UVM Clinical and Translational Science (CTS): August 2013

Saturday, August 31, 2013

Radiology Grand Rounds-August 30, 2013

On Friday, August 30th, 2013 guest speaker Alex Norbash, MD, MHCM, FACR from Boston Medical Center presented: Leadership: Historical Examples, Leadership Science and Lessons Learned. Dr. Norbash has served as a department chair for several years, and he also serves on the board of the Radiology Leadership Institute. His talk was very well delivered, and at times I had to remind myself that I was listening to a neuro-interventional radiologist. He came across as more of a motivational speaker than a radiologist! His talk focused on various types of leaders and leadership styles. He presented several examples of presumed "great" leaders, as well as some not so great leaders. Jack Welch, Nelson Mandela, George Patton were all the right type of leader at the right time in history. Each of them might not be a great leader in all situations. A story Dr. Norbash gave that I felt was very convincing describes how one of his mentors was able to elevate the mood, and work ethic of entire radiology staff simply by his presence. Athough fluoroscopy can be a less desirable aspect of radiology, this particular radiologist had the ability to change the mindset of the technologists and the radiologists that he worked alongside. He was the type of leader that was able to encourage everyone to give their very best effort, without having to say a negative word. To me, that is the best kind of leader! There are many examples of leaders who carry a "big hammer"; who like the "shoot one, scare many" approach. Dr. Norbash strikes me as a leader, who is able to motivate people to do great things, without a hammer! I enjoyed attending this non-technical presentation.

Monday, August 26, 2013

Clinical Research Oriented Workshop (CROW) Meeting: Aug 28, 2013

Present: Marianne Burke, Kat Cheung, Abby Crocker, Ben Littenberg, Charlie MacLean, Connie van Eeghen

Guest: Thomas Joseph visiting from India

Start Up: Next semester’s schedule will start August 29 on Thursday’s from 11:45 to 12:15. Noted also were the finer points of grant writing, which includes planning and delivering letters of support – and which also includes the fine art of diplomacy.

1. Discussion: Abby provided an update on a group research project around the Natural History of Opioid Use, based on the all payor claims data base for VT. The question is: what predictors can we find that result in long term opioid use. The original time frame has been adjusted due to access barriers and usability of the source data.

a. How to maintain momentum: keep this project going and define a more focused topic that could move forward more quickly using, for example, another national data base.

b. Barriers related to the current project: hardware, software, and political issues

i. Software: it takes time to use the tools

ii. Policies: a new IRB-like entity to sort out; similar process

iii. Steve is able to do extract (1% of total, but without medical claims – it just has pharmacy claims); resides on Charlie’s desktop. Have not yet tackled linking the data sets.

iv. Hardware: keep running into size limits. NATIZA hardware does have capacity but cannot run STATA (SPSS only, which no one uses any more). Limited to IBM based software. Picking the right DB server is really important; Ben will follow up on this.

v. Will take a while, but is moving forward.

c. Alternate plans: YRBSS (Youth Risk Behavior Survey System) or BRFSS (Behavior Risk Factors Survey System - adults) to use for a group study.

i. BRFSS includes a county field (FIPS code – 5 digit)

1. Ben has 3 years downloaded; many more years available

2. Rich data set; many variables, included SES, employment, income, demographics…

3. No EtOH or drug taking behavior questions

ii. YRBSS: consider using to develop a predictive model for answering “yes” to opioid use.

1. Consider an explanatory model: what risk factors are related to opioid use

2. Seek clusters: smoking and other behaviors of concern

3. Key characteristics: age, sex, region…

4. Changes over time (assuming stability in the survey question)

5. Some health questions: asthma (for some states), height and weight

iii. Cross over study from YRBSS to BRFSS for opioid patterns?

iv. State/territory data are only available upon request; county data in North Carolina and Florida

d. NAMCS: National Ambulatory Medical Care Survey – a sample of O/P providers, including private practices, EDs, walk-in centers… asking about the clinic, the characteristics of the visit, the drugs on the med list. No county, but do have MSA and rural/urban designation.

i. The data support time series study

ii. Abby has already started learning about this source (kid data is in age categories; low numbers)

iii. Consider determining prevalence of “starts” vs. “continued use” by age group. What are the patient characteristics (diagnosis)? May look at a time series later.

e. DAWN: Emergency room visits involving drugs – a sample from selected cities

f. The group discussed the research question that will support the longer term study on the natural history of opioid use.

i. What can we say about opioid users vs. non-opioid users?

ii. NAMCS: is available up to 2011; there has been a rising trend of opioid prescriptions, may now be declining – identify the temporal trend in opioid prescribing

iii. Where does opioid prescribing start? What setting and how does it change?

g. Next steps

i. Look at NAMCS data – what variables do we have?

ii. Literature review – now novel?

iii. Develop a FINER research question

h. Addendum: After the close of the meeting, Kat circulated a link to a recent NEJM “Perspectives” article entitled: “Abusive Prescribing of Controlled Substances — A Pharmacy View” by Betses and Brennan (Aug 21, 2013), describing a CVS-based program to identify prescribers with abusive prescribing habits, resulting in a policy of refusing to fill the controlled substance prescriptions of selected providers. Very interesting - to view the article, go to: http://www.nejm.org/doi/full/10.1056/NEJMp1308222. Thank you Kat!

2. Next Workshop Meeting(s): Starting next week: Thursday, 11:45 a.m. – 1:15 p.m., at Given Courtyard South Level 4.

a. Thursday, August 29, from 11:45 – 1:15?: Rodger: Review of Lexicon Checklist

b. September 5: Abby – Natural History of Opioids projects

c. September 12:

d. Future agenda to consider:

i. Peter Callas or other faculty on multi-level modeling

ii. Charlie MacLean: demonstration of Tableau; or Rodger’s examples of Prezi

iii. Journal article: Gomes, 2013, Opioid Dose and MVA in Canada (Charlie)

iv. Ben: Tukey chapter reading assignments, or other book of general interest

v. Summer plan: each week, one person will send out an article or prezi ahead for review or discussion by all. Alternatively, if a participant is working on a key document for their professional development, this is also welcome (e.g. K awards, F awards, etc.)

Recorder: Connie van Eeghen

Monday, August 19, 2013

Clinical Research Oriented Workshop (CROW) Meeting: Aug 14, 2013

Present: Kat Cheung, Kairn Kelley (by phone), Amanda Kennedy, Rodger Kessler, Ben Littenberg, Charlie MacLean, Connie van Eeghen

Guest: Thomas Joseph visiting from India

1. Start Up: Next semester’s schedule will NOT be Thursday’s at 11:30 but from 12:00 – 1:15, pending Abbie’s approval, starting August 29. Sylvie to send out meeting invitations. Introductions all around. Thomas is a medical graduate from India, now working as a house officer in India, and visiting to observe how research works in the U.S. and at UVM.

2. Discussion: Kat: Proposal to identify whether measures of frailty can help prognosticate older adults with chronic kidney disease, looking for general input about protocol

a. Background: End stage renal disease takes up over $20billion in Medicare funds; it usually affects older people. Interested in how people make decisions around dialysis, especially aged 80 and up. Many older adults will die within 6 months of starting dialysis (~20%); this increases to ~40% at 1 year. How to advise an 80 year old patient on whether to start dialysis?

i. Some studies exist (US/UK) comparing populations that did/didn’t choose dialysis. If an older person does not start dialysis, death usuallyoccurs in 3-4 weeks. No data exist that helps predict the outcome of dying within 6 months. Having the data will help inform not only whether to dialyze but what methods to use (catheters vs. other routes of access)

ii. Will measures of frailty predict these outcomes? Previous study at Stanford focused on veterans with GFR < 20 and are therefore making a decision on dialysis. Six months of follow up outcomes, including mortality, morbidity, decisions about whether to withdraw from dialysis, are pending.

iii. It could be possible to enroll more subjects in Stanford, but it would take an investment in time.

iv. There is no guideline for dialysis re: age; recommendations address only the most extreme cases of patients not able to receive the benefits of dialysis. There is an administrative process to set dialysis up, but as it is covered by Medicare it is a treatment that is often applied more easily than withheld.

b. Significance

i. Effects a large part of the population; decision rules or framework would be welcome by providers

ii. Would support shared-decision making, in an environment that allows decision-making in the on-going process of care (e.g. a patient may start dialysis on a trial basis, and then be helped to make further decisions about continuation)

c. Other resources:

i. Ursula McVeigh: research on deciding whether older patients should get their hips fixed. See Amanda for an introduction.

d. Preliminary research question: Does frailty predict mortality for end stage seniors (65+ years)

i. Is this already known? Or is this question about how to measure and what the scale is for making the decision?

1. Frailty is measured by physical or functional limits; hard to operationalize (e.g. gait speed)

2. There may be other measures of frailty: social, psycho/social, and other aspects of vulnerability who need support – this could lead to a descriptive analysis, with qualitative data, but is a different study

3. The data must be collected prospectively; they don’t exist in our data sets

ii. Does dialysis make the patient more frail?

iii. Is this a prediction model or is the study evaluating an association? “Here are the variables that matter” or “Here is a weighted formula?”

iv. What are the other variables that affect the outcome: age, sex, education, income, polypharmacy… a predictive model can look at more than GFR, frailty, functional status… and considers them all.

1. If interested in frailty, then control for these variables as possible confounders.

2. If interested in predictive advice, then look at the cancer literature/research as a model

v. Other good measures to consider

1. Functionality: SF36

2. Cognition

3. Co-morbidities

4. Frailty: does it have a crisp definition? Freid definition: exhaustion, weakness, gait speed, weight loss, and other elements – can this be improved on? Flourishing scale, grief/depression scale… ?

vi. Ultimate goal: decision-making about going onto dialysis

1. Deciding what to use as a set of measures depends on the PI’s interest:

a. Test the variable of interest, the outcome, and any confounders

b. Test the predictability of an outcome: start with a large group of variables and filter (a much larger project, with many thousands of cases)

c. Test a new index for prediction (fewer cases)

e. Analysis: Consider survival analysis, which could include the Stanford data. A Kaplan-Meyer analysis provides the opportunity to evaluate censored data: patients who dropped out of the study, patients who withdrew from dialysis, and mortality. May also be able to evaluate hospitalizations.

f. Population:

i. Consider analyzing the first study, and then deciding what the next question will be from that specific population (veterans). By end of 2013, will have 6 months of data on them all and can move quickly into publication.

ii. At FAHC:

1. Obtain list of those with renal replacement therapy

2. Obtain list of GFR < 20

3. Look for the patients on List #2 and not on List #1

iii. Consider the new research direction around “Patient Centered Outcomes Research” (PCOR), in which patients are engaged in defining the research question

iv. Consider the psychological variables of those who cope well with decision making and those who cope poorly, which is related to immediate and long term outcomes

g. Next steps: access the data…

3. Next Workshop Meeting(s): Wednesday, 11:30 a.m. – 1:00 p.m., at Given Courtyard South Level 4.

a. August 21: Abby: Opiate project update (no Amanda, Kairn?)

b. August 29: Rodger: Review of Lexicon Checklist

c. September