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      Systematic review of prediction models for delirium in the older adult inpatient

      systematic-review

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          Abstract

          Objective

          To identify existing prognostic delirium prediction models and evaluate their validity and statistical methodology in the older adult (≥60 years) acute hospital population.

          Design

          Systematic review.

          Data sources and methods

          PubMed, CINAHL, PsychINFO, SocINFO, Cochrane, Web of Science and Embase were searched from 1 January 1990 to 31 December 2016. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses and CHARMS Statement guided protocol development. Inclusion criteria: age >60 years, inpatient, developed/validated a prognostic delirium prediction model. Exclusion criteria: alcohol-related delirium, sample size ≤50. The primary performance measures were calibration and discrimination statistics. Two authors independently conducted search and extracted data. The synthesis of data was done by the first author. Disagreement was resolved by the mentoring author.

          Results

          The initial search resulted in 7,502 studies. Following full-text review of 192 studies, 33 were excluded based on age criteria (<60 years) and 27 met the defined criteria. Twenty-three delirium prediction models were identified, 14 were externally validated and 3 were internally validated. The following populations were represented: 11 medical, 3 medical/surgical and 13 surgical. The assessment of delirium was often non-systematic, resulting in varied incidence. Fourteen models were externally validated with an area under the receiver operating curve range from 0.52 to 0.94. Limitations in design, data collection methods and model metric reporting statistics were identified.

          Conclusions

          Delirium prediction models for older adults show variable and typically inadequate predictive capabilities. Our review highlights the need for development of robust models to predict delirium in older inpatients. We provide recommendations for the development of such models.

          Related collections

          Most cited references57

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          Translating clinical research into clinical practice: impact of using prediction rules to make decisions.

          Clinical prediction rules, sometimes called clinical decision rules, have proliferated in recent years. However, very few have undergone formal impact analysis, the standard of evidence to assess their impact on patient care. Without impact analysis, clinicians cannot know whether using a prediction rule will be beneficial or harmful. This paper reviews standards of evidence for developing and evaluating prediction rules; important differences between prediction rules and decision rules; how to assess the potential clinical impact of a prediction rule before translating it into a decision rule; methodologic issues critical to successful impact analysis, including defining outcome measures and estimating sample size; the importance of close collaboration between clinical investigators and practicing clinicians before, during, and after impact analysis; and the need to measure both efficacy and effectiveness when analyzing a decision rule's clinical impact. These considerations should inform future development, evaluation, and use of all clinical prediction or decision rules.
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            Precipitating factors for delirium in hospitalized elderly persons. Predictive model and interrelationship with baseline vulnerability.

            To prospectively develop and validate a predictive model for delirium based on precipitating factors during hospitalization, and to examine the interrelationship of precipitating factors and baseline vulnerability. Two prospective cohort studies, in tandem. General medical wards, university teaching hospital. For the development cohort, 196 patients aged 70 years and older with no delirium at baseline, and for the validation cohort, 312 comparable patients. New-onset delirium by hospital day 9, defined by the Confusion Assessment Method diagnostic criteria. Delirium developed in 35 patients (18%) in the development cohort. Five independent precipitating factors for delirium were identified; use of physical restraints (adjusted relative risk [RR], 4.4; 95% confidence interval [CI], 2.5 to 7.9), malnutrition (RR, 4.0; 95% CI, 2.2 to 7.4), more than three medications added (RR, 2.9; 95% CI, 1.6 to 5.4), use of bladder catheter (RR, 2.4; 95% CI, 1.2 to 4.7), and any iatrogenic event (RR, 1.9; 95% CI, 1.1 to 3.2). Each precipitating factor preceded the onset of delirium by more than 24 hours. A risk stratification system was developed by adding 1 point for each factor present. Rates of delirium for low-risk (0 points), intermediate-risk (1 to 2 points), and high-risk groups (> or equal to 3 points) were 3%, 20%, and 59%, respectively (P < .001). The corresponding rates in the validation cohort, in which 47 patients (15%) developed delirium, were 4%, 20%, and 35%, respectively (P < .001). When precipitating and baseline factors were analyzed in cross-stratified format, delirium rates increased progressively from low-risk to high-risk groups in all directions (double-gradient phenomenon). The contributions of baseline and precipitating factors were documented to be independent and statistically significant. A simple predictive model based on the presence of five precipitating factors can be used to identify elderly medical patients at high risk for delirium. Precipitating and baseline vulnerability factors are highly interrelated and contribute to delirium in independent substantive, and cumulative ways.
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              Postoperative delirium in the elderly: risk factors and outcomes.

              The purpose of this study was to describe the natural history, identify risk factors, and determine outcomes for the development of postoperative delirium in the elderly. Postoperative delirium is a common and deleterious complication in geriatric patients. Subjects older than 50 years scheduled for an operation requiring a postoperative intensive care unit admission were recruited. After preoperative informed written consent, enrolled subjects had baseline cognitive and functional assessments. Postoperatively, subjects were assessed daily for delirium using the confusion assessment method-intensive care unit. Patients were also followed for outcomes. During the study period, 144 patients were enrolled before major abdominal (40%), thoracic (53%), or vascular (7%) operations. The overall incidence of delirium was 44% (64/144). The average time to onset of delirium was 2.1 +/- 0.9 days and the mean duration of delirium was 4.0 +/- 5.1 days. Several preoperative variables were associated with an increased risk of delirium including older age (P < 0.001), hypoalbuminemia (P < 0.001), impaired functional status (P < 0.001), pre-existing dementia (P < 0.001), and pre-existing comorbidities (P < 0.001). In a multivariable logistic regression model, pre-existing dementia remains the strongest risk factor for the development of postoperative delirium. Worse outcomes, including increased length of stay (P < 0.001), postdischarge institutionalization (P < 0.001), and 6 month mortality (P = 0.001), occurred in subjects who developed delirium. In the current study, delirium occurred in 44% of elderly patients after a major operation. Pre-existing cognitive dysfunction was the strongest predictor of the development of postoperative delirium. Outcomes, including an increased rate of 6 month mortality, were worse in patients who developed postoperative delirium.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2018
                28 April 2018
                : 8
                : 4
                : e019223
                Affiliations
                [1 ] departmentDepartment of Anesthesiology , University of Wisconsin Madison School of Medicine and Public Health , Madison, Wisconsin, USA
                [2 ] departmentSchool of Nursing , University of Wisconsin Madison , Madison, Wisconsin, USA
                [3 ] departmentSchool of Nursing , University of Wisconsin-Madison , Madison, Wisconsin, USA
                [4 ] departmentDepartment of Nursing , University Hospital , Madison, Wisconsin, USA
                [5 ] departmentDepartment of Anesthesiology , University Hospital RWTH Aachen , Aachen, Germany
                [6 ] departmentDepartment of Medicine , Western University , London, Ontario, Canada
                [7 ] departmentAnesthesia and Intensive Care , The Chinese University of Hong Kong , Shatin, Hong Kong
                [8 ] departmentMRC Unit for Lifelong Health and Ageing , University College London , London, UK
                [9 ] departmentDivision of Anesthesiology Critical Care Medicine , Vanderbilt University School of Medicine , Nashville, Tennessee, USA
                [14 ] departmentDepartment of Medicine, Division of Geriatrics , University of Wisconsin School of Medicine and Public Health , Madison, Wisconsin, USA
                [15 ] departmentGeriatric Research, Education, and Clinical Center (GRECC) , William S. Middleton Memorial Veterans Hospital , Madison, Wisconsin, USA
                [16 ] Wisconsin Alzheimer’s Disease Research Center , Madison, Wisconsin, USA
                [17 ] Wisconsin Alzheimer’s Institute , Madison, Wisconsin, USA
                Author notes
                [Correspondence to ] Heidi Lindroth; hlindroth@ 123456wisc.edu
                Author information
                http://orcid.org/0000-0002-5389-4701
                http://orcid.org/0000-0002-8349-4696
                Article
                bmjopen-2017-019223
                10.1136/bmjopen-2017-019223
                5931306
                29705752
                27d37e03-3b6f-4edd-9426-fea3e4dbc885
                © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

                History
                : 18 August 2017
                : 13 March 2018
                : 19 March 2018
                Funding
                Funded by: University of Wisconsin Madison School of Medicine and Public Health;
                Funded by: FundRef http://dx.doi.org/10.13039/100000050, National Heart, Lung, and Blood Institute;
                Funded by: Hospira, Inc;
                Funded by: FundRef http://dx.doi.org/10.13039/100000049, National Institute on Aging;
                Categories
                Geriatric Medicine
                Research
                1506
                1698
                1329
                Custom metadata
                unlocked

                Medicine
                delirium,geriatric medicine,statistic
                Medicine
                delirium, geriatric medicine, statistic

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