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

Development and validation of a new drug-focused predictive risk score for postoperative delirium in orthopaedic and trauma surgery patients

BACKGROUND

Postoperative delirium (POD) is the most common complication following surgery in elderly patients. During pharmacist-led medication reconciliation (PhMR), a predictive risk score considering delirium risk-increasing drugs and other available risk factors could help to identify risk patients.

METHODS

Orthopaedic and trauma surgery patients aged ≥ 18 years with PhMR were included in a retrospective observational single-centre study 03/2022-10/2022. The study cohort was randomly split into a development and a validation cohort (6:4 ratio). POD was assessed through the 4 A's test (4AT), delirium diagnosis, and chart review. Potential risk factors available at PhMR were tested via univariable analysis. Significant variables were added to a multivariable logistic regression model. Based on the regression coefficients, a risk score for POD including delirium risk-increasing drugs (DRD score) was established.

RESULTS

POD occurred in 42/328 (12.8%) and 30/218 (13.8%) patients in the development and validation cohorts, respectively. Of the seven evaluated risk factors, four were ultimately tested in a multivariable logistic regression model. The final DRD score included age (66-75 years, 2 points; > 75 years, 3 points), renal impairment (eGFR < 60 ml/min/1.73m2, 1 point), anticholinergic burden (ACB-score ≥ 3, 1 point), and delirium risk-increasing drugs (n ≥ 2; 2 points). Patients with ≥ 4 points were classified as having a high risk for POD. The areas under the receiver operating characteristic curve of the risk score model were 0.89 and 0.81 for the development and the validation cohorts, respectively.

CONCLUSION

The DRD score is a predictive risk score assessable during PhMR and can identify patients at risk for POD. Specific preventive measures concerning drug therapy safety and non-pharmacological actions should be implemented for identified risk patients.

External validity of an automated delirium prediction model (DEMO) and comparison to the manual VMS-questions: a retrospective cohort study

BACKGROUND

It is estimated that one-third of delirium cases in hospitals could be prevented with appropriate interventions. In Dutch hospitals a manual instrument (VMS-questions) is used to identify patients at-risk for delirium. Delirium Model (DEMO) is an automated model which could support delirium prevention more efficiently. However, it has not been validated beyond the hospital it was developed in.

AIM

To externally validate the DEMO and compare its performance to the VMS-questions.

METHOD

A retrospective cohort study between July and December 2018 was conducted. Delirium cases were identified through a chart review, and the VMS-questions were extracted from the electronic health records. The DEMO was validated in patients ≥ 60 years, and a comparison with the VMS-questions was made in patients ≥ 70 years.

RESULTS

In total 1,345 admissions were included. The DEMO predicted 59 out of 75 delirium cases (sensitivity 0.79, 95% CI = 0.68-0.87; specificity 0.75, 95% CI = 0.72-0.77). Compared to the VMS-questions, the DEMO showed a lower specificity (0.64 vs. 0.72; p < 0.001) and a comparable sensitivity (0.83 vs. 0.80; p = 0.56). The VMS-questions were missing in 20% of admissions, in which the DEMO correctly predicted 10 of 12 delirium cases.

CONCLUSION

The DEMO showed acceptable performance for delirium prediction. Overall the DEMO predicted more delirium cases because the VMS-questions were missing in 20% of admissions. This study shows that automated instruments such as DEMO could play a key role in the efficient and timely deployment of measures to prevent delirium.

Delirium among people aging with and without HIV: Role of alcohol and Neurocognitively active medications

BACKGROUND

People aging with and without HIV (PWH and PWoH) want to avoid neurocognitive dysfunction, especially delirium. Continued use of alcohol in conjunction with neurocognitively active medications (NCAMs) may be a largely underappreciated cause, especially for PWH who experience polypharmacy a decade earlier than PWoH. We compare absolute and relative risk of delirium among PWH and PWoH by age, level of alcohol use, and exposure to NCAMs.

METHODS

Using the VACS cohort, we compare absolute and relative risk of inpatient delirium among PWH and PWoH by age, level of alcohol use, and exposure to NCAMs between 2007 and 2019. We matched each case based on age, race/ethnicity, sex, HIV, baseline year, and observation time with up to 5 controls. The case/control date was defined as date of admission for cases and the date corresponding to the same length of time on study for controls. Level of alcohol use was defined using Alcohol Use Disorder Identification Test-Consumption (AUDIT-C). Medication exposure was measured from 45 to 3 days prior to index date; medications were classified as anticholinergic NCAM, non-anticholinergic NCAM, or non NCAM and counts generated. We used logistic regression to determine odds ratios (ORs) for delirium associated with medication counts stratified by HIV status and adjusted for demographics, severity of illness, and related diagnoses.

RESULTS

PWH experienced a higher incidence of delirium (5.6, [95% CI 5.3-5.9/1000 PY]) than PWoH (5.0, [95% CI 4.8-5.1/1000 PY]). In multivariable analysis, anticholinergic and non-anticholinergic NCAM counts and level of alcohol use demonstrated strong independent dose-response associations with delirium.

CONCLUSIONS

Decreasing alcohol use and limiting the use of neurocognitively active medications may help decrease excess rates of delirium, especially among PWH.

Development and validation of a delirium risk prediction preoperative model for cardiac surgery patients (DELIPRECAS): An observational multicentre study

STUDY OBJECTIVE

To develop and validate a delirium risk prediction preoperative model for patients undergoing cardiac surgery.

DESIGN

Observational prospective multicentre study.

SETTING

Six intensive care units in Spain.

PATIENTS

689 patients undergoing cardiac surgery consecutively, aged ≥18 years.

MEASUREMENTS

The primary outcome measure was the development of delirium, diagnosed using the Confusion Assessment Method in Intensive Care Units (CAM-ICU), during the stay in the intensive care unit after cardiac surgery.

MAIN RESULTS

The model was developed with 345 consecutive patients undergoing cardiac surgery at six hospitals and validated with another 344 patients from the same hospitals. The prediction model contained four preoperative risk factors: age over 65 years, Mini-Mental State Examination (MMSE) score of 25-26 points (possible impairment of cognitive function) or < 25 (impairment of cognitive function), insomnia needing medical treatment and low physical activity (walk less than 30 min a day). The model had an area under the receiver operating characteristics curve of 0.825 (95% confidence interval: 0.76-0.89). The validation resulted in an area under the curve of 0.79 (0.73-0.85) and the pooled area under the receiver operating characteristics curve (n = 689) was 0.81 (0.76-0.85). We stratified patients in groups of low (0%-20%), moderate (> 20%-40%), high (> 40%-60%) and very high (> 60%) risk of developing delirium, with a positive and negative predictive value for the very high risk group of 70.97% and 85.56%, respectively.

CONCLUSION

The DELIPRECAS model (DELIrium PREvention CArdiac Surgery), consisting of four well-defined clinical risk factors, can predict in the preoperative period the risk of developing postoperative delirium in patients undergoing cardiac surgery. An automatic version of the risk calculator is available.

Risk Factors for Delirium and Cognitive Decline Following Coronary Artery Bypass Grafting Surgery: A Systematic Review and Meta-Analysis

Background

Coronary artery bypass grafting (CABG) is known to improve heart function and quality of life, while rates of surgery‐related mortality are low. However, delirium and cognitive decline are common complications. We sought to identify preoperative, intraoperative, and postoperative risk or protective factors associated with delirium and cognitive decline (across time) in patients undergoing CABG.

Methods and Results

We conducted a systematic search of Medline, PsycINFO, EMBASE, and Cochrane (March 26, 2019) for peer‐reviewed, English publications reporting post‐CABG delirium or cognitive decline data, for at least one risk factor. Random‐effects meta‐analyses estimated pooled odds ratio for categorical data and mean difference or standardized mean difference for continuous data. Ninety‐seven studies, comprising data from 60 479 patients who underwent CABG, were included. Moderate to large and statistically significant risk factors for delirium were as follows: (1) preoperative cognitive impairment, depression, stroke history, and higher European System for Cardiac Operative Risk Evaluation (EuroSCORE) score, (2) intraoperative increase in intubation time, and (3) postoperative presence of arrythmia and increased days in the intensive care unit; higher preoperative cognitive performance was protective for delirium. Moderate to large and statistically significant risk factors for acute cognitive decline were as follows: (1) preoperative depression and older age, (2) intraoperative increase in intubation time, and (3) postoperative presence of delirium and increased days in the intensive care unit. Presence of depression preoperatively was a moderate risk factor for midterm (1–6 months) post‐CABG cognitive decline.

Conclusions

This meta‐analysis identified several key risk factors for delirium and cognitive decline following CABG, most of which are nonmodifiable. Future research should target preoperative risk factors, such as depression or cognitive impairment, which are potentially modifiable.

Registration

URL: https://www.crd.york.ac.uk/prosp​ero/; Unique identifier: CRD42020149276.

The Incidence and Predictors of Postoperative Delirium After Brain Tumor Resection in Adults: A Cross-Sectional Survey

BACKGROUND

Postoperative delirium (POD) describes a multifactorial disease process occurring after surgery. However, few studies have focused on patients undergoing brain tumor resection, and its influencing factors are unclear.

METHODS

We performed a 1-year, single-center, cross-sectional, retrospective survey at Huashan Hospital. Patients were screened using the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU), Confusion Assessment Method, and Richmond Agitation Sedation Scale by trained bedside nurses. Perioperative data were collected using demographic and disease-related questionnaires. The primary outcome measures were the incidence of POD and subtype of POD. Independent predictors of POD were estimated from multivariate logistic regression models, and receiver operating characteristic analysis was used to compare the predictive performance of the models.

RESULTS

Of the 916 patients included in the study, 893 were analyzed. The overall incidence was 14.78%, 67 had hyperactive delirium (50.76%), 55 had hypoactive delirium (41.67%), and 10 had mixed delirium (7.57%). Age, sex, working status, tobacco use history, comorbidities, physical restraint, axillary temperature (>38.5°C), electrolyte disturbances, duration of anesthesia, pathologic diagnosis, tumor site, length of disease, and duration of operation were risk factors for POD. Conversely, saddle area mass was a protective factor. Age, tobacco use history, electrolyte disturbances, physical restraint, and duration of operation were included in the model.

CONCLUSIONS

POD is harmful to patients undergoing brain tumor resection, increasing length of stay in the intensive care unit and hospitalization costs. Intraoperative factors and postoperative factors, in addition to older age and tobacco use history, are associated with POD.

Delirium in hospitalized older adults

Delirium is a common and underdiagnosed problem in hospitalized older adults. It is associated with an increased risk of poor cognitive and functional outcomes, institutionalization, and death. Timely diagnosis of delirium and non-pharmacological prevention and management strategies can improve patient outcomes. The Confusion Assessment Method (CAM) is the most widely used clinical assessment tool for the diagnosis of delirium. Multiple variations of the CAM have been developed for ease of administration and for the unique needs of specific patient populations, including the 3-min diagnostic CAM (3D CAM), CAM-Intensive Care Unit (CAM-ICU), Delirium Triage Screen (DTS)/Brief CAM (b-CAM), 4AT tool, and ultrabrief delirium assessment. Strong evidence supports the effectiveness of nonpharmacologic strategies as the primary intervention for the prevention of delirium. Multicomponent delirium prevention strategies can reduce the incidence of delirium by 40%. Investigation of underlying medical precipitants and optimization of non-pharmacological interventions are first line in the management of delirium. Despite a lack of evidence supporting use of antipsychotics, low dose antipsychotics remain second line for off-label treatment of distressing psychoses and/or agitated behaviors that are refractory to non-pharmacological behavioral interventions and pose an imminent risk of harm to self or others. Any antipsychotic prescription for delirium should be accompanied by an appropriate taper plan. Follow up with primary care providers on discharge from hospital for ongoing screening of cognitive impairment is important.

Impact of a delirium prevention project among older hospitalized patients who underwent orthopedic surgery: a retrospective cohort study

Background

Postoperative delirium (POD) is a common clinical syndrome with significant negative outcomes. Thus, we aimed to evaluate the feasibility and effectiveness of a delirium screening tool and multidisciplinary delirium prevention project.

Methods

A retrospective cohort study was conducted at a single teaching center in Korea. A cohort of patients who underwent a delirium prevention program using a simple delirium screening tool from December 2018 to February 2019 (intervention group, N = 275) was compared with the cohort from the year before implementation of the delirium prevention program (December 2017 to February 2018) (control group, N = 274). Patients aged ≥65 years who were admitted to orthopedic wards and underwent surgery were included. The incidence rates of delirium before and after implementation of the delirium prevention program, effectiveness of the delirium screening tool, change in the knowledge score of nurses, and length of hospital stay were assessed.

Results

The sensitivity and specificity of the screening tool for the incidence of POD were 94.1 and 72.7%, respectively. The incidence rates of POD were 10.2% (control group) and 6.2% (intervention group). The odds ratio for the risk reduction effect of the project related to the incidence of POD was 0.316 (95% confidence interval: 0.125–0.800, p = 0.015) after adjustment for possible confounders. The delirium knowledge test score increased from 40.52 to 43.24 out of 49 total points (p < 0.001). The median length of hospital stay in the intervention and control groups was 6.0 (interquartile range, 4–9) and 7.0 (interquartile range, 4–10) days, respectively (p = 0.062).

Conclusion

The screening tool successfully identified patients at a high risk of POD at admission. The POD prevention project was feasible to implement, effective in preventing delirium, and improved knowledge regarding delirium among the medical staff.

Trial registration

None.

Delirium risk in non-surgical patients: systematic review of predictive tools

OBJECTIVE

Delirium is a common, serious condition associated with poor hospital outcomes. Guidelines recommend screening for delirium risk to target diagnostic and/or prevention strategies. This study critically reviews multicomponent delirium risk prediction tools in adult non-surgical inpatients.

STUDY DESIGN

Systematic review of studies incorporating at least two clinical factors in a multicomponent tool predicting risk of delirium during hospital admission. Derivation and validation studies were included. Study design, risk factors and tool performance were extracted and tabulated, and study quality was assessed by CHARMS criteria.

DATA SOURCES

PubMed, Embase, PsycINFO, and Cumulative Index to Nursing Health Literature (CINAHL) to 11^th March 2018.

DATA SYNTHESIS

22 derivation studies enrolling 38,874 participants (9 with a validation component) and 4 additional validation studies were identified, from a range of ward types. All studies had at least moderate risk of bias. Older age and cognitive, functional and sensory impairment were important predisposing factors. Precipitating risk factors included infection, illness severity, renal and electrolyte disturbances. Tools mostly did not differentiate between predisposing and precipitating risk factors mathematically or conceptually Most tools showed fair to good discrimination, and identified more than half of older inpatients at risk.

CONCLUSIONS

Several validated delirium risk prediction tools can identify patients at increased risk of delirium, but do not provide clear advice for clinical application. Most recommended cut-points are sensitive but have low specificity. Implementation studies demonstrating how risk screening can better direct clinical interventions in specific clinical settings are needed to define the potential value of these tools.