Identifying patients at risk of nursing home admission: The Leeds Elderly Assessment Dependency Screening tool (LEADS)

Anita Slade, Jon Fear, Alan Tennant

    Research output: Contribution to journalArticlepeer-review

    19 Citations (Scopus)


    BACKGROUND: Discharge from hospital to a nursing home represents a major event in the life of an older person and should only follow a comprehensive functional and medical assessment. A previous study identified 3 dependency scales able to discriminate across outcomes for older people admitted to an acute setting. We wished to determine if a single dependency scale derived from the 3 scales could be created. In addition could this new scale with other predictors be used as a comprehensive tool to identify patients at risk of nursing home admission.

    METHODS: Items from the 3 scales were combined and analysed using Rasch Analysis. Sensitivity and specificity analysis and ROC curves were applied to identify the most appropriate cut score. Binary logistic regression using this cut-off, and other predictive variables, were used to create a predictive algorithm score. Sensitivity, specificity and likelihood ratio scores of the algorithm scores were used to identify the best predictive score for risk of nursing home placement.

    RESULTS: A 17-item (LEADS) scale was derived, which together with four other indicators, had a sensitivity of 88% for patients at risk of nursing home placement, and a specificity of 85% for not needing a nursing home placement, within 2 weeks of admission.

    CONCLUSION: A combined short 17-item scale of dependency plus other predictive variables can assess the risk of nursing home placement for older people in an acute care setting within 2 weeks of admission. This gives an opportunity for either early discharge planning, or therapeutic intervention to offset the risk of placement.

    Original languageEnglish
    Pages (from-to)31
    JournalBMC Health Services Research
    Publication statusPublished - 2006


    • Aftercare
    • Aged
    • Aged, 80 and over
    • Algorithms
    • Female
    • Geriatric Assessment
    • Great Britain
    • Health Services Research
    • Humans
    • Logistic Models
    • Male
    • Nursing Homes
    • Patient Discharge
    • Risk Assessment
    • Risk Factors
    • Severity of Illness Index
    • Surveys and Questionnaires


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