Rationale and design of the Novel Uses of adaptive Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) pragmatic adaptive randomized trial: a trial protocol

Background: The prescribing of high-risk medications to older adults remains extremely common and results in potentially avoidable health consequences. Efforts to reduce prescribing have had limited success, in part because they have been sub-optimally timed, poorly designed, or not provided actionable information. Electronic health record (EHR)-based tools are commonly used but have had limited application in facilitating deprescribing in older adults. The objective is to determine whether designing EHR tools using behavioral science principles reduces inappropriate prescribing and clinical outcomes in older adults. Methods: The Novel Uses of Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) project uses a two-stage, 16-arm adaptive randomized pragmatic trial with a “pick-the-winner” design to identify the most effective of many potential EHR tools among primary care providers and their patients ≥ 65 years chronically using benzodiazepines, sedative hypnotic (“Z-drugs”), or anticholinergics in a large integrated delivery system. In stage 1, we randomized providers and their patients to usual care (n = 81 providers) or one of 15 EHR tools (n = 8 providers per arm) designed using behavioral principles including salience, choice architecture, or defaulting. After 6 months of follow-up, we will rank order the arms based upon their impact on the trial’s primary outcome (for both stages): reduction in inappropriate prescribing (via discontinuation or tapering). In stage 2, we will randomize (a) stage 1 usual care providers in a 1:1 ratio to one of the up to 5 most promising stage 1 interventions or continue usual care and (b) stage 1 providers in the unselected arms in a 1:1 ratio to one of the 5 most promising interventions or usual care. Secondary and tertiary outcomes include quantities of medication prescribed and utilized and clinically significant adverse outcomes. Discussion: Stage 1 launched in October 2020. We plan to complete stage 2 follow-up in December 2021. These results will advance understanding about how behavioral science can optimize EHR decision support to improve prescribing and health outcomes. Adaptive trials have rarely been used in implementation science, so these findings also provide insight into how trials in this field could be more efficiently conducted. Trial registration: Clinicaltrials.gov (NCT04284553, registered: February 26, 2020

Can purchasing information be used to predict adherence to cardiovascular medications? An analysis of linked retail pharmacy and insurance claims data

Objective: The use of retail purchasing data may improve adherence prediction over approaches using healthcare insurance claims alone. Design: Retrospective. Setting and participants A cohort of patients who received prescription medication benefits through CVS Caremark, used a CVS Pharmacy ExtraCare Health Care (ECHC) loyalty card, and initiated a statin medication in 2011. Outcome We evaluated associations between retail purchasing patterns and optimal adherence to statins in the 12 subsequent months. Results: Among 11 010 statin initiators, 43% were optimally adherent at 12 months of follow-up. Greater numbers of store visits per month and dollar amount per visit were positively associated with optimal adherence, as was making a purchase on the same day as filling a prescription (p<0.0001 for all). Models to predict adherence using retail purchase variables had low discriminative ability (C-statistic: 0.563), while models with both clinical and retail purchase variables achieved a C-statistic of 0.617. Conclusions: While the use of retail purchases may improve the discriminative ability of claims-based approaches, these data alone appear inadequate for adherence prediction, even with the addition of more complex analytical approaches. Nevertheless, associations between retail purchasing behaviours and adherence could inform the development of quality improvement interventions

External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure.

BackgroundEjection fraction (EF) is an important prognostic factor in heart failure (HF), but administrative claims databases lack information on EF. We previously developed a model to predict EF class from Medicare claims. Here, we evaluated the performance of this model in an external validation sample of commercial insurance enrollees.MethodsTruven MarketScan claims linked to electronic medical records (EMR) data (IBM Explorys) containing EF measurements were used to identify a cohort of US patients with HF between 01-01-2012 and 10-31-2019. By applying the previously developed model, patients were classified into HF with reduced EF (HFrEF) or preserved EF (HFpEF). EF values recorded in EMR data were used to define gold-standard HFpEF (LVEF ≥45%) and HFrEF (LVEFResultsA total of 7,001 HF patients with an average age of 71 years were identified, 1,700 (24.3%) of whom had HFrEF. An overall accuracy of 0.81 (95% CI: 0.80-0.82) was seen in this external validation sample. For HFpEF, the model had sensitivity of 0.96 (95%CI, 0.95-0.97) and PPV of 0.81 (95% CI, 0.81-0.82); while for HFrEF, the sensitivity was 0.32 (95%CI, 0.30-0.34) and PPV was 0.73 (95%CI, 0.69-0.76). These results were consistent with what was previously published in US Medicare claims data.ConclusionsThe successful validation of the Medicare claims-based model provides evidence that this model may be used to identify patient subgroups with specific EF class in commercial claims databases as well

Additional file 1 of Disentangling drug contributions: anticholinergic burden in older adults linked to individual medications: a cross-sectional population-based study

Additional file 1: Appendix Table A. Baseline demographic and clinical characteristics of anticholinergic users: Stratification of the mixed group. Appendix Figure A. Distribution of individual anticholinergic prescription medication fills across groups. Appendix Figure B. Patient characteristics associated with different categories of high anticholinergic burden: Full set of covariates. Appendix Table B. Patient characteristics associated with different categories of patients in the mixed group. Appendix Table C. Patient characteristics associated with different categories of patients in the mixed group compared with the moderate/strong group. Appendix Table D. Patient characteristics associated with different categories of patients in the mixed group compared with light/possible group. Appendix Table E. Patient characteristics associated with different categories of high anticholinergic burden adjusted for Gagne comorbidity index rather than individual comorbidities. Appendix Table F. Patient characteristics associated with different categories of high anticholinergic burden in patients with >5 prescription fills

Hydroxychloroquine lowers Alzheimer's disease and related dementias risk and rescues molecular phenotypes related to Alzheimer's disease

10.1038/s41380-022-01912-0MOLECULAR PSYCHIATRY28

International trends in antipsychotic use:A study in 16 countries, 2005-2014

The objective of this study was to assess international trends in antipsychotic use, using a standardised methodology. A repeated cross-sectional design was applied to data extracts from the years 2005 to 2014 from 16 countries worldwide. During the study period, the overall prevalence of antipsychotic use increased in 10 of the 16 studied countries. In 2014, the overall prevalence of antipsychotic use was highest in Taiwan (78.2/1000 persons), and lowest in Colombia (3.2/1000). In children and adolescents (0-19 years), antipsychotic use ranged from 0.5/1000 (Lithuania) to 30.8/1000 (Taiwan). In adults (20-64 years), the range was 2.8/1000 (Colombia) to 78.9/1000 (publicly insured US population), and in older adults (65+ years), antipsychotic use ranged from 19.0/1000 (Colombia) to 149.0/1000 (Taiwan). Atypical antipsychotic use increased in all populations (range of atypical/typical ratio: 0.7 (Taiwan) to 6.1 (New Zealand, Australia)). Quetiapine, risperidone, and olanzapine were most frequently prescribed. Prevalence and patterns of antipsychotic use varied markedly between countries. In the majority of populations, antipsychotic utilisation and especially the use of atypical antipsychotics increased over time. The high rates of antipsychotic prescriptions in older adults and in youths in some countries merit further investigation and systematic pharmacoepidemiologic monitoring

Reproducibility of real-world evidence studies using clinical practice data to inform regulatory and coverage decisions

Studies that generate real-world evidence on the effects of medical products through analysis of digital data collected in clinical practice provide key insights for regulators, payers, and other healthcare decision-makers. Ensuring reproducibility of such findings is fundamental to effective evidence-based decision-making. We reproduce results for 150 studies published in peer-reviewed journals using the same healthcare databases as original investigators and evaluate the completeness of reporting for 250. Original and reproduction effect sizes were positively correlated (Pearson’s correlation = 0.85), a strong relationship with some room for improvement. The median and interquartile range for the relative magnitude of effect (e.g., hazard ratiooriginal/hazard ratioreproduction) is 1.0 [0.9, 1.1], range [0.3, 2.1]. While the majority of results are closely reproduced, a subset are not. The latter can be explained by incomplete reporting and updated data. Greater methodological transparency aligned with new guidance may further improve reproducibility and validity assessment, thus facilitating evidence-based decision-making. Study registration number: EUPAS19636