• Hilde van Nieuw Amerongen, Sandra Stapel, Jan Jaap Spijkstra, Dagmar Ouweneel, and Jimmy Schenk.
• Hilde van Nieuw Amerongen is a registered nurse and clinical epidemiologist, Department of Intensive Care, Amsterdam UMC (VUmc), Amsterdam, the Netherlands.
• Am. J. Crit. Care. 2023 Jan 1; 32 (1): 435043-50.

BackgroundDelirium is a severe complication in critical care patients. Accurate prediction could facilitate determination of which patients are at risk. In the past decade, several delirium prediction models have been developed.ObjectivesTo compare the prognostic accuracy of the PRE-DELIRIC, E-PRE-DELIRIC, and Lanzhou models, and to investigate the difference in prognostic accuracy of the PRE-DELIRIC model between patients receiving and patients not receiving mechanical ventilation.MethodsThis retrospective study involved adult patients admitted to the intensive care unit during a 2-year period. Delirium was assessed by using the Confusion Assessment Method for the Intensive Care Unit or any administered dose of haloperidol or quetiapine. Model discrimination was assessed by calculating the area under the receiver operating characteristic curve (AUC); values were compared using the DeLong test.ResultsThe study enrolled 1353 patients. The AUC values were calculated as 0.716 (95% CI, 0.688-0.745), 0.681 (95% CI, 0.650-0.712), and 0.660 (95% CI, 0.629-0.691) for the PRE-DELIRIC, E-PRE-DELIRIC, and Lanzhou models, respectively. The difference in model discrimination was statistically significant for comparison of the PRE-DELIRIC with the E-PRE-DELIRIC (AUC difference, 0.035; P = .02) and Lanzhou models (AUC difference, 0.056; P < .001). In the PRE-DELIRIC model, the AUC was 0.711 (95% CI, 0.680-0.743) for patients receiving mechanical ventilation and 0.664 (95% CI, 0.586-0.742) for those not receiving it (difference, 0.047; P = .27).ConclusionStatistically significant differences in prognostic accuracy were found between delirium prediction models. The PRE-DELIRIC model was the best-performing model and can be used in patients receiving or not receiving mechanical ventilation.©2023 American Association of Critical-Care Nurses.

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