Dataset Information

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

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.

SUBMITTER: Lindroth H

PROVIDER: S-EPMC5931306 | biostudies-other | 2018 Apr

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

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

Lindroth Heidi H Bratzke Lisa L Purvis Suzanne S Brown Roger R Coburn Mark M Mrkobrada Marko M Chan Matthew T V MTV Davis Daniel H J DHJ Pandharipande Pratik P Carlsson Cynthia M CM Sanders Robert D RD

BMJ open 20180428 4

<h4>Objective</h4>To identify existing prognostic delirium prediction models and evaluate their validity and statistical methodology in the older adult (≥60 years) acute hospital population.<h4>Design</h4>Systematic review.<h4>Data sources and methods</h4>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 developme ...[more]

PMID: 29705752

Similar Datasets

Project description:ObjectivesEmergence delirium (ED) occurs in approximately 25% of paediatric general anaesthetics and has significant adverse effects. The goal of the current systematic review was to identify the existing literature investigating performance of predictive models for the development of paediatric ED following general anaesthesia and to determine their usability.DesignSystematic review using the Prediction model study Risk Of Bias Assessment Tool (PROBAST) framework.Data sourcesMedline (Ovid), PubMed, Embase (Ovid), Cochrane Database of Systematic Reviews (Ovid), Cochrane CENTRAL (Ovid), PsycINFO (Ovid), Scopus (Elsevier) and Web of Science (Clarivate Analytics), ClinicalTrials.gov, International Clinical Trials Registry Platform and ProQuest Digital Dissertations and Theses International through 17 November 2020.Eligibility criteria for selecting studiesAll randomised controlled trials and cohort studies investigating predictive models for the development of ED in children undergoing general anaesthesia.Data extraction and synthesisFollowing title, abstract and full-text screening by two reviewers, data were extracted from all eligible studies, including demographic parameters, details of anaesthetics and performance characteristics of the predictive scores for ED. Evidence quality and predictive score usability were assessed according to the PROBAST framework.ResultsThe current systematic review yielded 9242 abstracts, of which only one study detailing the development and validation of the Emergence Agitation Risk Scale (EARS) met the inclusion criteria. EARS had good discrimination with c-index of 0.81 (95% CI 0.72 to 0.89). Calibration showed a non-significant Homer-Lemeshow goodness-of-fit test (p=0.97). Although the EARS demonstrated low concern of applicability, the high risk of bias compromised the overall usability of this model.ConclusionsThe current systematic review concluded that EARS has good discrimination performance but low usability to predict ED in a paediatric population. Further research is warranted to develop novel models for the prediction of ED in paediatric anaesthesia.Prospero registration numberCRD42019141950.

Project description:BACKGROUND:Accurate 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. METHODS:This 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. RESULTS:In 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. CONCLUSIONS:While 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 REGISTRATION:ClinicalTrials.gov, NCT02518646 . Registered on 21 July 2015.

Project description:BackgroundSelecting appropriate outcome measures for vulnerable, multimorbid, older patients with acute and chronic impairments poses specific challenges, which may have caused inconsistent findings of previous intervention trials on early inpatient rehabilitation in acutely hospitalized older patients. The aim of this review was to describe primary outcome measures that have been used in randomized controlled trials (RCTs) on early rehabilitation in acutely hospitalized older patients, to analyze their matching, and to evaluate the effects of matching on the main findings of these RCTs.MethodsA systematic literature search was conducted in PubMed, Cochrane CENTRAL, CINAHL, and PEDro databases. Additional studies were identified through reference and citation tracking. Inclusion criteria were: RCT, patients aged ≥65 years, admission to an acute hospital medical ward (but not to an intensive medical care unit), physical exercise intervention (also as part of multidisciplinary programs), and primary outcome measure during hospitalization. Two independent reviewers extracted the data, assessed the methodological quality, and analyzed the matching of primary outcome measures to the intervention, study sample, and setting. Main study findings were related to the results of the matching procedure.ResultsTwenty-eight articles reporting on 24 studies were included. A total of 33 different primary outcome measures were identified, which were grouped into six categories: functional status, mobility status, hospital outcomes, adverse clinical events, psychological status, and cognitive functioning. Outcome measures differed considerably within each category and showed a large heterogeneity in their matching to the intervention, study sample, and setting. Outcome measures that specifically matched the intervention contents were more likely to document intervention-induced benefits. Mobility instruments seemed to be the most sensitive outcome measures to reveal such benefits.ConclusionsThis review highlights that the selection of outcome measures has to be highly specific to the intervention contents as this is a key factor to reveal benefits attributable to early rehabilitation in acutely hospitalized older patients. Inappropriate selection of outcome measures may represent a major cause of inconsistent findings reported on the effectiveness of early rehabilitation in this setting.Trial registrationPROSPERO CRD42017063978 .

Project description:ContextEarly detection of kidney cancer improves survival; however, low prevalence means that population-wide screening may be inefficient. Stratification of the population into risk categories could allow for the introduction of a screening programme tailored to individuals.ObjectiveThis review will identify and compare published models that predict the risk of developing kidney cancer in the general population.Evidence acquisitionA search identified primary research reporting or validating models predicting the risk of kidney cancer in Medline and EMBASE. After screening identified studies for inclusion, we extracted data onto a standardised form. The risk models were classified using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines and evaluated using the PROBAST assessment tool.Evidence synthesisThe search identified 15 281 articles. Sixty-two satisfied the inclusion criteria; performance measures were provided for 11 models. Some models predicted the risk of prevalent undiagnosed disease and others future incident disease. Six of the models had been validated, two using external populations. The most commonly included risk factors were age, smoking status, and body mass index. Most of the models had acceptable-to-good discrimination (area under the receiver-operating curve >0.7) in development and validation. Many models also had high specificity; however, several had low sensitivity. The highest performance was seen for the models using only biomarkers to detect kidney cancer; however, these were developed and validated in small case-control studies.ConclusionsWe identified a small number of risk models that could be used to stratify the population according to the risk of kidney cancer. Most exhibit reasonable discrimination, but a few have been validated externally in population-based studies.Patient summaryIn this review, we looked at mathematical models predicting the likelihood of an individual developing kidney cancer. We found several suitable models, using a range of risk factors (such as age and smoking) to predict the risk for individuals. Most of the models identified require further testing in the general population to confirm their usefulness.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets