Use of medicines that may precipitate delirium prior to hospitalisation in older Australians with delirium: An observational study

Adopting STOPP/START Criteria Version 3 in Clinical Practice: A Q&A Guide for Healthcare Professionals

The growing complexity of geriatric pharmacotherapy necessitates effective tools for mitigating the risks associated with polypharmacy. The Screening Tool of Older Persons' Potentially Inappropriate Prescriptions (STOPP)/Screening Tool to Alert doctors to Right Treatment (START) criteria have been instrumental in optimizing medication management among older adults. Despite their large adoption for improving the reduction of potentially inappropriate medications (PIM) and patient outcomes, the implementation of STOPP/START criteria faces notable challenges. The extensive number of criteria in the latest version and time constraints in primary care pose practical difficulties, particularly in settings with a high number of older patients. This paper critically evaluates the challenges and evolving implications of applying the third version of the STOPP/START criteria across various clinical settings, focusing on the European healthcare context. Utilizing a "Questions & Answers" format, it examines the criteria's implementation and discusses relevant suitability and potential adaptations to address the diverse needs of different clinical environments. By emphasizing these aspects, this paper aims to contribute to the ongoing discourse on enhancing medication safety and efficacy in the geriatric population, and to promote more person-centred care in an aging society.

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.

Thoracic delirium index for predicting postoperative delirium in elderly patients following thoracic surgery: A retrospective case-control study

BACKGROUND

Postoperative delirium (POD) is an acute neurological complication in the elderly undergoing thoracic surgery and can result in serious adverse consequences.

AIMS

This study aimed to identify the related risk factors for POD following thoracic surgery, primarily focusing on preoperative serum biomarkers, and further to establish a novel delirium index to better predict POD.

METHODS

A total of 279 patients aged ≥60 years who underwent elective thoracic surgery from August 2021 to August 2022 were enrolled in this observational study. The platelet-to-white blood cell ratio (PWR) was calculated as number the of platelets divided by the number of white blood cells. POD was defined by the confusion assessment method twice daily during the postoperative first 3 days. Multivariate regression analysis was performed to identify all potential variables for POD. Moreover, a novel thoracic delirium index (TDI) was developed based on the related risk factors. The accuracy of TDI and its component factors in predicting POD was determined by the curve of receiver operating characteristic (ROC).

RESULTS

In total, 25 of 279 patients developed POD (8.96%). Age, PWR, and average pain scores within the first 3 days after surgery were regarded as the independent risk factors for POD. Moreover, the ROC analysis showed the TDI, including age, PWR, and average pain scores within the first 3 days after surgery, can more accurately predict POD with the largest area under the curve of 0.790 and the optimal cutoff value of 9.072, respectively.

CONCLUSION

The TDI can scientifically and effectively predict POD to provide optimal clinical guidance for older patients after thoracic surgery.

Association of Delirium and Depression with Respiratory and Outcome Measures in COVID-19 Inpatients

Delirium (DEL) and depression (DEP) may impair the course and severity of acute respiratory illness. The impact of such syndromes on respiratory and outcome parameters in inpatients with COVID-19 needs clarification. To clarify the relationship between DEL and DEP and respiratory outcome measures, we enrolled 100 inpatients from COVID-19 units of the Fondazione Policlinico Universitario Agostino Gemelli IRCCS of Rome. Participants were divided into those with DEL, DEP, or absence of either delirium or depression (CONT). Delirium severity was assessed with the Neelson and Champagne Confusion Scale (NEECHAM). Psychopathology was assessed with the Hamilton Rating Scale for Depression (HAM-D), the Hamilton Rating Scale for Anxiety (HAM-A), and the Brief Psychiatric Rating Scale (BPRS). Dependent variables include: (a) respiratory parameters, i.e., partial pressure of oxygen in arterial blood (PaO2), oxygen saturation (SpO2), ratio between arterial partial pressure of oxygen (PaO2), and fraction of inspired oxygen (PaO2/FiO2); (b) outcome parameters, i.e., duration of hospitalization and number of pharmacological treatments used during the hospitalization. We investigated between-group differences and the relationships between severity of delirium/depression and the dependent variables. Duration of hospitalization was longer for DEL than for either DEP or CONT and for DEP compared to CONT. NEECHAM and HAM-D scores predicted lower PaO2 and PaO2/FiO2 levels in the DEL and DEP groups, respectively. In DEP, BPRS scores positively correlated with duration of hospitalization. Delirium impacted the course of COVID-19 more severely than depression. The mechanisms by which delirium and depression worsen respiratory parameters differ.

Hospitalizations of older people in an emergency department related to potential medication-induced hyperactive delirium: a cross-sectional study

Background Although delirium is one of the most common adverse drug reactions observed in hospitalized older people, it remains underdiagnosed. Aim To estimate the prevalence of hospitalization of older people with potential medication-induced hyperactive delirium in the emergency department (ED); to identify the risk factors and the medicines frequently associated with the occurrence of the syndrome. Method A cross-sectional, retrospective study was performed with older people (age ≥ 60) admitted in 2018 to a Brazilian ED. The hospitalizations with suspected hyperactive delirium were screened with the aid of trigger-tools: International Code of Diseases-10th Revision, intra-hospital prescriptions of antipsychotics, and trigger-words related to the syndrome. A chart-review and medication review were developed to establish the causality assessment between adverse event and medicine. Logistic regression was used to determine risk factors for occurrence. Results Among the hospitalizations included, 67.5% (193/286) were screened by at least one trigger-tool. Of these, potential medication-induced hyperactive delirium was observed in 26.0% (50/193). The prevalence estimated in the ward was 17.5% (50/286). Opioids (31.9%), benzodiazepines (18.8%) and corticosteroids (10.6%) were the commonest medicines associated with delirium. Long-lived patients (p = 0.005), potentially inappropriate medicines (PIMs) (p = 0.025), and high weighted deliriogenic load (p = 0.014) were associated with potential medication-induced hyperactive delirium. Conclusion Approximately one in six hospitalizations of older people in the ED showed potential medication-induced hyperactive delirium. Data suggest PIMs and high weighted deliriogenic load, rather than polypharmacy or anticholinergic burden, are considered the most important characteristics of pharmacotherapy associated with avoidable hyperactive delirium among long-lived patients.

Presentation and management of anxiety in individuals with acute symptomatic or asymptomatic COVID-19 infection, and in the post-COVID-19 recovery phase

COVID-19 is associated with neuropsychiatric complications, the most frequent one being anxiety. Multiple biological and psychosocial factors contribute to anxiety in COVID-19. Among the biological factors, stress, genetics, gender, immune system, resilience, anosmia, hypogeusia, and central nervous system infection with SARS-CoV-2 are key. Anxiety is a complication of COVID-19 that may exacerbate the infection course, and the infection may exacerbate anxiety. We present the mechanisms of anxiety in symptomatic or asymptomatic COVID-19. We discuss the presentation of anxiety in patients without or with prior psychiatric illness, and with co-morbidities. Timely diagnosis and management of anxiety in COVID-19 patients is important. Given the frequent complication of COVID-19 with Acute Respiratory Distress Syndrome and Intensive Care Unit stay, anxiety may be a long-term complication. We review the diagnostic tools for anxiety in COVID-19, and summarise pharmacologic and non-pharmacologic treatments. We provide recommendations for diagnosis, treatment, prevention and follow up of anxiety in COVID-19.Key pointsPatients with COVID-19 (symptomatic or asymptomatic) exhibit a high frequency of neuropsychiatric complications with highest percentage attributed to anxiety.Multiple biological and psychosocial risk factors for anxiety exist in COVID-19-ill individuals. Biological risk factors include stress, resilience, genetics, gender, age, immune system, direct infection of the central nervous system (CNS) with SARS-CoV-2, comorbid psychiatric and general medical illnesses, ARDS and ICU stay. Anosmia and hypogeusia are COVID-19-specific anxiety risk factors. Knowledge of the anxiety risk factors is essential to focus on timely interventions, because anxiety may be a complication of and exacerbate the COVID-19 course.An inverse correlation exists between resilience and anxiety because of COVID-19, and therefore efforts should be made to increase resilience in COVID-19 patients.In COVID-19, important anxiety mechanism is neuroinflammation resulting from activation of the immune system and an ensuing cytokine storm.The general approach to management of anxiety in COVID-19 should be compassionate, similar to that during trauma or disaster, with efforts focussed on instilling a sense of hope and resilience.In selecting pharmacological treatment of anxiety, the stress response and immune system effects should be key. Medications with cardio-respiratory adverse effects should be avoided in patients with respiratory problems.Anxiety is a disorder that will require for long-term follow up at least one month after COVID-19.

Identifying frequent drug combinations associated with delirium in older adults: Application of association rules method to a case-time-control design

BACKGROUND

Older adults are at an increased risk of delirium because of age, polypharmacy, multiple comorbidities, frailty, and acute illness. Although medication-induced delirium in older adults is well understood, limited population-level evidence is available, particularly on combinations of medications associated with delirium in older adults.

OBJECTIVES

We aimed to apply association rule analysis to identify drug combinations contributing to delirium risk in adults aged 65 and older using a case-time-control design.

METHOD

We sourced a nationwide representative sample of New Zealander's aged ≥65 years from the pharmaceutical collections and hospital discharge information. Prescription records (2005-2015) were obtained from New Zealand pharmaceutical collections (Pharms). Medication exposures were coded as binary variables (exposed vs. not exposed) at the individual drug level. All medications, including antimicrobials, antihistamines, diuretics, opioids, and nonsteroidal anti-inflammatory medications, were considered drugs of interest. The first-time coded diagnosis of delirium was extracted from the National Minimal Dataset (NMDS). A unique patient identifier linked the prescription dataset to the event dataset to set up a case-time-control cohort, indexed at the first delirium event. Association rules were then applied to identify frequent drug combinations in the case and the control periods (l-day with a 35-day washout period) that are statistically associated with delirium, and the association was tested by computing a time-trend adjusted matched odds-ratio (MOR) and its 95% confidence interval (CI).

RESULTS

We identified 28 503 individuals (mean age 84.1 years) from 2005 to 2015 with delirium. Our combined association rule and case-time-control analysis identified several drug classes, including antipsychotics, benzodiazepines, opioids, and diuretics associated with delirium. Our analysis also identified frequently used drug combinations that are associated with delirium. Examples include combined exposures to quetiapine and furosemide (MOR = 6.17; 95%CI = [2.05-18.54]), haloperidol (MOR = 4.81; 95%CI = [3.16-6.69]), combined exposures to furosemide, omeprazole, and lorazepam (MOR = 3.94; 95%CI = [3.03-5.10]), and fentanyl exposure (MOR = 3.46; 95%CI [2.05-9.21]).

CONCLUSION

The association rule method applied to a case-time-control design is a novel approach to identifying drug combinations contributing to delirium with adjustment for any temporal trends in exposures. The study provides new insight into the combination of medicines linked to delirium.

The Prevalence and Characteristics of Psychotropic-Related Hospitalizations in Older People: A Systematic Review and Meta-Analysis

OBJECTIVES

To assess the prevalence and characteristics of psychotropic medication-related hospitalizations in older people.

DESIGN

Systematic review with meta-analysis.

SETTING AND PARTICIPANTS

Older adults (≥65 years of age) with psychotropic-related hospitalizations.

METHODS

A search of published literature was performed in Medline, Embase, CINAHL, and Scopus from 2010 to March 2020. Three authors independently screened titles, abstracts, and full texts of relevant studies for relevance. Two authors independently extracted full text data, including characteristics, measures of causality, prevalence data, and performed quality assessment. A meta-analysis was conducted to estimate pooled prevalence and 95% confidence intervals (CIs) of psychotropic-related hospitalizations using random effects models. Heterogeneity was explored using subgroup analyses.

RESULTS

Of 815 potentially relevant studies, 11 were included in the final analysis. Five studies were cross-sectional studies, 5 were cohort studies, and 1 was a case control study. The majority of studies were rated as good quality. Psychotropic medications contributed to 2.1% (95% CI 1.2%-3.3%) of total hospitalizations and 11.3% (95% CI 8.2%-14.8%) of adverse drug event-related hospitalizations. The main psychotropic medications attributable to hospitalizations were antidepressants, hypnotics, sedatives, and antipsychotics.

CONCLUSIONS AND IMPLICATIONS

Psychotropic medications are a significant contributor to hospitalizations in older adults. The risk of hospitalization was greatest for those taking antidepressants, antipsychotics, hypnotics, and sedatives. Future studies should aim to address specific medication subgroups and implement uniform adverse drug event-related classification systems to improve comparability across studies.