# Presentation: 313540

## Does aprotinin increase the risk of renal failure in cardiac surgery patients?

- Anthony P. Furnary, MD, FAHA
-  YingXing Wu, MD
- Loren F. Hiratzka, MD, FAHA
- Gary L. Grunkemeier, PhD
- U. Scott Page, MD

- Providence Health Systems, Portland , OR
- Health Data Research /Merged Cardiac Registry, Portland, OR
- TriHealth Systems, Cincinatti, OH

**Notes:**
Members of the panel and guests,

## Disclosure Information

- FINANCIAL DISCLOSURE:  **NONE**
- UNLABELED/UNAPPROVED USES DISCLOSURE:  **NONE**

- Does Aprotinin Increase the Risk of Renal Failure
- in Cardiac Surgery Patients?
- Furnary AP, Wu YX, Hiratzka LF, Grunkemeier GL, Page US

- Disclosure Information

**AHA annual Scientific Sessions 2006; Oral Presentation: Chicago, IL November 15, 2006**

- AHA Presentation

- Circulation.  September 11, 2007; 116(suppl I): I-127--I-133

- Publication

**Notes:**
My coauthors and I have no conflicts to disclose.

## Does aprotinin increase the risk of renal failure in cardiac surgery patients?

- Non-Randomized Retrospective Multicenter Observational study
- 66 centers / 16 countries
- 1374 Patients “Control”  / 1295 Patients Aprotinin
- Increased risk of “Renal  Event” by 2.3 - 2.6 times; p < 0.001
- Conclusion: “Continued use [of Aprotinin] is not prudent”

**Notes:**
Aprotinin is frequently used in high-risk cardiac surgery patients to decrease bleeding complications and transfusions of packed red blood cells.

A recent highly publicized report in the New England Journal of Medicine implicated Aprotinin as an independent causal factor for postoperative renal failure.  

This non-randomized Retrospective study from 66 centers involving 43-hundred patients, in which the Aprotinin use was based solely on surgical judgment, 

concluded that continued use of Aprotinin was not prudent.

## Hypothesis

- Aprotinin (Trasylol) _***is***_ an independent risk factor for new onset, dialysis-dependent renal failure (ARF) following open heart surgery.

**Notes:**
We therefore sought to confirm this finding and hypothesized that Aprotinin IS a risk factor for new onset, dialysis -dependent, renal failure following Cardiac surgery

## Assessment of Pre-op ARF Risk: CCF Risk Model

-  CHF
-  LVEF < 35%
-  Preop IABP
-  COPD

- C-Index = 0.82

-  IDDM
-  Redo OHS
-  Emergency Surgery
-  Surgery type
-  Preop Creatinine

- 31,677 OHS patients: 1992 - 2002

- Assessment of Pre-op ARF Risk: CCF Risk Model
- Thakar, et al:J AM Soc Nephrol 16:162-8, 2005

**Notes:**
A recently published risk model for predicting Acute dialysis-dependent Renal failure  following cardiac surgery was used to calculate a baseline risk score for all patients. The risk model, which emanated from the Cleveland Clinic, utilizes the variables shown here. In this presentation all predicted risk data will be represented in BLUE.

## Methods: Database

- Locally validated, prospective perioperative data entry
- January 2001 - Feb 2006

- 23,105 patients
- 12 centers

- Validated Aprotinin Usage Data

**Notes:**
The Merged Cardiac registry was used to identify 23 thousand patients with validated Aprotinin usage data who were operated on between January 2001 and February 2006. 

## Methods: Patient Population

- 23,105 OHS patients
- 12 centers

- 15,174 patients
- 11 centers

- 3976 pts missing transfusion (PRBC) data

- 11,198 patients
- 10 centers
- Aprotinin
- CCF ARF
- PRBC

- Validate CCF ARF Risk model

- 7931 pts incomplete CCF ARF data

**Notes:**
8 thousand patients were excluded for incomplete risk score data.  

An additional 4 thousand patients were excluded for lack of transfusion data .

The remaining 11,198 patients were used for risk-adjusted assessment of Renal Failure in relation to Aprotinin use. 

25% per cent -- or 27-hundred -- of these patients were selected to receive Aprotinin at the discretion of their surgeon, Their data will be presented in YELLOW.
The 85-hundred patients who did not receive Aprotinin will be represented in GREEN.

The overall procedural distribution is shown here.

The Cleveland Clinic Risk Model was first validated with all 15-thousand patients with available scoring data..

## ROC of CCF ARF Risk model

**Sensitivity (%)**

**1 - ****Specificity (%)**

**Notes:**
ROC analysis revealed that the predictability Cleveland Clinic’s risk model on our data was excellent, with a c-index of nearly 82%.

## Renal Failure by Aprotinin Use

- P < 0.0001

**Notes:**
The incidence of new onset postoperative renal failure in the non-Aprotinin group was 1.3%...

and was twice as high in the Aprotinin subset -- a highly significant univariate difference.

## Demographics

**CCF Risk Variable for ARF**

**Notes:**
However, demographic comparison showed that patients who were selected to receive Aprotinin were, PREOPERATIVELY, at a higher risk for renal failure than the non Aprotinin group. Including higher incidences of risk model variables such as .... Complex sugery and redo surgery,..... congestive failure and low ejection  fraction... COPD and Female gender... and higher average preoperative creatinine.

## Observed ARF vs CCF Expected

| O:E Ratio | 1.06 | 1.22 | P = 0.042 |
| --- | --- | --- | --- |

- P < 0.001

- No Aprotinin

- Aprotinin

- P < 0.001

**Notes:**
Thus the preoperative risk model predicts a significantly higher incidence of renal  failure in the Aprotinin population as compared  to the non- aprotinin group.

Which is what we saw

The O to E ratio for the non-Aprotinin group was as expected.  But the actual incidence of renal failure was significantly HIGHER than expected in the aprotinin group

## Multivariable Analysis of Renal Failure

**CCF Risk score****	****< 0.001****	****2.4**
**   ****(12 variables)**
**	****Aprotinin****	****0.008****	****1.5**
- Area under the ROC curve = 0.817

**Multivariable Analysis of Renal Failure**
**CCF Model**** + ****Aprotinin**

**n = 15,174**

**Notes:**
The composite Cleveland risk score,
representing 12 variables, was entered into a multivariable analysis along with Aprotinin.

This model suggests, as did Magnano’s, that Aprotinin independently increases renal failure by a factor of 1.5 

 However,  aprotinin is not the only perioperative variable capable of potentially inducing renal failure.

## Renal Failure vs. # PRBC

- Renal Failure vs. # PRBC

**P < 0.0001**

**Notes:**
INTERESTINGLY, our data show -- as does that of others -- 

that the number of packed red blood cells transfused in the perioperative period directly and significantly increases the incidence of new onset renal failure

## PRBC Utilization by Aprotinin Use

- P < 0.0001

- Aprotinin

- No Aprotinin

**Notes:**
Importantly, the number of packed cells transfused per patient in the Aprotinin group was significantly higher than in the non aprotinin group

## Transfusions (# PRBC)

| % transfused | 48.4% | 50.2% | P = NS |
| --- | --- | --- | --- |
| O:E Ratio | 1.06 | 0.88 | P < 0.001 |

- P < 0.001

- No Aprotinin

- Aprotinin

**Notes:**
Again, this is not unusual as the predicted transfusion requirements of the surgeon-selected Aporotinin group were significantly higher than those of the non-Aprotinin group. Indicating appropriate utilization of this drug.

Although there were no differences in the PERCENT of patients transfused,

 in fact the Aprotinin patients received significantly less Packed cells than predicted by the model,  Thus Validating the usefulness of this drug.

## Multivariable Analysis of Renal Failure

**CCF Risk score****	****< 0.001****	****1.9**
**   ****(12 variables)**
**# PRBC****	**** < 0.001****	****1.2**
**	****Aprotinin****	****.231****	**
- Area under the ROC curve = 0.910

**Multivariable Analysis of Renal Failure**
**CCF Model**** +**** ****#PRBC ****- ****(Aprotinin)**

**n = 11,198**

**Notes:**
Further risk adjustment then with the addition of the highly significant # of transfused PRBC variable to the previous model ATTENUATES the supposed independent affect of Aprotinin on renal failure.  AND INCREASES the Predictability of this model  from 82% to 91%.

## Nontransfused Patients

- 23,105 OHS patients
- 12 centers

- 15,174 patients
- 11 centers

- 11,198 patients
- 10 centers
- Aprotinin
- CCF ARF
- PRBC

**Aprotinin**
_**No PRBC**_
**1372**

**No Aprotinin**
_**No PRBC**_
**4357**

| % NOT transfused |
| --- |
| 49.8% |
| 51.6% |
| P = NS |

**Notes:**
Finally we repeated these analyses on the non-transfused patient subgroups...

 in which had equal proportions of non-transfused patients.

## CCF Expected vs Observed ARFNon-transfused subgroup

| O:E Ratio | 0.2 | 0.33 | P = NS |
| --- | --- | --- | --- |

- No Aprotinin

- Aprotinin

**Notes:**
The predicted risk of renal  failure was slightly, but  not  significantly, higher in the non-transfused Aprotinin group

However, the ACTUAL rates of renal  failure were significantly LOWER than predicted in BOTH non-transfused subgroups.  

But there was no significant difference in the risk-adjusted incidence of renal failure between the non-transfused subgroups

## Multivariable Analysis of Renal Failure

**CCF Risk score****	****< 0.001****	****3.8**
**   ****(12 variables)**
**	****Aprotinin****	****0.654****	**

**Multivariable Analysis of Renal Failure**
**CCF Model**** - ****(Aprotinin)**
_**Non-transfused subgroup**_

**n = 5729**

**Notes:**
Multivariable testing in the non-transfused subgroup confirms that Aprotinin has no independent affect on Renal  failure.

## Aprotinin as a Risk Factor for ARF

- Key:  U = Univariate; M = Multivariable

- AP

- CCF + AP

- CCF + PRBC

- CCF (No PRBC)

**Notes:**
Exclusion of important predictive prognosticators will hamper any statistical analysis, causing it to render the wrong conclusion.  This is demonstrated with a sequential analysis of our own data.  This figure shows several odds ratios, with 95% confidence intervals, depicting the significance of aprotinin in relation to ARF in each of the four sequential patient populations and for the three multivariate risk models I have just shown you.

The univariate risk of aprotinin, that is before any risk adjustment, for causing renal failure in each of the four population sample sets is shown in the red bars “U0”, “UA”, “UB” and “UC”. Note that the odds ratios for the univariate analysis of aprotinin in relation to ARF in these subsequent sample populations are all between 2.0 and 2.5 and are not statistically different from that of the original cohort.  

The multivariable (risk-adjusted) odds ratios for aprotinin in relation to ARF are shown in BLUE.   Bar “MA” represents the odds ratio for aprotinin after CCF-ARF risk adjustment, which did not include PRBC. Bar “MB” represents the odds ratio from the second model after additional risk adjustment was made for numerical transfusion data. Bar “MC” represents the odds ratio from “model C” -- after CCF-ARF risk adjustment in just those patients who did not receive any PRBC transfusions.   

Note that aprotinin appears to be significant in all sample sets by univariate analysis. It also appears to be significant using the model A (PRBC-excluding) methodology.  However, it is only with the addition of the important prognostic numerical transfusion data that aprotinin is finally seen not to be a true independent predictor of ARF.  Further validation of this clarifying concept is provided by the non-transfused subset of patients, which confirms no relationship between aprotinin use and ARF.

## Summary

- Aprotinin does _**NOT**_ independently increase the risk of postoperative ARF Following CT surgery.
- Increasing PRBC use is independently related to an increased risk of postoperative ARF
- Aprotinin lowers predicted PRBC requirements
- CCF risk model for ARF was validated, but further _***postoperative***_ ARF risk assessment could be enhanced with the addition of #PRBC data to the model

**Notes:**
IN summary:

Aprotinin does NOT independently increase the risk of postoperative renal failure Following cardiac surgery.
Rather, Increasing packed red blood cell transfusions in the perioperative period are independently related to an increased risk of postoperative Renal failure
We confirmed that Aprotinin lowers predicted transfusion requirements
In addition the Cleveland Clinic risk model for Renal failure was validated by our data, but further POSTOPERATIVE risk assessment would be enhanced with the addition of numerical tPRBC data to the model

## Conclusions

- The implication of aprotinin as a causal factor for postoperative ARF was incorrect and premature.
- Previously published results were likely faulty due to the absence of PRBC data in the Magnano study.
- All efforts to minimize PRBC usage should be undertaken to prevent higher occurrence rates of ARF.
- All subsequent analyses of postoperative ARF should adjust for PRBC usage, as this is a highly significant risk factor for the subsequent development of ARF.
- The FDA should consider withdrawal of aprotinin advisory warnings based on these data.

**Notes:**
We conclude that:

The implication that aprotinin is a causal factor for postoperative renal failure was premature.

And that the Previously published results were likely faulty due to the absence of numerical transfusion data in the Magnano study.

We believe that ALL efforts to minimize packed red blood cell transfusion should be undertaken to prevent higher occurrence rates of Renal Failure.

All subsequent analyses of postoperative ARF, whether prospective or retrospective, should take into account the number of transfusions used in the perioperative period, as this is a highly significant risk factor for the ARF outcome endpoint.

Furthermore, The FDA should consider withdrawal of aprotinin advisory warnings based on these data.

and Tort lawyers in this country should use caution in this area as aprotinin appears to be both safe and effective.

We would like to thank the FDA for the privlidge of presenting these data.