ArticlePloS one2022
Delirium diagnosis without a gold standard: Evaluating diagnostic accuracy of combined delirium assessment tools.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Delirium detection in the emergency department: A diagnostic accuracy meta-analysis of history, physical examination, laboratory tests, and screening instruments.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2024Pooled it
- Diagnose Pediatric Delirium Preferentially by Psychiatric Examination, as CAPD Has Low Specificity.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2025Article
- Evaluating the predictive accuracy of cognitive screeners BAMCOG and MoCA in identifying postoperative delirium risk in aortic valve replacement patients: A cohort study.PLOS digital health · 2025Article
- Predicting ICU Delirium in Critically Ill COVID-19 Patients Using Demographic, Clinical, and Laboratory Admission Data: A Machine Learning Approach.Life (Basel, Switzerland) · 2025Article
- Evaluating the Effectiveness of Educational Intervention on ICU Nurses' Knowledge of Delirium: A Quasi-Experimental Approach.Nursing reports (Pavia, Italy) · 2025Article
- Predictive model for assessment of ICU delirium among patients in critical care units: Prospective observational study.Journal of education and health promotion · 2025Article
- Analysis of Risk Factors and Development of a Predictive Model for Delirium in ICU Patients: Construction, Validation, and Clinical Implications.International journal of general medicine · 2025Article
- Exploration of the factors affecting different delirium subtypes in hospitalized COVID-19 patients: a multicentre cross-sectional study.Scientific reports · 2024Article
- Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing.Proceedings. IEEE International Conference on Healthcare Informatics · 2024Article
- Translation, cultural debugging, and validation of the Chinese version of the Sour Seven Questionnaire: a cross-sectional study.Frontiers in medicine · 2024Article
- Pediatric Delirium Educational Tool Development With Intensive Care Unit Clinicians and Caregivers in Canada: Focus Group Study.JMIR pediatrics and parenting · 2023Article
- Delirium and Healthcare Utilization in Patients Undergoing Hematopoietic Stem Cell Transplantation.Transplantation and cellular therapy · 2023Article
- Characterizing and detecting delirium with clinical and computational measures of speech and language disturbance.Journal of psychiatry & neuroscience : JPNArticle
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundFluctuating course of delirium and complexities of ICU care mean delirium symptoms are hard to identify or commonly confused with other disorders. Delirium is difficult to diagnose, and clinicians and researchers may combine assessments from multiple tools. We evaluated diagnostic accuracy of different combinations of delirium assessments performed in each enrolled patient.
methodsData were obtained from a previously conducted cross-sectional study. Eligible adult patients who remained admitted to ICU for >24 hours with at least one family member present were consecutively enrolled as patient-family dyads. Clinical delirium assessments (Intensive Care Delirium Screening Checklist [ICSDC] and Confusion Assessment Method-ICU [CAM-ICU]) were completed twice daily by bedside nurse or trained research assistant, respectively. Family delirium assessments (Family Confusion Assessment Method and Sour Seven) were completed once daily by family members. We pooled all delirium assessment tools in a single two-class latent model and pairwise (i.e., combined, clinical or family assessments) Bayesian analyses.
resultsSeventy-three patient-family dyads were included. Among clinical delirium assessments, the ICDSC had lower sensitivity (0.72; 95% Bayesian Credible [BC] interval 0.54-0.92) and higher specificity (0.90; 95%BC, 0.82-0.97) using Bayesian analyses compared to pooled latent class analysis and CAM-ICU had higher sensitivity (0.90; 95%BC, 0.70-1.00) and higher specificity (0.94; 95%BC, 0.80-1.00). Among family delirium assessments, the Family Confusion Assessment Method had higher sensitivity (0.83; 95%BC, 0.71-0.92) and higher specificity (0.93; 95%BC, 0.84-0.98) using Bayesian analyses compared to pooled latent class analysis and the Sour Seven had higher specificity (0.85; 95%BC, 0.67-0.99) but lower sensitivity (0.64; 95%BC 0.47-0.82).
conclusionsResults from delirium assessment tools are often combined owing to imperfect reference standards for delirium measurement. Pairwise Bayesian analyses that explicitly accounted for each tool's (performed within same patient) prior sensitivity and specificity indicate that two combined clinical or two combined family delirium assessment tools have fair diagnostic accuracy.
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