Opportunistic and systematic tools can be used to predict falls without additional mobility assessments, shows a recent UK study.
Data on 135,433 patients over the age of 65 years from 127 general practices were included in the study as part of the quality improvement in chronic kidney disease trial. Five categories of potential predictors for falls were identified – physical, diagnostic, pharmaceutical, lifestyle factors, and records of falls or fractures over five years. These categories featured specific predictors, and these were used to generate a decision tree and risk score, and assess falls risk in the study population. Follow-up took place over a 30-month period, with outcomes assessed including the occurrence of either a fall, a fracture, or both.
Of the individuals included, 10,766 experienced a fall or fracture during follow-up. The strongest predictors of falls from the risk profile were age over 70, female sex, previous fall, nocturia, antidepressant use, and urinary incontinence – each of these factors increased falls risk by at least 30%, compared to individuals aged under 70. Medication for hypertension did not increase the risk of falls. Females aged over 75 years and subjects with a previous fall were at the highest risk of falls. Females under the age of 80 years who had previously had a fall comprised 20% of the total falls, with females under the age of 80 years who had not previously had a fall comprising 7%. Males under the age of 80 years who had previously had a fall comprised 13% of the total falls, while males under the age of 80 years who had not previously had a fall accounted for 3%.
The researchers noted that the study shows that ‘falls can be predicted in an elderly population using information that is readily available on GP databases’ and that ‘the risk score can be integrated into existing GP databases to provide an automated screening tool for falls’.