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Commentary
Adult nursing
Improving delirium detection with the 3D-CAM: a path towards enhanced patient outcomes
  1. Hui-Chen (Rita) Chang
  1. School of Nursing and Midwifery, Western Sydney University, Penrith South, NSW 2116, Australia
  1. Correspondence to Dr Hui-Chen (Rita) Chang, School of Nursing and Midwifery, Western Sydney University, Building EB/LG, Room 36, Parramatta South Campus, Victoria Rd, Rydalmere, NSW 2116, Australia; R.Chang2{at}westernsydney.edu.au

https://doi.org/10.1136/ebnurs-2023-103792

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    Commentary on: Ma R, Zhao J, Li C, Qin Y, Yan J, Wang Y, Yu Z, Zhang Y, Zhao Y, Huang B, Sun S, Ning X. Diagnostic accuracy of the 3-minute diagnostic interview for confusion assessment method-defined delirium in delirium detection: a systematic review and meta-analysis. Age Ageing. 2023 May 1;52(5):afad074. doi: 10.1093/ageing/afad074.

    Implications for practice and research

    • For practice: The study emphasises timely delirium recognition and management to mitigate adverse outcomes. The 3D-CAM emerges as a practical and accurate tool for delirium detection, facilitating clinicians to promptly initiate interventions.

    • For research: Future research should validate its performance in specific populations and comparing it with other diagnostic tools to optimise delirium detection strategies.

    Context

    The study1 underscores the critical need for timely recognition and management of delirium, highlighting the 3D-CAM as a practical and accurate detection tool in healthcare. Despite its impact, a considerable portion of delirium cases remains undetected.2 This study centres …

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    Footnotes

    • Competing interests None declared.

    • Provenance and peer review Commissioned; internally peer reviewed.