Introduction: Adverse drug events (ADEs) are associated with poor outcomes and increased costs but may be prevented with prediction tools. With the National Institute of Health All of Us (AoU) database, we employed machine learning (ML) to predict selective serotonin reuptake inhibitor (SSRI)-associated bleeding. Methods: The AoU program, beginning in 05/2018, continues to recruit ≥ 18 years old individuals across the United States. Participants completed surveys and consented to contribute electronic health record (EHR) for research. Using the EHR, we determined participants who were exposed to SSRIs (citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline, vortioxetine). Features (n = 88) were selected with clinicians’ input and comprised sociodemographic, lifestyle, comorbidities, and medication use information. We identified bleeding events with validated EHR algorithms and applied logistic regression, decision tree, random forest, and extreme gradient boost to predict bleeding during SSRI exposure. We assessed model performance with area under the receiver operating characteristic curve statistic (AUC) and defined clinically significant features as resulting in > 0.01 decline in AUC after removal from the model, in three of four ML models. Results: There were 10,362 participants exposed to SSRIs, with 9.6% experiencing a bleeding event during SSRI exposure. For each SSRI, performance across all four ML models was relatively consistent. AUCs from the best models ranged 0.632–0.698. Clinically significant features included health literacy for escitalopram, and bleeding history and socioeconomic status for all SSRIs. Conclusions: We demonstrated feasibility of predicting ADEs using ML. Incorporating genomic features and drug interactions with deep learning models may improve ADE prediction.

Diabetes complications remain a leading cause of death, which may be due to poor glycemic control resulting from medication nonadherence. The relationship between adherence status and HbA1c (glycemic control) has not been well-studied for clinical pharmacist interventions. This study evaluated medication adherence, patient satisfaction, and HbA1c, in a collaborative pharmacist-endocrinologist diabetes clinic over 6 months. Of 127 referred, 83 patients met the inclusion criteria. Mean medication adherence scores, considered “good” at baseline, 1.4 ± 1.2, improved by 0.05 points (p = 0.018), and there was a 26% increase in patients with good adherence. A significant improvement of 0.40 percentage points (95% CI: −0.47, −0.34) was observed in mean HbA1c across the three time points (p < 0.001). Mean total satisfaction scores were high and increased, with mean 91.3 ± 12.2 at baseline, 94.7 ± 9.6 at 3 months, and 95.7 ± 10.8 at 6 months (p = 0.009). A multimodal personalized treatment approach from a pharmacist provider significantly and positively impacted glycemic control regardless of self-reported medication adherence, and patient satisfaction remained high despite changing to a clinical pharmacist provider and increased care intensity.

The National Institutes of Health All of Us (AoU) Research Program is currently building a database of 1million+ adult subjects. With it, we describe the characteristics of those with documented vaccinations. To describe the sociodemographic, health status, and lifestyle factors associated with vaccinations. This is a retrospective study involving data from the AoU program (R2020Q4R2, N = 315,297). Five vaccine cohorts [influenza, hepatitis B (HBV), pneumococcal <65 years old, pneumococcal ≥65 years old, and human papillomavirus (HPV)] were generated based on vaccination history. The influenza cohort comprised participants with documented influenza vaccinations in electronic health records (EHRs) from September 2017 to May 2018. Other vaccine cohorts comprised participants with ≥1 lifetime record(s) of vaccination documented in the EHR by December 2018. The vaccine cohorts were compared to the overall AoU cohort. Descriptive statistics were generated using EHR- and survey-based sociodemographic, health, and lifestyle information. The SAMBA (0.9.0) R package was utilized to adjust for EHR selection and outcome misclassification biases to infer sources of disparity for pneumococcal vaccinations in older adults. Cohort counts were as follows: influenza (n = 15,346), HBV (n = 6323), pneumococcal <65 (n = 15,217), pneumococcal ≥65 (n = 15,100), and HPV (n = 2125). All vaccine cohorts had higher proportions of White and non-Hispanic/Latino participants compared to the overall AoU cohort. The largest differences were found in pneumococcal age ≥65, with 80.2% White participants compared to 52.9% in the overall study population. Multivariable analysis revealed that race/ethnic disparities in pneumococcal vaccination among older adults were explained by biological sex, income, health insurance, and education-related variables. Racial, ethnic, education, and income characteristics differ across the vaccine cohorts among AoU participants. These findings inform future utilization of large health databases in vaccine epidemiology research and emphasize the need for more targeted interventions that address differences in vaccine uptake.