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Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study

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BMJ-BRITISH MEDICAL JOURNAL
卷 344, 期 -, 页码 -

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BMJ PUBLISHING GROUP
DOI: 10.1136/bmj.e420

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Objectives To develop and validate a delirium prediction model for adult intensive care patients and determine its additional value compared with prediction by caregivers. Design Observational multicentre study. Setting Five intensive care units in the Netherlands (two university hospitals and three university affiliated teaching hospitals). Participants 3056 intensive care patients aged 18 years or over. Main outcome measure Development of delirium (defined as at least one positive delirium screening) during patients' stay in intensive care. Results The model was developed using 1613 consecutive intensive care patients in one hospital and temporally validated using 549 patients from the same hospital. For external validation, data were collected from 894 patients in four other hospitals. The prediction (PRE-DELIRIC) model contains 10 risk factors-age, APACHE-II score, admission group, coma, infection, metabolic acidosis, use of sedatives and morphine, urea concentration, and urgent admission. The model had an area under the receiver operating characteristics curve of 0.87 (95% confidence interval 0.85 to 0.89) and 0.86 after bootstrapping. Temporal validation and external validation resulted in areas under the curve of 0.89 (0.86 to 0.92) and 0.84 (0.82 to 0.87). The pooled area under the receiver operating characteristics curve (n=3056) was 0.85 (0.84 to 0.87). The area under the curve for nurses' and physicians' predictions (n=124) was significantly lower at 0.59 (0.49 to 0.70) for both. Conclusion The PRE-DELIRIC model for intensive care patients consists of 10 risk factors that are readily available within 24 hours after intensive care admission and has a high predictive value. Clinical prediction by nurses and physicians performed significantly worse. The model allows for early prediction of delirium and initiation of preventive measures.

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