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A regression tree for identifying combinations of fall risk factors associated to recurrent falling: a cross-sectional elderly population-based study

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Abstract

Background

Regression tree (RT) analyses are particularly adapted to explore the risk of recurrent falling according to various combinations of fall risk factors compared to logistic regression models. The aims of this study were (1) to determine which combinations of fall risk factors were associated with the occurrence of recurrent falls in older community-dwellers, and (2) to compare the efficacy of RT and multiple logistic regression model for the identification of recurrent falls.

Methods

A total of 1,760 community-dwelling volunteers (mean age ± standard deviation, 71.0 ± 5.1 years; 49.4 % female) were recruited prospectively in this cross-sectional study. Age, gender, polypharmacy, use of psychoactive drugs, fear of falling (FOF), cognitive disorders and sad mood were recorded. In addition, the history of falls within the past year was recorded using a standardized questionnaire.

Results

Among 1,760 participants, 19.7 % (n = 346) were recurrent fallers. The RT identified 14 nodes groups and 8 end nodes with FOF as the first major split. Among participants with FOF, those who had sad mood and polypharmacy formed the end node with the greatest OR for recurrent falls (OR = 6.06 with p < 0.001). Among participants without FOF, those who were male and not sad had the lowest OR for recurrent falls (OR = 0.25 with p < 0.001). The RT correctly classified 1,356 from 1,414 non-recurrent fallers (specificity = 95.6 %), and 65 from 346 recurrent fallers (sensitivity = 18.8 %). The overall classification accuracy was 81.0 %. The multiple logistic regression correctly classified 1,372 from 1,414 non-recurrent fallers (specificity = 97.0 %), and 61 from 346 recurrent fallers (sensitivity = 17.6 %). The overall classification accuracy was 81.4 %.

Conclusions

Our results show that RT may identify specific combinations of risk factors for recurrent falls, the combination most associated with recurrent falls involving FOF, sad mood and polypharmacy. The FOF emerged as the risk factor strongly associated with recurrent falls. In addition, RT and multiple logistic regression were not sensitive enough to identify the majority of recurrent fallers but appeared efficient in detecting individuals not at risk of recurrent falls.

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Acknowledgments

We acknowledge for their contribution to this study C. Nitenberg from HEC of Lyon, and B Bongue, A Colvez and N Deville from CETAF Saint-Etienne. We are also grateful to the participants for their cooperation.

Conflict of interest and disclosures

Miss A. Kabeshova reports no conflicts of interest.

Dr. Annweiler: has served as an unpaid consultant for Ipsen Pharma company, and serves as an associate editor for Gériatrie, Psychologie et Neuropsychiatrie du Vieillissement and for the Journal of Alzheimer’s Disease. He has no relevant financial interest in this manuscript. Dr Fantino reports no conflicts of interest. Dr Phillip reports no conflicts of interest. Dr Launay reports no conflicts of interest. Dr Gromov reports no conflicts of interest. Prof. Beauchet has served as an unpaid consultant for Ipsen Pharma company, and serves as an associate editor for Gériatrie, Psychologie et Neuropsychiatrie du Vieillissement. He has no relevant financial interest in this manuscript.

Authors’ contribution

Kabeshova has full access to the data in the study. Study concept and design: Beauchet, Annweiler and Launay. Acquisition of data: Launay, Philip and Fantino. Analysis and interpretation of data: Launay, Philip, Fantino, Kabeshova, Annweiler, Gromov and Beauchet. Drafting of the manuscript: Annweiler and Kabeshova. Critical revision of the manuscript for important intellectual content: Beauchet, Gromov. Obtained funding: Not applicable. Statistical expertise: Kabeshova. Administrative, technical, or material support: Annweiler. Study supervision: Beauchet.

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Authors and Affiliations

  1. Division of Geriatric Medicine, Department of Neuroscience, Angers University Hospital, 49933, Angers cedex 9, France

    A. Kabeshova, C. Annweiler, B. Fantino, C. P. Launay & O. Beauchet

  2. UPRES EA 4336, UNAM, Angers University Hospital, Angers, France

    A. Kabeshova & C. P. Launay

  3. Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, Canada

    C. Annweiler

  4. Department of Primary Care, Angers Medical School, University of Angers, Angers, France

    T. Philip

  5. Computational Mathematics and Mathematical Cybernetics Department, Faculty of Applied Mathematics, Oles Honchar Dnepropetrovsk National University Dnepropetrovsk, Dnepropetrovsk, Ukraine

    V. A. Gromov

Authors
  1. A. Kabeshova
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  2. C. Annweiler
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  3. B. Fantino
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  4. T. Philip
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  5. V. A. Gromov
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  6. C. P. Launay
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  7. O. Beauchet
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Corresponding author

Correspondence to O. Beauchet.

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Kabeshova, A., Annweiler, C., Fantino, B. et al. A regression tree for identifying combinations of fall risk factors associated to recurrent falling: a cross-sectional elderly population-based study. Aging Clin Exp Res 26, 331–336 (2014). https://doi.org/10.1007/s40520-014-0232-0

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  • DOI: https://doi.org/10.1007/s40520-014-0232-0

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