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Introduction: Kidney transplant recipients expect to survive the procedure with sufficient renal function for reliable dialysis freedom., Methods: Transplant outcomes (survival and estimated renal function) were assessed after live and deceased donor transplantation from the US national database. Outcomes were stratified by age (donor and recipient) and donor type., Results: Aggregate recipient outcomes were better transplanting living vs deceased donated kidneys. However, when stratified by the one-year renal function (within KDIGO CKD stage stratifications), surviving recipients had clinically similar dialysis-freedom, irrespective of donor type or age. The major outcome differences for recipients of age-stratified live and deceased kidneys was 1) the increasing frequency of one-year graft failures and 2) the increasing likelihood of severely limited renal function (CKD 4/5) with advancing donor age. Over 30% of recipients of deceased kidneys >65 years had either one-year graft failure or severely limited renal function contrasted to less than 15% of recipients of live kidneys aged >65 years., Conclusions: Evolving techniques to reduce adverse events after urgent vs elective procedures, plus improved transplant outcome predictability with increased-age deceased donor kidneys using advanced predictive analytics (using age-stratified live kidney transplantation outcomes as a relevant reference point) should facilitate similar kidney transplant outcomes, irrespective of donor type., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Pruett, Martin and Gupta.)

This paper provides a methodology for predicting post-transplant kidney function, that is, the 1-year post-transplant estimated Glomerular Filtration Rate (eGFR-1) for each donor-candidate pair. We apply customized machine-learning algorithms to pre-transplant donor and recipient data to determine the probability of achieving an eGFR-1 of at least 30 ml/min. This threshold was chosen because there is insufficient survival benefit if the kidney fails to generate an eGFR-1 ≥ 30 ml/min. For some donor-candidate pairs, the developed algorithm provides highly accurate predictions. For others, limitations of previous transplants' data results in noisier predictions. However, because the same kidney is offered to many candidates, we identify those pairs for whom the predictions are highly accurate. Out of 6977 discarded older-donor kidneys that were a match with at least one transplanted kidney, 5282 had one or more identified candidate, who were offered that kidney, did not accept any other offer, and would have had ≥80% chance of achieving eGFR-1 ≥ 30 ml/min, had the kidney been transplanted. We also show that transplants with ≥80% chance of achieving eGFR-1 ≥ 30 ml/min and that survive 1 year have higher 10-year death-censored graft survival probabilities than all older-donor transplants that survive 1 year (73.61% vs. 70.48%, respectively)., Competing Interests: None of the authors has any affiliation or financial involvement that conflicts with the material presented in this report., (© 2022 The Authors. Naval Research Logistics published by Wiley Periodicals LLC.)

Operating rooms (ORs) in US hospitals are costly to staff, generate about 70 % of a hospital's revenues, and operate at a staffed-capacity utilization of 60-70 %. Many hospitals allocate blocks of OR time to individual or groups of surgeons as guaranteed allocation, who book surgeries one at a time in their blocks. The booking procedure frequently results in unused time between surgeries. Realizing that this presents an opportunity to improve OR utilization, hospitals manually reschedule surgery start times one or two days before each day of surgical operations. The purpose of rescheduling is to decrease OR staffing costs, which are determined by the number of concurrently staffed ORs. We formulate the rescheduling problem as a variant of the bin-packing problem with interrelated items, which are the surgeries performed by the same surgeon. We develop a lower bound (LB) construction algorithm and prove that the LB is at least (2/3) of the optimal staffing cost. A key feature of our approach is that we allow hospitals to have two shift lengths. Our analytical results form the basis of a branch-and-bound algorithm, which we test on data obtained from three hospitals. Experiments show that rescheduling saves significant staffing costs.

S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH) link one-carbon metabolism to methylation status. However, it is unknown whether regulation of SAM and SAH by nutrient availability can be directly sensed to alter the kinetics of key histone methylation marks. We provide evidence that the status of methionine metabolism is sufficient to determine levels of histone methylation by modulating SAM and SAH. This dynamic interaction led to rapid changes in H3K4me3, altered gene transcription, provided feedback regulation to one-carbon metabolism, and could be fully recovered upon restoration of methionine. Modulation of methionine in diet led to changes in metabolism and histone methylation in the liver. In humans, methionine variability in fasting serum was commensurate with concentrations needed for these dynamics and could be partly explained by diet. Together these findings demonstrate that flux through methionine metabolism and the sensing of methionine availability may allow direct communication to the chromatin state in cells., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Simulated allocation models (SAMs) are used to evaluate organ allocation policies. An important component of SAMs is a module that decides whether each potential recipient will accept an offered organ. The objective of this study was to develop and test accept-or-decline classifiers based on several machine-learning methods in an effort to improve the SAM for liver allocation. Feature selection and imbalance correction methods were tested and best approaches identified for application to organ transplant data. Then, we used 2011 liver match-run data to compare classifiers based on logistic regression, support vector machines, boosting, classification and regression trees, and Random Forests. Finally, because the accept-or-decline module will be embedded in a simulation model, we also developed an evaluation tool for comparing performance of predictors, which we call sample-path accuracy. The Random Forest method resulted in the smallest overall error rate, and boosting techniques had greater accuracy when both sensitivity and specificity were simultaneously considered important. Our comparisons show that no method dominates all others on all performance measures of interest. A logistic regression-based classifier is easy to implement and allows for pinpointing the contribution of each feature toward the probability of acceptance. Other methods we tested did not have a similar interpretation. The Scientific Registry of Transplant Recipients decided to use the logistic regression-based accept-decline decision module in the next generation of liver SAM.

Hospitals develop nurse schedules that cover a period of 4-6 weeks and are posted several weeks in advance. Once posted, changes to the schedule require voluntary participation by the nurses, making it difficult for hospitals to respond to changes in nursing needs and availability of nurses. At the same time, nursing needs' forecasts developed several weeks in advance are often wrong. In each hospital setting, there may exist several promising strategies to enhance scheduling flexibility and reduce the mismatch between the nursing needs and the availability of nurses. However, methodologies to evaluate such strategies, before testing them in expensive pilot implementation, do not exist. We demonstrate how such evaluations can be carried out using historical data. Furthermore, we demonstrate the use of our approach by evaluating the benefits of a strategy where nurses are divided into two cohorts and schedules are phase shifted for the two cohorts. Staggering schedules allows nursing unit managers to benefit from more frequent updating of needs' assessments without having to change work rules. Upon applying our approach to data from a large urban hospital, we discovered that in this example staggering did not improve the performance of nurse schedules. We discuss possible reasons for this result, its implications for hospital managers, and other potential uses of our approach.

Background: Excessive waiting for procedures such as cardiac catheterization is an important issue for health care systems. Delays are generally attributed to a mismatch between demand and available capacity. Furthermore, due to the dynamic nature of short-term referral rates, procedure times, and patients' medical urgency, all of which are important contributors to the problem of excessive waiting time, it has been difficult to predict capacity needs accurately. The objective of our paper is to demonstrate how such calculations could be performed., Methods: After constructing a patient flow model and populating it with appropriate data from 16 consecutive months of operations (n=6215 referrals) of a regional cardiac centre in Ontario, we used computer simulation to simulate the operations of catheterization laboratories in several "what-if" scenarios. We divided the patients into three urgency categories: U1--hospitalized patients, U2--urgent outpatients, U3--elective outpatients. We tested the accuracy of the model by comparing a 1-year sample of computer simulation with actual data which resulted in a highly significant correlation of 0.94., Results: We observed from the referral cohort that waiting times were long, both overall and within each urgency category. We observed from the simulation models that: (1) a one-time infusion of capacity to clear the backlog failed to reduce the waiting times; (2) targeting extra capacity to highest urgency categories reduced waiting times overall and also benefited low urgency patients for whom specific increased capacity was not earmarked; (3) there were no significant effects on waiting times if in some cases patients or referring physicians were able to choose their cath physician; and (4) in situations where the arrival rates increased overall or within specific urgency categories, waiting times increased dramatically and failed to return to baseline for several months to years for the low urgency patients. Efficiency of the labs within the existing capacity could be improved by: (1) reducing changeover time between cases (2) externalizing and standardizing many of the pre- and post-procedural management of the patients, and (3) more carefully balancing the booking to reduce both slack and overtime., Interpretation: Capacity determination is a complex and dynamic process. A combination of available clinical and administrative data, along with a computer simulation model, helps predict capacity needs and is the most appropriate strategy to minimize waiting of patients for procedures. This approach is generalizable and can lead to more effective management of waiting lists for a variety of procedures.

Advanced access is an outpatient scheduling technique that aims to provide sameday appointment access. It is designed to reduce the time patients must wait for a scheduled appointment and to improve continuity of care by matching daily appointment supply and demand. Factors that make it difficult to sustain initial success in achieving supply-demand balance include different practice styles of doctors, differences in panel compositions and patient preferences, and time-varying demand patterns. This article proposes several performance measures that can help clinic directors monitor and evaluate their advanced access implementation. We also discuss strategies for sustaining advanced access in the long run.