Kim A Eagle MD

1
Quality of care from theory to practice

• Kim A Eagle MD
• Albion Walter Hewlett Professor of Internal
  Medicine
• Chief, Clinical Cardiology
• Co-Director, Heart Care Program
• University of Michigan
• Ann Arbor, MI
• Mauro Moscucci MD
• Assistant Professor of Medicine
• Director, Interventional Cardiology Program
• University of Michigan
• Ann Arbor, MI

2
PCI outcomes
In-hospital mortality

• Factors that effect in-hospital mortality have
  been well defined.
• Risk of mortality can now be assessed on the
  basis of comorbid conditions.
• Standards for appropriate modeling, risk
  adjustment, and evaluation for percutaneous
  coronary intervention (PCI) have not been well
  developed

3
Risk adjustment models
Predictors of in-hospital mortality

• Risk adjustment models have proven very effective
  in accounting for mortality rate
• the northern New England risk adjustment model
  for in-hospital mortality
• the Cleveland Clinic model

4
New England model
PCI and in-hospital mortality

• A prospective cohort study of in-hospital
  mortality after PCI in northern New England was
  conducted from 1994 to 1996.
• Data from 52 interventional cardiologists on 15
  331 consecutive hospital admissions for PCI were
  collected (98.5 of all patients who underwent a
  PCI during the study period).
• The data were used to develop and internally
  validate a multivariate prediction equation for
  in-hospital mortality that required only
  routinely collected data known before the PCI.

OConnor, et al. J Am Coll Cardiol 199934681-691
5
New England model
Univariate assessment
Variables associated with an increased risk of
in-hospital mortality

• older age
• congestive heart failure
• peripheral or cerebrovascular disease
• increased creatinine levels
• lowered ejection fraction
• cardiogenic shock
• acute myocardial infarction
• urgent priority
• emergent priority
• preprocedure insertion of an intra-aortic balloon
  pump
• PCI of a type C lesion

OConnor, et al. J Am Coll Cardiol 199934681-691
6
New England model
Multivariate prediction equation

• Variables included
• age
• indication for intra-aortic balloon pump (IABP)
• procedural priority for IABP
• and preprocedure use of an IABP
• congestive heart failure
• peripheral or cerebrovascular disease
• elevated creatinine level
• EF
• intervention on a type C lesion

• Variables not included
• sex
• history of MI
• use of preprocedure intravenous nitroglycerin
• LVEDP
• number of diseased coronary arteries
• intervention on a proximal left anterior
  descending coronary artery

OConnor, et al. J Am Coll Cardiol 199934681-691
7
Cleveland Clinic model
Model predictive of death after PCI

• Data from 12 985 consecutively treated patients
  were taken from quality-controlled databases at 5
  high-volume centers.
• Multivariable logistic regression models were
  used to examine individual and interaction
  relations between baseline characteristics of
  patients and death and also the composite of
  death, Q-wave infarction, or bypass surgery.
• These models were used for risk adjustment, and
  the relations between both yearly caseload and
  years of interventional experience and the 2
  adverse outcome measures were explored for all 38
  physicians who performed at least 30 procedures
  per year.

Ellis SG, et al Circulation 1997952479-2484
8
Cleveland Clinic model
Results

• Risk-adjusted measures of both death and the
  composite adverse outcome were inversely related
  to the number of procedures each operator
  performed annually, but were not related to years
  of experience.
• High-volume operators had a lower incidence of
  major complications than did lower-volume
  operators, but the difference was not consistent
  for all operators.
• Both adverse outcomes were more closely related
  to the logarithm of caseload (for death, r.37,
  p0.01 for death, Q-wave infarction, or bypass
  surgery, r.58, plt0.001) than to linear caseload.
  

Ellis SG, et al Circulation 1997952479-2484
9
Mathematical models
Expected mortality rates

• Mathematical formulas are used to calculate the
  expected mortality rate of an institution.
• The formulas make adjustments for patient
  population and compare expected and observed
  mortality rates.
• The expected mortality rate of an institution
  serving a high-risk population is not necessarily
  higher than that for an institution serving a
  low-risk population.

10
University of Michigan
Operator feedback

• Every operator receives cardiac reports that
  include observed and expected mortality rates and
  the baseline comorbidities of his or her
  patients.
• A multicenter registry is used to provide the
  same type of feedback to operators from 6 other
  hospitals in Michigan.

11
Mathematical models
Statistical confidence

• Modern mathematical science gives physicians
  outcome data that is mathematically robust in
  terms of risk assessment. 
• Because the mathematical model provides an
  accurate estimate of patient risk, risk-adjusted
  data can be used to help patients understand the
  risks of certain procedures.

12
Mathematical models
Predicting death

• The mathematical models may identify situations
  in which the expected risk of death may be so
  high as to render care futile.
• In such situations, a realistic estimate of the
  likelihood of death can be provided to the
  patient, so the patient will not have unwarranted
  expectations.

13
Mathematical models
Application to other conditions

• A model has been developed to assess the risk of
  mortality in acute renal failure patients in ICU
  requiring dialysis.
• Significant factors
• male gender
• respiratory failure requiring intubation
• hematologic dysfunction
• bilirubin lt 2.0 mg/dL
• the absence of surgery
• serum creatinine on the first dialysis treatment
  day
• an increasing number of failed organ systems
• an increased BUN from the time of admission

Paganini EP, et al. Clin Nephrol
199646(3)206-211
14
A controversy
Operator volume and outcome

• Several studies have shown that there appears to
  be a relation between operator volume and
  outcome.
• However, with new technology (particularly
  coronary stent) even low-volume operators can
  still have a good outcome.

15
ACC recommendations
Coronary interventional procedures

• Statistical data support the premise that a
  physician who performs coronary interventional
  procedures infrequently is unlikely to be as
  proficient as one who performs them often.
• The low-volume operator has fewer opportunities
  to maintain skills, and is less able to acquire
  the additional skills needed to become proficient
  in the use of new techniques and devices.
• The low-volume operator is likely to be less
  experienced at recognizing and managing
  procedural complications.
• Statistical data demonstrate that operators who
  perform lt75 procedures annually have the highest
  complication rates this trend is most pronounced
  in institutions with an annual procedural volume
  lt600.

Hirshfeld JW, et al. J Am Coll Cardiol 1998
31(3)722-743
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Volume and outcome
Quality of procedures

• Evidence suggests that quality might be
  acceptable for operators who perform fewer than
  75 procedures annually but who do them in a
  high-volume center.
• There is substantial evidence suggesting that the
  introduction of new technology such as coronary
  stent has led to a significant improvement in
  acute outcome.

17
Understanding process
Benchmarking

• Analyzing practice variations among operators and
  among institutions is very beneficial.
•  
• Benchmarking and comparisons identify differences
  among operators and institutions.
• Once areas needing improvement are identified,
  processes can be studied to determine why
  discrepancies exist and changes can be
  implemented.

18
University of Michigan
Benchmarking among cath labs

• A team visits other sites to evaluate and compare
  processes.
• Problem areas and important differences have been
  identified using this strategy.
• Team cohesion is fostered that is reflected in
  in-hospital work.
• Team members
• a physician
• a cath lab technician
• a cath lab nurse
• a nurse manager

19
University of Michigan
Implementing changes

• The common goals of the team make implementing
  changes easier.
• Knowing that a particular process is working
  elsewhere makes workers less resistant to change.

20
University of Michigan
Justifying new technology

• Cost procedure analysis
• higher use of coronary stents
• higher use of GP IIb/IIIa receptor blockers
  (abciximab)
• much higher use of expensive devices
• Total lower procedure cost
• decreases in vascular complications
• decreases in number of transfusions
• decreases in number of emergency bypasses
• decreases in length of stay
• fewer emergency cath lab procedures
• potential for less restenosis and fewer repeat
  procedures

21
University of Michigan
Reliable cost data

• Collaborative effort with department of clinical
  affairs.
• Annual cost data are available on all procedures
  performed (direct and indirect costs).
• Cost data are linked to outcome database.

22
University of Michigan
New database in development

• Interventional cardiology program will have
  access to clinical, financial, and patient data.
• Areas where costs are the result of practice
  variation, not actual procedures, can be
  identified.
• Costs can be closely monitored and areas can be
  identified where costs can be reduced.

23
Assessing appropriateness
Indicator of procedure performance

• If a procedure is not appropriate, the care
  provided is not good care.
• Some payers that are beginning to assess
  appropriateness in a clinical fashion.
• In Michigan, Blue Cross is assessing the
  appropriateness of procedures performed in the
  past 2 years by applying criteria based on
  national guidelines.

24
Assessing quality
High-cost / high-risk procedures

• Having a data base is not the final answer
• a good system to collect data is required
• data must be assessed in a clinical fashion
• problem areas identified must be addressed
• By examining, benchmarking and steadily
  improving, change is possible.