Falls in the elderly were predicted opportunistically using a decision tree and systematically using a database-driven screening tool.
OBJECTIVE
To identify risk factors for falls and generate two screening tools: an opportunistic tool for use in consultation to flag at risk patients and a systematic database screening tool for comprehensive falls assessment of the practice population.


METHODS
This multicenter cohort study was part of the quality improvement in chronic kidney disease trial. Routine data for participants aged 65 years and above were collected from 127 general practice (GP) databases across the UK, including sociodemographic, physical, diagnostic, pharmaceutical, lifestyle factors, and records of falls or fractures over 5 years. Multilevel logistic regression analyses were performed to identify predictors. The strongest predictors were used to generate a decision tree and risk score.


RESULTS
Of the 135,433 individuals included, 10,766 (8%) experienced a fall or fracture during follow-up. Age, female sex, previous fall, nocturia, anti-depressant use, and urinary incontinence were the strongest predictors from our risk profile (area under the receiver operating characteristics curve = 0.72). Medication for hypertension did not increase the falls risk. Females aged over 75 years and subjects with a previous fall were the highest risk groups from the decision tree. The risk profile was converted into a risk score (range -7 to 56). Using a cut-off of ≥9, sensitivity was 68%, and specificity was 60%.


CONCLUSIONS
Our study developed opportunistic and systematic tools to predict falls without additional mobility assessments.
Department of Health Care Management and Policy, University of Surrey, Guildford, Surrey, GU2 7XH, UK. Electronic address: mrafiq@doctors.org.uk.
Meena
M
Rafiq
Department of Health Care Management and Policy, University of Surrey, Guildford, Surrey, GU2 7XH, UK.
Andrew
A
McGovern
Department of Health Care Management and Policy, University of Surrey, Guildford, Surrey, GU2 7XH, UK.
Simon
S
Jones
Department of Nephrology, Leicester General Hospital and Leicester University, Leicester, LE5 4PW, UK.
Kevin
K
Harris
Department of Renal Medicine, Southmead Hospital, North Bristol NHS Trust, Bristol, BS10 5NB, UK.
Charles
C
Tomson
Division of Population Health Sciences and Education, St. George's, University of London, London, SW17 0RE, UK; South West Thames Renal Unit, St. Helier Hospital, Carshalton, SM5 1AA, UK.
Hugh
H
Gallagher
Department of Health Care Management and Policy, University of Surrey, Guildford, Surrey, GU2 7XH, UK; Division of Population Health Sciences and Education, St. George's, University of London, London, SW17 0RE, UK.
Simon
S
de Lusignan
D000058:Accidental Falls / Q000706:statistics & numerical data*; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000928:Antidepressive Agents / Q000008:administration & dosage / Q000009:adverse effects; D015331:Cohort Studies; D016208:Databases, Factual; D003663:Decision Trees; D005260:Female; D050723:Fractures, Bone / Q000453:epidemiology / Q000209:etiology; D006801:Humans; D008297:Male; D012372:ROC Curve; D012307:Risk Factors; D012737:Sex Factors; D006113:United Kingdom / Q000453:epidemiology; D014549:Urinary Incontinence / Q000453:epidemiology / Q000503:physiopathology
D000928:Antidepressive Agents
Computerised; Elderly; Falls risk; Fractures; General practice; Medical records systems; Screening tool