Evaluating the Safety of an Educational Deprescribing Intervention: Lessons from the Optimize Trial.

BACKGROUND: Patients, family members, and clinicians express concerns about potential adverse drug withdrawal events (ADWEs) following medication discontinuation or fears of upsetting a stable medical equilibrium as key barriers to deprescribing. Currently, there are limited methods to pragmatically assess the safety of deprescribing and ascertain ADWEs. We report the methods and results of safety monitoring for the OPTIMIZE trial of deprescribing education for patients, family members, and clinicians. METHODS: This was a pragmatic cluster randomized trial with multivariable Poisson regression comparing outcome rates between study arms. We conducted clinical record review and adjudication of sampled records to assess potential causal relationships between medication discontinuation and outcomes. This study included adults aged 65+ with dementia or mild cognitive impairment, one or more additional chronic conditions, and prescribed 5+ chronic medications. The intervention included an educational brochure on deprescribing that was mailed to patients prior to primary care visits, a clinician notification about individual brochure mailings, and an educational tip sheets was provided monthly to primary care clinicians. The outcomes of the safety monitoring were rates of hospitalizations and mortality during the 4 months following brochure mailings and results of record review and adjudication. The adjudication process was conducted throughout the trial and included classifications: likely, possibly, and unlikely. RESULTS: There was a total of 3012 (1433 intervention and 1579 control) participants. There were 420 total hospitalizations involving 269 (18.8%) people in the intervention versus 517 total hospitalizations involving 317 (20.1%) people in the control groups. Adjusted risk ratios comparing intervention to control groups were 0.92 [95% confidence interval (CI) 0.72, 1.16] for hospitalization and 1.19 (95% CI 0.67, 2.11) for mortality. Both groups had zero deaths "likely" attributed to a medication change prior to the event. A total of 3 out of 30 (10%) intervention group hospitalizations and 7 out of 35 (20%) control group hospitalizations were considered "likely" due to a medication change. CONCLUSIONS: Population-based deprescribing education is safe in the older adult population with cognitive impairment in our study. Pragmatic methods for safety monitoring are needed to further inform deprescribing interventions. TRIAL REGISTRATION: NCT03984396. Registered on 13 June 2019.

Willingness of older people living with dementia and mild cognitive impairment and their caregivers to have medications deprescribed.

BACKGROUND: people living with cognitive impairment commonly take multiple medications including potentially inappropriate medications (PIMs), which puts them at risk of medication related harms. AIMS: to explore willingness to have a medication deprescribed of older people living with cognitive impairment (dementia or mild cognitive impairment) and multiple chronic conditions and assess the relationship between willingness, patient characteristics and belief about medications. METHODS: cross-sectional study using results from the revised Patients' Attitudes Towards Deprescribing questionnaire (rPATDcog) collected as baseline data in the OPTIMIZE study, a pragmatic, cluster-randomised trial educating patients and clinicians about deprescribing. Eligible participants were 65+, diagnosed with dementia or mild cognitive impairment, and prescribed at least five-long-term medications. RESULTS: the questionnaire was mailed to 1,409 intervention patients and 553 (39%) were returned and included in analysis. Participants had a mean age of 80.1 (SD 7.4) and 52.4% were female. About 78.5% (431/549) of participants said that they would be willing to have one of their medications stopped if their doctor said it was possible. Willingness to deprescribe was negatively associated with getting stressed when changes are made and with previously having a bad experience with stopping a medication (P < 0.001 for both). CONCLUSION: most older people living with cognitive impairment are willing to deprescribe. Addressing previous bad experiences with stopping a medication and stress when changes are made to medications may be key points to discuss during deprescribing conversations.

Intervention design in cognitively impaired populations-Lessons learned from the OPTIMIZE deprescribing pragmatic trial.

BACKGROUND: Polypharmacy is common in older adults with cognitive impairment and multiple chronic conditions, increasing risks of adverse drug events, hospitalization, and death. Deprescribing, the process of reducing or stopping potentially inappropriate medications, may improve outcomes. The OPTIMIZE pragmatic trial examined whether educating and activating patients, family members and clinicians about deprescribing reduces number of chronic medications and potentially inappropriate medications. Acceptability and challenges of intervention delivery in cognitively impaired older adults are not well understood. METHODS: We explored mechanisms of intervention implementation through post hoc qualitative interviews and surveys with stakeholder groups of 15 patients, 7 caregivers, and 28 clinicians. We assessed the context in which the intervention was delivered, its implementation, and mechanisms of impact. RESULTS: Acceptance of the intervention was affected by contextual factors including cognition, prior knowledge of deprescribing, communication, and time constraints. All stakeholder groups endorsed the acceptability, importance, and delivery of the intervention. Positive mechanisms of impact included patients scheduling specific appointments to discuss deprescribing and providers being prompted to consider deprescribing. Recollection of intervention materials was inconsistent but most likely shortly after intervention delivery. Short visit times remained the largest provider barrier to deprescribing. CONCLUSIONS: Our work identifies key learnings in intervention delivery that can guide future scaling of deprescribing interventions in this population. We highlight the critical roles of timing and repetition in intervention delivery to cognitively impaired populations and the barrier posed by short consultation times. The acceptability of the intervention to patients and family members highlights the potential to incorporate deprescribing education into routine clinical practice and expand proven interventions to other vulnerable populations.

Deprescribing Education vs Usual Care for Patients With Cognitive Impairment and Primary Care Clinicians: The OPTIMIZE Pragmatic Cluster Randomized Trial.

Background: Individuals with dementia or mild cognitive impairment frequently have multiple chronic conditions (defined as ≥2 chronic medical conditions) and take multiple medications, increasing their risk for adverse outcomes. Deprescribing (reducing or stopping medications for which potential harms outweigh potential benefits) may decrease their risk of adverse outcomes. Objective: To examine the effectiveness of increasing patient and clinician awareness about the potential to deprescribe unnecessary or risky medications among patients with dementia or mild cognitive impairment. Design, Setting, and Participants: This pragmatic, patient-centered, 12-month cluster randomized clinical trial was conducted from April 1, 2019, to March 31, 2020, at 18 primary care clinics in a not-for-profit integrated health care delivery system. The study included 3012 adults aged 65 years or older with dementia or mild cognitive impairment who had 1 or more additional chronic medical conditions and were taking 5 or more long-term medications. Interventions: An educational brochure and a questionnaire on attitudes toward deprescribing were mailed to patients prior to a primary care visit, clinicians were notified about the mailing, and deprescribing tip sheets were distributed to clinicians at monthly clinic meetings. Main Outcomes and Measures: The number of prescribed long-term medications and the percentage of individuals prescribed 1 or more potentially inappropriate medications (PIMs). Analysis was performed on an intention-to-treat basis. Results: This study comprised 1433 individuals (806 women [56.2%]; mean [SD] age, 80.1 [7.2] years) in 9 intervention clinics and 1579 individuals (874 women [55.4%]; mean [SD] age, 79.9 [7.5] years) in 9 control clinics who met the eligibility criteria. At baseline, both groups were prescribed a similar mean (SD) number of long-term medications (7.0 [2.1] in the intervention group and 7.0 [2.2] in the control group), and a similar proportion of individuals in both groups were taking 1 or more PIMs (437 of 1433 individuals [30.5%] in the intervention group and 467 of 1579 individuals [29.6%] in the control group). At 6 months, the adjusted mean number of long-term medications was similar in the intervention and control groups (6.4 [95% CI, 6.3-6.5] vs 6.5 [95% CI, 6.4-6.6]; P = .14). The estimated percentages of patients in the intervention and control groups taking 1 or more PIMs were similar (17.8% [95% CI, 15.4%-20.5%] vs 20.9% [95% CI, 18.4%-23.6%]; P = .08). In preplanned subgroup analyses, adjusted differences between the intervention and control groups were -0.16 (95% CI, -0.34 to 0.01) for individuals prescribed 7 or more long-term medications at baseline (n = 1434) and -0.03 (95% CI, -0.20 to 0.13) for those prescribed 5 to 6 medications (n = 1578) (P = .28 for interaction; P = .19 for subgroup interaction for PIMs). Conclusions and Relevance: This large-scale educational deprescribing intervention for older adults with cognitive impairment taking 5 or more long-term medications and their primary care clinicians demonstrated small effect sizes and did not significantly reduce the number of long-term medications and PIMs. Such interventions should target older adults taking relatively more medications. Trial Registration: ClinicalTrials.gov Identifier: NCT03984396.

Developing an algorithm across integrated healthcare systems to identify a history of cancer using electronic medical records.

OBJECTIVE: Tumor registries in integrated healthcare systems (IHCS) have high precision for identifying incident cancer but often miss recently diagnosed cancers or those diagnosed outside of the IHCS. We developed an algorithm using the electronic medical record (EMR) to identify people with a history of cancer not captured in the tumor registry to identify adults, aged 40-65 years, with no history of cancer. MATERIALS AND METHODS: The algorithm was developed at Kaiser Permanente Colorado, and then applied to 7 other IHCS. We included tumor registry data, diagnosis and procedure codes, chemotherapy files, oncology encounters, and revenue data to develop the algorithm. Each IHCS adapted the algorithm to their EMR data and calculated sensitivity and specificity to evaluate the algorithm's performance after iterative chart review. RESULTS: We included data from over 1.26 million eligible people across 8 IHCS; 55 601 (4.4%) were in a tumor registry, and 44848 (3.5%) had a reported cancer not captured in a registry. The common attributes of the final algorithm at each site were diagnosis and procedure codes. The sensitivity of the algorithm at each IHCS was 90.65%-100%, and the specificity was 87.91%-100%. DISCUSSION: Relying only on tumor registry data would miss nearly half of the identified cancers. Our algorithm was robust and required only minor modifications to adapt to other EMR systems. CONCLUSION: This algorithm can identify cancer cases regardless of when the diagnosis occurred and may be useful for a variety of research applications or quality improvement projects around cancer care.

Validation of diagnosis codes to identify hospitalized COVID-19 patients in health care claims data.

PURPOSE: Health plan claims may provide complete longitudinal data for timely, real-world population-level COVID-19 assessment. However, these data often lack laboratory results, the standard for COVID-19 diagnosis. METHODS: We assessed the validity of ICD-10-CM diagnosis codes for identifying patients hospitalized with COVID-19 in U.S. claims databases, compared to linked laboratory results, among six Food and Drug Administration Sentinel System data partners (two large national insurers, four integrated delivery systems) from February 20-October 17, 2020. We identified patients hospitalized with COVID-19 according to five ICD-10-CM diagnosis code-based algorithms, which included combinations of codes U07.1, B97.29, general coronavirus codes, and diagnosis codes for severe symptoms. We calculated the positive predictive value (PPV) and sensitivity of each algorithm relative to laboratory test results. We stratified results by data source type and across three time periods: February 20-March 31 (Time A), April 1-30 (Time B), May 1-October 17 (Time C). RESULTS: The five algorithms identified between 34 806 and 47 293 patients across the study periods; 23% with known laboratory results contributed to PPV calculations. PPVs were high and similar across algorithms. PPV of U07.1 alone was stable around 93% for integrated delivery systems, but declined over time from 93% to 70% among national insurers. Overall PPV of U07.1 across all data partners was 94.1% (95% CI, 92.3%-95.5%) in Time A and 81.2% (95% CI, 80.1%-82.2%) in Time C. Sensitivity was consistent across algorithms and over time, at 94.9% (95% CI, 94.2%-95.5%). CONCLUSION: Our results support the use of code U07.1 to identify hospitalized COVID-19 patients in U.S. claims data.