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Research

Detection of frailty in older patients using a mobile app: cross-sectional observational study in primary care

Vanessa Aznar-Tortonda, Antonio Palazón-Bru, David Manuel Folgado-de la Rosa, Virginia Espínola-Morel, Bierca Fermina Pérez-Pérez, Ana Belén León-Ruiz and Vicente Francisco Gil-Guillén
British Journal of General Practice 4 November 2019; bjgp19X706577. DOI: https://doi.org/10.3399/bjgp19X706577
Vanessa Aznar-Tortonda
National Institute of Social Security, Valencia, Spain.
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Antonio Palazón-Bru
Department of Clinical Medicine, Miguel Hernández University, San Juan de Alicante, Alicante, Spain.
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David Manuel Folgado-de la Rosa
Department of Clinical Medicine, Miguel Hernández University, San Juan de Alicante, Alicante, Spain.
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Virginia Espínola-Morel
National Institute of Social Security, Valencia, Spain.
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Bierca Fermina Pérez-Pérez
Altabix Health Center, Elche, Alicante, Spain.
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Ana Belén León-Ruiz
Emergency Service, Marina Baixa Hospital, La Vila Joiosa, Alicante, Spain.
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Vicente Francisco Gil-Guillén
Department of Clinical Medicine, Miguel Hernández University, San Juan de Alicante, Alicante, Spain.
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    Figure 1.

    Scoring system to predict frailty in older people using easy-to-use parameters.

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    Figure 2.

    AUC distribution in the internal validation of the scoring system (bootstrapping). AUC = area under the receive operating characteristic curve. SD = standard deviation.

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    Figure 3.

    Smooth calibration in the internal validation of the scoring system (bootstrapping).

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    Frailty is a common geriatric syndrome associated with a high risk of mortality. The two most common measurements for frailty are the Fried phenotype and the Fatigue, Resistance, Ambulation, Illnesses, and Loss of Weight scale, better known as the FRAIL scale. As these are difficult to use for screening in daily clinical practice (having too many people in the health centre would be a problem if these scales were used for screening), a scoring system using only clinical variables to predict frailty was developed. It was found to have high standards in terms of internal validation but must be validated externally.
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    Table 1.

    Descriptive characteristics and adjusted odds ratios for predicting frailty in older people

    Total sample, n (%)aWith/at risk of frailty, n (%)a,bAdjusted ORc (95% CI)P-value
    Total621 (100)126 (20.3)
    Variable
    Male sex256 (41.2)34 (13.3)0.43 (0.27 to 0.69)<0.001
    Mean age, years73.1 (SD 8.0)77.4 (SD 7.9)1.07 (1.04 to 1.10)<0.001
    Atrial fibrillation61 (9.8)21 (34.4)n/mn/m
    Stroke29 (4.7)14 (48.3)n/mn/m
    CHD71 (11.4)27 (38.0)n/mn/m
    Parkinson’s disease7 (1.1)4 (57.1)n/mn/m
    COPD43 (6.9)12 (27.9)n/mn/m
    Arthrosis or advanced musculoskeletal disease307 (49.4)80 (26.1)n/mn/m
    Hearing loss or visual deficit229 (36.9)60 (26.2)n/mn/m
    Polypharmacy452 (72.8)121 (26.8)6.95 (2.73 to 17.70)<0.001
    Hospital admission in the last year104 (16.7)41 (39.4)2.81 (1.69 to 4.66)<0.001
    Diabetes147 (23.7)49 (33.3)1.98 (1.25 to 3.14)0.004
    Dementia21 (3.4)13 (61.9)n/mn/m
    Peripheral vascular disease38 (6.1)15 (39.5)n/mn/m
    • ↵a Unless otherwise specified.

    • ↵b Percentage calculated from subsample with relevant variable, not whole sample.

    • ↵c Variables in the multivariate model are those with an OR. Goodness-of-fit of the model: χ2 = 6.41, P = 0.602 (Hosmer–Lemeshow test). Area under the receiver operating characteristic curve = 0.78 (standard error 0.021). CHD = coronary heart disease. CI = confidence interval. COPD = chronic obstructive pulmonary disease. n(%) = absolute frequency (relative frequency). n/m = not in the multivariate model. OR = odds ratio.

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Detection of frailty in older patients using a mobile app: cross-sectional observational study in primary care
Vanessa Aznar-Tortonda, Antonio Palazón-Bru, David Manuel Folgado-de la Rosa, Virginia Espínola-Morel, Bierca Fermina Pérez-Pérez, Ana Belén León-Ruiz, Vicente Francisco Gil-Guillén
British Journal of General Practice 4 November 2019; bjgp19X706577. DOI: 10.3399/bjgp19X706577

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Detection of frailty in older patients using a mobile app: cross-sectional observational study in primary care
Vanessa Aznar-Tortonda, Antonio Palazón-Bru, David Manuel Folgado-de la Rosa, Virginia Espínola-Morel, Bierca Fermina Pérez-Pérez, Ana Belén León-Ruiz, Vicente Francisco Gil-Guillén
British Journal of General Practice 4 November 2019; bjgp19X706577. DOI: 10.3399/bjgp19X706577
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Keywords

  • frail elderly
  • frailty
  • general practice
  • mobile applications
  • statistical models

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© 2019 British Journal of General Practice

Print ISSN: 0960-1643
Online ISSN: 1478-5242