The OPTIMIZE patient- and family-centered, primary care-based deprescribing intervention for older adults with dementia or mild cognitive impairment and multiple chronic conditions: study protocol for a pragmatic cluster randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Most individuals with dementia or mild cognitive impairment (MCI) have multiple chronic conditions (MCC). The combination leads to multiple medications and complex medication regimens and is associated with increased risk for significant treatment burden, adverse drug events, cognitive changes, hospitalization, and mortality. Optimizing medications through deprescribing (the process of reducing or stopping the use of inappropriate medications or medications unlikely to be beneficial) may improve outcomes for MCC patients with dementia or MCI. METHODS: With input from patients, family members, and clinicians, we developed and piloted a patient-centered, pragmatic intervention (OPTIMIZE) to educate and activate patients, family members, and primary care clinicians about deprescribing as part of optimal medication management for older adults with dementia or MCI and MCC. The clinic-based intervention targets patients on 5 or more medications, their family members, and their primary care clinicians using a pragmatic, cluster-randomized design at Kaiser Permanente Colorado. The intervention has two components: a patient/ family component focused on education and activation about the potential value of deprescribing, and a clinician component focused on increasing clinician awareness about options and processes for deprescribing. Primary outcomes are total number of chronic medications and total number of potentially inappropriate medications (PIMs). We estimate that approximately 2400 patients across 9 clinics will receive the intervention. A comparable number of patients from 9 other clinics will serve as wait-list controls. We have > 80% power to detect an average decrease of - 0.70 (< 1 medication). Secondary outcomes include the number of PIM starts, dose reductions for selected PIMs (benzodiazepines, opiates, and antipsychotics), rates of adverse drug events (falls, hemorrhagic events, and hypoglycemic events), ability to perform activities of daily living, and skilled nursing facility, hospital, and emergency department admissions. DISCUSSION: The OPTIMIZE trial will examine whether a primary care-based, patient- and family-centered intervention educating patients, family members, and clinicians about deprescribing reduces numbers of chronic medications and PIMs for older adults with dementia or MCI and MCC. TRIAL REGISTRATION: NCT03984396. Registered on 13 June 2019.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".