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Validation of an automated delirium prediction model (DElirium MOdel (DEMO)): an observational study.

ABSTRACT: OBJECTIVES:Delirium is an underdiagnosed, severe and costly disorder, and 30%-40% of cases can be prevented. A fully automated model to predict delirium (DEMO) in older people has been developed, and the objective of this study is to validate the model in a hospital setting. SETTING:Secondary care, one hospital with two locations. DESIGN:Observational study. PARTICIPANTS:The study included 450 randomly selected patients over 60 years of age admitted to Zuyderland Medical Centre. Patients who presented with delirium on admission were excluded. PRIMARY OUTCOME MEASURES:Development of delirium through chart review. RESULTS:A total of 383 patients were included in this study. The analysis was performed for delirium within 1, 3 and 5 days after a DEMO score was obtained. Sensitivity was 87.1% (95% CI 0.756 to 0.939), 84.2% (95% CI 0.732 to 0.915) and 82.7% (95% CI 0.734 to 0.893) for 1, 3 and 5 days, respectively, after obtaining the DEMO score. Specificity was 77.9% (95% CI 0.729 to 0.882), 81.5% (95% CI 0.766 to 0.856) and 84.5% (95% CI 0.797 to 0.884) for 1, 3 and 5 days, respectively, after obtaining the DEMO score. CONCLUSION:DEMO is a satisfactory prediction model but needs further prospective validation with in-person delirium confirmation. In the future, DEMO will be applied in clinical practice so that physicians will be aware of when a patient is at an increased risk of developing delirium, which will facilitate earlier recognition and diagnosis, and thus will allow the implementation of prevention measures.

SUBMITTER: Mestres Gonzalvo C

PROVIDER: S-EPMC5695379 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Validation of an automated delirium prediction model (DElirium MOdel (DEMO)): an observational study.

Mestres Gonzalvo Carlota C de Wit Hugo A J M HAJM van Oijen Brigit P C BPC Deben Debbie S DS Hurkens Kim P G M KPGM Mulder Wubbo J WJ Janknegt Rob R Schols Jos M G A JMGA Verhey Frans R FR Winkens Bjorn B van der Kuy Paul-Hugo M PM

BMJ open 20171108 11

<h4>Objectives</h4>Delirium is an underdiagnosed, severe and costly disorder, and 30%-40% of cases can be prevented. A fully automated model to predict delirium (DEMO) in older people has been developed, and the objective of this study is to validate the model in a hospital setting.<h4>Setting</h4>Secondary care, one hospital with two locations.<h4>Design</h4>Observational study.<h4>Participants</h4>The study included 450 randomly selected patients over 60 years of age admitted to Zuyderland Med ...[more]

PMID: 29122789

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Project description:To develop and validate a simple clinical prediction rule, based on variables easily measurable at admission, to identify patients at high risk of developing delirium during their hospital stay on an internal medicine ward. Prospective study of two cohorts of patients admitted between 1 May and 30 June 2008 (derivation cohort), and between 1 May and 30 June 2009 (validation cohort). A tertiary hospital in Donostia-Gipuzkoa (Spain). In total 397 patients participated in the study. The mean age and incidence of delirium were 75.9 years and 13%, respectively, in the derivation cohort, and 75.8 years and 25% in the validation cohort. The predictive variables analysed and finally included in the rule were: being aged 85 years old or older, being dependent in five or more activities of daily living, and taking two or more psychotropic drugs (antipsychotics, benzodiazepines, antidepressants, anticonvulsant and/or antidementia drugs). The variable of interest was delirium as defined by the short Confusion Assessment Method, which assesses four characteristics: acute onset and fluctuating course, inattention, disorganised thinking and altered level of consciousness. We developed a rule in which the individual risk of delirium is obtained by adding one point for each criterion met (age≥85, high level of dependence, and being on psychotropic medication). The result is considered positive if the score is ≥1. The rule accuracy was: sensitivity=93.4% (95% CI 85.5% to 97.2%), specificity=60.6% (95% CI 54.1% to 66.8%), positive predictive value=44.4% (95% CI 36.9% to 52.1%) and negative predictive value=96.5% (95% CI: 92% to 98.5%). The area under the receiver operator characteristic (ROC) curve was 0.85 for the validation cohort. The presence or absence of any of the three predictive factors (age≥85, high level of dependence and psychotropic medication) allowed us to classify patients on internal medicine wards according to the risk of developing delirium. The simplicity of the variables in our clinical prediction rule means that the data collection required is feasible in busy medicine units.

Project description:BackgroundRisk prediction models allow clinicians to forecast which individuals are at a higher risk for developing a particular outcome. We developed and internally validated a delirium prediction model for incident delirium parameterized to patient ICU admission acuity.MethodsThis retrospective, observational, fourteen medical-surgical ICU cohort study evaluated consecutive delirium-free adults surviving hospital stay with ICU length of stay (LOS) greater than or equal to 24 hours with both an admission APACHE II score and an admission type (e.g., elective post-surgery, emergency post-surgery, non-surgical) in whom delirium was assessed using the Intensive Care Delirium Screening Checklist (ICDSC). Risk factors included in the model were readily available in electric medical records. Least absolute shrinkage and selection operator logistic (LASSO) regression was used for model development. Discrimination was determined using area under the receiver operating characteristic curve (AUC). Internal validation was performed by cross-validation. Predictive performance was determined using measures of accuracy and clinical utility was assessed by decision-curve analysis.ResultsA total of 8,878 patients were included. Delirium incidence was 49.9% (n = 4,431). The delirium prediction model was parameterized to seven patient cohorts, admission type (3 cohorts) or mean quartile APACHE II score (4 cohorts). All parameterized cohort models were well calibrated. The AUC ranged from 0.67 to 0.78 (95% confidence intervals [CI] ranged from 0.63 to 0.79). Model accuracy varied across admission types; sensitivity ranged from 53.2% to 63.9% while specificity ranged from 69.0% to 74.6%. Across mean quartile APACHE II scores, sensitivity ranged from 58.2% to 59.7% while specificity ranged from 70.1% to 73.6%. The clinical utility of the parameterized cohort prediction model to predict and prevent incident delirium was greater than preventing incident delirium by treating all or none of the patients.ConclusionsOur results support external validation of a prediction model parameterized to patient ICU admission acuity to predict a patients' risk for ICU delirium. Classification of patients' risk for ICU delirium by admission acuity may allow for efficient initiation of prevention measures based on individual risk profiles.

Project description:BackgroundDelirium often occurs in children with congenital heart disease in the early postoperative period, which is not conducive to the rehabilitation and prognosis. There is little evidence to prove the effectiveness and safety of drug treatment of delirium in children, and the prevention has become an important topic. The purpose of this study is to analyze the early risk factors of delirium in children after congenital heart surgery, establish a nomogram prediction model, and explore the application efficiency of the model, so as to provide reference for early prevention of delirium.MethodsA total of 362 children treated in the cardiac intensive care unit (CICU) of Shanghai Children's Medical Center after congenital heart surgery from February 15 to April 15, 2021 were enrolled for the construction of the model. Bedside nurses who received unified training used the Cornell Assessment of Pediatric Delirium (CAPD) to evaluate delirium and recorded sixteen preoperative- and intraoperative-related influencing factors. A nomogram prediction model was created using multivariate logistic regression. The prediction effect of the model was evaluated by C-index and Brier value, and 96 children from April 16 to May 15, 2021 were included for effect verification. The model's effectiveness was validated by comparing the occurrence of delirium in children predicted by the model with the actual occurrence.ResultsMultivariate logistic regression analysis showed that male gender [odds ratio (OR) =1.786, 95% confidence interval (CI): 1.018-3.134, P=0.043], age <6.5 months (OR =0.224, 95% CI: 0.126-0.399, P=0.000), disease severity ≥4 points (OR =6.955, 95% CI: 3.564-13.576, P=0.003), and operation time ≥148 min (OR =2.401, 95%CI: 1.336-4.315, P=0.000) were independent risk factors for delirium in children after cardiac surgery. The C-index of the nomogram prediction model was 0.808, sensitivity was 76.1%, specificity was 70%, and the Brier value was 0.142. The validation of the model showed that the model predicted 20 cases and the actual occurrence was 20 cases, of which 8 cases were false negative and 8 cases were false positive, and the sensitivity, specificity, and accuracy of the model were 60%, 89.5%, and 83.3%, respectively.ConclusionsThe prediction model constructed in this study could provide early prediction of the occurrence of delirium in children after congenital heart surgery to a certain extent.

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Validation of an automated delirium prediction model (DElirium MOdel (DEMO)): an observational study.

Publications

Validation of an automated delirium prediction model (DElirium MOdel (DEMO)): an observational study.

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