Recent Publications by CFE Educators

OBJECTIVE

Ankylosing spondylitis (AS) is associated with elevated cardiovascular risk, and obesity is a common, modifiable risk factor. Our aims were to assess the relationship of body mass index (BMI) with disease activity in AS patients and to assess the extent to which the effect is mediated through exercise.

METHODS

We used data from a prospective AS cohort with a median follow-up of 7 years. To determine the association of BMI (kg/m ) with disease activity as measured by the Ankylosing Spondylitis Disease Activity Score (ASDAS), we used generalized estimating equations with inverse probability weighting to account for repeated measures per subject and time-varying confounding. To estimate the direct effect of overweight/obese BMI on disease activity and the indirect effect through exercise, we performed a mediation analysis.

RESULTS

There were 183 subjects with available BMI and disease activity data (77% male, 70% White, mean ± SD age 40.8 ± 13.3 years). Higher BMI was significantly associated with higher disease activity over time; on average, for a 1 kg/m higher BMI, the ASDAS was 0.06 units higher (95% confidence interval 0.04-0.08) after adjustment for important confounders. The direct effect of an overweight/obese BMI accounted for most of the total effect on disease activity, with a smaller indirect effect mediated by exercise (7%).

CONCLUSION

Higher BMI was associated with higher disease activity in a prospective AS cohort. We found that being overweight/obese largely influenced disease activity directly rather than indirectly through exercise. Other mechanisms, such as increased inflammation, may better explain the obesity-disease activity association.

Background

Clinical experience suggests an increased hospitalization rate for alcohol-related hepatitis (AH) in the winter months; however, seasonal variations in the prevalence of hospitalizations for AH have not been described previously. We hypothesized that AH hospitalizations would be higher in the winter months due to the holiday season and increased alcohol sales.

Methods

Patients with primary or secondary discharge diagnosis of AH were included in the study (International Classification of Diseases, Clinical Modification-10th Revision codes K70.4 and K70.1) between January 2016 and December 2019. The primary outcome measure for this study was daily hospitalizations by each month of the year. Secondary outcome measures included the rate of in-hospital mortality associated with AH, for each month.

Results

The highest number of AH-related admissions was reported in July (n = 56,800; 9%), followed by August (n = 55,700; 8.8%) and May (n = 54,865; 8.7%). February had the lowest number of admissions (n = 46,550; 7.37%). The adjusted mortality was highest in December (overall mortality: 9.6%; adjusted odds ratio: 1.29; 95% confidence interval: 1.142 - 1.461; P 0.0001) and lowest in May (overall mortality rate: 7.7%). No difference was noted between length of stay and total hospitalization cost between months.

Conclusion

Our findings demonstrate that seasonal variations in hospitalizations related to AH do exist across the United States. Regional differences also exist and follow unique patterns. The increase in admissions for AH is in line with other studies suggesting that heavy drinking happens during the warm season. Hospital administrators and other stewards of healthcare resources can use seasonal patterns to guide allocation of resources.

BACKGROUND

The coproduction of care involves patients and families partnering with their clinicians and care teams, with the premise that each brings their own perspective, knowledge, and expertise, as well as their own values, goals, and preferences, to the partnership. Dashboards can display meaningful patient and clinical data to assess how a patient is doing and inform shared decision-making. Increasing communication between patients and care teams is particularly important for children with chronic conditions. Juvenile idiopathic arthritis (JIA), the most common chronic pediatric rheumatic condition, is associated with increased pain, decreased function, and decreased quality of life.

OBJECTIVE

The aim of this study is to design a dashboard prototype for use in coproducing care in patients with JIA. We evaluated the use and needs of end users, obtained a consensus on the necessary dashboard data elements, and constructed display prototypes to inform meaningful discussions for coproduction.

METHODS

A human-centered design approach involving parents, patients, clinicians, and care team members was used to develop a dashboard to support the coproduction of care in 4 ambulatory pediatric rheumatology clinics. We engaged a multidisciplinary team (n=18) of patients, parents, clinicians, nurses, and staff during an in-person kick-off meeting followed by biweekly meetings. We also leveraged advisory panels. Teams mapped workflows and patient journeys, created personas, and developed dashboard sketches. The final dashboard components were determined via Delphi consensus voting. Low-tech dashboard testing was completed during clinic visits, and visual display prototypes were iterated by using the Plan-Do-Study-Act methodology. Patients and clinicians were surveyed regarding their experiences.

RESULTS

Teams achieved consensus on what data mattered most at the point of care to support patients with JIA, families, and clinicians collaborating to make the best possible health care decisions. Notable themes included the right data in the right place at the right time, data in once for multiple purposes, patient and family self-management components, and the opportunity for education and increased transparency. A final set of 11 dashboard data elements was identified, including patient-reported outcomes, clinical data, and medications. Important design considerations featured the incorporation of real-time data, clearly labeled graphs, and vertical orientation to facilitate review and discussion. Prototype paper-testing with 36 patients and families yielded positive feedback, with 89% (8/9) to 100% (9/9) of parents (n=9) and 80% (8/10) to 90% (9/10) of clinicians (n=10) strongly agreeing or agreeing that the dashboard was useful during clinic discussions, helped to talk about what mattered most, and informed health care decision-making.

CONCLUSIONS

We developed a dashboard prototype that displays patient-reported and clinical data over time, along with medications that can be used during a clinic visit to support meaningful conversations and shared decision-making among patients with JIA, their families, and their clinicians and care teams.