Kidney Disease Quality Measure Development, Maintenance, and Support

Principal Investigator: Joseph Messana

measuresThe University of Michigan Kidney Epidemiology and Cost Center (UM-KECC) is the contractor for the MIDS Kidney Disease Quality Measure Development, Maintenance, and Support Task Order from the Centers for Medicare & Medicaid Services (CMS). Over the next five years, UM- KECC will provide the support necessary to revise and enhance quality measurement within the CKD and ESRD setting. This support includes work to develop, maintain and update the technical specifications for current and future ESRD measures. Once measures are in use, CMS requires that they periodically be reevaluated. In addition, UM-KECC will maintain and calculate the quality measures publicly reported on the Dialysis Facility Compare (DFC) website, a venue which allows patients, families and others to compare dialysis facilities on a number of characteristics and quality measures.

This Task Order is one of the initial task orders competed under the Measure & Instrument Development and Support (MIDS) IDIQ Umbrella contract for 2018-2028. MIDS program promotes the quality of healthcare by emphasizing major strategies for evaluating and improving care. Under multiple task orders, the MIDS contractors will assist the Centers for Medicare and Medicaid Services (CMS) in developing, testing, maintaining, implementing, and publically reporting quality healthcare- specific measures that fill critical gaps within the National Quality Strategy.


ESRD Data Utilization: Dialysis Facility Reports (Oversights/DFR)

Principal Investigator: Joseph Messana

oversightsThe Dialysis Facility Reports (DFR) have been produced since 1995 under contract to the Centers for Medicare & Medicaid Services. These Reports are provided to dialysis facilities, ESRD Networks, State Survey Agencies, and Regional Offices annually on the DialysisData.org website. An individual DFR is provided to each dialysis facility in the U.S. in the interest of stimulating quality improvement efforts and facilitating the quality improvement process. State surveyors also use data reported in the DFRs to make decisions on which facilities to survey during the upcoming year. Quarterly reports (which include updates to a subset of the metrics reported the annual DFR) became available to facilities, ESRD Networks and Survey Agencies in Q4 of 2018. 
 
The DFRs include information about directly actionable practice patterns such as dose of dialysis, vascular access, and anemia management, as well as patient outcomes (such as mortality, hospitalization, and transplantation) that can be used to inform and motivate reviews of practices. The information in the report facilitates comparisons of facility patient characteristics, treatment patterns, and outcomes to local and national averages. Such comparisons help to evaluate patient outcomes and to account for important differences in the patient mix - including age, sex, race, and patients’ diabetic status - which in turn enhances each facility’s understanding of the clinical experience relative to other facilities in the state, Network, and nation. 


The Evaluation of the End-Stage Renal Disease Treatment
Choices Model and Kidney Care Choices Model

Co-Principal Investigators: Claudia Dahlerus and Richard A. Hirth

cec_kccThe Centers for Medicare and Medicaid Services (CMS) Innovation Center is testing two new care delivery models aimed at improving the quality of care for people living with kidney disease. This project is an independent evaluation of these models. In collaboration with The Lewin Group (Sponsor) the University of Michigan (subcontractor) is serving on the independent evaluation team for these two new models. The End Stage Renal Disease (ESRD) Treatment Choices (ETC) Model includes incentives for kidney doctors and dialysis clinics to increase use of home dialysis and increase successful kidney transplants. The other new approach is the Kidney Care Choices (KCC) Model which includes incentives for kidney doctors to slow down chronic kidney disease (CKD), increase home dialysis, and shared incentives for kidney doctors and transplant providers to increase kidney transplantation. The aims of the Evaluation are to assess how well model participants (nephrology practices along with dialysis facilities, transplant providers) were able to increase patient uptake of home dialysis; increase access to kidney transplant, and for the KCC Model, slow CKD progression to ESRD. UM-KECC will analyze metrics assessing CKD progression and access to transplant, as well as interviewing providers participating in the KCC Model, kidney disease patients, patient care partners, and potential living donors on how they think the Models are doing in achieving the above stated goals.


End Stage Renal Disease Quality Incentive Program (ESRD QIP) Support

Principal Investigator: Yi Li

QIPUM-KECC serves as a subcontractor to Arbor Research to provide support for the CMS ESRD Quality Incentive Program (QIP) that was first implemented for Payment Year (PY) 2012 to guard against any potential unintended consequences and a decline in the quality of care under the new ESRD bundled payment system, CMS’s first national value-based purchasing program.


CDC Chronic Kidney Disease (CKD) Surveillance System

Principal Investigator: Rajiv Saran

CKDUM Nephrology in collaboration with the Centers for Disease Control & Prevention and University of California, San Francisco, has successfully established a Chronic Kidney Disease Surveillance System for the entire United States. It systematically tracks; reports trends in burden; awareness and impact on population health in the following six major topics: (1) the burden of disease, (2) burden of risk factors, (3) disease awareness, (4) quality and processes of care, (5) health consequences associated with CKD and (6) health system capacity available to deal with CKD.


Optimization and Simulation of Kidney Paired Donation Programs

Principal Investigator: Jack D. Kalbfleisch

KPDNetwork

The above is an illustration of a KPD network, with pairs represented by red nodes and altruistic donors represented by purple nodes.  Edges connecting nodes represent donor-candidate matches based on a virtual (computer based) cross match.  A selection with five subsets is highlighted and the possible transplants are indicated by purple edges.  These subsets are locally relevant subsets of size four or less and are chosen to present fall back opportunities should some transplants not be viable.  We proceed by evaluating all transplants for viability indicated by the purple arrows, then choosing the best possible disjoint set of cycles and chains for transplant.  (See Bray et al., 2015)

Kidney paired donation (KPD) provides an approach to overcome the barriers faced by many patients with kidney failure who present with willing, but immunologically or blood type incompatible living donors. KPD programs use a computerized algorithm to match one incompatible donor/recipient pair to another pair with a complementary incompatibility, such that the donor of the first pair gives to the recipient of the second, and vice versa. More complex exchanges of organs involving three or more pairs are also considered as are altruistic or non-directed donors (NDD) who donate a kidney voluntarily and thereby have the potential to create a chain of kidney transplants.  Such donors and chains have become increasingly important in KPD programs. Checking the viability of all potential transplants in a pool is not logistically possible, and so a fundamental problem in a KPD program is selecting an optimal subset of matches to consider among the many possibilities that exist.  We are developing methods of selecting  potential matches that take account of the uncertainty in the process; namely that potential transplants that are identified on a computer algorithm often fail when an attempt is made to put them into practice. Our approaches select subsets of patients with many options or fallbacks that could be implemented depending upon which potential transplants are found to be viable, and have been shown to have the potential to greatly increase the number and/or utility of transplants performed. We are also developing user friendly and efficient software to implement these approaches. In this work, we utilize data from the University of Michigan Paired Donation Program and the Alliance for Paired Donation.

Kidney Graft Survival Calculator:
Our calculator estimates the probability of graft survival given the characteristics of the candidate and potential donor, based on a Cox proportional hazards model fitted to SRTR data. This calculator has particular potential as a guide to candidates with a compatible living donor who may be able to improve their predicted graft or patient survival through participation in a KPD program.

Link to Calculator

Software:
KPD GUI: Software and Graphical Interface (download)

Our software platform can be used to manage and visualize exchanges suggested by optimization criteria in KPD, offering several advantages over other available software: 

Interactive visual display of the state of the KPD
Implementation of optimization methods from previous literature, accounting for probabilities of failure, as well as fallback options (uncertainties and contingencies)
Optimization extended to more general subsets of pairs and NDDs that facilitate fallback options. 

Related Publications:
Chen Y, Li Y, Kalbfleisch JD, Zhou Y, Leichtman A, Song PXK. Graph-based optimization algorithm and software on kidney exchanges. IEEE Transactions on Biomedical Engineering. 2012; 59:  1985-1991. doi: 10.1109/TBME.2012.2195663. Epub 2012 Apr 20.

Li Y, Song PXK, Leichtman AB, Rees MA, Kalbfleisch JD. Decision making in kidney paired donation programs with altruistic donors. SORT (Barcelona). 2014; 38(1): 53-72. PMID: 25309603 [PubMed]  PMCID: PMC4193813.

Li Y, Song PXK, Zhou Y, Leichtman A, Rees MA, Kalbfleisch JD. Optimal decisions for organ exchanges in a kidney paired donation program. Statistics in Biosciences. 2014; 6:  85-104. PMID:24795783 [PubMed]  PMCID: PMC4004760

Fumo D, Kapoor V, Reece IJ, Stepkowski SM, Kopke JE, Rees SE, Smith C, Roth AE, Leichtman AB, Rees MA. Historical matching strategies in kidney paired donation: The 7-year evolution of a web-based virtual matching system. American Journal of Transplantation. 2015; 15(10): 2646-2654. doi: 10.1111/ajt.13337. Epub 2015 May 26.

Bray M, Wang W, Song PK, Leichtman A, Rees M, Ashby V, Eikstadt R, Goulding A, Kalbfleisch J. Planning for uncertainty and fallbacks can increase the number of transplants in a kidney-paired donation program. American Journal of Transplantation. 2015; 15(10): 2636–2645. doi: 10.1111/ajt.13413. Epub 2015 Aug 4.

Melcher ML, Roberts JP, Leichtman AB, Roth AE, Rees MA. Utilization of deceased donor kidneys to initiate living donor chains. American Journal of Transplantation. 2016. doi: 10.1111/ajt.13740. Epub 2016 March.9

Ashby VA, Leichtman AB, Rees MA, Song PXK, Bray M, Wang W, and Kalbfleisch JD (2017).  A Kidney Graft Survival Calculator that Accounts for Mismatches in Age, Sex, HLA, and Body Size.  Clinical Journal of the American Society of Nephrology 12(7): 1148-60. doi: 10.2215/CJN.09330916 

Wang W, Bray M, Song PXK, Kalbflesich JD (2017).  A Look-Ahead Strategy for Non-Directed Donors in Kidney Paired-Donation. Statistics in Biosciences 9(2): 453-69. doi: 10.1007/s12561-016-9155-y

Bray M, Wang W, Song PXK, and Kalbfleishch JD (2018).  Valuing Sets of Potential Transplants in a Kidney Paired Donation Network.  Statistics in Biosciences (Published Online March 2018). doi: 10.1007/s12561-018-9214-7

Wang W, Bray M, Song PXK, Kalbfleisch JD (2018).  An Efficient Algorithm to Enumerate Sets with Fallbacks in a Kidney Paired Donation Program.  Operations Research for Health Care.  (Submitted)

Posters: 
Bray M, Wang W, Song PXK, Leichtman AB, Rees MA, Ashby VB, Eikstadt R, Kalbfleisch JD. Incorporating Uncertainties and Contingencies in a Paired Donation Program. ASN Kidney Week 2013. Atlanta, GA (November 7). American Society of Nephrology

Wang W, Bray M, Song PXK, Leichtman AB, Kalbfleisch JD. Multiple Decision Allocation Strategies in Kidney Paired Donation Program.  ASN Kidney Week 2013. Atlanta, GA (November 7). American Society of Nephrology.

Bray M, Wang W, Song PXK, Kalbfleisch JD. Incorporating Transplant Candidates with Multiple Associated Incompatible Donors in Kidney Paired-Donation. ENAR Spring Meeting 2016. Austin, TX (March 6). Eastern North American Region of the International Biometrics Society.

Wang W, Bray M, Song PXK, Kalbfleisch JD. Locally Relevant Subgraph Enumeration in Transplant Patient Network. ENAR Spring Meeting 2016. Austin, TX (March 6). Eastern North American Region of the International Biometrics Society.

Bray M, Wang W, Rees MA, Song PXK, Leichtman AB, Ashby VB, Kalbfleisch JD. A Visualization Software Platform for Managing a Kidney Paired-Donation Program.  American Transplant Congress 2016. Boston, MA (June14). American Society of Transplant Surgeons & American Society of Transplantation

Ashby VB, Leichtman AB, Rees MA, Song PXK, Bray M, Wang W, Kalbfleisch JD. Mismatch in Age, Sex, and Body Size Inform a Calculator for Kidney Graft Survival. American Transplant Congress 2016. Boston, MA (June 11). American Society of Transplant Surgeons & American Society of Transplantation

Related Projects
Optimization and Simulation of Kidney Paired Donation Programs

Kidney paired donation (KPD) provides an approach to overcome the barriers faced by many patients with kidney failure who present with willing, but immunologically or blood type incompatible living donors. KPD programs use a computerized algorithm to match one incompatible donor/recipient pair to another pair with a complementary incompatibility, such that the donor of the first pair gives to the recipient of the second, and vice versa. More complex exchanges of organs involving three or more pairs are also considered as are altruistic or non-directed donors (NDD) who donate a kidney voluntarily and thereby have the potential to create a chain of kidney transplants.  Such donors and chains have become increasingly important in KPD programs. Checking the viability of all potential transplants in a pool is not logistically possible, and so a fundamental problem in a KPD program is selecting an optimal subset of matches to consider among the many possibilities that exist.  We are developing methods of selecting  potential matches that take account of the uncertainty in the process; namely that potential transplants that are identified on a computer algorithm often fail when an attempt is made to put them into practice. Our approaches select subsets of patients with many options or fallbacks that could be implemented depending upon which potential transplants are found to be viable, and have been shown to have the potential to greatly increase the number and/or utility of transplants performed. We are also developing user friendly and efficient software to implement these approaches. In this work, we utilize data from the University of Michigan Paired Donation Program and the Alliance for Paired Donation. 


Enhancing the Cardiovascular Safety of Hemodialysis Care:
Patient-Centered Outcomes Research Institute

Principal Investigator: Rajiv Saran

Enhancing

Full Project Title: Enhancing the Cardiovascular Safety of Hemodialysis Care: A Cluster-Randomized, comparative Effectiveness Trial of Multimodal Provider Education and Patient Activation Interventions

Design: Cluster-Randomized controlled trial (CRCT) using Fresenius medical centers for the randomization, comparing two interventions to improve the CV safety of HD care by pursuing the following aims:

Aim 1 To translate two evidence-based interventions (multi-modal provider education and patient activation using peer mentoring) from their prior application settings into the context of outpatient hemodialysis (HD) care cardiovascular/hemodynamic safety.
Aim 2 Conduct a cluster-randomized controlled trial (CRCT) to compare the effects of the above HD facility-level interventions on the primary outcome of dialysis session safety over a period of 12 months.
Aim 3 Compare the effects of the two HD facility-level interventions on secondary patient-centered clinical outcomes, including: patient symptoms, fluid adherence, dialysis adherence, quality of life, hospitalizations and mortality over the same timeframe.
Aim 4 Identify factors associated with successful implementation of the interventions, and conduct analyses of mediators and moderators associated with primary and secondary outcomes.


Reducing Kidney Disease Through Acute Kidney
Injury Risk Prediction and Prevention

Principal Investigator: Michael Heung

Reducing

The study aims to a) develop a high fidelity AKI risk prediction model for hospitalized Veterans; and b) develop an accompanying clinical decision support system (CDSS) to provide
management recommendations for patients identified as being at higher risk for AKI. A future goal would be to incorporate these tools into the VA electronic health record (EHR) and to perform usability testing to establish their efficacy and impact.


Prediction Analytics to Guide Prevention of Kidney Disease Among U.S. Veterans

Principal Investigator: Rajiv Saran

Prediction

The goal the study is to develop a data-driven approach to reduce the incidence and progression of kidney disease among Veterans in the U.S. The study propose is to carry out predictive analytics (using traditional statistical and machine learning tools) and geospatial analyses, to identify high risk individuals as well as those with identifiable kidney disease, and the geographic areas they live in, to better understand risk and progression factors for kidney disease. This work will set the stage for enabling targeted population health interventions to prevent kidney disease as well as slow progression of kidney disease among U.S. veterans.