A Simple Algorithm to Predict Falls in Primary Care Patients Aged 65 to 74 Years: The International Mobility in Aging Study

Abstract

Objective: Primary care practitioners need simple algorithms to identify older adults at higher risks of falling. Classification and regression tree (CaRT) analyses are useful tools for identification of clinical predictors of falls.

Design: Prospective cohort.

Setting: Community-dwelling older adults at 5 diverse sites: Tirana (Albania), Natal (Brazil), Manizales (Colombia), Kingston (Ontario, Canada), and Saint-Hyacinthe (Quebec, Canada).

Participants: In 2012, 2002 participants aged 65-74 years from 5 international sites were assessed in the International Mobility in Aging Study. In 2014 follow-up, 86% of the participants (n = 1718) were reassessed.

Measurements: These risk factors for the occurrence of falls in 2014 were selected based on relevant literature and were entered into the CaRT as measured at baseline in 2012: age, sex, body mass index, multimorbidity, cognitive deficit, depression, number of falls in the past 12 months, fear of falling (FoF) categories, and timed chair-rises, balance, and gait.

Results: The 1-year prevalence of falls in 2014 was 26.9%. CaRT procedure identified 3 subgroups based on reported number of falls in 2012 (none, 1, ≥2). The 2014 prevalence of falls in these 3 subgroups was 20%, 30%, and 50%, respectively. The "no fall" subgroup was split using FoF: 30% of the high FoF category (score >27) vs 20% of low and moderate FoF categories (scores: 16-27) experienced a fall in 2014. Those with multiple falls were split by their speed in the chair-rise test: 56% of the slow category (>16.7 seconds) and the fast category (<11.2 seconds) had falls vs 28% in the intermediate group (between 11.2 and 16.7 seconds). No additional variables entered into the decision tree.

Conclusions: Three simple indicators: FoF, number of previous falls, and time of chair rise could identify those with more than 50% probability of falling.

Keywords: Accidental falls; logistic regression tree; older adults; risk factors.