Medication exposure of the dyad patient-caregiver in Alzheimer's disease and related dementias: a cross-sectional study.

BACKGROUND: Patients with Alzheimer's disease and related dementias and their caregivers can be defined as people with higher risk of developing medication-related problems due to aging and polypharmacy. AIM: To assess the medication exposure of patient with Alzheimer's disease and related dementias and their caregivers. METHOD: Ancillary cross-sectional study based on baseline medication data of the PHARMAID RCT. The PHARMAID study was a multi-center RCT assessing an integrated pharmaceutical care at a psychosocial program. Older outpatients with Alzheimer's disease and related dementias and their older caregivers were eligible for inclusion. Baseline medication data were used to assess the medication exposure, illustrated by the number of medications, the prevalence of potentially inappropriate medications (PIMs) using the EU(7)-PIM list and the Medication Regimen Complexity Index (MRCI). RESULTS: Seventy-three dyads were included in this ancillary study. The mean numbers (SD) of medications used by patients was 6.8 (2.6) and by caregivers was 4.7 (3.7). Overall, 60.3% of patients used at least one PIM and 47.9% of caregivers. Regarding the medication regimen complexity, the mean MRCI was 16.3(8.1) for patients and 11.3(10.5) for caregivers. CONCLUSION: The results of this study confirm the relevance of carrying out medication review with patients, but also with their caregivers who can be considered as hidden patients.

Impact of pharmaceutical care integrated at a psychosocial intervention to reduce caregiver's burden in Alzheimer's disease or related dementias: Negative results at 18 months and difficulties to conduct PHARMAID RCT.

Background: Psychosocial interventions for caregivers of patients with Alzheimer disease and relative dementias (ADRD) reported a caregiver burden improvement. Multicomponent intervention integrating pharmaceutical care has not yet been evaluated while ADRD patients and their caregivers are exposed to high risk of drug-related problems. The PHARMAID study aimed to assess the impact of personalized pharmaceutical care integrated to a psychosocial program on the burden of ADRD caregivers at 18 months. Methods: The PHARMAID RCT was conducted between September 2016 and June 2020 [ClinicalTrials.gov: NCT02802371]. PHARMAID study planned to enroll 240 dyads, i.e. ADRD patients and caregivers, whose inclusion criteria were: outpatient with mild or major neurocognitive disorders due to ADRD, living at home, receiving support from a family caregiver. Three parallel groups compared a control group with two interventional groups: psychosocial intervention and integrated pharmaceutical care at a psychosocial intervention. The main outcome was the caregiver burden assessed by the Zarit Burden Index (ZBI, score range 0-88) at 18 months. Results: Overall, 77 dyads were included (32% of the expected sample size). At 18 months, the mean ZBI scores were 36.7 ± 16.8 in the control group, 30.3 ± 16.3 for the group with psychosocial intervention, and 28.8 ± 14.1 in group with integrated pharmaceutical care at psychosocial intervention. No significant difference was demonstrated between the three groups (p = 0.326). Conclusions: The findings suggest that PHARMAID program had no significant impact on caregiver burden at 18 months. Several limitations have been highlighted and discussed by the authors in order to formulate recommendations for further research.

Risk factors associated with unintentional medication discrepancies at admission in an internal medicine department.

At admission, unintentional medication discrepancies (UMDs) can occur and may led to severe adverse events. Some of them are preventable through medication reconciliation (MR). As MR is a time-consuming activity, a better identification of high-risk patients of UMDs is mandatory. The objective was to identify risk factors associated with UMDs at admission in an internal medicine department. This prospective observational study was conducted from April 2017 to June 2019. At admission, inpatients had MR to obtain a complete list of home medications. This list was compared to prescriptions made at admission. All discrepancies were classified as intentional or UMDs. Univariate and multivariate analyses to identify the risk factors associated with UMDs were performed. MR was performed on 1157 patients (70.1 ± 16.8 years old); 550 MR (47.5%) contained at least one UMD. More than half of the UMDs (n = 892, 65.6%) corresponded to drug omission. The univariate analysis showed that age (> 60 years old), "living at home", medication preparation not performed by patient, medication-intake difficulties, number of sources consulted, MR duration, presence of a high-risk drug and the number of home medications were associated with UMDs. In the multivariate analysis, adjusted on the number of sources consulted, independent risk factors were "living at home" and the number of home medications. At admission to an internal medicine department, UMDs were frequent and associated with "living at home" and poly-medication. Our findings might help physicians to identify high-risk patients of UMDs since their admission.

Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively?

The characterization of drug-drug interactions (DDIs) may require the use of several different tools, such as the thesaurus issued by our national health agency (i.e., ANSM), the metabolic pathways table from the Geneva University Hospital (GUH), and DDI-Predictor (DDI-P). We sought to (i) compare the three tools' respective abilities to detect DDIs in routine clinical practice and (ii) measure the pharmacist intervention rate (PIR) and physician acceptance rate (PAR) associated with the use of DDI-P. The three tools' respective DDI detection rates (in %) were measured. The PIRs and PARs were compared by using the area under the curve ratio given by DDI-P (RAUC) and applying a chi-squared test. The DDI detection rates differed significantly: 40.0%, 76.5%, and 85.2% for ANSM (The National Agency for the Safety of Medicines and Health Products), GUH and DDI-P, respectively (p < 0.0001). The PIR differed significantly according to the DDI-P's RAUC: 90.0%, 44.2% and 75.0% for RAUC ≤ 0.5; RAUC 0.5-2 and RAUC > 2, respectively (p < 0.001). The overall PAR was 85.1% and did not appear to depend on the RAUC category (p = 0.729). Our results showed that more pharmacist interventions were issued when details of the strength of the DDI were available. The three tools can be used in a complementary manner, with a view to refining medication adjustments.