The Regulatory Alphabet: CMS, OPTN, HRSA, SRTR, UNOS And Monitoring of Transplant Outcomes

Transcription

1 The Regulatory Alphabet: CMS, OPTN, HRSA, SRTR, UNOS And Monitoring of Transplant Outcomes John Paul Roberts M.D. University of California San Francisco

2 NONE Disclosures

3 Outcome Monitoring Outcome monitoring has existed for transplantation for many years. Centers of Excellence Payor designation of centers SRTR reports of observed vs. expected outcomes Publically reported Used by OPTN as a trigger for peer review process Used by CMS as trigger for review of transplant center

4 Organ Transplant Policy In the United States the legal basis for organ transplantation is the National Organ Transplant Act (NOTA). The regulatory interpretation of NOTA is called the Final Rule The Final Rule governs transplant centers.

5 Final Rule Make available to the public timely and accurate program specific information on the performance of transplant programs. This shall include riskadjusted probabilities of receiving a transplant or dying while awaiting a transplant, risk adjusted graft and patient survival following the transplant, and risk adjusted overall survival following listing OPTN Final Rule Page 21 October 20, 1999

6 Program Specific Report Report of patient and graft survival of each transplant center. Reports are published every 6 months. Center outcomes are compared to national outcomes. Observed results at the center are compared to expected results for patients transplanted with similar characteristics nationally.

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8 Center Specific Survival Reports posted between 1/2005 and 1/2010. Reports posted every 6 months Survival is calculated at 1 and 3 years Centers with both 1 year and 3 year survival significantly better than expected

9 UCSF Waitlist Mortality Since 2005 UCSF has been better than expected in 10/12 reporting periods Only one other center in country has done as well Comparing to California CPMC 4/12 (4/4 since they stole our hepatologists) Stanford 0/12 Other California centers 0/12 (UCLA, USC, San Diego) Other big national players Mayo Clinic(1/12), MGH (0/12), U of MN (0/12), Columbia (3/12), Pittsburgh (1/12)

10 CSR Patient Survival 1 and 3 Years Hospital of the University of Pennsylvania (PAUP) Mayo Clinic Florida (FLSL) NY Presbyterian Hospital/Columbia Univ. Medical Center (NYCP) Rochester Methodist Hospital (Mayo Clinic) (MNMC) St Luke's Episcopal Hospital (TXHI) University of California San Francisco Medical Center (CASF)

11 7 CSR Graft Survival Better than Expected at Both 1 and 3 year Hospital of the University of Pennsylvania (PAUP) NY Presbyterian Hospital/Columbia Univ. Medical Center (NYCP) Rochester Methodist Hospital (Mayo Clinic) (MNMC) St Luke's Episcopal Hospital (TXHI) University of California San Francisco Medical Center (CASF)

12 Waitlist Survival Mortality on the waiting list. Are patients alive 1 year after listing Risk adjusted UCSF transplant rates are lower than expected. Patients we put on list are less likely to be transplanted within a year MELD score at transplant higher than most of the country

13 UCSF Waitlist Mortality Since 2005 UCSF has been better than expected in 10/12 reporting periods Only one other center in country has done as well Comparing to California CPMC 4/12 (4/4 since they stole our hepatologists) Stanford 0/12 Other California centers 0/12 (UCLA, USC, San Diego) Other big national players Mayo Clinic(1/12), MGH (0/12), U of MN (0/12), Columbia (3/12), Pittsburgh (1/12)

14 What Are The Regulatory Uses Of The PSR? Used by both the OPTN and CMS in center evaluation OPTN process is meant to be a peer review process and not generally punitive. CMS process can result in center closures but allows for center improvement plans.

15 How Are Poorly Performing Centers Identified? The centers submit data on recipients and living donors. Multi variate Cox models used to compare expected patient and graft outcome based upon national data to the observed data at the center based upon individual patient characteristics Risk adjusted Observed vs. Expected outcomes calculated

16 Risk Adjustment Neutralizes the effect of higher risk donor and recipient selection on post transplant outcome for factors measured Risk adjustment may allow for the transplantation of higher risk recipients and use of higher risk donors Is risk adjustment for recipients appropriate when we are rationing organs?

17 Post Transplant Survival Model: Covariates (Order of Relative Import) Recipient factors re transplant; life support; malignant neoplasms other than HCC; functional status; portal vein thrombosis, recipient age>65; HCV; recipient age 60 64, abdominal surgery; creatinine, albumin; Donor factors: donation after cardiac death, split liver; donor age>70, ischemic time>12 hours; Ischemic time 9 11 hours; race, cause of death

18 Model Flagging Risk Adjusted Outcomes (Graft + Patient Survival) Deficient if Observed/Expected Failures > 1.5 (One Year Post Tx) p <.05 (one sided value) Observed minus Expected Absolute Deaths > 3

19 Many Facilities Have More Observed Than Expected Deaths SRTR Each point represents a KI, LI, HR, or LU center in the July 2005 CSRs. 33 facilities with expected deaths > 20 or observed deaths > 30 are not shown for

20 Important: More Than Three Excess Deaths SRTR Each point represents a KI, LI, HR, or LU center in the July 2005 CSRs. 33 facilities with expected deaths > 20 or observed deaths > 30 are not shown for

21 Actionable: More Than 50% Excess Deaths SRTR Each point represents a KI, LI, HR, or LU center in the July 2005 CSRs. 33 facilities with expected deaths > 20 or observed deaths > 30 are not shown for

22 Significant: Excess Deaths Are Unlikely Due to Chance O/E > 1.5 (Actionable) Observed Deaths p <.05 (Significant) O = E (45 o ) O - E > 3 (Important) 0 SRTR Expected Deaths Each point represents a KI, LI, HR, or LU center in the July 2005 CSRs. 33 facilities with expected deaths > 20 or observed deaths > 30 are not shown for

23 CMS Conditions of Participation CMS issued COP in 2007 Centers whose observed graft or patient survival below expected at risk for adverse action. Adverse action could mean loss of Medicare certification and loss of government payment Loss of certification impacts Center of Excellence (COE) status and therefore loss of patients from private payors. Cost of loss of COE status can cost a liver transplant program millions of dollars

24 CMS Criteria and Sequelae of Flagging Based on PSR Flag 1 SRTR Report meeting 3 flagging criteria Standard Deficiency Condition Level Citation 2 SRTR Reports of Last 5 meet flagging criteria Mitigating Factors Process Up to 210 days (~20% of new patients) Allows for 3d SRTR Report Allows time for additional improvements Systems Improvement Agreement if Progress

25 Mitigating Factors Main Types of Mitigating Factors Natural Disasters (e.g. Hurricane) Innovation (high HLA population, etc. Robust Program Improvement Evidence of Improved Outcomes Hamilton, Tom 2012 ASTS Leadership Development Program

26 What Effect Are Regulatory Efforts Having on Poorly Performing Centers? Initial wave of center closures when CMS regulations took effect. Analysis First 334 Programs with Completed Process 300 approved (89.8%) without mitigating factors (any deficiencies were corrected + confirmed via revisit) 18 approved with mitigating factors approval (6.0%) 8 denied mitigating factors(2.4%) withdrew or Medicare terminated 8 other MF requests still in process (2.4%) Hamilton Presentation

27 Centers with Poor Outcomes Outcomes correlate with problems with internal center processes.

28 Types of Program Deficiencies N=334 (Programs w/ Poor Outcomes v All Others, 26 v. 308) 70% 60% 50% 40% 30% 20% 10% 30% 62% 31% 27% 27% 17% 17% 15% Programs Not Cited for Outcomes Programs Cited for Outcomes 15% 12% 9% 9% 8% 19% 0%

29 Program Specific Reports Liver center reports posted every 6 months. Examination of reports for liver transplant centers posted between 1/2005 and 1/2010. Many centers have multiple reports where the graft survival is less than expected One center had outcomes worse than expected in all periods.

30 Number of Reporting Periods with 1 year Graft Survival Less than Expected by Individual Center Individual Center (blinded) 0 Number of Reporting Periods with Graft Survival Less than Expected

31 OPTN Adverse Actions Kidney and Liver Centers KI programs threatened with an adverse action relating to outcomes: 7 KI programs that received an adverse action relating to outcomes: 0 LI programs threatened with an adverse action relating to outcomes: 3 LI programs that received an adverse action relating to outcomes: 0

32 Do PSRs Effect Center Behavior

33 Other Effects of Poor PSR Report Payors use the PSR to determine Centers of Excellence (COE) Flagged program usually lose this designation Loss of designation generally results in loss of patients directed to COE May lose all patients from a particular payor

34 Schold, J Progress Transpl 2010

35 Can You Limit Your Risk? Does your center do better or worse with a given category that is adjusted e.g. DCD donors?

36 Post Transplant Survival Model: Covariates (Order of Relative Import) Recipient factors re transplant; life support; malignant neoplasms other than HCC; functional status; portal vein thrombosis, recipient age>65; HCV; recipient age 60 64, abdominal surgery; creatinine, albumin; Donor factors: donation after cardiac death, split liver; donor age>70, ischemic time>12 hours; Ischemic time 9 11 hours; race, cause of death

37 Many things are not "adjusted" for: Donor Biopsy Cardiovascular disease Nutritional status Income Education Patient support networks Noncompliance Prior malignancies Ancillary quality of care Smoking status Employment status Other comorbidites Drug use Psychological conditions Genetics

38 High risk Transplants Donor Factors: How They Are Accounted For DCD: adjusted Older donors: age adjusted Donor Biopsy: Not Adjusted DCD donor risk adjustment may not help if you use DCDs with much longer warm time than other centers

39 Limit Risk? Because of the risk adjustment, excluding DCD donors may not improve the outcome and will decrease the number or transplants. Choosing donors on basis of biopsy may limit risk. 1. Dickinson DM, et al. Am J Transplant. 2008;8(Pt 2): (B)

40 High Risk Transplant Unadjusted (usually uncaptured) factors that are more prevalent in your patients than at other centers Desensitization Cardiovascular disease Re re transplantation Organs that seem OK by captured factors but have bad biopsies

41 Center Risk Aversion SRTR Risk adjustment is known to inadequately adjust for cardiovascular risk. Have centers responded to CMS rules by not transplanting patients with CV risks, who previously would have been transplanted? Abecassis PSR Consensus Conference 2012

42 Effect of CMS Conditions of Participation Kidney recipients Liver recipients Wang, et al. Oral Presentation AASLD 2011.

43 Innovation Transplantation at the cutting edge Trials of new therapies may turn out badly and result in decreased patient and/or graft survival If the trials involve significant number of a center s patients, the center s outcome maybe flagged.

44 Innovation Down staging hepatocellular cancers to Milan criteria Transplantation of the HIV positive patient Left lobe grafts with inflow modulation De sensitization protocols

45 Innovation Larger centers may have ability to innovate as failures maybe buried in overall success Center may not be able to innovate if results are borderline Center may plead mitigating factors once flagged and CMS review process has begun

46 Innovation How do we allow for innovation with the program specific report framework? Designation of a therapy as innovation Allow exclusion of a patient from the reports

47 Innovation Agreement by CMS, OPTN and HRSA to examine the program specific reports to see if they can be improved One possible outcome will exclusion of patients in trials

48 Outcomes and Innovation Outcome monitoring effects center behavior as it is supposed to. Outcome monitoring can have a negative effect on innovation

49 Thanks SRTR Slides Dorry Segev Provided slides effect of CMS on centers David Axelrod Provided CUSUM Slides Tom Hamilton CMS slides