Clinical Research Oriented Workshop (CROW) Meeting: June 3, 2021

 

Present:   Levi Bonnell, Justine Dee, Nancy Gell, Ben Littenberg, Charlie MacLean, Jen Oshita, Liliane Savard, Adam Sprouse-Blum, Connie van Eeghen (9)

1.                   Warm Up: (the recorder was late…)

2.                   Adam’s Biosketch for his K award:

a.       The goal is to present oneself as the researcher who has already done this kind of work and is sure to deliver

b.       Biosketches are revised with each submission

c.       Department of Medicine has a great resource in Gretchen Argraves

d.       Personal Statement

                                                   i.      First person singular is correct

                                                 ii.      Sales message: the K award is an investment to create a long term researcher with ongoing returns to the field of knowledge and the benefits to patients/society. Communicate the “I” in FINER – what is the life-long passion about?  Have made many efforts in this direction and need the award to take the next step to complete this preparation process.  Make it a story arch that ends in the elevator speech.

                                               iii.      Rephrase weaknesses to gaps and be consistent; indicate what is needed to become an R01 ready scientist

                                               iv.      Center on the dissertation as part of the story; it’s not a side issue.  How does what the dissertation study is focusing on fit into the long term trajectory of the plan, with the skills that it brings. “The PhD program taught me these skills which resulted in these accomplishments….  And I will build on them by…”

e.       Contributions to Science section:

                                                   i.      Should tell the story that supports the I in FINER

                                                 ii.      Good to present as a progression that connects across the story arch

                                               iii.      Some of the contributions can be in terms of collaborations with other peers to help their work and to learn about science

                                               iv.      Group by “treatment” and “etiology” – rebinning works

                                                 v.      Remember to include in the proposal, the plan to move from novice to mastery, leveraging the LCOM resources

f.        Sharing the document with line numbers is great for CROW; remember to remove for submission

3.                   Next week:  Jen may take the slot or will prompt for other volunteers

Clinical Research Oriented Workshop (CROW) Meeting: May 27, 2021

 

Present:   Levi Bonnell, Justine Dee, Nancy Gell, Emily Houston, Ben Littenberg, Jen Oshita, Liliane Savard, Adam Sprouse-Blum, Connie van Eeghen (9)

1.                   Warm Up: Masks are starting to come off!

2.                   Justine Dee’s RCT Introduction and Methods: Review the latest draft of intro and methods, as well as initial results. I am looking at ways to clearly communicate the findings. I have a lot of outcome measures. I am hoping for feedback on the tables formatting and style and any other suggestions you might have.

a.       How best to share univariate and multivariable regressions, both unadjusted and adjusted models: Levi provided an example; Ben provided an alternate table of presenting Justine’s results to include coefficients, confidence intervals, and p values.  Discussion: why include the unadjusted results?

                                                   i.      Unadjusted coef: 1.48 with a CE from -0.1 to 3.9,  p of 0.22

1.       For every one unit increase in DSEN,  Physical Function increased of about 1.5

                                                 ii.      Adjusted coef: 2.16, CI -.03 to 4.6, p of 0.08 (so far)

1.       The DSEN group came out 2.16 higher than the comparison group

a.       Years of chronic Pain CI -0.09: older people have worse physical function while controlling for sex, age, and which group they were assigned to (the association between the covariate and the outcome)

                                               iii.      This model does not explain relationships, and it shows effect on DSENS group

                                               iv.      For the secondary outcome, Pain Interference, use the same rationale for use of covariates: the 10% rule.  Report on all covariates used; reviewers often insist on seeing everything.

1.       Include telehealth as a logical potential confounder

2.       Consider a sensitivity analysis for telehealth, which was 10% of the group

3.       Be careful about too many covariates: they may be interdependent and if there’s too much to analyze, it becomes incomprehensible. Go with the five or six strongest, or at least test them out.

                                                 v.      Proposed language: “All models adjusted for potential confounders that influenced the main outcome… by …”

                                               vi.      Randomized, groups matched, we tested for confounders, no effect, our unadjusted effects are about right (or adjusted effects)

                                              vii.      Adjust for covid burden: all in VT/Chittenden County, during pandemic, so no adjustment needed, but may need to explain this

b.       Discussion section: there was a reduction in a secondary outcome; everyone got better in both function and pain (both groups).  Why did everyone get better?  Placebo, common elements in both treatment arms, engagement with therapist, a self-healing condition (not likely), regression to mean – they enrolled on a bad day/week/month of a fluctuating condition.

                                                   i.      Protection against regression to the mean: enroll, wait six months (depending on how long it takes for poor status to regress), retake the baseline, and then run the study.  

c.       Reporting on race/nationality: create a category of Asian or Other, depending on the population included, rather than listing out nationalities (for which the data are incomplete) or including these peoples under “Non-Hispanic/white). Be aware of how the reporting of data can be used to counter discrimination, rather than reinforce it.

d.       Recruitment was higher than planned; is this OK to recruit 107 rather than 100?

                                                   i.      Powered for 84 analyzable records.  Planned for 100 to account for drop offs. 

                                                 ii.      Limits are determined by IRB protocol – get an amendment if enrolling more than 100

                                               iii.      Higher samples provide power for smaller effect size – but does that effect size matter?  If not, then it’s still an intervention that doesn’t matter.

3.                   Next week:  TBA

 

 

New Publication for Maria Ramos-Nino

Anybody remember the Vermont Diabetes Information System? Back in the early 2000s, it was a large regional study of a quality improvement system that generated a big database of adults with diabetes. One of our old collaborators, Maria Ramos-Nino, PhD, now Associate Professor Microbiology, Immunology, and Pharmacology at St. George’s University in Grenada, West Indies remembers. She recently dug into the data to investigate the relationship between obesity and lung disease and the work was published this week. Congratulations, Maria!

Ramos-Nino, M.E., MacLean, C.D. & Littenberg, B. Association between prevalence of obstructive lung disease and obesity: results from The Vermont Diabetes Information System. Asthma Res and Pract 7, 6 (2021). https://doi.org/10.1186/s40733-021-00073-1

The association of obesity with the development of obstructive lung disease, namely asthma and/or chronic obstructive pulmonary disease, has been found to be significant in general population studies, and weight loss in the obese has proven beneficial in disease control. Obese patients seem to present with a specific obstructive lung disease phenotype including a reduced response to corticosteroids. Obesity is increasingly recognized as an important factor to document in obstructive lung disease patients and a critical comorbidity to report in diabetic patients, as it may influence disease management. This report presents data that contributes to establishing the relationship between obstructive lung disease in a diabetic cohort, a population highly susceptible to obesity.

A total of 1003 subjects in community practice settings were interviewed at home at the time of enrolment into the Vermont Diabetes Information System, a clinical decision support program. Patients self-reported their personal and clinical characteristics, including any history of obstructive lung disease. Laboratory data were obtained directly from the clinical laboratory, and current medications were obtained by direct observation of medication containers. We performed a cross-sectional analysis of the interviewed subjects to assess a possible association between obstructive lung disease history and obesity.

In a multivariate logistic regression model, a history of obstructive lung disease was significantly associated with obesity (body mass index ≥30) even after correcting for potential confounders including gender, low income (<$30,000/year), number of comorbidities, number of prescription medications, cigarette smoking, and alcohol problems (adjusted odds ratio (OR) = 1.58, P = 0.03, 95% confidence interval (CI) = 1.05, 2.37). This association was particularly strong and significant among female patients (OR = 2.18, P = < 0.01, CI = 1.27, 3.72) but not in male patients (OR = 0.97, P = 0.93, CI = 0.51, 1.83).

These data suggest an association between obesity and obstructive lung disease prevalence in patients with diabetes, with women exhibiting a stronger association. Future studies are needed to identify the mechanism by which women disproportionately develop obstructive lung disease in this population.