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Transcript of Relative Efficacy and Safety of Aprotinin and Tranexamic Acid in Cardiac Surgery Keyvan Karkouti,...
Relative Efficacy and Safety of Relative Efficacy and Safety of Aprotinin and Tranexamic Acid Aprotinin and Tranexamic Acid
in Cardiac Surgeryin Cardiac Surgery
Keyvan Karkouti, MD, FRCPC, MScKeyvan Karkouti, MD, FRCPC, MScToronto General HospitalToronto General HospitalUniversity Health NetworkUniversity Health Network
University of TorontoUniversity of Toronto
DisclosureDisclosure
I have received $3,000 from Bayer Inc. for speaking I have received $3,000 from Bayer Inc. for speaking engagements engagements
OverviewOverview
Propensity analysis primerPropensity analysis primer Aprotinin versus Tranexamic Acid - Karkouti et al.Aprotinin versus Tranexamic Acid - Karkouti et al.
Methodology and ResultsMethodology and Results Aprotinin not superior to tranexamic acidAprotinin not superior to tranexamic acid
Major criticisms and putting it all in contextMajor criticisms and putting it all in context Not randomized, therefore “results are irredeemably biased”Not randomized, therefore “results are irredeemably biased” Results not consistent with findings of placebo-controlled RCTs, Results not consistent with findings of placebo-controlled RCTs,
therefore must be wrongtherefore must be wrong My ConclusionsMy Conclusions
No convincing evidence that aprotinin is better, or worse, than No convincing evidence that aprotinin is better, or worse, than alternativesalternatives
Propensity Analysis PrimerPropensity Analysis Primer
Multivariable analyses in observational studiesMultivariable analyses in observational studies
Multiple logistic regression versus propensity analysisMultiple logistic regression versus propensity analysis Newgard et al. Acad Emerg Med 2004;11:953-961Newgard et al. Acad Emerg Med 2004;11:953-961 Shah et al. J Clin Epid 2005;58:550-559Shah et al. J Clin Epid 2005;58:550-559
Logistic Regression vs. Propensity AnalysisLogistic Regression vs. Propensity Analysis
Logistic RegressionLogistic Regression Objective: to reduce bias by Objective: to reduce bias by
adjusting for measured adjusting for measured confoundersconfounders
Models for outcome, covariates Models for outcome, covariates include exposure and include exposure and confoundersconfounders
Get adjusted odds ratio of Get adjusted odds ratio of outcome for exposure outcome for exposure
Propensity AnalysisPropensity Analysis Objective: to reduce bias by Objective: to reduce bias by
adjusting for measured adjusting for measured confoundersconfounders
Models for exposure, covariates Models for exposure, covariates include confoundersinclude confounders
Get a propensity score (PS) for Get a propensity score (PS) for exposure for each patientexposure for each patient
Difference between exposed and Difference between exposed and unexposed with equal PS is an unexposed with equal PS is an unbiased est. of treatment effectunbiased est. of treatment effect
Logistic Regression vs. Propensity AnalysisLogistic Regression vs. Propensity Analysis
Logistic RegressionLogistic Regression Cannot adjust for confounders with Cannot adjust for confounders with
large differences in distribution large differences in distribution between treatment groupsbetween treatment groups
Assumes linear relationship Assumes linear relationship between confounder and outcomebetween confounder and outcome
# of covariates that can be included # of covariates that can be included is limited by # of outcomesis limited by # of outcomes
Rare covariates cannot be includedRare covariates cannot be included
Propensity AnalysisPropensity Analysis Can adjust for confounders with Can adjust for confounders with
large differences in distribution large differences in distribution between treatment groupsbetween treatment groups
No underlying assumptions in No underlying assumptions in modelingmodeling
Unlimited # of covariates can be Unlimited # of covariates can be included because it is used to included because it is used to balance treatment groups, not to balance treatment groups, not to make inferential statements make inferential statements about treatment groupsabout treatment groups
What Makes for a ‘Good’ Propensity Score What Makes for a ‘Good’ Propensity Score Analysis?Analysis?
Exploit its advantages over logistic regressionExploit its advantages over logistic regression Use it appropriatelyUse it appropriately
Use of PS to match exposed/unexposed patients vs. adjusting Use of PS to match exposed/unexposed patients vs. adjusting for PS as part of logistic regression model for outcomefor PS as part of logistic regression model for outcome
Confirm its validityConfirm its validity Must demonstrate if adequately balanced confoundersMust demonstrate if adequately balanced confounders
Realize its limitationsRealize its limitations Does not match for unmeasured confoundersDoes not match for unmeasured confounders
Use of PS as part of logistic regressionUse of PS as part of logistic regression
Newgard et al. Acad Emerg Med 2004;11:953-961Newgard et al. Acad Emerg Med 2004;11:953-961
Aprotinin vs. Tranexamic Acid - Karkouti et al.Aprotinin vs. Tranexamic Acid - Karkouti et al.
Hypothesis: Aprotinin is superior to tranexamic acid; Hypothesis: Aprotinin is superior to tranexamic acid; therefore, it is associated with reduced transfusions and therefore, it is associated with reduced transfusions and improved outcomesimproved outcomes
Site: Tertiary care, aprotinin used in high risk cases onlySite: Tertiary care, aprotinin used in high risk cases only Sample: Consecutive CPB patients from 1999-2004Sample: Consecutive CPB patients from 1999-2004 Data Source: Prospectively collected clinical databaseData Source: Prospectively collected clinical database Analysis: Propensity score matching of aprotinin with Analysis: Propensity score matching of aprotinin with
tranexamic acidtranexamic acid Propensity score derivation model developed for likelihood of Propensity score derivation model developed for likelihood of
aprotinin use using perioperative clinical variablesaprotinin use using perioperative clinical variables
Study ConclusionStudy Conclusion
Study Hypothesis: Aprotinin is superior to tranexamic acid; Study Hypothesis: Aprotinin is superior to tranexamic acid; therefore, it is associated with reduced transfusions and therefore, it is associated with reduced transfusions and improved outcomesimproved outcomes
Study Result: Aprotinin was not associated with reduced Study Result: Aprotinin was not associated with reduced transfusions or improved outcomestransfusions or improved outcomes
Study Conclusion: Reject hypothesisStudy Conclusion: Reject hypothesis
Major criticisms and putting it in contextMajor criticisms and putting it in context
Not randomized, therefore “results are irredeemably biased” Not randomized, therefore “results are irredeemably biased” because of confounding by indicationbecause of confounding by indication Aprotinin group was higher risk, and no statistical analysis can Aprotinin group was higher risk, and no statistical analysis can
fully adjust for this.fully adjust for this. Thus, aprotinin was more effective because transfusion rates were Thus, aprotinin was more effective because transfusion rates were
similar to a lower risk groupsimilar to a lower risk group Thus, aprotinin was not more harmful because a higher renal Thus, aprotinin was not more harmful because a higher renal
dysfunction rate is expected in the higher risk group dysfunction rate is expected in the higher risk group
Are observational studies irredeemably Are observational studies irredeemably biased?biased?
Best evidence on EFFICACY of therapy comes from Best evidence on EFFICACY of therapy comes from randomized trials randomized trials Caveat: Low quality RCTs may overestimate benefits of therapyCaveat: Low quality RCTs may overestimate benefits of therapy
Best evidence on HARM of therapy will often come from large, Best evidence on HARM of therapy will often come from large, properly analyzed nonrandomized trialsproperly analyzed nonrandomized trials RCTs are not ideal for identifying adverse events if:RCTs are not ideal for identifying adverse events if:
Low frequency owing to restrictive inclusion/exclusion criteriaLow frequency owing to restrictive inclusion/exclusion criteria Short follow-up periodsShort follow-up periods Small sample sizesSmall sample sizes
Vandenbroucke (Editorial) CMAJ 2006 174(5)Vandenbroucke (Editorial) CMAJ 2006 174(5)
Randomized versus Nonrandomized StudiesRandomized versus Nonrandomized Studies
Data from observational studies on adverse effects may be as Data from observational studies on adverse effects may be as valid as those from RCTsvalid as those from RCTs Adverse effects often not linked to treatment indicationsAdverse effects often not linked to treatment indications
confounding by indication not an issueconfounding by indication not an issue little need for randomization to quantify themlittle need for randomization to quantify them
The same is not true for beneficial effectsThe same is not true for beneficial effects Beware of observational studies that claim beneficial effects, even Beware of observational studies that claim beneficial effects, even
if unanticipatedif unanticipated Patients taking the drug were sufficiently well to continue taking Patients taking the drug were sufficiently well to continue taking
them over a longer periodthem over a longer period
Vandenbroucke (Editorial) CMAJ 2006 174(5)Vandenbroucke (Editorial) CMAJ 2006 174(5)
Panagiotis CMAJ 2006;175:635-41Panagiotis CMAJ 2006;175:635-41
Comparison of evidence on HARMS of medical interventions in Comparison of evidence on HARMS of medical interventions in randomized and nonrandomized studiesrandomized and nonrandomized studies Targeted medical interventions that had randomized and Targeted medical interventions that had randomized and
nonrandomized studies with > 4000 subjectsnonrandomized studies with > 4000 subjects Compared relative risksCompared relative risks Found 15 harms that could be assessed Found 15 harms that could be assessed
Papanikolaou, P. N. et al. CMAJ 2006;174:635-641
Comparison of relative risk estimates for specific harms of medical interventions from randomized and nonrandomized studies
Randomized versus Nonrandomized StudiesRandomized versus Nonrandomized Studies
Thus: “It may be unfair to invoke bias and confounding to Thus: “It may be unfair to invoke bias and confounding to discredit observational studies as a source of evidence on discredit observational studies as a source of evidence on harms.”harms.”
Caveat: Observational study must be of high qualityCaveat: Observational study must be of high quality Large sample sizeLarge sample size Proper adjustment for baseline differences to reduce Proper adjustment for baseline differences to reduce
confounding by indicationconfounding by indication TransparentTransparent
Example: Risk of platelet transfusion in cardiac surgeryExample: Risk of platelet transfusion in cardiac surgery
Spiess et al. Transfusion 2004Spiess et al. Transfusion 2004
Used data from phase III RCT aprotinin studiesUsed data from phase III RCT aprotinin studies N = 284 platelets; 1436 no plateletsN = 284 platelets; 1436 no platelets Logistic regression and propensity analysisLogistic regression and propensity analysis Adjusted for: Adjusted for: age, sex, race, weight, history of diabetes, history of unstable
angina, previous history of coronary artery disease (CAD), history of hypertension, history of chronic obstructive pulmonary disease (COPD), history of congestive heart failure (CHF), New York Heart Association classification (identified as ≥II [NYHA II], ≥III [NYHA III], ≥IV [NYHA IV]), left ventricular ejection fraction less than 30 percent, left ventricular ejection fraction less than 50 percent, return to surgery for reexploration for surgical bleeding, return to surgery for reexploration for diffuse bleeding, return to surgery for any reason, left ventricular assist device, volume of reinfused shed mediastinal blood, RBC transfusion (yes/no), duration of surgery, total heparin dose, total protamine dose, minimum intraoperative Hct, minimum intraoperative Hb.
Platelets Associated with Adverse OutcomesPlatelets Associated with Adverse Outcomes
Karkouti et al. CJA 2006Karkouti et al. CJA 2006
Used data from our large databaseUsed data from our large database N = 2174 platelets, 9285 no plateletsN = 2174 platelets, 9285 no platelets Logistic regression and propensity analysisLogistic regression and propensity analysis When adjusted for same variables as previous study, platelets When adjusted for same variables as previous study, platelets
were associated with LOS, renal failure, and deathwere associated with LOS, renal failure, and death Then we adjusted for three additional variables: baseline Then we adjusted for three additional variables: baseline
platelet count, difficult wean from CPB, and massive blood platelet count, difficult wean from CPB, and massive blood lossloss
Platelets NOT Associated with Adverse Platelets NOT Associated with Adverse OutcomesOutcomes
Major criticisms and putting it in contextMajor criticisms and putting it in context
Not randomized, therefore “results are irredeemably biased”* Not randomized, therefore “results are irredeemably biased”* because of confounding by indicationbecause of confounding by indication Thus, this assertion is irredeemably invalidThus, this assertion is irredeemably invalid However, we agree that neither our study, nor the study by However, we agree that neither our study, nor the study by
Mangano et al., is conclusive Mangano et al., is conclusive Not yet reproduced Not yet reproduced Cannot rule out the effect of unmeasured confoundersCannot rule out the effect of unmeasured confounders
• The results of our study on 3500 patients who had surgery at seven The results of our study on 3500 patients who had surgery at seven Canadian hospitals in 2004 highlight the importance of the site-effect Canadian hospitals in 2004 highlight the importance of the site-effect in multi-center observational studiesin multi-center observational studies
*Weiskopf RB. J Thromb Haemost 2066;4:2074-8
Major criticisms and putting it in contextMajor criticisms and putting it in context
Results not consistent with findings of placebo-controlled Results not consistent with findings of placebo-controlled RCTs, therefore must be wrongRCTs, therefore must be wrong
Placebo-controlled RCTsPlacebo-controlled RCTs
Levi et al. Lancet 1999;354:1940-47
Placebo-controlled RCTsPlacebo-controlled RCTs
Brown NEJM 2006
What about head-to-head studies?What about head-to-head studies?
Meta-analysis: Carless et al. BMC Cardiovascular Disorders Meta-analysis: Carless et al. BMC Cardiovascular Disorders 2005,5:192005,5:19
20 head-to-head RCTs identified20 head-to-head RCTs identified Total N = 2430Total N = 2430 Median trial arms N = 25; Range = 14-522Median trial arms N = 25; Range = 14-522 Methodological quality = PoorMethodological quality = Poor Very little high-risk data (e.g., primary CABG = 12 studies)Very little high-risk data (e.g., primary CABG = 12 studies)
24-hour blood loss24-hour blood loss
RBC Transfusion RateRBC Transfusion Rate
37.2%37.2% 36.5%36.5%
Other OutcomesOther Outcomes
Uninformative because the data are sparseUninformative because the data are sparse
Major criticisms and putting it in contextMajor criticisms and putting it in context
Results not consistent with findings of placebo-controlled Results not consistent with findings of placebo-controlled RCTs, therefore must be wrongRCTs, therefore must be wrong Our results are consistent with totality of existing evidenceOur results are consistent with totality of existing evidence
ConclusionsConclusions
No conclusive evidence exists that aprotinin is better, or No conclusive evidence exists that aprotinin is better, or worse, than tranexamic acidworse, than tranexamic acid
There is a glaring lack of high-quality data in high-risk patients There is a glaring lack of high-quality data in high-risk patients that the BART trial will only partly resolvethat the BART trial will only partly resolve