. Conclusion: Our findings demonstrate the feasibility of predicting the TUG test time using a machine learning model that does not depend on mobility data. This establishes a basis for identifying patients at risk automatically and objectively assessing the physical capacity of currently immobilized patients. Such advancements could significantly contribute to enhancing patient care and treatment planning in orthogeriatric settings.

Background: Low energy falls (LEF) in older adults constitute a relevant cause for emergency department (ED) visits, hospital admission and in-hospital mortality. Patient-reported outcome measures containing information about patients’ medical, mental and social health problems might support disposition and therapy decisions. We investigated the value of a tablet-based (self-)assessment in predicting hospital admission and in-hospital mortality. Methods: Patients 65 years or older, consecutively presenting with LEF to our level I trauma center ED (from November 2020 to March 2021), were eligible for inclusion in this prospective observational study. The primary endpoint was hospital admission; secondary endpoints were in-hospital mortality and the use of the tablet for self-reported assessment. Multivariate logistic regression models were calculated to measure the association between clinical findings and endpoints. Results: Of 618 eligible patients, 201 patients were included. The median age was 82 years (62.7% women). The hospital admission rate was 45.3% (110/201), with an in-hospital mortality rate of 3.6% (4/110). Polypharmacy (odds ratio (OR): 8.48; 95% confidence interval (95%CI) 1.21–59.37, p = 0.03), lower emergency severity index (ESI) scores (OR: 0.33; 95%CI 0.17–0.64, p = 0.001) and increasing injury severity score (ISS) (OR: 1.54; 95%CI 1.32–1.79, p < 0.001) were associated with hospital admission. The Charlson comorbidity index (CCI) was significantly associated with in-hospital mortality (OR: 2.60; 95%CI: 1.17–5.81, p = 0.03). Increasing age (OR: 0.94; 95%CI: 0.89–0.99, p = 0.03) and frailty (OR: 0.71; 95%CI: 0.51–0.99, p = 0.04) were associated with the incapability of tablet use. Conclusions: The severity of fall-related injuries and the clinical acuity are easily accessible, relevant predictors for hospital admission. Tablet-based (self-)assessment may be feasible and acceptable during ED visits and might help facilitate comprehensive geriatric assessments during ED stay.