David Axelrod, MD, MBA. Associate Professor of Surgery Section Chief- Solid Organ Transplantation Dartmouth- Hitchcock Medical Center

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1 David Axelrod, MD, MBA Associate Professor of Surgery Section Chief- Solid Organ Transplantation Dartmouth- Hitchcock Medical Center

2 Owner of XynManagement which produces software to track and improve center performance XynManagement received contract support from Sanofi Corporation to develop free software for patients

3 Regulatory alphabet soup: The Basics SRTR PSRs CMS/SIA Desensitization data Impact on outcomes Programmatic Risk Risk minimization strategies Risk capture Program performance monitoring

4 Transplant centers are the most highly regulated health delivery system in the US National Organ Transplant Act (NOTA) The Scientific Registry of Transplant Recipients (SRTR) Program Specific Reports Post- transplant graft and patient outcomes at 1 and 3 years Used by UNOS, CMS, and private payers

5 The SRTR produces Program Specific Reports (PSRs) for every program and every organ Publically available every 6 months Compares center outcomes to national expectations adjusted for donor and recipient characteristics Flags centers for lower than expected performance 1 year graft and patient survival data No penalty (currently) for waitlist mortality or low transplant rate

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7 Use 2.5 year cohorts with 1 year follow- up data Report Observed:Expected Ratio Observed- actual number of deaths or graft losses Expected- number of deaths or graft losses expected if center was functioning at the national average performance Expected rates are adjusted using donor, recipient, and transplant variables

8 RECIPIENT DONOR/TRANSPLANT Age Race Gender HCV status Years of dialysis BMI Diagnosis Previous Transplant Peak PRA NO ADJUSTMENT FOR Age Race Donor:Recipient Weight Ratio HLA mismatch Donor type (LD,DD) Serum Creatinine DCD Cold Ischemic Time ECD DESENSITIZATION TREATMENT, ANTIBODY STRENGTH, OR CLASS I vs. CLASS II POSITIVITY

9 Changing from Frequentist to Bayesian Statistics Instead of assuming the center performance is distributed as a bell curve, the Bayesian methods start with an asymmetric curve Bayesian methods place more weight on the pre- existing belief Use data to change this belief; however, a significant amount of data is needed to move the curve Continue to use current risk adjustment models CMS will continue to use the old system for a period of time

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11 Large Volume Program Small Volume Program Prior Belief Large Program Prior Belief Small Program Data Data About Average Data About Average About Average Data About Average Data

12 Current Output: Number of Transplants: 299 Observed 1- Year Patient Deaths: 13 Expected 1- Year Patient Deaths: 6.97 O/E Ratio: % Confidence Interval: (0.99, 3.19) Two- sided p- value: Bayesian Output:

13 Current Output: Number of Transplants: 6 Observed 1- Year Patient Deaths: 1 Expected 1- Year Patient Deaths: 0.18 O/E Ratio: % Confidence Interval: (0.14, 30.20) Two- sided p- value: Bayesian Output:

14 Current Output: Number of Transplants: 6 Observed 1- Year Patient Deaths: 0 Expected 1- Year Patient Deaths: 0.22 O/E Ratio: 0 95% Confidence Interval: (0.00, 13.75) Two- sided p- value: Bayesian Output:

15 LARGE CENTER SMALL CENTER

16 Current: Is a particular center performing as expected Produces a Yes/No Decision p- value shows how extreme the program s performance would be if the program truly had expected performance. Bayesian: What is the probability that a particular center is underperforming? Produces a probability that the program s true mortality rate exceeds a given standard. Produces a probability distribution for center performance.

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18 Bayes Criteria Mean Hazard Ra+o (HR) Probability HR > 1.20 Probability HR > 2.50 Flagged by Proposed Criteria % 0.1% FALSE

19 Significant imprecision in the risk adjustment Evaluated using C- statistic (0.5 is chance, 1.0 is perfect) STS post- CABG mortality c- statistic: 0.76 (Shanian et al. Circulation 2012) SRTR post kidney transplant c- statistic: 0.64 (SRTR 2009 annual report) SRTR Program Specific Reports are episodic, time delayed, and statistically incorrect 2.5 year cohorts transplanted up to 3.5 years prior to data release Too slow for active program management Statistical limitations of repeated measures

20 Membership and Professional Standards Committee (MPSC) reviews all centers every report 1 : flag requires plan of correction Identify root cause of poor perfromance Develop mitigation strategies MPSC actions include Ongoing monitoring Letter of reprimand Designation of a member not in good standing

21 2 periods of poor performance is a violation of the Conditions of Participation Three options for centers Mitigating factors application Demonstrate program is in compliance in recent data Problems are resolved Systems improvement agreement (SIA) Forced program of improvement $500,000 to $5 million in direct expenses and lost revenue Withdrawal from Medicare Program

22 Private payer Center of Excellence (COE) networks Based on volume and performance standards Require annual review Exclusion is possible with 1 flag for below expected performance Can often be appealed with data on program performance May be willing to consider innovative programs if results are quantified

23 Regulatory alphabet soup: The Basics SRTR PSRs CMS/SIA Desensitization data Impact on outcomes Programmatic Risk Risk minimization strategies Risk capture Program performance monitoring

24 Living donor transplant 1 Patient Survival 1 year 3 years 5 years Desensitization 90.6% 85.7% 80.6% Waiting list with possible transplant 91.1% 67.2% 51.5% Dialysis alone 93.1% 77.0% 65.6% Deceased donor transplant 2 Patient Survival Desensitization with transplant Waiting list with possible transplant 3 years 96.6% 77.7% 1 Montgomery et al. NEJM Vo et al. Transplantation 2013

25 1 Year Graft Survival ahr (p- value) 1 Year Patient Survival ahr (p- value) Desensitization PLNF 96.2% 0.91 (p=0.81) 98.4% 0.83 (p=0.83) PFNC 93.1% 1.64 (p.007) 96.1% 2.04 (p=.003) PCC 80.6% 5.01 (p<.001) 91.1% 4.59 (P<.001) Compatible 97.1% Reference 98% Reference PLNF= Positive Lumimex-Negative Flow PFNC= Positive Flow- Negative CDC PCC= Positive Cytotoxic Crossmatch Orandi et al. Amer J Trans 2014

26 CLINICIANS 1 REGULATORS 2 1 Montgomery et al. NEJM Orandi et al. Amer J Transplant 2014

27 Odds Ratio For Regulatory Flagging % ILDKT PFNC PCCC No ILDKT Reference Reference 5% ILDKT 1.19 ( ) 2.22 ( ) 10% ILDKT 1.33 ( ) 4.09 ( ) 20% ILDKT 1.73 ( ) ( ) Orandi et al. Amer J Trans 2014

28 Risk Quotient: Relationship of Center Performance to Average Performance

29 Regulatory alphabet soup: The Basics SRTR PSRs CMS/SIA Desensitization data Impact on outcomes Programmatic Risk Risk minimization strategies Risk capture Program performance monitoring

30 Improve capture of the risks that are accounted for by the PSR models Diabetes Peak PRA Donor diabetes Appropriate cause of death Re- transplant Accurate, real time monitoring of program outcomes Appreciate where the program is relative to expected Reduce risk from avoidable graft losses Maximize the use of KPD to lower antibody strength

31 Graphical representation of outcomes of a care process Can be risk- adjusted charts for important donor and recipient characteristics Plot outcomes over time to compare the results with expected outcomes based on a national model of mortality or graph failure 2 types: O - E charts and One- sided charts Trends in the plot line suggest improving or declining outcomes Once the trend line reaches a certain predefined level (one- sided charts) or exceeds a certain slope (O - E charts) the CUSUM signals

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35 HLA incompatible kidney transplantation improves patient survival vs. dialysis Current PSR models compare HLA incompatible outcomes with compatible transplant Increases the risk of flagging for participating centers Risk mitigation strategies Complete capture of all accounted for risk factors Real time monitoring of center outcomes to limit exposure to regulatory risk

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