Adaptation and Validation of a Chart-Based Delirium Detection Tool for the ICU (CHART-DEL-ICU).
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ABSTRACT: Objective
To adapt and validate a chart-based delirium detection tool for use in critically ill adults.Design
Validation study.Setting
Medical-surgical intensive care unit (ICU) in an academic hospital.Measurements
A chart-based delirium detection tool (CHART-DEL) was adapted for use in critically ill adults (CHART-DEL-ICU) and compared with prospective delirium assessments (i.e., clinical assessments (reference standard) by a research nurse trained by a neuropsychiatrist and routine delirium screening tools Confusion Assessment Method (CAM-ICU)) and (Intensive Care Delirium Screening Checklist (ICDSC)). The original CHART-DEL tool was adapted to include physician-reported ICDSC score (for probable delirium) and Richmond-Agitation Sedation Scale score (for altered level of consciousness and agitation). Two trained chart abstractors blinded to all delirium assessments manually abstracted delirium-related information from medical charts and electronic medical records and rated if delirium was present (four levels: uncertain, possible, probable, definite) or absent (no evidence).Results
Charts were manually abstracted for delirium-related information for 213 patients who were included in a prospective cohort study that included prospective delirium assessments. The CHART-DEL-ICU tool had excellent interrater reliability (kappa = 0.90). Compared to the reference standard, the sensitivity was 66.0% (95% CI = 59.3-72.3%) and specificity was 82.1% (95% CI = 78.0-85.7%), with a cut-point that included definite, probable, possible, and uncertain delirium. The AUC of the CHART-DEL-ICU alone is 74.1% (95% CI = 70.4-77.8%) compared with the addition of the CAM-ICU and ICDSC (CAM-ICU/CHART-DEL-ICU: 80.9% (95% CI = 77.8-83.9%), P = .01; ICDSC/CHART-DEL-ICU: 79.2% (95% CI = 75.9-82.6%), P = .03).Conclusion
A chart-based delirium detection tool has improved diagnostic accuracy when combined with routine delirium screening tools (CAM-ICU and ICDSC), compared to a chart-based method on its own. This presents a potential for retrospective detection of delirium from medical charts for research or to augment routine delirium screening methods to find missed cases of delirium.
SUBMITTER: Krewulak KD
PROVIDER: S-EPMC8049975 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundDelirium is a brain condition associated with poor outcomes in rehabilitation. It is therefore important to assess delirium incidence in rehabilitation.PurposeTo develop and validate a chart-based method to identify incident delirium episodes within the electronic database of a Swiss rehabilitation clinic, and to identify a study population of validated incident delirium episodes for further research purposes.DesignRetrospective validation study.SettingsRoutinely collected inpatient clinical data from ZURZACH Care.ParticipantsAll patients undergoing rehabilitation at ZURZACH Care, Rehaklinik Bad Zurzach between 2015 and 2018 were included.MethodsWithin the study population, we identified all rehabilitation stays for which ≥2 delirium-predictive key words (common terms used to describe delirious patients) were recorded in the medical charts. We excluded all prevalent delirium episodes and defined the remaining episodes to be potentially incident. At least two physicians independently confirmed or refuted each potential incident delirium episode by reviewing the patient charts. We calculated the positive predictive value (PPV) with 95% confidence interval (95% CI) for all potential incident delirium episodes and for specific subgroups.ResultsWithin 10,515 rehabilitation stays we identified 554 potential incident delirium episodes. Overall, 125 potential incident delirium episodes were confirmed by expert review. The PPV of the chart-based method varied from 0.23 (95% CI 0.19-0.26) overall to 0.69 (95% CI 0.56-0.79) in specific subgroups.ConclusionsOur chart-based method was able to capture incident delirium episodes with low to moderate accuracy. By conducting an additional expert review of the medical charts, we identified a study population of validated incident delirium episodes. Our chart-based method contributes towards an automated detection of potential incident delirium episodes that, supplemented with expert review, efficiently yields a validated population of incident delirium episodes for research purposes.
| S-EPMC8705493 | biostudies-literature
Project description:Guidelines recommend daily delirium monitoring of hospitalized patients. Available delirium-screening tools have not been validated for use by nurses among diverse inpatients.We sought to validate the Nursing Delirium-Screening Scale (Nu-DESC) under these circumstances.A blinded cross-sectional and quality-improvement study was conducted from August 2015-February 2016. Nurses? Nu-DESC scores were compared to delirium diagnosis according to Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5) criteria. A total of 405 consecutive hospitalized patients were included. Nu-DESC-positive (threshold score ?2) patients were matched with equal numbers of Nu-DESC-negative patients, by sex, age, and nursing unit. Nurses recorded a Nu-DESC score for each patient on every 12-hour shift. A Nu-DESC-blinded evaluator interviewed patients for 2 consecutive days. Delirium diagnosis was determined by physicians using DSM-5 criteria applied to collected research data. Sensitivity and specificity of the Nu-DESC were calculated. In an exploratory analysis, the performance of the Nu-DESC was analyzed with the addition of bedside measures of attention.The sensitivity of the Nu-DESC at a threshold of ?2 was 42% (95% CI: 33-53%). Specificity was 98% (97-98%). At a threshold of ?1, sensitivity was 67% (52-80%) and specificity 93% (90-95%). Similar results were found with the addition of attention tasks.The Nu-DESC is a specific delirium detection tool, but it is not sensitive at the usually proposed cut point of ?2. Using a threshold of ?1 or adding a test of attention increase sensitivity with a minor decrease in specificity.
| S-EPMC5798858 | biostudies-literature
Project description:IntroductionIn critically ill patients, delirium is a serious and frequent disorder that is associated with a prolonged intensive care and hospital stay and an increased morbidity and mortality. Without the use of a delirium screening instrument, delirium is often missed by ICU nurses and physicians. The effects of implementation of a screening method on haloperidol use is not known. The purpose of this study was to evaluate the implementation of the confusion assessment method-ICU (CAM-ICU) and the effect of its use on frequency and duration of haloperidol use.MethodsWe used a tailored implementation strategy focused on potential barriers. We measured CAM-ICU compliance, interrater reliability, and delirium knowledge, and compared the haloperidol use, as a proxy for delirium incidence, before and after the implementation of the CAM-ICU.ResultsCompliance and delirium knowledge increased from 77% to 92% and from 6.2 to 7.4, respectively (both, P < 0.0001). The interrater reliability increased from 0.78 to 0.89. More patients were treated with haloperidol (9.9% to 14.8%, P < 0.001), however with a lower dose (18 to 6 mg, P = 0.01) and for a shorter time period (5 [IQR:2-9] to 3 [IQR:1-5] days, P = 0.02).ConclusionsWith a tailored implementation strategy, a delirium assessment tool was successfully introduced in the ICU with the main goals achieved within four months. Early detection of delirium in critically ill patients increases the number of patients that receive treatment with haloperidol, however with a lower dose and for a shorter time period.
| S-EPMC2750188 | biostudies-literature
Project description:RATIONALE:Delirium incidence in intensive care unit (ICU) patients is high and associated with poor outcome. Identification of high-risk patients may facilitate its prevention. PURPOSE:To develop and validate a model based on data available at ICU admission to predict delirium development during a patient's complete ICU stay and to determine the predictive value of this model in relation to the time of delirium development. METHODS:Prospective cohort study in 13 ICUs from seven countries. Multiple logistic regression analysis was used to develop the early prediction (E-PRE-DELIRIC) model on data of the first two-thirds and validated on data of the last one-third of the patients from every participating ICU. RESULTS:In total, 2914 patients were included. Delirium incidence was 23.6%. The E-PRE-DELIRIC model consists of nine predictors assessed at ICU admission: age, history of cognitive impairment, history of alcohol abuse, blood urea nitrogen, admission category, urgent admission, mean arterial blood pressure, use of corticosteroids, and respiratory failure. The area under the receiver operating characteristic curve (AUROC) was 0.76 [95% confidence interval (CI) 0.73-0.77] in the development dataset and 0.75 (95% CI 0.71-0.79) in the validation dataset. The model was well calibrated. AUROC increased from 0.70 (95% CI 0.67-0.74), for delirium that developed <2 days, to 0.81 (95% CI 0.78-0.84), for delirium that developed >6 days. CONCLUSION:Patients' delirium risk for the complete ICU length of stay can be predicted at admission using the E-PRE-DELIRIC model, allowing early preventive interventions aimed to reduce incidence and severity of ICU delirium.
| S-EPMC4477716 | biostudies-literature
Project description:ObjectivesBoth delirium duration and delirium severity are associated with adverse patient outcomes. Serum biomarkers associated with delirium duration and delirium severity in ICU patients have not been reliably identified. We conducted our study to identify peripheral biomarkers representing systemic inflammation, impaired neuroprotection, and astrocyte activation associated with delirium duration, delirium severity, and in-hospital mortality.DesignObservational study.SettingThree Indianapolis hospitals.PatientsThree-hundred twenty-one critically ill delirious patients.InterventionsNone.Measurements and main resultsWe analyzed the associations between biomarkers collected at delirium onset and delirium-/coma-free days assessed through Richmond Agitation-Sedation Scale/Confusion Assessment Method for the ICU, delirium severity assessed through Confusion Assessment Method for the ICU-7, and in-hospital mortality. After adjusting for age, gender, Acute Physiology and Chronic Health Evaluation II score, Charlson comorbidity score, sepsis diagnosis and study intervention group, interleukin-6, -8, and -10, tumor necrosis factor-α, C-reactive protein, and S-100β levels in quartile 4 were negatively associated with delirium-/coma-free days by 1 week and 30 days post enrollment. Insulin-like growth factor-1 levels in quartile 4 were not associated with delirium-/coma-free days at both time points. Interleukin-6, -8, and -10, tumor necrosis factor-α, C-reactive protein, and S-100β levels in quartile 4 were also associated with delirium severity by 1 week. At hospital discharge, interleukin-6, -8, and -10 retained the association but tumor necrosis factor-α, C-reactive protein, and S-100β lost their associations with delirium severity. Insulin-like growth factor-1 levels in quartile 4 were not associated with delirium severity at both time points. Interleukin-8 and S-100β levels in quartile 4 were also associated with higher in-hospital mortality. Interleukin-6 and -10, tumor necrosis factor-α, and insulin-like growth factor-1 were not found to be associated with in-hospital mortality.ConclusionsBiomarkers of systemic inflammation and those for astrocyte and glial activation were associated with longer delirium duration, higher delirium severity, and in-hospital mortality. Utility of these biomarkers early in delirium onset to identify patients at a higher risk of severe and prolonged delirium, and delirium related complications during hospitalization needs to be explored in future studies.
| S-EPMC7242000 | biostudies-literature
Project description:ObjectivesTo develop and validate a delirium prediction model for adult intensive care patients and determine its additional value compared with prediction by caregivers.DesignObservational multicentre study.SettingFive intensive care units in the Netherlands (two university hospitals and three university affiliated teaching hospitals).Participants3056 intensive care patients aged 18 years or over.Main outcome measureDevelopment of delirium (defined as at least one positive delirium screening) during patients' stay in intensive care.ResultsThe 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.ConclusionThe 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. Trial registration Clinical trials NCT00604773 (development study) and NCT00961389 (validation study).
| S-EPMC3276486 | biostudies-literature
Project description:BackgroundPostoperative delirium is an important problem for surgical inpatients and was the target of a multidisciplinary quality improvement project at our institution. We developed and tested a semiautomated delirium risk stratification instrument, Age, WORLD backwards, Orientation, iLlness severity, Surgery-specific risk (AWOL-S), in 3 independent cohorts from our tertiary care hospital and describe its performance characteristics and impact on clinical care.MethodsThe risk stratification instrument was derived with elective surgical patients who were admitted at least overnight and received at least 1 postoperative delirium screen (Nursing Delirium Screening Scale [NuDESC] or Confusion Assessment Method for the Intensive Care Unit [CAM-ICU]) and preoperative cognitive screening tests (orientation to place and ability to spell WORLD backward). Using data pragmatically collected between December 7, 2016, and June 15, 2017, we derived a logistic regression model predicting probability of delirium in the first 7 postoperative hospital days. A priori predictors included age, cognitive screening, illness severity or American Society of Anesthesiologists physical status, and surgical delirium risk. We applied model odds ratios to 2 subsequent cohorts ("validation" and "sustained performance") and assessed performance using area under the receiver operator characteristic curves (AUC-ROC). A post hoc sensitivity analysis assessed performance in emergency and preadmitted patients. Finally, we retrospectively evaluated the use of benzodiazepines and anticholinergic medications in patients who screened at high risk for delirium.ResultsThe logistic regression model used to derive odds ratios for the risk prediction tool included 2091 patients. Model AUC-ROC was 0.71 (0.67-0.75), compared with 0.65 (0.58-0.72) in the validation (n = 908) and 0.75 (0.71-0.78) in the sustained performance (n = 3168) cohorts. Sensitivity was approximately 75% in the derivation and sustained performance cohorts; specificity was approximately 59%. The AUC-ROC for emergency and preadmitted patients was 0.71 (0.67-0.75; n = 1301). After AWOL-S was implemented clinically, patients at high risk for delirium (n = 3630) had 21% (3%-36%) lower relative risk of receiving an anticholinergic medication perioperatively after controlling for secular trends.ConclusionsThe AWOL-S delirium risk stratification tool has moderate accuracy for delirium prediction in a cohort of elective surgical patients, and performance is largely unchanged in emergent/preadmitted surgical patients. Using AWOL-S risk stratification as a part of a multidisciplinary delirium reduction intervention was associated with significantly lower rates of perioperative anticholinergic but not benzodiazepine, medications in those at high risk for delirium. AWOL-S offers a feasible starting point for electronic medical record-based postoperative delirium risk stratification and may serve as a useful paradigm for other institutions.
| S-EPMC7669577 | biostudies-literature
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.
| S-EPMC7437909 | biostudies-literature
Project description:ObjectiveSummarize performance and development of ICU delirium-prediction models published within the past 5 years.Data sourcesSystematic electronic searches were conducted in April 2019 using PubMed, Embase, Cochrane Central, Web of Science, and Cumulative Index to Nursing and Allied Health Literature to identify peer-reviewed studies.Study selectionEligible studies were published in English during the past 5 years that specifically addressed the development, validation, or recalibration of delirium-prediction models in adult ICU populations.Data extractionScreened citations were extracted independently by three investigators with a 42% overlap to verify consistency using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies.Data synthesisEighteen studies featuring 23 distinct prediction models were included. Model performance varied greatly, as assessed by area under the receiver operating characteristic curve (0.62-0.94), specificity (0.50-0.97), and sensitivity (0.45-0.96). Most models used data collected from a single time point or window to predict the occurrence of delirium at any point during hospital or ICU admission, and lacked mechanisms for providing pragmatic, actionable predictions to clinicians.ConclusionsAlthough most ICU delirium-prediction models have relatively good performance, they have limited applicability to clinical practice. Most models were static, making predictions based on data collected at a single time-point, failing to account for fluctuating conditions during ICU admission. Further research is needed to create clinically relevant dynamic delirium-prediction models that can adapt to changes in individual patient physiology over time and deliver actionable predictions to clinicians.
| S-EPMC7746201 | biostudies-literature
Project description:BackgroundWe investigated the effect of delirium burden in mechanically ventilated patients, beginning in the ICU and continuing throughout hospitalization, on functional neurologic outcomes up to 2.5 years following critical illness.MethodsProspective cohort study of enrolling 178 consecutive mechanically ventilated adult medical and surgical ICU patients between October 2013 and May 2016. Altogether, patients were assessed daily for delirium 2941days using the Confusion Assessment Method for the ICU (CAM-ICU). Hospitalization delirium burden (DB) was quantified as number of hospital days with delirium divided by total days at risk. Survival status up to 2.5 years and neurologic outcomes using the Glasgow Outcome Scale were recorded at discharge 3, 6, and 12 months post-discharge.ResultsOf 178 patients, 19 (10.7%) were excluded from outcome analyses due to persistent coma. Among the remaining 159, 123 (77.4%) experienced delirium. DB was independently associated with >4-fold increased mortality at 2.5 years following ICU admission (adjusted hazard ratio [aHR], 4.77; 95% CI, 2.10-10.83; P < .001), and worse neurologic outcome at discharge (adjusted odds ratio [aOR], 0.02; 0.01-0.09; P < .001), 3 (aOR, 0.11; 0.04-0.31; P < .001), 6 (aOR, 0.10; 0.04-0.29; P < .001), and 12 months (aOR, 0.19; 0.07-0.52; P = .001). DB in the ICU alone was not associated with mortality (HR, 1.79; 0.93-3.44; P = .082) and predicted neurologic outcome less strongly than entire hospital stay DB. Similarly, the number of delirium days in the ICU and for whole hospitalization were not associated with mortality (HR, 1.00; 0.93-1.08; P = .917 and HR, 0.98; 0.94-1.03, P = .535) nor with neurological outcomes, except for the association between ICU delirium days and neurological outcome at discharge (OR, 0.90; 0.81-0.99, P = .038).ConclusionsDelirium burden throughout hospitalization independently predicts long term neurologic outcomes and death up to 2.5 years after critical illness, and is more predictive than delirium burden in the ICU alone and number of delirium days.
| S-EPMC8638853 | biostudies-literature