Risk Prediction Models of Subsyndromal Delirium in Critically Ill Patients: A Systematic Review and Meta‐Analysis.
Background: The number of predictive models for assessing the risk of subsyndromal delirium (SSD) in critically ill patients is increasing, yet the quality and applicability of these models in clinical practice remain unclear. Aim: To systematically review and critically evaluate the existing risk p...
| Publicado en: | Nursing in Critical Care Vol. 30; no. 3; pp. 1 - 14 |
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| Autores principales: | , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
May2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185589238&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185589238 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13621017 Q15 jtl: Nursing in Critical Care issn: 13621017 maglogo: Y pubinfo: dt: May2025 vid: 30 iid: 3 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 185589238 185589238 185589238 10.1111/nicc.70063 185589238 ppf: 1 ppct: 13 formats: tig: atl: Risk Prediction Models of Subsyndromal Delirium in Critically Ill Patients: A Systematic Review and Meta‐Analysis. aug: au: Wu, Fei Wang, Tong Xing, Yana Cai, Weixin Zhang, Ran affil: Nursing Department, Beijing TianTan Hospital, Capital Medical University, Beijing, China sug: subj: Delirium Diagnosis Delirium Epidemiology Critical Illness Intensive Care Units Risk Assessment Critical Care Predictive Value of Tests Human Critically Ill Patients Psychosocial Factors Prediction Models Artificial Intelligence Decision Support Systems, Clinical China Japan PubMed Embase Clinical Assessment Tools Descriptive Statistics Data Analysis Software Confidence Intervals Systematic Review Meta Analysis Funding Source ab: Background: The number of predictive models for assessing the risk of subsyndromal delirium (SSD) in critically ill patients is increasing, yet the quality and applicability of these models in clinical practice remain unclear. Aim: To systematically review and critically evaluate the existing risk prediction models. Study Design: Eleven Chinese and English databases, including PubMed, Web of Science and Embase, were searched from their inception to August 16, 2024. Two researchers independently screened the literature, extracted data and assessed the risk of bias and applicability using the prediction model risk of bias assessment tool. Meta‐analysis was conducted using Stata 17.0. Results: Eight studies were included. The SSD incidence in ICU patients ranged from 8.97% to 34.5%. The most commonly used predictors were the APACHE II score and age. The reported area under the curve (AUC) ranged from 0.788 to 0.923, with the pooled AUC value for the five validated models being 0.87 (95% CI: 0.82–0.92). Six studies had a high risk of bias, while two had an unclear risk. Conclusions: The eight included models demonstrated good performance in early identification and screening of high‐risk critically ill patients for SSD, but they all exhibited a high risk of bias regarding model quality. Relevance to Clinical Practice: ICU professionals should carefully select and validate existing models based on their specific clinical settings before applying them. Alternatively, they can conduct new models incorporating multimodal data and artificial intelligence algorithms, utilizing large sample sizes, robust research designs and multi‐center external validation. pubtype: Academic Journal doctype: meta analysis research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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