BackgroundAccurate prediction of delirium in the intensive care unit (ICU) may facilitate efficient use of early preventive strategies and stratification of ICU patients by delirium risk in clinical research, but the optimal delirium prediction model to use is unclear. We compared the predictive performance and user convenience of the prediction model for delirium (PRE-DELIRIC) and early prediction model for delirium (E-PRE-DELIRIC) in ICU patients and determined the value of a two-stage calculation.MethodsThis 7-country, 11-hospital, prospective cohort study evaluated consecutive adults admitted to the ICU who could be reliably assessed for delirium using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. The predictive performance of the models was measured using the area under the receiver operating characteristic curve. Calibration was assessed graphically. A physician questionnaire evaluated user convenience. For the two-stage calculation we used E-PRE-DELIRIC immediately after ICU admission and updated the prediction using PRE-DELIRIC after 24 h.ResultsIn total 2178 patients were included. The area under the receiver operating characteristic curve was significantly greater for PRE-DELIRIC (0.74 (95% confidence interval 0.71–0.76)) compared to E-PRE-DELIRIC (0.68 (95% confidence interval 0.66–0.71)) (z score of − 2.73 (p < 0.01)). Both models were well-calibrated. The sensitivity improved when using the two-stage calculation in low-risk patients. Compared to PRE-DELIRIC, ICU physicians (n = 68) rated the E-PRE-DELIRIC model more feasible.ConclusionsWhile both ICU delirium prediction models have moderate-to-good performance, the PRE-DELIRIC model predicts delirium better. However, ICU physicians rated the user convenience of E-PRE-DELIRIC superior to PRE-DELIRIC. In low-risk patients the delirium prediction further improves after an update with the PRE-DELIRIC model after 24 h.Trial registrationClinicalTrials.gov, NCT02518646. Registered on 21 July 2015.Electronic supplementary materialThe online version of this article (10.1186/s13054-018-2037-6) contains supplementary material, which is available to authorized users.
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With today's hospital demands and financial constraints, hospital inpatient bed management is becoming increasingly complex. The use of decision support systems could enable hospital staff and health decision makers to perform more focused management of the hospital inpatient beds, thus potentially reducing costs and inpatient length of stay. A literature review was carry out on both PubMed and ISI Web of Knowledge in order to identify studies evaluating the use of decision support systems when applied to hospital inpatient bed management. Two different approaches were identified: one approach based on the use of mathematical models to support the planning and allocation of hospital inpatient beds and another approach consisting in the utilization of information technologies to support timely inpatient placement. It was perceived that mathematical models could be safely used to model annual patient arrival rates and bed occupancy, thus forecasting hospital/department bed demand and underlying cost structures/revenues. It was also perceived that the use of bed management information systems provides hospital staff (administrative clerk, clinicians and housekeepers) with the necessary information to timely assess performance measures based on the hospital/department activity thus increasing resource effectiveness, optimizing established clinical pathways, reducing inpatient length of stay and associated costs.
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