Validation of an automated delirium prediction model (DElirium MOdel (DEMO)): an observational study. Issue 11 (8th November 2017)
- Record Type:
- Journal Article
- Title:
- Validation of an automated delirium prediction model (DElirium MOdel (DEMO)): an observational study. Issue 11 (8th November 2017)
- Main Title:
- Validation of an automated delirium prediction model (DElirium MOdel (DEMO)): an observational study
- Authors:
- Mestres Gonzalvo, Carlota
de Wit, Hugo A J M
van Oijen, Brigit P C
Deben, Debbie S
Hurkens, Kim P G M
Mulder, Wubbo J
Janknegt, Rob
Schols, Jos M G A
Verhey, Frans R
Winkens, Bjorn
van der Kuy, Paul-Hugo M - Abstract:
- Abstract : Objectives: Delirium is an underdiagnosed, severe and costly disorder, and 30%–40% of cases can be prevented. A fully automated model to predict delirium (DEMO) in older people has been developed, and the objective of this study is to validate the model in a hospital setting. Setting: Secondary care, one hospital with two locations. Design: Observational study. Participants: The study included 450 randomly selected patients over 60 years of age admitted to Zuyderland Medical Centre. Patients who presented with delirium on admission were excluded. Primary outcome measures: Development of delirium through chart review. Results: A total of 383 patients were included in this study. The analysis was performed for delirium within 1, 3 and 5 days after a DEMO score was obtained. Sensitivity was 87.1% (95% CI 0.756 to 0.939), 84.2% (95% CI 0.732 to 0.915) and 82.7% (95% CI 0.734 to 0.893) for 1, 3 and 5 days, respectively, after obtaining the DEMO score. Specificity was 77.9% (95% CI 0.729 to 0.882), 81.5% (95% CI 0.766 to 0.856) and 84.5% (95% CI 0.797 to 0.884) for 1, 3 and 5 days, respectively, after obtaining the DEMO score. Conclusion: DEMO is a satisfactory prediction model but needs further prospective validation with in-person delirium confirmation. In the future, DEMO will be applied in clinical practice so that physicians will be aware of when a patient is at an increased risk of developing delirium, which will facilitate earlier recognition and diagnosis, andAbstract : Objectives: Delirium is an underdiagnosed, severe and costly disorder, and 30%–40% of cases can be prevented. A fully automated model to predict delirium (DEMO) in older people has been developed, and the objective of this study is to validate the model in a hospital setting. Setting: Secondary care, one hospital with two locations. Design: Observational study. Participants: The study included 450 randomly selected patients over 60 years of age admitted to Zuyderland Medical Centre. Patients who presented with delirium on admission were excluded. Primary outcome measures: Development of delirium through chart review. Results: A total of 383 patients were included in this study. The analysis was performed for delirium within 1, 3 and 5 days after a DEMO score was obtained. Sensitivity was 87.1% (95% CI 0.756 to 0.939), 84.2% (95% CI 0.732 to 0.915) and 82.7% (95% CI 0.734 to 0.893) for 1, 3 and 5 days, respectively, after obtaining the DEMO score. Specificity was 77.9% (95% CI 0.729 to 0.882), 81.5% (95% CI 0.766 to 0.856) and 84.5% (95% CI 0.797 to 0.884) for 1, 3 and 5 days, respectively, after obtaining the DEMO score. Conclusion: DEMO is a satisfactory prediction model but needs further prospective validation with in-person delirium confirmation. In the future, DEMO will be applied in clinical practice so that physicians will be aware of when a patient is at an increased risk of developing delirium, which will facilitate earlier recognition and diagnosis, and thus will allow the implementation of prevention measures. … (more)
- Is Part Of:
- BMJ open. Volume 7:Issue 11(2017)
- Journal:
- BMJ open
- Issue:
- Volume 7:Issue 11(2017)
- Issue Display:
- Volume 7, Issue 11 (2017)
- Year:
- 2017
- Volume:
- 7
- Issue:
- 11
- Issue Sort Value:
- 2017-0007-0011-0000
- Page Start:
- Page End:
- Publication Date:
- 2017-11-08
- Subjects:
- geriatric medicine -- psychiatry
Medicine -- Research -- Periodicals
610.72 - Journal URLs:
- http://www.bmj.com/archive ↗
http://bmjopen.bmj.com/ ↗ - DOI:
- 10.1136/bmjopen-2017-016654 ↗
- Languages:
- English
- ISSNs:
- 2044-6055
- Deposit Type:
- Legaldeposit
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
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