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A Machine Learning Approach for Investigating Delirium as a Multifactorial Syndrome

Author

Listed:
  • Honoria Ocagli

    (Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan 18, 35121 Padova, Italy)

  • Daniele Bottigliengo

    (Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan 18, 35121 Padova, Italy)

  • Giulia Lorenzoni

    (Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan 18, 35121 Padova, Italy)

  • Danila Azzolina

    (Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan 18, 35121 Padova, Italy
    Department of Medical Science, University of Ferrara, Via Fossato di Mortara 64B, 44121 Ferrara, Italy)

  • Aslihan S. Acar

    (Department of Actuarial Sciences, Hacettepe University, Ankara 06800, Turkey)

  • Silvia Sorgato

    (Health Professional Management Service (DPS) of the University Hospital of Padova, 35128 Padova, Italy)

  • Lucia Stivanello

    (Health Professional Management Service (DPS) of the University Hospital of Padova, 35128 Padova, Italy)

  • Mario Degan

    (Health Professional Management Service (DPS) of the University Hospital of Padova, 35128 Padova, Italy)

  • Dario Gregori

    (Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan 18, 35121 Padova, Italy)

Abstract

Delirium is a psycho-organic syndrome common in hospitalized patients, especially the elderly, and is associated with poor clinical outcomes. This study aims to identify the predictors that are mostly associated with the risk of delirium episodes using a machine learning technique (MLT). A random forest (RF) algorithm was used to evaluate the association between the subject’s characteristics and the 4AT (the 4 A’s test) score screening tool for delirium. RF algorithm was implemented using information based on demographic characteristics, comorbidities, drugs and procedures. Of the 78 patients enrolled in the study, 49 (63%) were at risk for delirium, 32 (41%) had at least one episode of delirium during the hospitalization (38% in orthopedics and 31% both in internal medicine and in the geriatric ward). The model explained 75.8% of the variability of the 4AT score with a root mean squared error of 3.29. Higher age, the presence of dementia, physical restraint, diabetes and a lower degree are the variables associated with an increase of the 4AT score. Random forest is a valid method for investigating the patients’ characteristics associated with delirium onset also in small case-series. The use of this model may allow for early detection of delirium onset to plan the proper adjustment in healthcare assistance.

Suggested Citation

  • Honoria Ocagli & Daniele Bottigliengo & Giulia Lorenzoni & Danila Azzolina & Aslihan S. Acar & Silvia Sorgato & Lucia Stivanello & Mario Degan & Dario Gregori, 2021. "A Machine Learning Approach for Investigating Delirium as a Multifactorial Syndrome," IJERPH, MDPI, vol. 18(13), pages 1-13, July.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:13:p:7105-:d:587523
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    References listed on IDEAS

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    1. Yuliya V Karpievitch & Elizabeth G Hill & Anthony P Leclerc & Alan R Dabney & Jonas S Almeida, 2009. "An Introspective Comparison of Random Forest-Based Classifiers for the Analysis of Cluster-Correlated Data by Way of RF++," PLOS ONE, Public Library of Science, vol. 4(9), pages 1-10, September.
    2. Emily Jane Tomlinson & Nicole M Phillips & Mohammadreza Mohebbi & Alison M Hutchinson, 2017. "Risk factors for incident delirium in an acute general medical setting: a retrospective case–control study," Journal of Clinical Nursing, John Wiley & Sons, vol. 26(5-6), pages 658-667, March.
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    Cited by:

    1. Guillermo Cano-Escalera & Manuel Graña & Jon Irazusta & Idoia Labayen & Ariadna Besga, 2022. "Survival of Frail Elderly with Delirium," IJERPH, MDPI, vol. 19(4), pages 1-18, February.

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