Disentangling drug contributions: anticholinergic burden in older adults linked to individual medications: a cross-sectional population-based study.

BACKGROUND: Medications with potent anticholinergic properties have well-documented adverse effects. A high cumulative anticholinergic burden may arise from the concurrent use of multiple medications with weaker anticholinergic effects. We sought to identify patterns of high anticholinergic burden and associated patient characteristics. METHODS: We identified patients aged ≥ 65 who filled ≥ 1 medication with anticholinergic adverse effects in 2019 and had a cumulative Anticholinergic Burden score (ACB) ≥ 4 (i.e., high anticholinergic burden) in a large US health insurer. We classified patients based on how they attained high burden, as follows: 1) only filling strong or moderate anticholinergic medications (i.e., ACB = 2 or 3, "moderate/strong"), 2) only filling lightly anticholinergic medications (i.e., ACB = 1, "light/possible"), and 3) filling any combination ("mix"). We used multinomial logistic regression to assess the association between measured patient characteristics and membership in the three anticholinergic burden classifications, using the moderate/strong group as the referent. RESULTS: In total, 83,286 eligible patients with high anticholinergic burden were identified (mean age: 74.3 years (SD:7.1), 72.9% female). Of these, 4.5% filled only strong/moderate anticholinergics, 4.3% filled only light/possible anticholinergics, and the rest filled a mix (91.2%). Within patients in the mixed group, 64.3% of medication fills were for light/possible anticholinergics, while 35.7% were for moderate/strong anticholinergics. Compared with patients in the moderate/strong anticholinergics group, patients filling only light/possible anticholinergics were more likely to be older (adjusted Odds Ratio [aOR] per 1-unit of age: 1.06, 95%CI: 1.05-1.07), less likely to be female (aOR: 0.56, 95%CI: 0.50-0.62 vs. male), more likely to have comorbidities (e.g., heart failure aOR: 3.18, 95%CI: 2.70-3.74 or depression aOR: 1.20, 95%CI: 1.09-1.33 vs. no comorbidity), and visited fewer physicians (aOR per 1-unit of change: 0.98, 95%CI: 0.97-0.98). Patients in the mixed group were older (aOR per 1-unit of age: 1.02, 95%CI: 1.02-1.03) and less likely to be female (aOR: 0.89, 95%CI: 0.82-0.97 vs. male) compared with those filling moderate/strong anticholinergics. CONCLUSION: Most older adults accumulated high anticholinergic burden through a combination of light/possible and moderate/strong anticholinergics rather than moderate/strong anticholinergics, with light/possible anticholinergics being the major drivers of overall anticholinergic burden. These insights may inform interventions to improve prescribing in older adults.

The impact of using reinforcement learning to personalize communication on medication adherence: findings from the REINFORCE trial.

Text messaging can promote healthy behaviors, like adherence to medication, yet its effectiveness remains modest, in part because message content is rarely personalized. Reinforcement learning has been used in consumer technology to personalize content but with limited application in healthcare. We tested a reinforcement learning program that identifies individual responsiveness ("adherence") to text message content and personalizes messaging accordingly. We randomized 60 individuals with diabetes and glycated hemoglobin A1c [HbA1c] ≥ 7.5% to reinforcement learning intervention or control (no messages). Both arms received electronic pill bottles to measure adherence. The intervention improved absolute adjusted adherence by 13.6% (95%CI: 1.7%-27.1%) versus control and was more effective in patients with HbA1c 7.5- < 9.0% (36.6%, 95%CI: 25.1%-48.2%, interaction p < 0.001). We also explored whether individual patient characteristics were associated with differential response to tested behavioral factors and unique clusters of responsiveness. Reinforcement learning may be a promising approach to improve adherence and personalize communication at scale.

Pragmatic trial evaluating the impact of simulation training on high-risk prescribing to older adults by junior physicians.

BACKGROUND: High-risk medications like benzodiazepines, sedative hypnotics, and antipsychotics are commonly prescribed for hospitalized older adults, despite guidelines recommending avoidance. Prior interventions have not fully addressed how physicians make such prescribing decisions, particularly when experiencing stress or cognitive overload. Simulation training may help improve prescribing decision-making but has not been evaluated for overprescribing. METHODS: In this two-arm pragmatic trial, we randomized 40 first-year medical resident physicians (i.e., interns) on inpatient general medicine services at an academic medical center to either intervention (a 40-minute immersive simulation training) or control (online educational training) groups. The primary outcome was the number of new benzodiazepine, sedative hypnotic, or antipsychotic orders for treatment-naïve older adults during hospitalization. Secondary outcomes included the same outcome by all providers, being discharged on one of the medications, and orders for related or control medications. Outcomes were measured using electronic health record data over each intern's service period (~2 weeks). Outcomes were evaluated using generalized estimating equations, adjusting for clustering. RESULTS: In total, 522 treatment-naïve older adult patients were included in analyses. Over follow-up, interns prescribed ≥1 high-risk medication for 13 (4.9%) intervention patients and 13 (5.0%) control patients. The intervention led to no difference in the number of new prescriptions (Rate Ratio [RR]: 0.85, 95%CI: 0.31-2.35) versus control and no difference in secondary outcomes. In secondary analyses, intervention interns wrote significantly fewer "as-needed" ("PRN") order types for the high-risk medications (RR: 0.29, 95%CI: 0.08-0.99), and instead tended to write more "one-time" orders than control interns, though this difference was not statistically significant (RR: 2.20, 95%CI: 0.60-7.99). CONCLUSIONS: Although this simulation intervention did not impact total high-risk prescribing for hospitalized older adults, it did influence how the interns prescribed, resulting in fewer PRN orders, suggesting possibly greater ownership of care. Future interventions should consider this insight and implementation lessons raised. TRIAL REGISTRATION: Clinicaltrials.gov(NCT04668248).