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A new tool for the evaluation of the rehabilitation outcomes in older persons: a machine learning model to predict functional status 1 year ahead

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Abstract

Purpose

To date, the assessment of disability in older people is obtained utilizing a Comprehensive Geriatric Assessment (CGA). However, it is often difficult to understand which areas of CGA are most predictive of the disability. The aim of this study is to evaluate the possibility to early predict—1 year ahead—the disability level of a patient using machine leaning models.

Methods

Community-dwelling older people were enrolled in this study. CGA was made at baseline and at 1 year follow-up. After collecting input/independent variables (i.e., age, gender, schooling followed, body mass index, information on smoking, polypharmacy, functional status, cognitive performance, depression, nutritional status), we performed two distinct Support Vector Machine models (SVMs) able to predict functional status 1 year ahead. To validate the choice of the model, the results achieved with the SVMs were compared with the output produced by simple linear regression models.

Results

218 patients (mean age = 78.01; SD = 7.85; male = 39%) were recruited. The combination of the two SVMs is able to achieve a higher prediction accuracy (exceeding 80% instances correctly classified vs 67% instances correctly classified by the combination of the two linear regression models). Furthermore, SVMs are able to classify both the three categories, self sufficiently, disability risk and disability, while linear regression model separates the population only in two groups (self-sufficiency and disability) without identifying the intermediate category (disability risk) which turns out to be the most critical one.

Conclusions

The development of such a model can contribute to the early detection of patients at risk of self-sufficiency loss.

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Acknowledgements

We would like to thank Prof. Paola Verico (Sapienza University of Rome) for her technical support at this work.

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Authors and Affiliations

  1. Division of Gerontology, Department of Cardiovascular, Respiratory, Nephrological, Anesthesiological and Geriatric Sciences (SCReNAG), Sapienza University of Rome, Viale del Policlinico 155, 00161, Rome, Italy

    Walter Verrusio, Mariagrazia Zaccone, Nicolò Gueli & Mauro Cacciafesta

  2. Department of Dynamic and Clinical Psychology, Sapienza University of Rome, Via degli Apuli 1, 00185, Rome, Italy

    Alessia Renzi

  3. ACTOR, Analytic Control Technology Operations Research, Rome, Italy

    Umberto Dellepiane & Stefania Renzi

Authors
  1. Walter Verrusio
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  2. Alessia Renzi
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  3. Umberto Dellepiane
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  5. Mariagrazia Zaccone
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  7. Mauro Cacciafesta
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Corresponding author

Correspondence to Walter Verrusio.

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The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in this study which involved human participants were in accordance with our institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Verrusio, W., Renzi, A., Dellepiane, U. et al. A new tool for the evaluation of the rehabilitation outcomes in older persons: a machine learning model to predict functional status 1 year ahead. Eur Geriatr Med 9, 651–657 (2018). https://doi.org/10.1007/s41999-018-0098-3

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  • DOI: https://doi.org/10.1007/s41999-018-0098-3

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