Dataset Information

Transition probabilities between changing sensitization levels, waitlist activity status and competing-risk kidney transplant outcomes using multi-state modeling.

ABSTRACT: Sensitization and activity status are associated with kidney transplant waitlist mortality. Unknown is how changes in these covariates after listing impact transplant outcomes.Two cohorts were created from the OPTN (Organ Procurement and Transplantation Network) database, one pre-KAS (new kidney allocation system) (10/01/2009-12/04/2013, n = 97,793) and one post-KAS (12/04/2014-06/17/2015, n = 13,113). Multi-state modeling provides transition probabilities between intermediate states (CPRA category/activity status combinations) and competing-risk outcomes: transplant (living), transplant (deceased), death, or other/well.Transition probabilities show chances of converting between intermediate states prior to a competing-risk outcome. One year transplant probabilities for post-KAS candidates with a CPRA of 0%(P, 0.123[95% CI, 0.117,0.129]), 1-79%(P, 0.125 [95% CI, 0.112,0.139]), 95-98%(P, 0.242[95% CI, 0.188, 0.295]) and 99-100%(P, 0.252 [95% CI, 0.195, 0.308]) were significantly higher than the pre-KAS cohort; they were lower for CPRA 80-89%(P, 0.152 [95% CI, 0.116,0.189]) and not statistically different for CPRA 90-94%(P, 0.180 [95% CI, 0.137,0.223]) candidates. Post-KAS, Whites had a statistically higher transplant probability only at a CPRA of 99-100%.Multi-state modeling provides transition probabilities between CPRA/activity status combinations, giving estimates on how changing patient characteristic's after listing impact outcomes. Preliminarily, across most CPRA categories, there was no statistical difference in transplant probabilities between Whites, Blacks and Hispanics following KAS implementation, however, this finding requires longer follow-up for validation.

SUBMITTER: Kulkarni S

PROVIDER: S-EPMC5747475 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Transition probabilities between changing sensitization levels, waitlist activity status and competing-risk kidney transplant outcomes using multi-state modeling.

Kulkarni Sanjay S Hall Isaac I Formica Richard R Thiessen Carrie C Stewart Darren D Gan Geliang G Greene Erich E Deng Yanhong Y

PloS one 20171229 12

<h4>Background</h4>Sensitization and activity status are associated with kidney transplant waitlist mortality. Unknown is how changes in these covariates after listing impact transplant outcomes.<h4>Methods</h4>Two cohorts were created from the OPTN (Organ Procurement and Transplantation Network) database, one pre-KAS (new kidney allocation system) (10/01/2009-12/04/2013, n = 97,793) and one post-KAS (12/04/2014-06/17/2015, n = 13,113). Multi-state modeling provides transition probabilities betw ...[more]

PMID: 29287087

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Project description:Interest in atomically detailed simulations has grown significantly with recent advances in computational hardware and Markov state modeling (MSM) methods, yet outstanding questions remain that hinder their widespread adoption. Namely, how do alternative sampling strategies explore conformational space and how might this influence predictions generated from the data? Here, we seek to answer these questions for four commonly used sampling methods: (1) a single long simulation, (2) many short simulations run in parallel, (3) adaptive sampling, and (4) our recently developed goal-oriented sampling algorithm, FAST. We first develop a theoretical framework for analytically calculating the probability of discovering select states on simple landscapes, where we uncover the drastic effects of varying the number and length of simulations. We then use kinetic Monte Carlo simulations on a variety of physically inspired landscapes to characterize the probability of discovering particular states and transition pathways for each of the four methods. Consistently, we find that FAST simulations discover each target state with the highest probability, while traversing realistic pathways. Furthermore, we uncover the potential pathology that short parallel simulations sometimes predict an incorrect transition pathway by crossing large energy barriers that long simulations would typically circumnavigate. We refer to this pathology as "pathway tunneling". To protect against this phenomenon when using adaptive-sampling and FAST simulations, we introduce the FAST-string method. This method enhances sampling along the highest-flux transition paths to refine an MSMs transition probabilities and discriminate between competing pathways. Additionally, we compare the performance of a variety of MSM estimators in describing accurate thermodynamics and kinetics. For adaptive sampling, we recommend simply normalizing the transition counts out of each state after adding small pseudocounts to avoid creating sources or sinks. Lastly, we evaluate whether our insights from simple landscapes hold for all-atom molecular dynamics simulations of the folding of the λ-repressor protein. Remarkably, we find that FAST-contacts predicts the same folding pathway as a set of long simulations but with orders of magnitude less simulation time.

Project description:BackgroundEarlier qualification for the kidney transplant waitlist expedites transplant and is therefore associated with improved outcomes. U.S. Organ Procurement and Transplantation Network policies state that "measured or calculated creatinine clearance or glomerular filtration rate less than or equal to 20 mL/min" triggers waitlist time accrual. The choice of qualification method is somewhat arbitrary, and the policy implies that decline in renal function is monotonic.Methods(1) We used survival analysis to quantify temporal differences in waitlist qualification by applying 3 kidney-function-estimating equations (Cockcroft-Gault, Modification of Diet in Renal Disease study, Chronic Kidney Disease Epidemiology Collaboration) to serial creatinine measurements from 3 patient cohorts: 1 of waitlisted patients at a major U.S. academic center and 2 national, multicenter cohorts of chronic kidney disease patients (African American Study of Kidney Disease and Hypertension, Modification of Diet in Renal Disease). (2) Survival analysis assessed whether requiring patients to demonstrate persistently reduced renal function on 2 occasions at least 90 days apart would meaningfully change qualification order.ResultsOn average, time to waitlist qualification would be delayed on the order of 1 to 2 years by using calculated creatinine clearance (per the Cockcroft-Gault equation). Compared with current policy, requiring demonstration of persistently reduced renal function delayed qualification by 0.6 to 2.1 years and caused 40% to 50% of patients to switch the order in which they qualify by 6 months or more.ConclusionsThe kidney transplantation policies should be revised, such that timing of waitlist qualification is more standardized. We suggest that mention of using calculated creatinine clearance be dropped from the Organ Procurement and Transplantation Network policy wording and the units to quantify kidney function be changed to mL/min per 1.73 m2. Some consideration should be given to whether requiring persistently reduced renal function would better identify patients most likely to benefit from earlier waitlist qualification.

Dataset Information

Transition probabilities between changing sensitization levels, waitlist activity status and competing-risk kidney transplant outcomes using multi-state modeling.

Publications

Transition probabilities between changing sensitization levels, waitlist activity status and competing-risk kidney transplant outcomes using multi-state modeling.

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