BackgroundAs questions have been raised about the appropriateness of direct oral anticoagulant (DOAC) dosing among outpatients with atrial fibrillation, we examined this issue in patients being managed by primary care providers.Methods and ResultsThis was a retrospective cohort new‐user study using electronic medical records from 744 Canadian primary care clinicians. Potentially inappropriate DOAC prescribing was defined as prescribing lower or higher doses than those recommended by guidelines for patients with nonvalvular atrial fibrillation. Of the 6658 patients with nonvalvular atrial fibrillation who were prescribed a DOAC (mean age: 74.8; 55% male), 626 (9.4%) had a CHADS 2 score of 0, and 168 (2.5%) had a CHADS‐VASc score of 0. Of the DOAC prescriptions, 527 (7.7%) were deemed potentially inappropriate: 496 (7.2%) were potentially underdosed, and 31 (0.5%) were prescribed a dose that was higher than recommended. Patients were more likely to be prescribed lower‐than‐recommended doses if they were female (adjusted odds ratio [aOR]: 1.3 [95% confidence interval (CI), 1.0–1.5]), had multiple comorbidities (aOR: 1.4 [95% CI, 1.1–1.8])—particularly heart failure (aOR: 1.6 [95% CI, 1.2–2.0]) or dementia (aOR: 1.4 [95% CI, 1.1–1.8])—or if they were also taking aspirin (aOR: 1.7 [95% CI, 1.3–2.1]) or nonsteroidal anti‐inflammatory drugs (aOR: 1.2 [95% CI, 1.02–1.5]). Potentially inappropriate DOAC dosing was more common in rural practices (aOR: 2.1 [95% CI, 1.7–2.6]) or smaller practices (aOR: 1.9 [95% CI, 1.6–2.4] for practices smaller than median).ConclusionsThe vast majority of DOAC prescriptions in our cohort of primary care–managed patients appeared to be for appropriate doses, particularly since prescribing a reduced dose of DOAC may be appropriate in frail patients or those taking other medications that predispose to bleeding.

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Objective:By surveying a multiple sclerosis (MS) population, we tested the hypothesis that influenza vaccine uptake would not meet public health targets in a large multiple and that vaccine misconceptions would contribute to lower than desired uptake.Methods:In Spring 2020, we surveyed participants in the North American Research Committee on Multiple Sclerosis (NARCOMS) Registry regarding vaccinations. Participants reported whether they had received hepatitis A, hepatitis B, pneumococcal, shingles, varicella, measles/mumps/rubella, tetanus or influenza vaccines. Participants who had not received influenza vaccine last year reported why not. We summarized responses descriptively. Using multivariable logistic regression, we assessed participant characteristics associated with uptake of seasonal influenza vaccine.Results:Of 5,244 eligible respondents, 80.8% were female, with a mean (SD) age of 61.8 (10.1) years. Overall, 43.0% (2161/5032) of participants reported that their neurologist had ever asked about their immunization history. The percentage of participants who received the seasonal flu vaccine last year ranged from 59.1% among those aged 18-24 to 79.9% for persons aged ≥65 years. Among those who did not get the influenza vaccination those most common reasons were personal preference (29.6%), concerns about possible adverse effects in general (29.3%), and concerns that the vaccine would worsen their MS (23.7%).Conclusion:Vaccination uptake is lower than desired in the MS population as compared to existing recommendations, including for seasonal influenza. Misconceptions about the safety of vaccination in the context of MS and personal preference appear to play important roles in vaccination choices, highlighting the importance of education about these issues.

This study proposes a predictive model that uses structured data and unstructured narrative notes from Electronic Medical Records to accurately identify patients diagnosed with Post-Traumatic Stress Disorder (PTSD). We utilize data from primary care clinicians participating in the Manitoba Primary Care Research Network (MaPCReN) representing 154,118 patients. A reference sample of 195 patients that had their PTSD diagnosis confirmed using a manual chart review of structured data and narrative notes, and PTSD negative patients is used as the gold standard data for model training, validation and testing. We assess structured and unstructured data from eight tables in the MaPCReN namely, patient demographics, disease case, examinations, medication, billing records, health condition, risk factors, and encounter notes. Feature engineering is applied to convert data into proper representation for predictive modeling. We explore serial and parallel mixed data models that are trained on both structured and unstructured data to identify PTSD. Model performances were calculated based on a highly skewed hold-out test dataset. The serial model that uses both structured and text data as input, yielded the highest values in sensitivity (0.77), F-measure (0.76), and AUC (0.88) and the parallel model that uses both structured and text data as the input obtained the highest positive predicted value (PPV) (0.75). Diseases such as PTSD are difficult to diagnose. Information recorded in the chart note over multiple visits of the patients with the primary care physicians has higher predictive power than structured data and combining these two data types can increase the predictive capabilities of machine learning models in diagnosing PTSD. While the deep-learning model outperformed the traditional ensemble model in processing text data, the ensemble classifier obtained better results in ingesting a combination of features obtained from both data types in the serial mixed model. The study demonstrated that unstructured encounter notes enhance a model’s ability to identify patients diagnosed with PTSD. These findings can enhance quality improvement, research, and disease surveillance related to PTSD in primary care populations.