Development and validation of the delirium risk assessment score (DRAS)

Postoperative delirium in oral and maxillofacial surgery: a scoping review

BACKGROUND

Postoperative delirium (POD) in the oral and maxillofacial settings has gained more attention in recent decades. Due to advances in medical technology, treatment possibilities have expanded treatment for elderly and frail patients. This scoping review explores the correlation between POD and oral and maxillofacial surgery, summarizing screening and management protocols and identifying risk factors in this surgical field.

METHODS

This review follows the Scoping Review extension of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-ScR). A comprehensive literature search was performed using multiple databases, focusing on articles published from 2002 to 2023 that discuss delirium in oral and maxillofacial surgery settings. The review was registered beforehand in the Open Science Framework ( https://osf.io/r2ebc ).

RESULTS

From the initial 644 articles, 68 met the inclusion criteria. These studies highlighted the significant heterogeneity in POD diagnosis methods. The review identifies multiple risk factors across the preoperative, intraoperative, and postoperative phases that influence the occurrence of POD. Significant and independent risk factors in multiple regression analysis were highlighted, creating a clinical prediction list for the occurrence of POD.

CONCLUSION

It is crucial to preoperatively identify patients at risk for POD and actively modify these risks throughout the patient's hospital stay. Implementing nonpharmacological preventive measures for at-risk patients is recommended to decrease the incidence of POD. Future research should focus on creating standardized specialty-specific protocols incorporating validated assessment tools and addressing the full spectrum of risk factors associated with POD.

Value of the Safety Management System (VMS) frailty instrument as a frailty screener in care for older hospital patients: a systematic review

BACKGROUND

Dutch hospitals are required to screen older patients for the risk of developing functional decline using the Safety Management System (VMS) which assesses four domains associated with functional decline; fall risk, risk of delirium, malnutrition, and physical impairment.

PURPOSE

The aim is twofold, first to compare the VMS frailty instrument as a frailty screener with existing frailty instruments and second to provide an overview of the available evidence.

METHODS

We performed a literature search to identify studies that used the VMS instrument as frailty screener to asses frailty or to predict adverse health outcomes in older hospitalized patients. Pubmed, Cinahl, and Embase were searched from January 1st 2008 to December 11th 2023.

RESULTS

Our search yielded 603 articles, of which 17 studies with heterogenous populations and settings were included. Using the VMS, frailty was scored in six different ways. The agreement between VMS and other frailty instruments ranged from 57 to 87%. The highest sensitivity and specificity of VMS for frailty were 90% and 67%, respectively. The association of the VMS with outcomes was studied in 14 studies, VMS was predictive for complications, delirium, falls, length of stay, and adverse events. Conflicting results were found for hospital (re)admission, complications, change in living situation, functional decline, and mortality.

CONCLUSION

The VMS frailty instrument were studied as a frailty screening instrument in various populations and settings. The value of the VMS instrument as a frailty screener looks promising. Our results suggest that the scoring method of the VMS could be adapted to specific requirements of settings or populations.

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.

Inclusion of frailty improved performance of delirium prediction for elderly patients in the cardiac intensive care unit (D-FRAIL): A prospective derivation and external validation study

BACKGROUND

The elderly patients admitted to cardiac intensive care unit (CICU) are at relatively high risk for developing delirium. A simple and reliable predictive model can benefit them from early recognition of delirium followed by timely and appropriate preventive strategies.

OBJECTIVE

To explore the role of frailty in delirium prediction and develop and validate a delirium predictive model including frailty for elderly patients in CICU.

DESIGN

A prospective, observational cohort study.

SETTINGS

CICU at China-Japan Friendship Hospital from March 1, 2022 to August 25, 2022 (derivation cohort); CICU at Beijing Anzhen Hospital affiliated to Capital Medical University from March 14, 2023 to May 8, 2023 (external validation cohort).

PARTICIPANTS

A total of 236 and 90 participants were enrolled in the derivation and external validation cohorts, respectively. Participants in the derivation cohort were assigned into either the delirium (n = 70) or non-delirium group (n = 166) based on the occurrence of delirium.

METHODS

The simplified Chinese version of the Confusion Assessment Method for the Diagnosis of Delirium in the Intensive Care Unit was used to assess delirium twice a day at 8:00-10:00 and 18:00-20:00 until the onset of delirium or discharge from the CICU. Frailty was assessed using the FRAIL scale during the first 24 h in the CICU. Other possible risk factors were collected prospectively through patient interviews and medical records review. After processing missing data via multiple imputations, univariate analysis and bootstrapped forward stepwise logistic regression were performed to select optimal predictors and develop the models. The models were internally validated using bootstrapping and evaluated comprehensively via discrimination, calibration, and clinical utility in both the derivation and external validation cohorts.

RESULTS

The study developed D-FRAIL predictive model using FRAIL score, hearing impairment, Acute Physiology and Chronic Health Evaluation-II score, and fibrinogen. The area under the receiver operating characteristic curve (AUC) was 0.937 (95% confidence interval [CI]: 0.907-0.967) and 0.889 (95%CI: 0.840-0.938) even after bootstrapping in the derivation cohort. Inclusion of frailty was demonstrated to improve the model performance greatly with the AUC increased from 0.851 to 0.937 (p < 0.001). In the external validation cohort, the AUC of D-FRAIL model was 0.866 (95%CI: 0.782-0.907). Calibration plots and decision curve analysis suggested good calibration and clinical utility of the D-FRAIL model in both the derivation and external validation cohorts.

CONCLUSIONS

For elderly patients in the CICU, FRAIL score is an independent delirium predictor and the D-FRAIL model demonstrates superior performance in predicting delirium.

Validation of Two Screening Tools for Detecting Delirium in Older Patients in the Post-Anaesthetic Care Unit: A Diagnostic Test Accuracy Study

(1) Background: Delirium is a common complication among surgical patients after major surgery, but it is often underdiagnosed in the post-anaesthetic care unit (PACU). Valid and reliable tools are required for improving diagnoses of delirium. The objective of this study was to evaluate the diagnostic test accuracy of the Three-Minute Diagnostic Interview for Confusion Assessment Method (3D-CAM) and the 4A's Test (4AT) as screening tools for detection of delirium in older people in the PACU. (2) Methods: A prospective diagnostic test accuracy study was conducted in the PACU and surgical wards of a university-affiliated tertiary care hospital in Victoria, Australia. A consecutive prospective cohort of elective and emergency patients (aged 65 years or older) admitted to the PACU were recruited between July 2021 and December 2021 following a surgical procedure performed under general anaesthesia and expected to stay in the hospital for at least 24 h following surgery. The outcome measures were sensitivity, specificity positive predictive value and negative predictive value for 3D-CAM and 4AT. (3) Results: A total of 271 patients were recruited: 16.2% (44/271) had definite delirium. For a diagnosis of definite delirium, the 3D-CAM (area under curve (AUC) = 0.96) had a sensitivity of 100% (95% CI 92.0 to 100.0) in the PACU and during the first 5 days post-operatively. Specificity ranged from 93% (95% CI 87.8 to 95.2) to 91% (95% CI 85.9 to 95.2) in the PACU and during the first 5 days post-operatively. The 4AT (AUC = 0.92) had a sensitivity of 93% (95% CI 81.7 to 98.6) in the PACU and during the first 5 days post-operatively, and specificity ranged from 89% (95% CI 84.6 to 93.1) to 87% (95%CI 80.9 to 91.8) in the PACU and during the first 5 days post-operatively. (4) Conclusions: The 3D-CAM and the 4AT are sensitive and specific screening tools that can be used to detect delirium in older people in the PACU. Screening with either tool could have an important clinical impact by improving the accuracy of delirium detection in the PACU and hence preventing adverse outcomes associated with delirium.

Recipe for primary prevention of delirium in hospitalized older patients

Delirium is an acute fluctuating syndrome characterized by a change in consciousness, perception, orientation, cognition, sleep-wake rhythm, psychomotor skills, and the mood and feelings of a patient. Delirium and delirium prevention remain a challenge for healthcare professionals, especially nurses who form the basis of patient care. It also causes distress for patients, their caregivers and healthcare professionals. However, delirium is preventable in 30-40% of cases. The aim of this article is to summarize the delirium risk models, delirium screening tools, and (non-pharmacological) delirium prevention strategies. A literature search of review articles supplemented by original articles published in PubMed, Cinahl, and Cochrane between 1 January 2000 and 31 December 2020 was carried out. Among the older patients, delirium is a common condition with major consequences in terms of mortality and morbidity, but prevention is possible. Despite the fact that delirium risk models, delirium screening scales and non-pharmacological prevention are available for the development of a hospital delirium prevention programme, such a programme is still not commonly used on a daily basis.

Clinical and Epidemiological Approach to Delirium in an Acute Care Unit: A Cross-Sectional Study

During hospital admissions, the union of various factors, those related to acute pathology, dependency conditions, cognitive impairment, change of habitual environment, and others, can cause delirium. Acute delirium in the elderly (ADE) occurs in around a third of patients over 70 years of age. The syndrome generates serious complications that increase hospital morbidity and mortality and a high cost for the health administration. This study aimed to determine the clinical and epidemiological profile of ADE in an internal medicine unit. A descriptive cross-sectional study was carried out using a convenience test. A total of 356 patients participated between September and November 2021. Sociodemographic variables, predisposing and precipitating factors of ADE, methods of action against ADE, and the impact on functional and cognitive deterioration were analyzed. A total of 35.1% of the patients developed ADE, mostly of the hyperactive type and of nocturnal appearance. ADE was mainly treated with psychoactive drugs and 22% required mechanical restraint, with non-pharmacological preventive strategies, support, and caregiver training being the main tools for controlling ADE during hospital admission.

Patient Profile and Management of Delirium in Older Adults Hospitalized Due to COVID-19

SARS-CoV-2 can cause neurologic symptoms, as well as respiratory ones. Older adults are at risk of developing acute delirium in older persons (ADOP). The combination of experiencing respiratory isolation due to COVID-19, as well as other associated risk factors for older adults, may have had an impact on ADOP and ADOP management in the acute hospital setting. This study aimed to analyze the characteristics of ADOP in patients admitted to a COVID-19 unit. An observational prospective study on a sample of 108 patients was carried out between November 2020 and May 2021. The following data were collected: sociodemographic characteristics, risk factors for ADOP, management of ADOP, and impact on ADOP on both functional and cognitive deterioration. A 29.6% proportion of older adults admitted to an acute COVID-19 unit presented hyperactive ADOP, mainly during the night. Management of ADOP in our sample involved mainly pharmacological treatment and had a serious impact on hospital stay and both functional and cognitive deterioration. Preventive strategies and being accompanied by a relative or a carer may be useful to manage ADOP during hospital admission due to COVID-19.