External validation of the Medication Risk Score in polypharmacy patients in general practice: A tool for prioritizing patients at greatest risk of potential drug-related problems

[Adverse drug reactions as cause of nonspecific symptoms in patients in the emergency department]

BACKGROUND

In a large proportion of patients admitted to the emergency department (ED), the initial main symptom is nonspecific. One possible reason for this, especially in older patients, may be adverse drug reactions (ADR) due to their frequent polypharmacy.

AIM

To illustrate the incidence of ADRs, the affected patient population including risk factors, and drug classes with ADRs leading to nonspecific symptoms. To provide practice recommendations for the management of ADRs in the ED.

MATERIAL AND METHODS

Presentation of the pharmacological principles on ADRs, statistics of pharmacovigilance centers as well as original literature including experiences from clinical practice and own projects.

RESULTS

In 10% of patients with nonspecific symptoms an ADR is responsible for presentation in the ED. In 60% of cases these ADRs are not correctly identified in the ED setting. A small number of drug classes are responsible for most of these referrals. Databases, risk stratification, clinical pharmacists, or clinical decision support systems are available to improve ADR identification and management. As these options are partly associated with considerable costs or the validation for German EDs is missing, a widespread application does not take place.

CONCLUSION

Correct identification of ADRs in patients with nonspecific symptoms in the ED is necessary to initiate adequate treatment. These ADRs are often overlooked because processes and tools for identification and management are not applied in the ED, leading to a lack of awareness. For high-risk patients in the ED, the focus should be on drug history, ideally considering patient-specific risk factors and specific drug classes.

[Estimation of the risk of drug-related problems in nursing home residents : A retrospective analysis of routine data]

BACKGROUND

Polypharmacy and drug-related problems are major challenges in the care and treatment of nursing home residents. Many interventional studies showed disappointing results, which lead to the question if this could also be due to the selection of the target parameters of these studies.

MATERIAL AND METHODS

A routine data set from six long-term care facilities was retrospectively analyzed. The question is if the recently validated medication risk score (MERIS) is suitable for carrying out a risk assessment in a population of nursing home residents. Associations between MERIS and the dependent variables hospital admissions and falls over 12 months and a weight loss of ≥ 5% over 3 months were examined.

RESULTS

Out of 495 residents 38.6% (n = 191) have a high risk of drug-related problems according to MERIS. A univariate regression analysis showed a significantly increased risk of hospital admissions (OR 2.2; p < 0.001) and weight loss of ≥ 5% (OR 1.95; p = 0.041) with high MERIS, but no significant association with falls. In the multivariate regression the risk of hospitalization was increased by diabetes mellitus (OR 1.88; p = 0.004), falls in the same period (OR 1.91; p = 0.001), positive MERIS (OR 1.75; p = 0.006) and decreased with stable weight (OR 0.88; p = 0.004).

CONCLUSION

The results indicate the potential of the score for future research projects and individual risk assessment; however, due to the limitations of retrospective secondary analyses further studies are required.

[Scoring tool to identify patients at increased risk for drug-related problems: results of a point prevalence study at hospital admission]

INTRODUCTION

Drug therapy is a high-risk process and requires special attention, especially at sectoral borders. Pharmaceutical services such as medication review are appropriate measures to identify drug-related problems and thus improve the safety of drug therapy. Risk-scoring tools have been described in the literature as helpful for prioritizing medication reviews for patients at high risk for drug-related problems.

METHODS

In a multi-centre point prevalence study, we identified patients at increased risk for medication-related problems at hospital admission using the medication risk tool. In addition, the current level of implementation of pharmacy services was surveyed.

RESULTS

A total of 11 (58%; 11/19) hospital pharmacies in Saxony participated in the point prevalence survey. The scoring tool identified 32% (279/875) of patients at increased risk for medication-related problems (Meris score >12 group) at admission. Thereby, the number of drugs in the Meris score >12 group was 10.6 (average; standard deviation 3.5; n=279), while in the Meris score ≤12 group it was only five drugs per patient (average 4.6; standard deviation 2.8; n=596). The age of patients in the Meris score >12 group averaged 75.9 ± 11 years, while the age of patients in the Meris score ≤12 group averaged 60.6 ± 17.9 years.

DISCUSSION

Prioritization with the help of a risk-scoring tool is essential as pharmacy services in Saxon hospitals still need to be regularly established and in order to identify patients with an increased risk for drug-related problems at an early stage.

Comparison of prioritisation algorithms for the selection of patients for medication reviews in the emergency department: a cross-sectional study

BACKGROUND

Risk prioritisation algorithms provide patients with a risk category that guides pharmacists to choose those needing medication reviews (MRs) the most. For this study the Medicine Risk Score (MERIS) and a modified Assessment of Risk Tool (ART) were used.

AIM

To examine how the selection of patients by the clinical pharmacists in an emergency department for MRs compared with the categorisation provided by MERIS and a modified version of ART (mART). Furthermore, examine the agreement between MERIS and mART.

METHOD

A cross-sectional study was conducted using data on all admitted patients during a two-month period. Data were entered into the prioritisation algorithms and independently ranked by the six pharmacists who were observed as they selected patients for MR. Risk scores and categorisations were compared between the algorithms and the pharmacists' ranking using t-test, Z-test, Chi square, Kruskal Wallis H-test, or Kappa statistics.

RESULTS

The study included 1133 patients. Significant differences were found between the pharmacists and the algorithms. The sensitivity and specificity of MERIS were 37.8% and 73.6%, for mART, 33.0% and 75.9%. Kappa was 0.58, showing moderate agreement. No significant differences were observed between the individual pharmacists' selection, but differences were significant between how pharmacists ranked the importance of the provided MRs.

CONCLUSION

Pharmacists disagreed with the risk categorisation provided by MERIS and mART. However, MERIS and mART had similar sensitivity, specificity, and moderate agreement. Further research should focus on how clinical algorithms affect the selection of patients and on the importance of the MRs carried out by pharmacists.

Increasing trend in hospitalisation due to adverse drug reactions: can we stem the tide?

Living with multiple long-term health conditions (multimorbidity) is increasingly common in older age. The more long-term conditions that an individual has, the more medicines they are likely to take. Hospitalisation as a consequence of medication-related harm is increasing and a concerted effort is needed to reduce the burden of harm caused by medication. However, making decisions about the balance between benefit and harm for an older person with multimorbidity and polypharmacy is very complex. There are various clinical tools that can help to identify patients at higher risk of harm and numerous strategies, including medicines optimisation reviews that incorporate personalised health information, to try to reduce risk. Further education and training of the healthcare professionals is needed to equip the multidisciplinary workforce with the skills and knowledge to address these challenges. This article discusses some of the changes that can be implemented now and highlights areas that will require more research before they can be introduced, in order to help patients to get the best out of their medicines.

Comparing risk prediction models aimed at predicting hospitalizations for adverse drug events in community dwelling older adults: a protocol paper

Background

The objective of this paper is to describe the creation, validation, and comparison of two risk prediction modeling approaches for community-dwelling older adults to identify individuals at highest risk for adverse drug event-related hospitalizations. One approach will use traditional statistical methods, the second will use a machine learning approach.

Methods

We will construct medication, clinical, health care utilization, and other variables known to be associated with adverse drug event-related hospitalizations. To create the cohort, we will include older adults (≥ 65 years of age) empaneled to a primary care physician within the Cedars-Sinai Health System primary care clinics with polypharmacy (≥ 5 medications) or at least 1 medication commonly implicated in ADEs (certain oral hypoglycemics, anti-coagulants, anti-platelets, and insulins). We will use a Fine-Gray Cox proportional hazards model for one risk modeling approach and DataRobot, a data science and analytics platform, to run and compare several widely used supervised machine learning algorithms, including Random Forest, Support Vector Machine, Extreme Gradient Boosting (XGBoost), Decision Tree, Naïve Bayes, and K-Nearest Neighbors. We will use a variety of metrics to compare model performance and to assess the risk of algorithmic bias.

Discussion

In conclusion, we hope to develop a pragmatic model that can be implemented in the primary care setting to risk stratify older adults to further optimize medication management.

Pharmaceutical care- urgency: Proposing a practical clinical framework for pharmacy students

BACKGROUND AND PURPOSE

Prioritizing a drug therapy problem (DTP) during an experiential placement is challenging for some pharmacy students, suggesting a gap in pre-placement preparedness and the need to modify existing resources. A modified DTP prioritizing framework is proposed to enhance clinical reasoning and increase students' confidence in performing this important step in the pharmaceutical care process.

EDUCATIONAL ACTIVITY AND SETTING

Students' baseline DTP prioritizing capability was assessed in an informal focus group consisting of pharmacy students and experienced hospital pharmacy preceptors. Participants ranked the urgency for addressing 47 common medical conditions and selected a time frame to resolve the DTP. Participants also provided feedback on a proposed DTP prioritizing framework. A modified, student-focused DTP prioritizing framework, incorporating elements of curricular knowledge, principal elements of urgency, and time frame for taking action to resolve the identified DTP is described.

FINDINGS

Students' DTP urgency rankings were heterogeneous and showed greater deviation from the anticipated ranking (R = 0.61) compared to the pharmacist cohort (R = 0.807), reinforcing our view of the need for a modified DTP prioritizing framework for students. In qualitative terms, students felt the framework's focus on curricular knowledge would contribute to the development of expertise. Preceptors felt the framework reflected their usual practice and would help guide discussions with students.

SUMMARY

The modified DTP prioritizing framework, described in this article, may be utilized both to enhance student success and preceptor development in the experiential setting.

Systematic Review of Risk Factors Assessed in Predictive Scoring Tools for Drug-Related Problems in Inpatients

Drug-related problems (DRP, defined as adverse drug events/reactions and medication errors) are a common threat for patient safety. With the aim to aid improved allocation of specialist resources and to improve detection and prevention of DRP, numerous predictive scoring tools have been proposed. The external validation and evidence for the transferability of these tools still faces limitations. However, the proposed scoring tools include partly overlapping sets of similar factors, which may allow a new approach to estimate the external usability and validity of individual risk factors. Therefore, we conducted this systematic review and analysis. We identified 14 key studies that assessed 844 candidate risk factors for inclusion into predictive scoring tools. After consolidation to account for overlapping terminology and variable definitions, we assessed each risk factor in the number of studies it was assessed, and, if it was found to be a significant predictor of DRP, whether it was included in a final scoring tool. The latter included intake of ≥ 8 drugs, drugs of the Anatomical Therapeutic Chemical (ATC) class N, ≥1 comorbidity, an estimated glomerular filtration rate (eGFR) <30 mL/min and age ≥60 years. The methodological approach and the individual risk factors presented in this review may provide a new starting point for improved risk assessment.

Is a High Medication Risk Score Associated With Increased Risk of 30-Day Readmission? A Population-Based Cohort Study From CROSS-TRACKS

OBJECTIVES

The primary aim of this study was to evaluate whether a high Medication Risk Score (MERIS) upon admission to an emergency department is associated with increased risk of 30-day readmission in patients discharged directly home. Mortality, visit to general practitioner, and drug changes within 30 days were included as secondary outcomes.

METHODS

This is a historical cohort study with data from the Danish population-based open-cohort CROSS-TRACKS. Cox regression analyses were used to determine whether a high MERIS score was associated with increased risk of 30-day readmission and mortality. Visit to general practitioner and drug changes were tested with χ2 test and Wilcoxon rank sum test.

RESULTS

A total of 2106 patients were eligible: 2017 had a MERIS score lower than 14 (low-risk group), and 89 had a score of 14 or higher (high-risk group). The proportion of patients in the high-risk group who were readmitted was 21.3% compared with 16.3% in the low-risk group, resulting in a hazard ratio for readmission of 1.43 (95% confidence interval, 0.9-1.3). The hazard ratio for mortality was 8.3 (95% confidence interval, 3.0-22.8). No statistical significant difference was found in general practitioner visits; however, significantly more drug changes were observed in the high-risk group.

CONCLUSIONS

A high MERIS score was associated with increased risk of readmissions and can potentially assist healthcare professionals in the prioritizing of patients who may benefit from further exam, for example, additional medication review in acute care setting. Further investigation of MERIS and exploration of causal inferences between medication-related harm and medication-related readmissions are warranted.