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J Clin Epidemiol. 2014 Aug;67(8):877-86. doi: 10.1016/j.jclinepi.2014.03.008. Epub 2014 Apr 29.

Falls in the elderly were predicted opportunistically using a decision tree and systematically using a database-driven screening tool.

Author information

1
Department of Health Care Management and Policy, University of Surrey, Guildford, Surrey, GU2 7XH, UK. Electronic address: mrafiq@doctors.org.uk.
2
Department of Health Care Management and Policy, University of Surrey, Guildford, Surrey, GU2 7XH, UK.
3
Department of Nephrology, Leicester General Hospital and Leicester University, Leicester, LE5 4PW, UK.
4
Department of Renal Medicine, Southmead Hospital, North Bristol NHS Trust, Bristol, BS10 5NB, UK.
5
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.
6
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.

Abstract

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.

STUDY DESIGN AND SETTING:

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%.

CONCLUSION:

Our study developed opportunistic and systematic tools to predict falls without additional mobility assessments.

KEYWORDS:

Computerised; Elderly; Falls risk; Fractures; General practice; Medical records systems; Screening tool

PMID:
24786593
DOI:
10.1016/j.jclinepi.2014.03.008
[Indexed for MEDLINE]

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