Deprescribing, Medication Adverse Effects, Older Adults (65 Years and Older), Polypharmacy, Polypharmacy Patients
Conditions
Keywords
Older adults, Polypharmacy, Deprescribing
Brief summary
This randomized controlled pragmatic pilot study examines the feasibility and acceptability of a population health-based deprescribing intervention that leverages a polypharmacy risk prediction model. It includes four arms (2 intervention and 2 control arms) and uses a parallel arm study design.
Detailed description
This pilot clinical trial will examine the feasibility and acceptability of a medication management intervention that uses risk stratification to identify older adults (including older adults with Mild Cognitive Impairment or Alzheimer's Disease and Related Dementias) at greatest risk for polypharmacy-related adverse events. The intervention will use a risk prediction model to identify potential study participants for inclusion and enroll them and their care partners into a polypharmacy clinic. The study will stratify patients by cognitive impairment status. The risk prediction model includes variables such as: age, sex, recent healthcare encounters, current and past medications, current and past lab tests, current and past diagnoses.
Interventions
BEHAVIORALPatient-tailored deprescribing assessment and intervention
Patients are identified for enrollment via a risk prediction model and enrolled in a pharmacist-led polypharmacy clinic. The pharmacist will conduct a comprehensive medication review, which will include: medication reconciliation, assessment of patient risk factors (clinical, socioeconomic), identification of potentially inappropriate medications, identification of potential prescribing omissions, shared decision-making with the patient and/or caregiver, and confirmation of the results of the comprehensive medication review with the primary care clinician. Once the primary care clinician has approved the recommendations, the pharmacist will make the appropriate changes to the medication regimen, will provide tailored medication education and counseling (including Motivational Interviewing) and will follow-up with the patient at least once per month via video visit or phone to assess side effects, adverse effects, and provide support.
Sponsors
Cedars-Sinai Medical Center
National Institute on Aging (NIA)
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
65 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Patients will be invited to enroll in the study if they are in the top decile of risk selected by a risk prediction model of polypharmacy-related adverse events. The risk prediction model includes variables such as: age, sex, recent healthcare encounters, current and past medications, current and past lab tests, current and past diagnoses. * 65 years of age or older * Have a primary care clinician within the Cedars-Sinai Health System who uses MyCSLink (electronic health record used by Cedars-Sinai). * Part of a Cedars-Sinai Health System population health registry (e.g., enrolled in an Accountable Care Organization, Medicare Advantage plan, or Primary Care First program) * Diagnosis of Mild Cognitive Impairment or Dementia at baseline (for one intervention group) on the problem list
Exclusion criteria
include: * Patients for whom there is an active substance use disorder (defined by a physician) and thus for whom the intervention may not be appropriate * Patients with an organ transplant * Visits with a Cedars-Sinai Medical Center pharmacist for a polypharmacy consult within the last 6 months * Patients who previously indicated in the EHR that they wish to opt out of participating in research studies. * Patients enrolled in CS360, a program for highly complex medical patients, who already receive polypharmacy services.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Number of all-cause hospitalizations | 12 months after enrollment | Number of all-cause inpatient visits (including observation visits) 12 months after enrollment. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Total number of medications | Baseline, 12 months | Total number of medications on the medication list, including prescription medications, over-the-counter medications, vitamins, and supplements at 12 months. |
| Total number of potentially inappropriate medications | Baseline, 12 months | Use of any potentially inappropriate medications, defined by the 2019 Beers Criteria Tables 2 and 7 |
| Number of medications with a dose reduction >10% over 12 months | 12 months | We will sum up the number of medications where the study participant has achieved at \>10% dose reduction compared to baseline for each study participant at baseline and 12 months in both study arms. |
| Number of medications discontinued for 90 days or more which are re-prescribed | Baseline, 12 months | We will sum up the number of medications where the study participant has had any re-prescription of medications which were discontinued for 90 days or more by 12 months in all study arms. |
| Number of times per day the patient is taking medications | Baseline, 12 months | Each study participant will be coded on a continuous scale the number of times a day medications are taken based on the prescription instructions at baseline and 12 months in all study arms. |
| Number of potential prescribing omissions (PPOs) based on the STOPP/START criteria | 12 months | Number of potential prescribing omissions (PPOs) based on the STOPP/START criteria |
| Number of drug-related problems | Baseline, 12 months | We will compare the total number of drug-related problems (identified retrospectively by a pharmacist) at baseline and 12 months. |
| Number of potential pharmacy recommendations | Baseline, 12 months | We will compare the total number of potential pharmacist recommendations (identified retrospectively by a pharmacist) at baseline and 12 months. |
| Change in risk prediction model score | 12 months | Each study participant's information will be run through the risk prediction model at the end of the study to see if there is a meaningful change (10% or more change) in the risk prediction score from baseline to the end of the study period. |
Countries
United States
Contacts
PRINCIPAL_INVESTIGATORMichelle Keller, PhD, MPH
University of Southern California
Outcome results
None listed