A Machine Learning Approach for Investigating Delirium as a Multifactorial Syndrome

Ocagli, Honoria; Bottigliengo, Daniele; Lorenzoni, Giulia; Azzolina, Danila; Acar, Aslıhan Şentürk; Sorgato, Silvia; Stivanello, Lucia; Degan, Mario; Gregori, Darío

doi:10.3390/ijerph18137105

Cited by 10 publications

(4 citation statements)

References 60 publications

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“…Most frequent precipitating factors in community-dwelling older individuals are infectious diseases, followed by drugs and hydro-electrolytic disorders [ 9 ]. Machine learning tools have been proposed for the identification of delirium onset risk factors [ 10 , 11 ]. The duration of delirium is variable, persisting for weeks or months in 20% of patients with dementia [ 12 ], which is a high risk factor for delirium in older patients [ 13 ], while delirium onset often leads to cognitive deterioration and dementia [ 3 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Survival of Frail Elderly with Delirium

Cano‐Escalera

Graña

Irazusta

et al. 2022

IJERPH

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This study aims to determine when frailty increases the risks of delirium mortality. Hospital patients falling into the elderly frail or pre-frail category were recruited, some without delirium, some with delirium at admission, and some who developed delirium during admission. We screened for frailty, cognitive status, and co-morbidities whenever possible and extracted drug information and mortality data from electronic health records. Kaplan–Meier estimates of survival probability functions were computed at four times, comparing delirium versus non delirium patients. Differences in survival were assessed by a log-rank test. Independent Cox’s regression was carried out to identify significant hazard risks (HR) at 1 month, 6 months, 1 year, and 2 years. Delirium predicted mortality (log-rank test, p < 0.0001) at all four censoring points. Variables with significant HRs were frailty indicators, comorbidities, polypharmacy, and the use of specific drugs. For the delirium cohort, variables with the most significant 2-year hazard risks (HR(95%CI)) were: male gender (0.43 20 (0.26,0.69)), weight loss (0.45 (0.26,0.74)), sit and stand up test (0.67 (0.49,0.92)), readmission within 30 days of discharge (0.50 (0.30,0.80)), cerebrovascular disease (0.45 (0.27,0.76)), head trauma (0.54 22 (0.29,0.98)), number of prescribed drugs (1.10 (1.03,1.18)), and the use of diuretics (0.57 (0.34,0.96)). These results suggest that polypharmacy and the use of diuretics increase mortality in frail elderly patients with delirium.

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Section: Introductionmentioning

confidence: 99%

Survival of Frail Elderly with Delirium

Cano‐Escalera

Graña

Irazusta

et al. 2022

IJERPH

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“…Delirium is often undocumented and underdiagnosed, despite being associated with a multitude of identifiable risk factors. One study utilized an RF ML approach to establish a correlation between patient characteristics and 4 ‘A’s Test (4AT) scores, a convenient screening tool for delirium [ 48 ]. The variables that correlated significantly with the 4AT score were age, physical restraint, dementia, diabetes, ward type, educational level, and gender.…”

Section: Reviewmentioning

confidence: 99%

Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool

Suresh,

Singh,

Vaish

et al. 2024

Cureus

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Artificial intelligence (AI) is a technique that attempts to replicate human intelligence, analytical behavior, and decision-making ability. This includes machine learning, which involves the use of algorithms and statistical techniques to enhance the computer's ability to make decisions more accurately. Due to AI's ability to analyze, comprehend, and interpret considerable volumes of data, it has been increasingly used in the field of healthcare. In critical care medicine, where most of the patient load requires timely interventions due to the perilous nature of the condition, AI's ability to monitor, analyze, and predict unfavorable outcomes is an invaluable asset. It can significantly improve timely interventions and prevent unfavorable outcomes, which, otherwise, is not always achievable owing to the constrained human ability to multitask with optimum efficiency. AI has been implicated in intensive care units over the past many years. In addition to its advantageous applications, this article discusses its disadvantages, prospects, and the changes needed to train future critical care professionals. A comprehensive search of electronic databases was performed using relevant keywords. Data from articles pertinent to the topic was assimilated into this review article.

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“…Machine learning (ML) methods [5] may offer one such approach to clinically characterizing DLB. Identification of subphenotypes using ML have assisted clinical decision-making in Alzheimer’s disease (most common form of dementia) [6], and disorders such as delirium [7] and sporadic Creutzfeldt-Jakob disease [8]. In contrast, most DLB studies to date have adopted traditional statistical methods to provide group-level insights that may not apply to all subgroups within DLB.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing Lewy Body Dementia from Alzheimer’s Disease using Machine Learning on Heterogeneous Data: A Feasibility Study

McCombe

Joshi

Finn

et al. 2022

Preprint

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Dementia with Lewy Bodies (DLB) is the second most common form of dementia, but diagnostic markers for DLB can be expensive and inaccessible, and many cases of DLB are undiagnosed. This work applies machine learning techniques to determine the feasibility of distinguishing DLB from Alzheimer's Disease (AD) using heterogeneous data features. The Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm was first applied using a Leave-One-Out Cross-Validation protocol to a dataset comprising DLB and AD cases. Then, interpretable association rule-based diagnostic classifiers were obtained for distinguishing DLB from AD. The various diagnostic classifiers generated by this process had high accuracy over the whole dataset (mean accuracy of 94%). The mean accuracy in classifying their out-of-sample case was 80.5%. Every classifier generated consisted of very simple structure, each using 1-2 classification rules and 1-3 data features. As a group, the classifiers were heterogeneous and used several different data features. In particular, some of the classifiers used very simple and inexpensive diagnostic features, yet with high diagnostic accuracy. This work suggests that opportunities may exist for incorporating accessible diagnostic assessments while improving diagnostic rate for DLB.

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A Machine Learning Approach for Investigating Delirium as a Multifactorial Syndrome

Cited by 10 publications

References 60 publications

Survival of Frail Elderly with Delirium

Survival of Frail Elderly with Delirium

Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool

Distinguishing Lewy Body Dementia from Alzheimer’s Disease using Machine Learning on Heterogeneous Data: A Feasibility Study

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