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Delirium risk stratification in consecutive unselected admissions to acute medicine: validation of externally derived risk scores.

ABSTRACT: reliable delirium risk stratification will aid recognition, anticipation and prevention and will facilitate targeting of resources in clinical practice as well as identification of at-risk patients for research. Delirium risk scores have been derived for acute medicine, but none has been prospectively validated in external cohorts. We therefore aimed to determine the reliability of externally derived risk scores in a consecutive cohort of older acute medicine patients.consecutive patients aged ?65 over two 8-week periods (2010, 2012) were screened prospectively for delirium using the Confusion Assessment Method (CAM), and delirium was diagnosed using the DSM IV criteria. The reliability of existing delirium risk scores derived in acute medicine cohorts and simplified for use in routine clinical practice (USA, n = 2; Spain, n = 1; Indonesia, n = 1) was determined by the area under the receiver operating characteristic curve (AUC). Delirium was defined as prevalent (on admission), incident (occurring during admission) and any (prevalent + incident) delirium.among 308 consecutive patients aged ?65 (mean age/SD = 81/8 years, 164 (54%) female), existing delirium risk scores had AUCs for delirium similar to those reported in their original internal validations ranging from 0.69 to 0.76 for any delirium and 0.73 to 0.83 for incident delirium. All scores performed better than chance but no one score was clearly superior.externally derived delirium risk scores performed well in our independent acute medicine population with reliability unaffected by simplification and might therefore facilitate targeting of multicomponent interventions in routine clinical practice.

SUBMITTER: Pendlebury ST

PROVIDER: S-EPMC4711661 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Delirium risk stratification in consecutive unselected admissions to acute medicine: validation of externally derived risk scores.

Pendlebury Sarah T ST Lovett Nicola N Smith Sarah C SC Cornish Emily E Mehta Ziyah Z Rothwell Peter M PM

Age and ageing 20160101 1

<h4>Background</h4>reliable delirium risk stratification will aid recognition, anticipation and prevention and will facilitate targeting of resources in clinical practice as well as identification of at-risk patients for research. Delirium risk scores have been derived for acute medicine, but none has been prospectively validated in external cohorts. We therefore aimed to determine the reliability of externally derived risk scores in a consecutive cohort of older acute medicine patients.<h4>Meth ...[more]

PMID: 26764396

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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.

Project description:ObjectiveTo determine whether the ability to stratify an individual patient's hazard for falling could facilitate development of focused interventions aimed at reducing these adverse outcomes.DesignClinical and sociodemographic data from electronic health records were utilized to derive multiple logistic regression models of hospital readmissions for injuries related to falls. Drugs used at admission were summarized based on reported adverse effect frequencies in published drug labeling.SettingTwo large academic medical centers in New England, United States.ParticipantsThe model was developed with 25,924 individuals age ≥ 40 with an initial hospital discharge. The resulting model was then tested in an independent set of 13,032 inpatients drawn from the same hospital and 36,588 individuals discharged from a second large hospital during the same period.Main outcome measureHospital readmissions for injury related to falls.ResultsAmong 25,924 discharged individuals, 680 (2.6%) were evaluated in the emergency department or admitted to hospital for a fall within 30 days of discharge, 1635 (6.3%) within 180 days of discharge, 2360 (9.1%) within one year, and 3465 (13.4%) within two years. Older age, female sex, white or African-American race, public insurance, greater number of drugs taken on discharge, and score for burden of adverse effects were each independently associated with hazard for fall. For drug burden, presence of a drug with a frequency of adverse effects related to fall of 10% was associated with 3.5% increase in odds of falling over the next two years (odds ratio 1.04, 95% confidence interval 1.02 to 1.05). In an independent testing set, the area under the receiver operating characteristics curve was 0.65 for a fall within two years based on cross sectional data and 0.72 with the addition of prior utilization data including age adjusted Charlson comorbidity index. Portability was promising, with area under the curve of 0.71 for the longitudinal model in a second hospital system.ConclusionsIt is potentially useful to stratify risk of falls based on clinical features available as artifacts of routine clinical care. A web based tool can be used to calculate and visualize risk associated with drug treatment to facilitate further investigation and application.

Project description:ObjectivesThere are no established mortality risk equations specifically for unplanned emergency medical admissions which include patients with SARS-19 (COVID-19). We aim to develop and validate a computer-aided risk score (CARMc19) for predicting mortality risk by combining COVID-19 status, the first electronically recorded blood test results and the National Early Warning Score (NEWS2).DesignLogistic regression model development and validation study.SettingTwo acute hospitals (York Hospital-model development data; Scarborough Hospital-external validation data).ParticipantsAdult (aged ≥16 years) medical admissions discharged over a 24-month period with electronic NEWS and blood test results recorded on admission. We used logistic regression modelling to predict the risk of in-hospital mortality using two models: (1) CARMc19_N: age+sex+NEWS2 including subcomponents+COVID19; (2) CARMc19_NB: CARMc19_N in conjunction with seven blood test results and acute kidney injury score. Model performance was evaluated according to discrimination (c-statistic), calibration (graphically) and clinical usefulness at NEWS2 thresholds of 4+, 5+, 6+.ResultsThe risk of in-hospital mortality following emergency medical admission was similar in development and validation datasets (8.4% vs 8.2%). The c-statistics for predicting mortality for CARMc19_NB is better than CARMc19_N in the validation dataset (CARMc19_NB=0.88 (95% CI 0.86 to 0.90) vs CARMc19_N=0.86 (95% CI 0.83 to 0.88)). Both models had good calibration (CARMc19_NB=1.01 (95% CI 0.88 to 1.14) and CARMc19_N:0.95 (95% CI 0.83 to 1.06)). At all NEWS2 thresholds (4+, 5+, 6+) model, CARMc19_NB had better sensitivity and similar specificity.ConclusionsWe have developed a validated CARMc19 scores with good performance characteristics for predicting the risk of in-hospital mortality. Since the CARMc19 scores place no additional data collection burden on clinicians, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.

Project description:BACKGROUND:A common postoperative complication found among patients who are critically ill is delirium, which has a high mortality rate. A predictive model is needed to identify high-risk patients in order to apply strategies which will prevent and/or reduce adverse outcomes. OBJECTIVES:To identify the incidence of, and the risk factors for, postoperative delirium (POD) in surgical intensive care unit (SICU) patients, and to determine predictive scores for the development of POD. METHODS:This study enrolled adults aged over 18 years who had undergone an operation within the preceding week and who had been admitted to a SICU for a period that was expected to be longer than 24 h. The CAM - ICU score was used to determine the occurrence of delirium. RESULTS:Of the 250 patients enrolled, delirium was found in 61 (24.4%). The independent risk factors for delirium that were identified by a multivariate analysis comprised age, diabetes mellitus, severity of disease (SOFA score), perioperative use of benzodiazepine, and mechanical ventilation. A predictive score (age + (5 × SOFA) + (15 × Benzodiazepine use) + (20 × DM) + (20 × mechanical ventilation) + (20 × modified IQCODE > 3.42)) was created. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 (95% CI: 0.786 to 0.897). The cut point of 125 demonstrated a sensitivity of 72.13% and a specificity of 80.95%, and the hospital mortality rate was significantly greater among the delirious than the non-delirious patients (25% vs. 6%, p < 0.01). CONCLUSIONS:POD was experienced postoperatively by a quarter of the surgical patients who were critically ill. A risk score utilizing 6 variables was able to predict which patients would develop POD. The identification of high-risk patients following SICU admission can provide a basis for intervention strategies to improve outcomes. TRIAL REGISTRATION:Thai Clinical Trials Registry TCTR20181204006 . Date registered on December 4, 2018. Retrospectively registered.

Project description:OBJECTIVES:This study assessed the utility of the pooled cohort equation (PCE) and/or coronary artery calcium (CAC) for atherosclerotic cardiovascular disease (ASCVD) risk assessment in smokers, especially those who were lung cancer screening eligible (LCSE). BACKGROUND:The U.S. Preventive Services Task Force recommended and the Centers for Medicare & Medicaid Services currently pays for annual screening for lung cancer with low-dose computed tomography scans in a specified group of cigarette smokers. CAC can be obtained from these low-dose scans. The incremental utility of CAC for ASCVD risk stratification remains unclear in this high-risk group. METHODS:Of 6,814 MESA (Multi-Ethnic Study of Atherosclerosis) participants, 3,356 (49.2% of total cohort) were smokers (2,476 former and 880 current), and 14.3% were LCSE. Kaplan-Meier, Cox proportional hazards, area under the curve, and net reclassification improvement (NRI) analyses were used to assess the association between PCE and/or CAC and incident ASCVD. Incident ASCVD was defined as coronary death, nonfatal myocardial infarction, or fatal or nonfatal stroke. RESULTS:Smokers had a mean age of 62.1 years, 43.5% were female, and all had a mean of 23.0 pack-years of smoking. The LCSE sample had a mean age of 65.3 years, 39.1% were female, and all had a mean of 56.7 pack-years of smoking. After a mean of 11.1 years of follow-up 13.4% of all smokers and 20.8% of LCSE smokers had ASCVD events; 6.7% of all smokers and 14.2% of LCSE smokers with CAC = 0 had an ASCVD event during the follow-up. One SD increase in the PCE 10-year risk was associated with a 68% increase risk for ASCVD events in all smokers (hazard ratio: 1.68; 95% confidence interval: 1.57 to 1.80) and a 22% increase in risk for ASCVD events in the LCSE smokers (hazard ratio: 1.22; 95% confidence interval: 1.00 to 1.47). CAC was associated with increased ASCVD risk in all smokers and in LCSE smokers in all the Cox models. The C-statistic of the PCE for ASCVD was higher in all smokers compared with LCSE smokers (0.693 vs. 0.545). CAC significantly improved the C-statistics of the PCE in all smokers but not in LCSE smokers. The event and nonevent net reclassification improvements for all smokers and LCSE smokers were 0.018 and -0.126 versus 0.16 and -0.196, respectively. CONCLUSIONS:In this well-characterized, multiethnic U.S. cohort, CAC was predictive of ASCVD in all smokers and in LCSE smokers but modestly improved discrimination over and beyond the PCE. However, 6.7% of all smokers and 14.2% of LCSE smokers with CAC = 0 had an ASCVD event during follow-up.

Dataset Information

Delirium risk stratification in consecutive unselected admissions to acute medicine: validation of externally derived risk scores.

Publications

Delirium risk stratification in consecutive unselected admissions to acute medicine: validation of externally derived risk scores.

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