Background Cell and gene therapy products belong to a diverse class of biopharmaceuticals known as advanced therapy medicinal products. Cell and gene therapy products are used for the treatment and prevention of diseases that until recently were only managed chronically.

Medication necessity and concern beliefs were associated with a definable subset of patients who may be at higher risk for non-persistence.

Liver disease is often associated with dysfunctional potassium homeostasis but is not a well-established risk factor for hyperkalemia. This retrospective cohort study examined the potential relationship between liver disease and recurrent hyperkalemia. Patients with ≥1 serum potassium measurement between January 2004 and December 2018 who experienced hyperkalemia (serum potassium >5.0 mmol/L) were identified from the United States Veterans Affairs database. A competing risk regression model was used to analyze the relationship between patient characteristics and recurrent hyperkalemia. Of 1,493,539 patients with incident hyperkalemia, 71,790 (4.8%) had liver disease (one inpatient or two outpatient records) within 1 year before the index hyperkalemia event. Recurrent hyperkalemia within 1 year after the index event occurred in 234,807 patients (15.7%) overall, 19,518 (27.2%) with liver disease, and 215,289 (15.1%) without liver disease. The risk of recurrent hyperkalemia was significantly increased in patients with liver disease versus those without (subhazard ratio, 1.34; 95% confidence interval, 1.32–1.37; p < 0.0001). Aside from vasodilator therapy, the risk of recurrent hyperkalemia was not increased with concomitant medication. In this cohort study, liver disease was an independent risk factor strongly associated with recurrent hyperkalemia within 1 year, independent of concomitant renin–angiotensin–aldosterone system inhibitor or potassium-sparing diuretic use.

OBJECTIVES: The goal of this research was to design a solution to detect non-reported incidents of injection. METHODS: We developed methods to process electronic medical records and automatically extract clinical notes describing incidents of injection by using the SVM based technique. First, we manually labeled a training set of clinical notes into two categories based on whether they included an incident report of injection or not, and then the machine learning models were created. This machine learning method arranges data in a vector space, using single words as the axes. For the training process, a few variations were tested: normalized versus non-normalized data. The extracted notes are treated as incident candidates which are shown to the safety management department for further analysis. RESULTS: Using the developed method based on the SVM, we implemented an incident candidate reporting system. To evaluate the system, we asked a staff of the safety management department to judge whether extracted incident candidates were incidents or not. The system used inpatients' clinical notes written from in Kyoto University Hospital. As a result, in the case of non-normalized data, 41 out of 364 incident candidates were judged clinical notes describing incidents. Furthermore, 21 of them were nonreported incidents. In the case of normalized data, 23 out of 91 incident candidates were judged clinical notes describing incidents. Moreover, 13 of them were nonreported incidents. CONCLUSIONS: In this research, we aimed to establish a method to extract incident candidates from clinical notes in order to detect nonreported incidents of injection. In addition, we created a reporting system that presents incident candidates extracted by using the developed method. The system successfully detected non-reported incidents to the safety management department, thus our goal was achieved.

Relative to beneficiaries with unintentional OOD, more individuals with intentional OOD had no opioid prescription in the study period (39.9% vs. 46.8%, p=0.0004). In our adjusted model, the odds of intentional vs. unintentional OOD was lower among beneficiaries age$65 versus age,65 (OR=0.22; 95%CI=0.16-0.30), Black versus White (OR=0.59; 95%CI=0.42-0.82), and with opioid therapy .90 days versus ,90 days (OR=0.65; 95%CI=0.52-0.80). The odds of mental illness diagnosis was almost 5 times higher among intentional vs. unintentional OOD groups (OR=4.86; 95%CI=3.912-6.037). Conclusions: One in ten OOD events among Medicare beneficiaries was intentional. Shorter duration of opioid therapy and mental illness may be associated with increased risk of intentional OOD. Findings suggest interventions aimed at preventing OOD should consider intentionality and include mental health support particularly among beneficiaries with psychiatric disorders.

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protecting children against both serotypes was introduced, after which the model estimated shifts in the new postvaccine serotype distribution equilibrium. Default parameters were identified, and scenario analyses were tested on vaccine coverage rate, levels of adult-to-child transmission rates, and relative population sizes. Results: The distributions between the two serotypes prior to vaccination were 25%:75% in children and 50%:50% in adults, and the proportions infected were 43% and 33%, respectively. For the default parameters, 80% vaccine coverage reduced the proportion of children infected by 92% and shifted the child serotype distribution at equilibrium toward the adult distribution to 47%:53%. Similar results were observed for all parameter scenarios, with the serotype distribution among infected children shifting closer to the adult distribution as vaccine coverage increased. Conclusions: The model demonstrates that serotype distribution in children after vaccination may be influenced by the adult serotype distribution, with higher vaccine coverage shifting the child distribution closer to the adult distribution. As adult-to-child infection becomes more common than childto-child infection, the adult serotype distribution becomes more prominent in the child population. These results support the hypothesis that rotavirus exposure from adult populations contributes to observed shifts in rotavirus serotype distributions in children after vaccine introduction.

The study aims to assess office-based visit trends for lupus patients and evaluate their medication burden, chronic conditions, and comorbidities. This cross-sectional study used data from the National Ambulatory Medical Care Survey (NAMCS), a survey sample weighted to represent national estimates of outpatient visits. Adult patients diagnosed with lupus were included. Medications and comorbidities that were frequently recorded were identified and categorized. Descriptive statistics and bivariate analyses were used to characterize visits by sex, age, race/ethnicity, insurance type, region, and reason for visit. Comorbidities were identified using diagnosis codes documented at each encounter. There were 27,029,228 visits for lupus patients from 2006 to 2016, and 87% them were on or were prescribed medications. Most visits were for female (88%), white (79%), non-Hispanic (88%) patients with private insurance (53%). The majority of patients were seen for a chronic routine problem (75%), and 29% had lupus as the primary diagnosis. Frequent medications prescribed were hydroxychloroquine (30%), prednisone (23%), multivitamins (14%), and furosemide (9%). Common comorbidities observed included arthritis (88%), hypertension (25%), and depression (13%). Prescription patterns are reflective of comorbidities associated with lupus. By assessing medications most frequently prescribed and comorbid conditions among lupus patients, we showcase the complexity of disease management and the need for strategies to improve care.