Machine Learning–Based Prediction Models for Delirium: A Systematic Review and Meta-Analysis

Xie, Qi; Wang, Xing‐Lei; Pei, Juhong; Wu, Yin-Ping; Guo, Qiang; Su, Yujie; Yan, Hui; Nan, Rui-Ling; Chen, Hạixia; Dou, Xin‐Man

doi:10.1016/j.jamda.2022.06.020

Cited by 9 publications

(4 citation statements)

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“…By simulating human learning processes to analyse data patterns, these algorithms have tremendous potential to uncover intricate relationships between variables and improve predictive accuracy (Liu et al., 2023). However, a recent systematic review and meta‐analysis found that while machine learning models perform well in predicting delirium, most of the studies associated with them have a high risk of bias (Xie et al., 2022). In order to apply these models to new patient populations and routine clinical settings, they must be thoroughly evaluated to reduce potential risks of bias.…”

Section: Discussionmentioning

confidence: 99%

“…However, a recent systematic review and meta-analysis found that while machine learning models perform well in predicting delirium, most of the studies associated with them have a high risk of bias (Xie et al, 2022). In order to apply these models to new patient populations and routine clinical settings, they must be thoroughly evaluated to reduce potential risks of bias.…”

Section: Ta B L Ementioning

confidence: 99%

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Can admission Braden skin score predict delirium in older adults in the intensive care unit? Results from a multicenter study

Cheng,

Huang,

Yuan

et al. 2023

Journal of Clinical Nursing

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Aims and ObjectivesTo investigate whether a low Braden Skin Score (BSS), reflecting an increased risk of pressure injury, could predict the risk of delirium in older patients in the intensive care unit (ICU).BackgroundDelirium, a common acute encephalopathy syndrome in older ICU patients, is associated with prolonged hospital stay, long‐term cognitive impairment and increased mortality. However, few studies have explored the relationship between BSS and delirium.DesignMulticenter cohort study.MethodsThe study included 24,123 older adults from the Medical Information Mart for Intensive Care IV (MIMIC‐IV) database and 1090 older adults from the eICU Collaborative Research Database (eICU‐CRD), all of whom had a record of BSS on admission to the ICU. We used structured query language to extract relevant data from the electronic health records. Delirium, the primary outcome, was primarily diagnosed by the Confusion Assessment Method for the ICU or the Intensive Care Delirium Screening Checklist. Logistic regression models were used to validate the association between BSS and outcome. A STROBE checklist was the reporting guide for this study.ResultsThe median age within the MIMIC‐IV and eICU‐CRD databases was approximately 77 and 75 years, respectively, with 11,195 (46.4%) and 524 (48.1%) being female. The median BSS at enrollment in both databases was 15 (interquartile range: 13, 17). Multivariate logistic regression showed a negative association between BSS on ICU admission and the prevalence of delirium. Similar patterns were found in the eICU‐CRD database.ConclusionsThis study found a significant negative relationship between ICU admission BSS and the prevalence of delirium in older patients.Relevance to Clinical PracticeThe BSS, which is simple and accessible, may reflect the health and frailty of older patients. It is recommended that BSS assessment be included as an essential component of delirium management strategies for older patients in the ICU.No Patient or Public ContributionThis is a retrospective cohort study, and no patients or the public were involved in the design and conduct of the study.

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Section: Discussionmentioning

confidence: 99%

Section: Ta B L Ementioning

confidence: 99%

Can admission Braden skin score predict delirium in older adults in the intensive care unit? Results from a multicenter study

Cheng,

Huang,

Yuan

et al. 2023

Journal of Clinical Nursing

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“…CDSS tools using machine learning algorithms have also been applied to the prediction of delirium [ 20 ], and to the prediction of depression among older adults [ 21 ]. Interestingly, a review in 2020 identified 35 articles studying the prediction of specific chronic diseases using AI in older people, with 9 studies claiming algorithm accuracy of at least 90% [ 5 ].…”

Section: Where Can Ai Provide the Most Benefit To The Care Of Older P...mentioning

confidence: 99%

New Horizons in artificial intelligence in the healthcare of older people

Shiwani,

Relton,

Evans

et al. 2023

Age and Ageing

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Artificial intelligence (AI) in healthcare describes algorithm-based computational techniques which manage and analyse large datasets to make inferences and predictions. There are many potential applications of AI in the care of older people, from clinical decision support systems that can support identification of delirium from clinical records to wearable devices that can predict the risk of a fall. We held four meetings of older people, clinicians and AI researchers. Three priority areas were identified for AI application in the care of older people. These included: monitoring and early diagnosis of disease, stratified care and care coordination between healthcare providers. However, the meetings also highlighted concerns that AI may exacerbate health inequity for older people through bias within AI models, lack of external validation amongst older people, infringements on privacy and autonomy, insufficient transparency of AI models and lack of safeguarding for errors. Creating effective interventions for older people requires a person-centred approach to account for the needs of older people, as well as sufficient clinical and technological governance to meet standards of generalisability, transparency and effectiveness. Education of clinicians and patients is also needed to ensure appropriate use of AI technologies, with investment in technological infrastructure required to ensure equity of access.

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“…Studies using either MA or ML need this data form to analyze. 1 2 3) Despite the promises, challenges persist in integrating MA and ML. Heterogeneities in study designs, data formats, data quality, and various ML models pose significant obstacles.…”

mentioning

confidence: 99%

Meta-Analysis and Machine Learning: Advancement of Analytic Methodology

Lee

2023

Korean J Neurotrauma

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Machine Learning–Based Prediction Models for Delirium: A Systematic Review and Meta-Analysis

Cited by 9 publications

References 65 publications

Can admission Braden skin score predict delirium in older adults in the intensive care unit? Results from a multicenter study

Can admission Braden skin score predict delirium in older adults in the intensive care unit? Results from a multicenter study

New Horizons in artificial intelligence in the healthcare of older people

Meta-Analysis and Machine Learning: Advancement of Analytic Methodology

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