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Validation of a Nurse-Based Delirium-Screening Tool for Hospitalized Patients.

ABSTRACT: 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.

SUBMITTER: Hargrave A

PROVIDER: S-EPMC5798858 | biostudies-literature | 2017 Nov - Dec

REPOSITORIES: biostudies-literature

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Validation of a Nurse-Based Delirium-Screening Tool for Hospitalized Patients.

Hargrave Anita A Bastiaens Jesse J Bourgeois James A JA Neuhaus John J Josephson S Andrew SA Chinn Julia J Lee Melissa M Leung Jacqueline J Douglas Vanja V

Psychosomatics 20170724 6

<h4>Background</h4>Guidelines recommend daily delirium monitoring of hospitalized patients. Available delirium-screening tools have not been validated for use by nurses among diverse inpatients.<h4>Objective</h4>We sought to validate the Nursing Delirium-Screening Scale (Nu-DESC) under these circumstances.<h4>Methods</h4>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 Dia ...[more]

PMID: 28750835

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Project description:ObjectiveTo adapt and validate a chart-based delirium detection tool for use in critically ill adults.DesignValidation study.SettingMedical-surgical intensive care unit (ICU) in an academic hospital.MeasurementsA 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).ResultsCharts 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).ConclusionA 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.

Project description:ImportanceCurrent methods for identifying hospitalized patients at increased risk of delirium require nurse-administered questionnaires with moderate accuracy.ObjectiveTo develop and validate a machine learning model that predicts incident delirium risk based on electronic health data available on admission.Design, setting, and participantsRetrospective cohort study evaluating 5 machine learning algorithms to predict delirium using 796 clinical variables identified by an expert panel as relevant to delirium prediction and consistently available in electronic health records within 24 hours of admission. The training set comprised 14 227 adult patients with non-intensive care unit hospital stays and no delirium on admission who were discharged between January 1, 2016, and August 31, 2017, from UCSF Health, a large academic health institution. The test set comprised 3996 patients with hospital stays who were discharged between August 1, 2017, and November 30, 2017.ExposuresPatient demographic characteristics, diagnoses, nursing records, laboratory results, and medications available in electronic health records during hospitalization.Main outcomes and measuresDelirium was defined as a positive Nursing Delirium Screening Scale or Confusion Assessment Method for the Intensive Care Unit score. Models were assessed using the area under the receiver operating characteristic curve (AUC) and compared against the 4-point scoring system AWOL (age >79 years, failure to spell world backward, disorientation to place, and higher nurse-rated illness severity), a validated delirium risk-assessment tool routinely administered in this cohort.ResultsThe training set included 14 227 patients (5113 [35.9%] aged >64 years; 7335 [51.6%] female; 687 [4.8%] with delirium), and the test set included 3996 patients (1491 [37.3%] aged >64 years; 1966 [49.2%] female; 191 [4.8%] with delirium). In total, the analysis included 18 223 hospital admissions (6604 [36.2%] aged >64 years; 9301 [51.0%] female; 878 [4.8%] with delirium). The AWOL system achieved a baseline AUC of 0.678. The gradient boosting machine model performed best, with an AUC of 0.855. Setting specificity at 90%, the model had a 59.7% (95% CI, 52.4%-66.7%) sensitivity, 23.1% (95% CI, 20.5%-25.9%) positive predictive value, 97.8% (95% CI, 97.4%-98.1%) negative predictive value, and a number needed to screen of 4.8. Penalized logistic regression and random forest models also performed well, with AUCs of 0.854 and 0.848, respectively.Conclusions and relevanceMachine learning can be used to estimate hospital-acquired delirium risk using electronic health record data available within 24 hours of hospital admission. Such a model may allow more precise targeting of delirium prevention resources without increasing the burden on health care professionals.

Project description:PurposeFall prevention is a safety goal in many hospitals. The performance of the Johns Hopkins Fall Risk Assessment Tool (JHFRAT) in older inpatients is largely unknown. We aimed to assess the JHFRAT performance in a large sample of Dutch older inpatients, including its trend over time.MethodsWe used an Electronic Health Records (EHR) dataset with hospitalized patients (≥ 70), admitted for ≥ 24 h between 2016 and 2021. Inpatient falls were extracted from structured and free-text data. We assessed the association between JHFRAT and falls using logistic regression. For test accuracy, we calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Discrimination was measured by the AUC. For calibration, we plotted the predicted fall probability with the actual probability of falls. For time-related effects, we calculated the AUC per 6 months (using data of patients admitted during the 6 months' time interval) and plotted these different AUC values over time. Furthermore, we compared the model (JHFRAT and falls) with and without adjusting for seasonal influenza, COVID-19, spring, summer, fall or winter periods.ResultsData included 17,263 admissions with at least 1 JHFRAT measurement, a median age of 76 and a percentage female of 47%. The in-hospital fall prevalence was 2.5%. JHFRAT [OR = 1.11 (1.03-1.20)] and its subcategories were significantly associated with falls. For medium/high risk of falls (JHFRAT > 5), sensitivity was 73%, specificity 51%, PPV 4% and NPV 99%. The overall AUC was 0.67, varying over time between 0.62 and 0.71 (for 6 months' time intervals). Seasonal influenza did affect the association between JHFRAT and falls. COVID-19, spring, summer, fall or winter did not affect the association.ConclusionsOur results show an association between JHFRAT and falls, a low discrimination by JHFRAT for older inpatients and over-prediction in the calibration. Improvements in the fall-risk assessment are warranted to improve efficiency.

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.

Dataset Information

Validation of a Nurse-Based Delirium-Screening Tool for Hospitalized Patients.

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Validation of a Nurse-Based Delirium-Screening Tool for Hospitalized Patients.

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