Correlation of Neuro-Qol Lower Extremity Function with legacy scores in older patients after hip fracture: influence of falls

Introduction

Scales for measuring quality of life and functionality are transforming to allow patients to describe how they perceive their health status. In this study, we aimed to use technology to evaluate whether the Neuro-QoL-Lower Extremity Function-CAT (NQoL-CAT) correlates with traditional functional assessment scales (Barthel Index and Functional Ambulation Classification-FAC) in elderly patients with hip fractures and check how history of falls affects it.

Method

Prospective study that included patients over 69 years admitted to the orthogeriatric unit for 12 months. Sociodemographic, functional and clinical variables were measured at baseline, 3 months and 12 months. Non-parametric analysis (Spearman's Rho) was performed to determine the observed correlation between the three scales at the 3 time points, and in patients with history of falls.

Results

105 patients were included, mean age 87 years. Baseline function: Barthel: 79, FAC: 4, NQoL: 42.2. 38% had at least 2 falls in the last year. At discharge, 66% received home physiotherapy, 20% recovery in a skilled nursing facility, and 13% were transferred for in-hospital rehabilitation. Functional status at 3 months: Barthel: 69, FAC: 3.5, NQoL: 33.2, and 12% were institutionalized. Functional status at 12 months: Barthel: 70, FAC: 3.7, NQoL: 38.5, and 20% of patients were institutionalized. The correlation of NQoL with Barthel and FAC was: 0.61 and 0.65 at baseline, 0.79 and 0.76 at 3 months, and 0.85 and 0.86 at 12 months (p=0.01). The correlation was stronger in the baseline falls subgroup.

Conclusions

There is a high correlation between the NQoL-CAT and traditional functional assessment scales in older adults after a hip fracture, and even stronger in patients with falls history, suggesting that the use of technology implementing this scale could optimize the measurement of their functional recovery in the future, to find predictive models and design potential intervention programs.