Drug–Drug Interactions with Oral Anticoagulants as Potentially Inappropriate Medications: Prevalence and Outcomes in Elderly Patients in Primary Care and Hospital Settings

Drug Burden Index Is a Modifiable Predictor of 30-Day Hospitalization in Community-Dwelling Older Adults With Complex Care Needs: Machine Learning Analysis of InterRAI Data

BACKGROUND

Older adults (≥65 years) account for a disproportionately high proportion of hospitalization and in-hospital mortality, some of which may be avoidable. Although machine learning (ML) models have already been built and validated for predicting hospitalization and mortality, there remains a significant need to optimize ML models further. Accurately predicting hospitalization may tremendously affect the clinical care of older adults as preventative measures can be implemented to improve clinical outcomes for the patient.

METHODS

In this retrospective cohort study, a data set of 14 198 community-dwelling older adults (≥65 years) with complex care needs from the International Resident Assessment Instrument-Home Care database was used to develop and optimize 3 ML models to predict 30-day hospitalization. The models developed and optimized were Random Forest (RF), XGBoost (XGB), and Logistic Regression (LR). Variable importance plots were generated for all 3 models to identify key predictors of 30-day hospitalization.

RESULTS

The area under the receiver-operating characteristics curve for the RF, XGB, and LR models were 0.97, 0.90, and 0.72, respectively. Variable importance plots identified the Drug Burden Index and alcohol consumption as important, immediately potentially modifiable variables in predicting 30-day hospitalization.

CONCLUSIONS

Identifying immediately potentially modifiable risk factors such as the Drug Burden Index and alcohol consumption is of high clinical relevance. If clinicians can influence these variables, they could proactively lower the risk of 30-day hospitalization. ML holds promise to improve the clinical care of older adults. It is crucial that these models undergo extensive validation through large-scale clinical studies before being utilized in the clinical setting.

Appropriateness of direct oral anticoagulant prescribing in older subjects with atrial fibrillation discharged from acute medical wards

AIMS

Knowledge on the prescriptive practice of direct oral anticoagulants (DOACs) in older subjects with atrial fibrillation (AF) hospitalized in acute medical wards is limited. This study aimed to evaluate the prevalence and appropriateness of DOAC prescriptions in hospitalized older subjects with AF, discharged from acute medical wards.

METHODS

We analysed a cohort of 609 subjects with AF, aged ≥65 years (mean age 85 years) enrolled from 39 geriatric and nephrology wards in Italy. DOAC prescriptive appropriateness was evaluated according to the summary of product characteristics (smPC), 2019 Beers and STOPP criteria, and drug-drug interactions (DDIs).

RESULTS

At hospital discharge, 33% of patients with AF were prescribed with DOAC, 26% with vitamin-K antagonist, while 41% did not receive any anticoagulant. Among subjects on DOAC therapy, 31% presented a violation of the smPC criteria (mainly underdosage-17%), while 48% and 18% presented a Beers/STOPP inappropriate prescription, or a DDI, respectively. Older age, lower body mass index (BMI), cancer and higher estimated glomerular filtration rate (eGFR) were independently associated with DOAC underdosage or missed prescription (age: adjusted odds ratio [aOR] 1.06, 95% confidence interval [95% CI] 1.00-1.12 for underdosage; eGFR: aOR 1.04, 95% CI 1.02-1.07 for underdosage; BMI: aOR 0.95, 95% CI 0.91-0.99 for missed prescription; cancer: aOR 1.93, 95% CI 1.19-3.13 for missed prescription).

CONCLUSIONS

This study showed a suboptimal DOAC prescriptive practice in older in-patients, with frequent missed prescription and DOAC underdosage. Contrary to current recommendations, physicians appear overly concerned by bleeding risk in real-life older and frailer subjects. Strategies should be developed to promote appropriate DOAC prescription in the hospital setting.

Application of machine learning approaches in predicting clinical outcomes in older adults - a systematic review and meta-analysis

BACKGROUND

Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care.

DESIGN

Systematic review and meta-analyses.

PARTICIPANTS

Older adults (≥ 65 years) in any setting.

INTERVENTION

Machine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months.

OUTCOME MEASURES

Studies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric.

RESULTS

Thirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 - 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 - 0.86) for mortality over 6 months, signifying good discriminatory power.

CONCLUSION

The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes.

Optimizing Pharmacotherapy for Direct Oral Anticoagulants in Older Adults: Strategies for Managing Drug Interactions

Direct oral anticoagulants (DOACs) have been increasingly used by older adults. Although these medications offer several therapeutic advantages over traditional anticoagulants, such as warfarin, they have limitations. One significant concern associated with DOACs is their potential for drug-drug interactions. These interactions may compromise the safe and effective use of DOACs and can potentially lead to serious adverse events and complications, particularly major bleeding. Polypharmacy is common among older adults with chronic diseases, which can increase the risk of drug-drug interactions with DOACs. The current article discusses the impact and risks of drug-drug interactions with DOACs in the context of older adults and explores ways to improve and manage these interactions through the collaboration of an interprofessional team. [Journal of Gerontological Nursing, 49(9), 7-13.].

Pharmacokinetic and Pharmacodynamic Drug-Drug Interactions: Research Methods and Applications

Because of the high research and development cost of new drugs, the long development process of new drugs, and the high failure rate at later stages, combining past drugs has gradually become a more economical and attractive alternative. However, the ensuing problem of drug-drug interactions (DDIs) urgently need to be solved, and combination has attracted a lot of attention from pharmaceutical researchers. At present, DDI is often evaluated and investigated from two perspectives: pharmacodynamics and pharmacokinetics. However, in some special cases, DDI cannot be accurately evaluated from a single perspective. Therefore, this review describes and compares the current DDI evaluation methods based on two aspects: pharmacokinetic interaction and pharmacodynamic interaction. The methods summarized in this paper mainly include probe drug cocktail methods, liver microsome and hepatocyte models, static models, physiologically based pharmacokinetic models, machine learning models, in vivo comparative efficacy studies, and in vitro static and dynamic tests. This review aims to serve as a useful guide for interested researchers to promote more scientific accuracy and clinical practical use of DDI studies.