A Machine Learning Approach for Investigating Delirium as a Multifactorial Syndrome.

Honoria Ocagli,Silvia Sorgato,Aslihan S Acar,Mario Degan,Dario Gregori,Danila Azzolina,Giulia Lorenzoni,Lucia Stivanello,Daniele Bottigliengo

doi:10.3390/ijerph18137105

Honoria Ocagli, Silvia Sorgato + Show 7 more

Open Access

https://doi.org/10.3390/ijerph18137105

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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.

Highlights

Delirium is a psycho-organic syndrome characterized by an alteration in attention and consciousness, with disorganized psychic activity, fragmentation of psychic processes that appear untied and upset [1]
The project was conducted within the framework of thesis preparation of the nursing student and it was approved by the internal offices at the University Hospital of Padova
The present results show that machine learning technique (MLT) can identify complex relationship among data, so in the future it would be interesting to use those techniques to define delirium risk factors instead of traditional approaches

Summary

Introduction

Delirium is a psycho-organic syndrome characterized by an alteration in attention and consciousness, with disorganized psychic activity, fragmentation of psychic processes that appear untied and upset [1]. Delirium has a multifactorial etiology, in which internal predisposing factors (susceptibility) interact with external precipitating ones [2,3]. There are many risk factors that may change according to the characteristics of the patient. Risk factors for delirium were evaluated in systematic reviews according to the type of patients. Risk of delirium is higher in very old patients [5,6]. Marquetand et al 2020 [6] in a recent work found that very old patients require only few precipitant factors to develop delirium. Age was a risk factor for patients after hip fracture surgery [7], vascular surgery [8], in knee and hip replacement patients [9]. Other risk factors are as follows: function dependency [7,10], hypertension [8], hearing or visual impairment [7,8], anesthetic use [11], and cognitive impairment [9,10]

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