Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jul 31:2022:1449277.
doi: 10.1155/2022/1449277. eCollection 2022.

Development of a Delirium Risk Predication Model among ICU Patients in Oman

Affiliations

Development of a Delirium Risk Predication Model among ICU Patients in Oman

Rasha Khamis Al-Hoodar et al. Anesthesiol Res Pract. .

Abstract

Background: Delirium is a common disorder among patients admitted to intensive care units. Identification of the predicators of delirium is very important to improve the patient's quality of life.

Methods: This study was conducted in a prospective observational design to build a predictive model for delirium among ICU patients in Oman. A sample of 153 adult ICU patients from two main hospitals participated in the study. The Intensive Care Delirium Screening Checklist (ICDSC) was used to assess the participants for delirium twice daily.

Result: The results showed that the incidence of delirium was 26.1%. Multiple logistic regression analysis showed that sepsis (odds ratio (OR) = 9.77; 95% confidence interval (CI) = 1.91-49.92; P < 0.006), metabolic acidosis (odds ratio (OR) = 3.45; 95% confidence interval [CI] = 1.18-10.09; P=0.024), nasogastric tube use (odds ratio (OR) 9.74; 95% confidence interval (CI) = 3.48-27.30; P ≤ 0.001), and APACHEII score (OR = 1.22; 95% CI = 1.09-1.37; P ≤ 0.001) were predictors of delirium among ICU patients in Oman (R 2=0.519, adjusted R 2=0.519, P ≤ 0.001).

Conclusion: To prevent delirium in Omani hospitals, it is necessary to work on correcting those predictors and identifying other factors that had effects on delirium development. Designing of a prediction model may help on early delirium detection and implementation of preventative measures.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Model of predictors of overall patient safety culture.

Similar articles

Cited by

References

    1. American Psychatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5®) Washington, DC, USA: American Psychiatric Association Publishing; 2013.
    1. Abla H.-A., Alshraideh J. A. Delirium post‐cardiac surgery: incidence and associated factors. Nursing in Critical Care . 2019;26(3) doi: 10.1111/nicc.12492. - DOI - PubMed
    1. Kotfis K., Szylińska A., Listewnik M., et al. Early delirium after cardiac surgery: an analysis of incidence and risk factors in elderly (a≥65 years) and very elderly (a≥80 years) patients. Clinical Interventions in Aging . 2018;13:1061–1070. doi: 10.2147/cia.s166909. - DOI - PMC - PubMed
    1. Hayhurst C. J., Pandharipande P. P., Hughes C. G. Intensive care unit delirium: a review of diagnosis, prevention, and treatment. Anesthesiology . 2016;125(6):1229–1241. doi: 10.1097/aln.0000000000001378. - DOI - PMC - PubMed
    1. Tilouche N., Hassen M. F., Ali H. B. S., Jaoued O., Gharbi R., El Atrous S. S. Delirium in the intensive care unit: incidence, risk factors, and impact on outcome. Indian Journal of Critical Care Medicine . 2018;22(3):144–149. doi: 10.4103/ijccm.ijccm_244_17. - DOI - PMC - PubMed

LinkOut - more resources