Differences of anticholinergic drug burden between older hospitalized patients with and without delirium: a systematic review and meta-analysis based on prospective cohort studies.

OBJECTIVES: This review aims to comprehensively summarize the differences in anticholinergic drug burden (ADB) scores between older hospitalized patients with and without delirium. METHODS: We searched PubMed, Embase, Web of Science, Cochrane Library and CINAHL EBSCOhost databases to identify prospective cohort studies exploring the relationship between ADB and the occurrence of delirium in older hospitalized patients. The primary outcome of the review was the mean ADB scores for the delirium and non-delirium groups, and the secondary outcome was the scores for the subsyndromal and non-delirium groups. The standardized mean difference (SMD) and corresponding 95% confidence intervals (95% CI) were incorporated using a fixed-effect method. Moreover, we performed subgroup analysis according to the admission type, age, the ADB scale type and the ADB classification. RESULTS: Nine prospective cohort studies involving 3791 older patients with a median age of 75.1 (71.6-83.9) were included. The ADB score was significantly higher in the delirium group than in the non-delirium group (SMD = 0.21, 95%CI 0.13-0.28). In subgroup analysis, the age subgroup was split into < 75 and ≥ 75 according to the median age of the older people. There were significant differences in ADB scores between older people with delirium and those without delirium in various subgroups: surgical (SMD = 0.20, 95%CI 0.12-0.28), internal medicine (SMD = 0.64, 95%CI 0.25-1.02), age < 75 (SMD = 0.17, 95%CI 0.08-0.26), age ≥ 75 (SMD = 0.27, 95%CI 0.15-0.39), ADS scale (SMD = 0.13, 95%CI 0.13-0.40), ARS scale (SMD = 0.15, 95%CI 0.03-0.26), ACB scale (SMD = 0.13, 95%CI 0.01-0.25), pre-admission ADB (SMD = 0.24, 95%CI 0.05-0.43) and ADB during hospitalization (SMD = 0.20, 95%CI 0.12-0.27). CONCLUSIONS: We found a quantitative relationship between ADB and delirium in older patients admitted for internal medicine and surgery. And this relationship remained significant in different age, ADB scale type and ADB classification subgroups. However, the actual difference in ADB scores between patients with delirium and without delirium was small. More high-quality observational studies should be conducted to explore the impact of ADB on delirium and subsyndromal delirium. CLINICAL TRIAL REGISTRATION: The protocol was published in the International Prospective Register of Systematic Reviews (PROSPERO) [Ref: CRD42022353649].

An Ontology-Based Approach to Improving Medication Appropriateness in Older Patients: Algorithm Development and Validation Study.

Background: Inappropriate medication in older patients with multimorbidity results in a greater risk of adverse drug events. Clinical decision support systems (CDSSs) are intended to improve medication appropriateness. One approach to improving CDSSs is to use ontologies instead of relational databases. Previously, we developed OntoPharma-an ontology-based CDSS for reducing medication prescribing errors. Objective: The primary aim was to model a domain for improving medication appropriateness in older patients (chronic patient domain). The secondary aim was to implement the version of OntoPharma containing the chronic patient domain in a hospital setting. Methods: A 4-step process was proposed. The first step was defining the domain scope. The chronic patient domain focused on improving medication appropriateness in older patients. A group of experts selected the following three use cases: medication regimen complexity, anticholinergic and sedative drug burden, and the presence of triggers for identifying possible adverse events. The second step was domain model representation. The implementation was conducted by medical informatics specialists and clinical pharmacists using Protégé-OWL (Stanford Center for Biomedical Informatics Research). The third step was OntoPharma-driven alert module adaptation. We reused the existing framework based on SPARQL to query ontologies. The fourth step was implementing the version of OntoPharma containing the chronic patient domain in a hospital setting. Alerts generated from July to September 2022 were analyzed. Results: We proposed 6 new classes and 5 new properties, introducing the necessary changes in the ontologies previously created. An alert is shown if the Medication Regimen Complexity Index is ≥40, if the Drug Burden Index is ≥1, or if there is a trigger based on an abnormal laboratory value. A total of 364 alerts were generated for 107 patients; 154 (42.3%) alerts were accepted. Conclusions: We proposed an ontology-based approach to provide support for improving medication appropriateness in older patients with multimorbidity in a scalable, sustainable, and reusable way. The chronic patient domain was built based on our previous research, reusing the existing framework. OntoPharma has been implemented in clinical practice and generates alerts, considering the following use cases: medication regimen complexity, anticholinergic and sedative drug burden, and the presence of triggers for identifying possible adverse events.

Evaluation of anticholinergic burden in elderly outpatients and the risk factors. / è€å¹´é—¨è¯Šæ‚£è€ æŠ—èƒ†ç¢±èƒ½å¤„æ–¹è´Ÿæ‹ è¯„ä»·åŠå ¶å±é™©å› ç´ .

OBJECTIVES: The use of anticholinergic drugs in the elderly may lead to negative events such as falls, delirium, urinary retention and cognitive decline, and the higher the number of anticholinergic drugs use, the more such negative events occur. This study aims to analyze the risk factors associated with the prescription of total anticholinergic drugs in elderly outpatients and evaluate the rationality of anticholinergic drugs, and to provide a reference for reducing the adverse effects of anticholinergic drugs. METHODS: A list of drugs with anticholinergic activity based on the Beers criteria was established. The basic information (such as age and gender), clinical diagnosis, and medications of elderly outpatient were extracted from hospital electronic medical records, and the Anticholinergic Cognitive Burden (ACB) Scale was used to calculate the anticholinergic burden for each patient. Logistic regression analysis was used to identify the potential risk factors for the occurrence of problems such as multiple medication and insomnia. RESULTS: A total of 1 840 prescriptions for elderly patients were reviewed. Of these patients, ACB score was more than or equal to 1 in 648 (35.22%) patients. Number of prescription medication (95% CI: 1.221 to 1.336) and insomnia (95% CI: 3.538 to 6.089) were independent factors affecting ACB scores (both P<0.01). Medications for patients of ACB scores were most commonly treated with the central nervous system drugs (such as alprazolam and eszopiclone) and for the cardiovascular system drugs (such as metoprolol and nifedipine). CONCLUSIONS: There is a high rate of ACB drugs use in geriatric patients, and the clinical focus should be on multiple medication prescriptions, especially on the central nervous system drugs (such as alprazolam and eszopiclone) and cardiovascular system drugs (such as metoprolol and nifedipine). The prescription review should be emphasized to reduce adverse reactions to anticholinergic drugs in elderly patients.

Evaluation of anticholinergic burden in elderly outpatients and the risk factors / 中南大学学报(医学版)

OBJECTIVES@#The use of anticholinergic drugs in the elderly may lead to negative events such as falls, delirium, urinary retention and cognitive decline, and the higher the number of anticholinergic drugs use, the more such negative events occur. This study aims to analyze the risk factors associated with the prescription of total anticholinergic drugs in elderly outpatients and evaluate the rationality of anticholinergic drugs, and to provide a reference for reducing the adverse effects of anticholinergic drugs.@*METHODS@#A list of drugs with anticholinergic activity based on the Beers criteria was established. The basic information (such as age and gender), clinical diagnosis, and medications of elderly outpatient were extracted from hospital electronic medical records, and the Anticholinergic Cognitive Burden (ACB) Scale was used to calculate the anticholinergic burden for each patient. Logistic regression analysis was used to identify the potential risk factors for the occurrence of problems such as multiple medication and insomnia.@*RESULTS@#A total of 1 840 prescriptions for elderly patients were reviewed. Of these patients, ACB score was more than or equal to 1 in 648 (35.22%) patients. Number of prescription medication (95% CI: 1.221 to 1.336) and insomnia (95% CI: 3.538 to 6.089) were independent factors affecting ACB scores (both P<0.01). Medications for patients of ACB scores were most commonly treated with the central nervous system drugs (such as alprazolam and eszopiclone) and for the cardiovascular system drugs (such as metoprolol and nifedipine).@*CONCLUSIONS@#There is a high rate of ACB drugs use in geriatric patients, and the clinical focus should be on multiple medication prescriptions, especially on the central nervous system drugs (such as alprazolam and eszopiclone) and cardiovascular system drugs (such as metoprolol and nifedipine). The prescription review should be emphasized to reduce adverse reactions to anticholinergic drugs in elderly patients.

Influence of physiological and psychological factors on cognitive dysfunction in heart failure patients.

AIM: Our study aimed to examine factors that contribute to cognitive dysfunction in patients with heart failure (HF). BACKGROUND: Although a majority of patients with HF have mild to moderate cognitive impairment, little is known about factors that influence progressive cognitive decline in this population. METHODS: We examined the influence of physiological factors (NYHA functional class II - IV, ejection fraction, co-morbidity burden, polypharmacy), psychosocial factors (anxiety, depression, evaluation for advanced therapy), and associated toxicities (anticholinergic drug burden), on cognitive dysfunction. Data were analyzed using mean (SE) for continuous variables and frequency and percent for categorical variables. Differences between NYHA functional classification (Class II vs. Class III/IV) were examined using Chi Square. Linear regression models were used to assess associations among model variables. RESULTS: Of the 113 participants with HF, Class III-IV HF were more cognitively impaired than those with NYHA Class II (p < 0.0001), had higher anxiety (p = 0.002), and depression (p = 0.003), and lower EF (p = 0.041). A majority of participants had a moderate anticholinergic drug burden, and NYHA Class III/IV participants had significantly higher medication counts than Class II participants (p = 0.034). Regression analysis found that NYHA Class III/IV, anxiety, depression and evaluation for advanced therapy significantly influenced cognitive dysfunction. CONCLUSIONS: Findings support a high prevalence of cognitive dysfunction, anxiety, and depression in NYHA class II-IV with a greater level of cognitive dysfunction in class III/IV patients.