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J Am Geriatr Soc. 2014 Dec;62(12):2383-90. doi: 10.1111/jgs.13138.

Risk prediction models for postoperative delirium: a systematic review and meta-analysis.

Author information

1
University of Amsterdam, Amsterdam, The Netherlands.

Abstract

Postoperative delirium (POD) is a common neuropsychiatric disorder characterized by inattention, fluctuating levels of consciousness, and disorganized thinking. POD can have serious consequences, including institutionalization and death. Risk stratification may target prevention to individuals at greater risk of POD. The objective of this study was to identify all published POD risk prediction models (RPMs) and to compare them with regard to their clinical practicability and predictive and discriminative performance. PubMed and EMBASE were searched from inception to January 1, 2013, for articles describing POD RPMs. Studies were included if they presented data from a cohort study, examined one or more RPMs, examined POD as an outcome, and assessed the performance of the RPM(s). Thirty of 2,246 articles were included, and 37 RPMs were found. Sixteen and six studies described individuals who had undergone cardiovascular and orthopedic surgery, respectively. The Confusion Assessment Method (CAM) for the intensive care unit checklist was the most often used diagnostic method (65%), followed by the Diagnostic and Statistical Manual of Mental Disorders (DSM), Fourth Edition criteria (16%). Predictors most often used in RPMs were age (20), preoperative Mini-Mental State Examination score (10), and preoperative increased alcohol use (7). Thirty RPMs were not validated, three were validated internally, and four were validated externally. Size of the models was not associated with their discriminatory performance. Instead of creating steadily new RPMs, existing RPMs should be further tested, improved, and meta-analytically integrated. It may be too early to implement a particular PODRPM in clinical practice with confidence.

KEYWORDS:

delirium; geriatrics; psychiatry; risk prediction models; surgery

PMID:
25516034
DOI:
10.1111/jgs.13138
[Indexed for MEDLINE]

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