Cardiac Subcommittee Minutes 7.20.20

Cardiac Anesthesia Subcommittee Minutes

July 20, 2020

1:00-2:00pm EST

Zoom

Danny Muehlschlegel, Brigham and Women’s

Eric Davies, Henry Ford Health System

Doug Shook, Brigham and Women’s

Michael Grant, Johns Hopkins

Dirk Varelmann, Brigham and Women’s

Jake Abernathy, Johns Hopkins

Cantwell Clark, Dartmouth

Amanda Rhee, Mount Sinai

Ying Low, Dartmouth

Peter Neuburger, NYU Langone

Brandi Bottiger, Duke

Brent Luria, NYU Langone

Eleanor Vega, Duke

Valerie Sera, OHSU

Eric JohnBull, Duke

Govind Rangrass, University of Chicago

Nazish Hasmi, Duke

Allison Janda, Michigan Medicine

Negmeldeed Mamoun, Duke

Anna Dubovoy, Michigan Medicine

Mihai Podgoreanu, Duke

Mike Mathis, Michigan Medicine

Gaurav Katta, Henry Ford Health System

Ian Gannon, Michigan Medicine

Jayakar (Jay) Guruswamy, Henry Ford Health

System

Thomas (TJ) Krall, University of California- San

Francisco

Srdjan Jelacic, University of Washington

Ashanpreet (Ashan) Grewal, University of Maryland

Josh Billings, Vanderbilt

Nirav Shah, MPOG Associate Director

Andrea Reidy, Washington University

Kate Buehler, MPOG Clinical Program Manager

Yunwei Chen, Washington University

Brooke Szymanski, MPOG QI Coordinator

Josh Douin, University of Colorado

Meridith Bailey,

Rob Schonberger, Yale

Victoria Lacca, MPOG Administrative Program

Manager

1. Introductions & Background of MPOG/ASPIRE

a. Roles within ASPIRE and Roll Call

i. Nirav Shah, MD – MPOG Director of Quality

ii. Michael Mathis, MD – MPOG Director of Research

iii. Kate Buehler, MSN – Clinical Program Manager

iv. Allison Janda, MD – MPOG Cardiac Anesthesia Subcommittee Lead

v. Roll call for attendance- see above

b. MPOG History

i. Formed in 2008

ii. >50 hospitals (Academic and private practice)

iii. 13 million cases

iv. 27 billion physiologic observations to date

v. Dual mission of research and quality improvement

c. Data Collected through MPOG

i. Demographic Information

ii. Preoperative H&P

iii. Medications / Infusions / Fluids / Outputs

iv. Physiologic values/ Laboratory values

v. Intraop events

vi. IV Access

vii. Staff in / out

viii. Professional fee CPT codes

ix. Discharge ICD 9/10 codes

x. Outcome record / Outcome registry

d. MPOG provides a reporting dashboard to assist sites with quality improvement as well

as provider feedback emails that are sent monthly to participating sites

i. Some of the ASPIRE measure are pertinent to cardiac cases though not specific

to cardiac anesthesia

ii. Individual providers can review their email to identify practice opportunities to

improve care

2. Current Status of Cardiac data/measures within MPOG

a. Cardiac cases: 4% of total cases in MPOG registry

i. Total: 194,819 cardiac cases

ii. 146,042 from 1/2014-12/2019

iii. 33,307 from 2018-2019

b. 1 cardiac-specific measure: FLUID-01-C: Minimizing Colloid Use (Cardiac)

c. MPOG offers the option for STS-integration for sites interested:

i. STS-Adult Cardiac Surgical Database (STS-ACSD, aka “STS Cardiac”)  3 sites

ii. STS-General Thoracic Surgical Database (STS-GTSD, “STS Thoracic”)  8 sites

iii. STS-Intemacs (LVAD database)  tentative

iv. More STS-MPOG integrated sites are in the pipeline

d. MPOG Cardiac-related resources: AKI Toolkit- Cardiac section

i. Please send any feedback you may have to: Allison Janda

(ajanda@med.umich.edu) and Kate Buehler (kjbucrek@med.umich.edu

)

3. 2020-2021 Plans

a. Call for Measure Survey Results

i. 16 providers completed the survey – Thank you!

ii. Highest rated measures (no overwhelming consensus)

1. Post-bypass hypothermia avoidance (62% listed in the top 3)

2. Glucose management (56% listed in the top 3)

3. Postoperative AKI avoidance (44% listed in the top 3)

4. Hypotension avoidance (44% listed in the top 3)

5. Antibiotic timing (38% listed in the top 3)

iii. FYI: MPOG data capture - measure limitations

1. 4 Hours before Anesthesia Start  6 hours after Anesthesia End (for

hemodynamic and medication administration granular data)

2. Laboratory values are included within 365 days of the procedure

3. What can’t we do?

a. Limited outcome data at this point

b. Unable to provide feedback for STS data as only a few sites have

merged STS data at this point

4. TJ Krall, UCSF - Is MPOG able to capture bypass start and end times?

a. Allison Janda, MPOG Coordinating Center- Yes, it’s not 100%

accurate, occasionally arterial line placement may be mis-

mapped to bypass cannulation if “arterial cannula inserted” was

selected by the in-room provider, for example, so the time may

be incorrect for a few cases but overall, data capture for those

times is pretty accurate

b. Nirav Shah, MPOG Director of Quality- MPOG uses phenotypes

to determine these times based on site mapping to standardized

MPOG concepts. A number of MPOG concepts make up the

phenotypes to determine specific times or case types. These

phenotypes are created and validated using MPOG data.

c. Mike Mathis, MPOG Director of Research – The phenotypes are

constructed by using a number of concepts (data fields) to serve

as ques or validation that something such as initiating bypass is

occurring.

b. Measure Goals

i. Discuss viable measure options with current state

1. Limitations exist within MPOG

ii. Build 1 cardiac-specific measure in 2020

iii. Build 2-3 cardiac-specific measures in 2021

iv. Table those topics requiring more discussion for later

v. Future potential for STS/INTERMACS-MPOG merged outcome reports

c. Post-bypass hypothermia avoidance

i. Current TEMP-03 Measure:

1. Percentage of patients, with procedures >60 minutes under

GA/neuraxial, with at least one body temperature ≥ 36

2. Excludes cardiac surgeries

ii. Considerations in new measure development:

1. Threshold?

2. Timing (post-CPB)?

3. Exclusions for specific cardiac cases (e.g. spinal protection w/ thoracic

aortic)?

iii. Govind Rangrass, University of Chicago- Another consideration in the

development would be source of temperature (Swan vs. esophageal vs. skin) Do

esophageal temperature from the TEE probe get recorded in some sites buy not

others? Blood temperature may be the optimal core temperature…we could also

discuss the merits of an absolute cutoff for the measure of ‘hypothermia’ vs.

temporal trends in temp changes

iv. Rob Schonberger, Yale- Need to make sure we are not encouraging

hyperthermia. Would ‘normothermia’ be a better measure- or perhaps two

separate measures?

1. Mike Mathis, MPOG Coordinating Center - Agrees with Rob. There will

be cases where hypothermia is actually helpful (e.g. spinal cord

protection for thoracic aortic procedures)

v. Jake Abernathy, Johns Hopkins- CPOM created an avoidance of hyperthermia

measure that will be incorporated soon- could look to that measure for guidance

vi. Mike Mathis, MPOG Coordinating Center - Some of these issues can be

addressed via carefully designed inclusions/exclusions; others addressed via

careful risk adjustment (in the case of outcome measures)

vii. Brandi Bottiger, Duke- If including all cardiac surgery, might be interesting for

the user to assess their performance by case type (e.g. aortic surgery v. CABG v.

valve v. LVAD, etc.) instead of excluding

1. Mike Mathis, MPOG Coordinating Center - Agrees with Brandi. To do

this, need surgical CPT codes which MPOG does not currently capture for

all sites

viii. Mihai Podgoreanu, Duke- Is DHCA captured as a procedural phenotype in

MPOG?

1. Mike Mathis, MPOG Coordinating Center- Not yet- although could

consider doing this, just depends on where we want to focus our effort.

To see what phenotypes we currently have developed, can go to MPOG

website (mpog.org) – Tools – Phenotype Browser:

https://collations.mpogresearch.org/Collations?type=general&query=na

ix. Josh Billings, Vanderbilt- Are there data that mild hypothermia post bypass or

surgical insult protects organs? Do we know or assume that normothermia is

indeed best practice before we make it a goal?

1. Allison Janda, MPOG Coordinating Center- Great point! If we move

forward with a measure, needs to evidence to support it and want to

make sure whatever measures we endorse that it is practice we want to

support.

d. Glucose Management

i. Current GLU-01 Measure:

1. Percentage of cases with perioperative glucose > 200 mg/dL with

administration of insulin or glucose recheck within 90 minutes of

original glucose measurement

ii. Considerations:

1. Lower glucose threshold?

2. Set a shorter threshold for rechecks?

3. Initiation of an insulin infusion or treatment requirement?

iii. Gaurav Katta, Henry Ford Health System - Is the current GLU 01 measure recheck

or treatment? Is the requirement to simply recheck within 90 minutes not

necessarily treat?

1. Nirav Shah, ASPIRE Director- That is correct. For cardiac, could modify

this measure to consider treatment alone if determine appropriate by

this committee.

e. Antibiotic Timing

i. Current ABX-01 Measure:

1. % of cesarean deliveries with documentation of antibiotic

administration initiated within one hour before surgical incision

2. Currently only applies to cesarean deliveries

ii. Considerations:

1. Timing?

2. Type of antibiotics?

f. Hypotension and AKI Avoidance

i. Hypotension avoidance considerations:

1. BP threshold?

2. Varying thresholds for on pump or off?

3. Couple with evidence of malperfusion (e.g. rising lactate?)

4. Excluding specific types of cardiac cases is challenging without

consistent contribution of surgical CPTs

ii. AKI avoidance considerations:

1. Current AKI-01 still includes cardiac cases

2. Change the threshold for flagged cases?

3. STS and KDIGO definitions are inconsistent

g. ASPIRE Opioid Dashboard: Is this of interest to the cardiac subcommittee?

h. Recommendations

i. Build 1 cardiac-specific measure in 2020

1. Glucose management?

2. Post-bypass hypothermia avoidance?

3. Antibiotic timing?

4. Where to start? Glucose management or temperature management?

a. Ashanpreet (Ashan) Grewal, University of Maryland - glucose

management may be the lowest hanging fruit

i. Gaurav Katta, - I agree. I think glucose management

would be better since there is likely more variability

between providers, it already has an MPOG framework,

and it is mostly affected by the anesthesia provider.

Post-bypass hypothermia is affected by multiple

providers (e.g. when perfusion and cardiac surgery

decide to separate from bypass). Not that we shouldn’t

tackle that and it’s an excellent measure, but glucose

would in my opinion be easier to tackle due to

anesthesia involvement/ownership.

b. Danny Muehlschlegel, Brigham and Women’s - Vote for temp

management

c. Jake Abernathy, Johns Hopkins- Which measure has more

variation with the general ASPIRE population? Perhaps pulling

some initial data to assess where the variation lies may help us

determine which measure to start with. CPOM has built

measures in the past that are topped out as soon as they’re

rolled out due to no variation in performance. Would be nice to

avoid that if possible.

i. Rob Schonberger, Yale- Fantastic point! The areas of

large variability overlapping with strong evidence should

be the focus.

ii. Mike Mathis, MPOG Coordinating Center – Would agree

with this approach

iii. TJ Krall, UCSF - Would be useful information

d. Yunwei Chen, Washington University- Glucose management

may be easier as there’s already a framework in MPOG

e. TJ Krall, UCSF - No particularly strong opinion but also feel that a

purely on-bypass hypotension avoidance would be reasonable to

tackle. Pre- and post- bypass hypotension avoidance would be

more challenging, but there seems to be a lot of variability in the

bypass MAP goal

f. Ashanpreet (Ashan) Grewal, University of Maryland – If CABG

data is easier to isolate, then we can start by focusing on those

specific cases

i. Andrea Reidy, Washington University - Not doing many

isolated CABGs at our center

ii. Build 2-3 cardiac-specific measures in 2021

1. Glucose management?

2. Post-bypass hypothermia avoidance?

3. Antibiotic timing?

iii. More discussion

1. Hypotension avoidance

2. AKI avoidance

a. Josh Douin, University of Colorado- Since MPOG data is the most

granular to only 6 hours postop, how are we capturing patients

with AKI on postop day 2 or 3? Do we have lab data which could

be used to satisfy KDIGO criteria?

i. Mike Mathis, MPOG Coordinating Center- Labs are

captured 365 days before and after surgery, so should

be able to get these POD #2 or 3 for AKI. We don’t have

ICU flowsheet data post 6 hours however, so hard to

capture urine output or RRT. Although again, STS-

cardiac integration could capture these components of

an AKI outcome.

iv. Opportunities for STS-merged outcome reports  requires institutions to

integrate with STS

1. Please reach out to Mike Mathis (mathism@med.umich.edu

) and

Allison Janda (ajanda@med.umich.edu) if you’re interested merging

MPOG and STS at your institution; we’re happy to help.

4. Subcommittee membership and meeting schedule

a. Open to all anesthesiologists or those interested in improving cardiothoracic measures

i. Do not have to practice at an active MPOG institution

b. Basecamp forum: best format for communication between members?

i. Yes, the group agreed that Basecamp would work great.

c. How often should this group meet?

i. Need help with measure build questions and the approval process

ii. Decided to meet quarterly with intermittent electronic communications via

Basecamp in between

d. Proposed 2020 – 2021 Meeting Schedule

i. Summer 2020 Meeting: July 20, 2020

ii. Fall 2020 Meeting: October/November, 2020

iii. Winter 2021 Meeting: January, 2021

iv. Spring 2021 Meeting: April, 2021

e. Doug Shook, BWH- Agree this meeting schedule seems appropriate and Basecamp is

great for continued conversation between meetings. Is the goal to provide this measure

feedback directly to the providers or will they remain at the department level?

i. Allison Janda, MPOG Coordinating Center- Initially will roll-out to department

first to confirm results are valid and then ultimately offer for all providers to

access via provider feedback emails and dashboards.

Meeting adjourned at 1400