Abstract
Objectives Toulouse Saint Louis University Mini Falls Assessment (TSLUMFA) tool has been designed to predict falls. It was initially validated in a geriatric clinic in 2018. The primary objective was to evaluate the predictive capacity of the TSLUMFA for incident falls in older adults residing in nursing homes. The secondary objective was to determine the TSLUMFA optimal cut-off value identifying those older adults with a high-risk of falling. Settings A longitudinal study was carried out over a period of six months. Participants 93 older adults residing in nursing homes were evaluated for the present study. Measurements The TSLUMFA (made up of 7 criteria) was administered at baseline, and incident falls were recorded based on a registry of falls. Comparisons of TSLUMFA scores between fallers and non-fallers were performed using the U Mann-Whitney test or Chi(2). Correlation between the total TSLUMFA score (/30 points) and incident fall(s) was explored using the Cox proportional hazard model. ROC analysis enabled an optimal cut-off value to be established to identify those adults at the highest-risk of falling. Results In the study, 93 older adults (61.3% women) with a median age of 80 (69-87) years were included. The median total TSLUMFA score was 21 (19-24.5) points. During the 6-month study period, 38 subjects (40.9%) experienced at least one fall. The total TSLUMFA score in older adults with incident fall(s) was significantly lower than in those who did not fall (20 (15.75-22.25) points versus 23 (20-25) points and a p-value of <0.001). For each 1-point higher score at the total TSLUMFA a 9% less chance of falling was observed during the study period (p-value = 0.006). The AUC was 0.736 (95%CI: 0.617-0.822) and p-value <0.001, clearly demonstrating its interesting performance as a screening tool. A score of <= 21 points was identified as the optimal cut-off to identify those older adults at a higher-risk of falling. Conclusion The TSLUMFA performed well and successfully identified older adults with a high risk of falling in a nursing home setting. Further comparisons with existing tools are warranted.