Allocation System Research Articles

Impact of Heart Transplant Allocation Change on Waitlist Mortality and Posttransplant Mortality in Patients With Left Ventricular Assist Devices.

In October 2018, the US heart transplant (HT) allocation system was revised giving patients with left ventricular assist device (LVAD) intermediate priority status. Few studies have examined the impact of this policy change on outcomes among patients with LVAD. We sought to determine how the allocation change impacted waitlist and posttransplant mortality in patients with LVAD. We retrospectively assessed the United Network for Organ Sharing registry for patients with LVAD who were listed for or underwent HT between October 2016 and October 2021. We evaluated waitlist mortality using competing risks analysis and a multivariable Fine-Gray model, and posttransplant mortality using Kaplan-Meier survival analysis and a multivariate proportional hazards model. We analyzed data from 3835 patients with LVAD listed for HT and 3486 patients with LVAD who underwent HT during the study period. Listing for HT preallocation change was significantly associated with an increased risk of waitlist mortality (Gray P=0.0058) compared with postallocation change. After adjustment for covariates, mortality differences by listing era were attenuated, but LVAD brand was significantly associated with waitlist mortality (HM3 versus HMII; hazard ratio, 0.38 [95% CI, 0.21-0.69]; P=0.002; HVAD versus HMII; hazard ratio, 0.79 [95% CI, 0.48-1.30]; P=0.36; overall P=0.004). In contrast, HT postallocation change was associated with increased posttransplant mortality (log-rank P=0.0172) compared with preallocation change. In a multivariable analysis, the association with posttransplant mortality between transplant eras was attenuated, but ischemic time (hazard ratio, 1.16 [95% CI, 1.07-1.26]; P<0.001) and status at time of HT (Status 1-3 versus 4; hazard ratio, 1.29 [95% CI, 1.04-1.61]; P=0.02) were significantly associated with posttransplant mortality. Among patients with LVAD, lower waitlist mortality postallocation change was likely driven by improved LVAD technology. Higher posttransplant mortality following the allocation change was largely attributable to longer ischemic times and patient acuity.

Fairer schooling for a better housing environment: Effect of abolishing school zones on the housing market

Access to high-quality public education is often closely linked to residential property ownership and location in many countries, contributing to rising housing costs in areas with better educational resources. This creates challenges in housing affordability and perpetuates inequality in education access. Our study explores the effectiveness of two policies aimed at reducing the association between property ownership and access to premium public education: the "six-year-one-position" policy, which limits school enrolment to one student per property every six years, and the "multi-dicing" policy, which uses a random allocation system for student placement. While this research focuses on Beijing, China, the implications of such policies are of broader relevance to cities worldwide facing similar issues. Using a spatial difference-in-difference approach, we find that eliminating school zoning can decrease housing prices by 2.5% in areas where these policies are implemented, with reductions of 4.4%–7.9% in districts previously associated with high-ranking schools. However, the announcement of these policies triggers short-term price spikes, with increases of 2.7%–4.7%, reflecting a panic-buying effect. Moreover, we observe a spillover effect, where demand shifts from regions that abolished school zones earlier to those that implemented the policy later, as revealed by a staggered difference-in-difference analysis. These findings offer insights for policymakers in other urban contexts where educational access is intertwined with housing markets, highlighting the need for balanced strategies to address both housing affordability and equitable education access.

A Weighted AIMD Congestion Control Algorithm for Device-to-Device Communication in 5G Network

ABSTRACT Device-to-Device (D2D) communication offers promising benefits in 5G networks in terms of improved throughput and reduced latency. However, efficient congestion control remains a critical challenge to ensure optimal performance and resource utilization. Existing congestion control mechanisms often struggle with adapting to dynamic network conditions, leading to inefficiencies, such as underutilization of network resources or excessive packet loss. This paper addresses these challenges by proposing a novel Weighted AIMD congestion control algorithm tailored specifically for D2D communication in 5G networks (WACC-D2DC-5GN). Here, the Weighted AIMD Congestion Control Algorithm is proposed for estimating available bandwidth and Hunger Games Search Optimization to optimize the weight parameter of WACC. The proposed algorithm integrates weighted factors that dynamically adjust congestion window sizes based on real-time network feedback. Simulations are run on the NS3-mmWave module, NS3 LTE-A module. The simulation outcomes show that the proposed WACC-D2DC-5GN method attains 27.26%, 20.41%, and 23.26% higher SNR and 16.20%, 26.97%, and 30.34% higher Throughput when analysed with existing techniques like NexGen D-TCP: next generation dynamic TCP congestion control approach (NGD-TCP-CCA), user-centric channel allocation system for 5G higher-speed users by using ML to decrease handover rate (UCC-HSU-MLA), and efficient with reliable hybrid deep learning-allowed method for congestion control in 5G/6G networks (HDL-EMC-CN) respectively.

Unified Fault-Tolerant Control and Adaptive Velocity Planning for 4WID-4WIS Vehicles under Multi-Fault Scenarios

Four-wheel independent drive and four-wheel independent steering (4WID-4WIS) vehicles provide increased redundancy in fault-tolerant control (FTC) schemes, enhancing heterogeneous fault-tolerant capabilities. This paper addresses the challenge of maintaining vehicle safety and maneuverability in the presence of actuator faults in autonomous vehicles, focusing on 4WID-4WIS systems. A novel unified hierarchical active FTC strategy is proposed to handle various actuator failures. The strategy includes an upper-layer motion controller that determines resultant force requirements based on trajectory tracking errors and a middle-layer allocation system that redistributes tire forces to fault-free actuators using fault information. This study, for the first time, considers multi-fault scenarios involving longitudinal and lateral coupling, calculating FTC boundaries for each fault type. Additionally, a fault tolerance index is introduced for 256 fault scenarios, using singular value decomposition to linearly represent the vehicle attainable force domain. Based on this, an adaptive velocity planning strategy is developed to balance safety and maneuverability under fault conditions. Matlab 2021a/Simulink and Carsim 2019 co-simulation results validate the proposed strategies, demonstrating significant improvements in fault-tolerant performance, particularly in complex and emergency scenarios.

How Are Diagnosis-Related Groups and Staffing Allocation Systems Associated with the Complexity of Nursing Care? An Observational Study.

In Italy, Diagnosis-Related Groups (DRGs) have been adopted for hospital services reimbursement. In some Italian regions, nurse staffing allocation is purely volume-based, with different minutes/patient/day categories determined by the type of hospital ward. The Information System of Nursing Performance (SIPI) is a valid and reliable tool assessing nursing care complexity as an indicator of the actual nursing care demand. Evidence is lacking about the ability of current resource allocation methods to account for the nursing care demand. To evaluate the association between (1) DRG rates and nursing care complexity and (2) hospital ward categories of nurse staffing and nursing care complexity. All patients discharged from the medical department of an Italian hospital over a data collection period were eligible. To assess the association between nursing care complexity (SIPI) and DRGs, the distribution of the DRG rate (median and first-third quartile) was compared for cases with high or low complexity. To evaluate the association between nursing care complexity (SIPI) and nurse staffing, the frequency of high complexity within nurse staffing categories (120/180/240 min/patient/day) was compared. Because the sample was very large, methods of statistical inference were not applied, and only descriptive measures were reported. 6872 hospitalizations were included. The median DRG rate for high and low complexity admissions were very similar (EUR 3536 and EUR 3285, respectively). The proportion of admissions with high complexity decreased for wards with higher staffing allocation rates. DRG reimbursement and the nurse staffing allocation systems were ineffective in accounting for nursing care complexity. The SIPI could help identify areas requiring more financial and staffing resources for nursing care.

A blockchain-enabled private parking space allocation with improved parking space utilization

With the rapid development of the world’s infrastructure, we are moving towards making cities smart, known as smart cities. To make smart cities sustainable, designing efficient solutions is needed to reduce traffic congestion due to the lack of parking spaces within the constrained land areas of many metropolitan city centres. Parking problems in urban areas can be reduced by sharing vacant private parking spaces with others. In this work, we propose a decentralized and shared private parking management system using blockchain, which helps private parking owners and users fulfil their demands without the involvement of central authority. The proposed parking allocation method assigns parking to a single user from multiple parking providers, but the requester does not need to relocate between different parking spaces. The proposed method significantly improves private parking space utilization compared to a state-of-the-art parking allocation system. Hence, the system satisfies more user requests and generates more profit for parking owners than the state-of-the-art parking allocation system. The proposed system is a winning strategy because it maximizes the utilization of private parking spaces for the entire community’s benefit and helps to reduce traffic congestion. Experiments conducted in the simulated environment validate the benefits of the proposed model and show that it outperforms the state-of-the-art parking allocation system. A prototype of the proposed system is also developed in the Ethereum blockchain to validate the work, and the gas cost used to deploy the proposed model is analyzed.

A novel two-stage network data envelopment analysis model for kidney allocation problem under medical and logistical uncertainty: a real case study.

Organ transplantation is one of the most complicated and challenging treatments in healthcare systems. Despite the significant medical advancements, many patients die while waiting for organ transplants because of the noticeable differences between organ supply and demand. In the organ transplantation supply chain, organ allocation is the most significant decision during the organ transplantation procedure, and kidney is the most widely transplanted organ. This research presents a novel method for assessing the efficiency and ranking of qualified organ-patient pairs as decision-making units (DMUs) for kidney allocation problem in the existence of COVID-19 pandemic and uncertain medical and logistical data. To achieve this goal, two-stage network data envelopment analysis (DEA) and credibility-based chance constraint programming (CCP) are utilized to develop a novel two-stage fuzzy network data envelopment analysis (TSFNDEA) method. The main benefits of the developed method can be summarized as follows: considering internal structures in kidney allocation system, investigating both medical and logistical aspects of the problem, the capability of expanding to other network structures, and unique efficiency decomposition under uncertainty. Moreover, in order to evaluate the validity and applicability of the proposed approach, a validation algorithm utilizing a real case study and different confidence levels is used. Finally, the numerical results indicate that the developed approach outperforms the existing kidney allocation system.

Associations Among Circle-Based Kidney Allocation, Center Waiting Time, and Likelihood of Deceased-Donor Kidney Transplantation

The kidney allocation system (KAS250), using circle-based distribution, attempts to address geographic disparities through broader sharing of deceased-donor kidney allografts. This study sought to evaluate the association between KAS250 and likelihood of deceased-donor kidney transplantation (DDKT) among waitlisted candidates, and whether the policy has differentially affected centers with shorter vs. longer waiting time. Retrospective cohort study. & Participants: 160,941 candidates waitlisted at 176 transplant centers between 3/2017-3/2024. KAS250 allocation policy. Rate of DDKT. Multivariable Cox regression, modeling KAS250 as a time-dependent variable. KAS250 was not independently associated with likelihood of DDKT overall (HR=1.01 vs. pre-KAS250, 95% C.I. 0.97-1.04). KAS250's association with likelihood of DDKT varied across centers from HR=0.18 (DDKT less likely after KAS250) to HR=17.12 (DDKT more likely) and varied even among neighboring centers. KAS250 was associated with decreased DDKT at 25.6% and increased DDKT at 18.2% of centers. Centers with previously long median waiting times (57+ months) experienced increased likelihood of DDKT after KAS250 (HR=1.20, 95% C.I. 1.15-1.26), whereas centers with previously short median waiting times (6-24mo.; HR=0.88, 0.84-0.92) experienced decreased likelihood of DDKT. Retrospective study of allocation policy changes, confounded by multiple changes over the study timeframe. Association between KAS250 and DDKT varied across centers. For one-in-four centers, DDKT was less likely after KAS250 relative to pre-KAS250 trends. Candidates at centers with previously long waiting times experienced increased likelihood of DDKT after KAS250. Thus, broader distribution of kidneys may be associated with improved equity in access to DDKT, but additional strategies may be needed to minimize disparities between centers.

Changing Landscape of Open Offers in Liver Transplantation in the Machine Perfusion Era: Exposure, Equity, and Economics.

Open offers (OOs) in liver transplantation (LT) result from bypassing the traditional allocation system. Little is known about the trends of OOs or the differences in donor/recipient characteristics compared to traditionally placed organs. We aim to quantify modern practices regarding OOs and understand NMP's impact, focusing on social determinants of health (SDH), cost, and graft-associated risk. LTs from 1/1/2018 to 12/31/2023 at a single center were included. NMP was implemented on 10/1/2022. The CDC (centers for disease control)-validated social vulnerability index (SVI) and donor risk index (DRI) were calculated. Comprehensive complications index (CCI), Clavien-Dindo grades, patient and graft survival, and costs of transplantation were included. 1162 LTs were performed; 193 (16.8%) from OOs. OOs were more common in the post-NMP era (26.5%vs. 13.3%, p < 0.001). Pre-NMP, patients receiving OOs had longer waitlist times (118 vs. 69 days, p < 0.001), lower MELDs (17 vs. 25 points, p < 0.001), and riskier grafts (DRI=1.8 vs. 1.6, p=0.004) compared to standard offers. Post-NMP, recipients receiving OOs demonstrated no difference in waitlist time (27vs. 20 days, p=0.21) or graft risk (DRI=2.03vs. 2.23, p=0.17). OO recipient MELD remained lower (16 vs. 22, p < 0.001). OO recipients were more socially vulnerable (SVI), pre-NMP (0.41vs. 0.36, p=0.004), but less vulnerable after NMP (0.23vs. 0.36, p=0.019). Despite increased graft risk, pre-NMP OO-LTs were less expensive in the 90-day global period ($154 939vs. $178 970, p=0.002) and the 180-days pre-/post-LT ($208 807vs. $228 091, p=0.021). Cost trends remained similar with NMP. OOs are increasingly utilized and may be appealing due to demonstrated cost reductions even with NMP. Although most OO-related metrics in our center remain similar before and after machine perfusion, programs should take caution that increasing use does not worsen organ access for socially vulnerable populations.

Personal Viewpoint: Navigating Challenges in Recipient Selection for End-Chain Kidneys

As a result of the increasing number of transplants being facilitated by kidney paired donation, and newer initiatives such as voucher donation, end-chain kidneys now constitute a considerable proportion of kidney paired donation transplants in the United States. Data on end-chain kidneys are limited. They may be lower in quality compared to non-end chain living donor kidneys. However, they can provide unique opportunities for recipient candidates without living donors. There are no data or algorithms available to guide recipient selection for end-chain kidneys accepted by a transplant center. Considering the ethical principles of utility, justice, and respect for persons that underlie organ allocation, we discuss three potential approaches for recipient selection: 1) adherence to the Kidney Allocation System, 2) utility maximization and 3) priority to high-risk candidates, along with examples from our own center’s experience. Similar considerations are also relevant to selection of recipients for non-directed donor organs, and to out-of-sequence allocation for deceased organ donors. Since end-chain kidneys represent an increasingproportion of kidney paired donation-facilitated living kidney donor transplantation in the United States, and will likely get more medically and surgically complex over time, ongoing research on their utilization and outcomes is needed.

Maṣādiru Tamwīlil Ta’līmi fī al-Madrasati al-Islāmīyati min Manẓūri Niẓami Tamwili at-Ta’limi fī Indūnisiyā

Education has a very important role in promoting the civilization of the nation. A good education can encourage the creation of a good, creative and productive society so that it can finally achieve the goals of the state. Through this Indonesian national education system, the government must be able to ensure the equitable distribution of educational opportunities, as well as the importance and efficiency of education management to meet challenges in line with the changing demands of local, national and global life. The budget allocation system for education in Indonesia is strongly influenced by government policies. The researcher used the library research method by collecting information and data with the help of various materials found in the library such as reference books, similar previous research results, articles, notes and various journals related to the problem to be solved. Research activities are carried out by collecting information and data with the help of various materials in the library such as reference books, similar previous research results, articles, notes and various journals related to the problem to be solved. The research concluded that the financing of education in Indonesia through seven sources: Sources of funding from the central government from Law No. 20 of 2003 on the national education system, Article 49 "Education funds other than education salaries and official education costs are allocated at a minimum of 20% of the state budget and a minimum of 20% of the local revenue budget called the Public Allocation Fund and partially added with the Special Allocation Fund, local governments, parents of students, adoptive parents, community groups, students themselves and sources of institutions or associations.

Epidemiological characteristics, diagnosis and treatment effect of rifampicin-resistant pulmonary tuberculosis (RR-PTB) in Guizhou Province

BackgroundRifampicin-resistant pulmonary tuberculosis (RR-PTB) presents a significant threat to global public health security. China bears a substantial burden of RR-PTB cases globally, with Guizhou Province experiencing particularly alarming trends, marked by a continual increase in patient numbers. Understanding the population characteristics and treatment modalities for RR-PTB is crucial for mitigating morbidity and mortality associated with this disease.MethodsWe gathered epidemiological, diagnostic, and treatment data of all RR-PTB cases recorded in Guizhou Province from January 1, 2017 to December 31, 2023. Utilizing composition ratios as the analytical metric, we employed Chi-square tests to examine the spatiotemporal distribution patterns of RR-PTB patients and the evolving trends among different patient classifications over the study period.ResultsIn our study, 3396 cases of RR-PTB were analyzed, with an average age of 45 years. The number of RR-PTB patients rose significantly from 176 in 2017 to 960 in 2023, peaking notably among individuals aged 23–28 and 44–54, with a rising proportion in the 51–80 age group (P < 0.001). Since 2021, there has been a notable increase in the proportion of female patients. While individuals of Han ethnic group comprised the largest group, their proportion decreased over time (P < 0.001). Conversely, the Miao ethnicity showed an increasing trend (P < 0.05). The majority of patients were farmers, with their proportion showing an upward trajectory (P < 0.001), while students represented 4.33% of the cases. Geographically, most patients were registered in Guiyang and Zunyi, with a declining trend (P < 0.001), yet household addresses primarily clustered in Bijie, Tongren, and Zunyi. The proportion of floating population patients gradually decreased, alongside an increase in newly treated patients and those without prior anti-tuberculosis therapy. Additionally, there was a notable rise in molecular biological diagnostic drug sensitivity (real-time PCR and melting curve analysis) (P < 0.001). However, the cure rate declined, coupled with an increasing proportion of RR-PTB patients lost to follow-up and untreated (P < 0.05).ConclusionsEnhanced surveillance is crucial for detecting tuberculosis patients aged 23–28 and 44–54 years. The distribution of cases varies among nationalities and occupations, potentially influenced by cultural and environmental factors. Regional patterns in RR-PTB incidence suggest tailored prevention and control strategies are necessary. Despite molecular tests advances, challenges persist with low cure rates and high loss to follow-up. Strengthening long-term management, resource allocation, and social support systems for RR-PTB patients is essential.

Advancing Kidney Transplantation: A Machine Learning Approach to Enhance Donor-Recipient Matching.

(1) Background: Globally, the kidney donor shortage has made the allocation process critical for patients awaiting a kidney transplant. Adopting Machine Learning (ML) models for donor-recipient matching can potentially improve kidney allocation processes when compared with traditional points-based systems. (2) Methods: This study developed an ML-based approach for donor-recipient matching. A comprehensive evaluation was conducted using ten widely used classifiers (logistic regression, decision tree, random forest, support vector machine, gradient boosting, boost, CatBoost, LightGBM, naive Bayes, and neural networks) across three experimental scenarios to ensure a robust approach. The first scenario used the original dataset, the second used a merged version of the dataset, and the last scenario used a hierarchical architecture model. Additionally, a custom ranking algorithm was designed to identify the most suitable recipients. Finally, the ML-based donor-recipient matching model was integrated into a web-based platform called Nephron. (3) Results: The gradient boost model was the top performer, achieving a remarkable and consistent accuracy rate of 98% across the three experimental scenarios. Furthermore, the custom ranking algorithm outperformed the conventional cosine and Jaccard similarity methods in identifying the most suitable recipients. Importantly, the platform not only facilitated efficient patient selection and prioritisation for kidney allocation but can be flexibly adapted for other solid organ allocation systems built on similar criteria. (4) Conclusions: This study proposes an ML-based approach to optimize donor-recipient matching within the kidney allocation process. Successful implementation of this methodology demonstrates significant potential to enhance both efficiency and fairness in kidney transplantation.

Aflatoxins in Cameroon: Occurrence, Potential Health Risks and the Way Forward in Implementing the Partnership for Aflatoxin Control in Africa (PACA) Country-Led Model

Aflatoxin hazard is present among the main food dangers in Cameroon. This paper gives insight on how affected crops and commodities such as maize and groundnuts are likely to be contaminated with aflatoxin. Possible negative effects of aflatoxin include hepatic injury, cancer risk, kwashiorkor, and marasmus kwashiorkor is also discussed. Therefore, this report recommends using the Partnership for Aflatoxin Control in Africa (PACA) method for Cameroon. This policy also targets aflatoxin control to be a country-led, multi-sectoral approach. The aims of the study propose useful strategies for guiding the PACA approach; these include raising awareness of the health risks of aflatoxins, enhancing the laws on food safety, supporting adequate agricultural practices, and improving proper disposal and utilization procedures of post-harvest storage and processing systems. Government authorities and research institutes must collaborate to successfully execute aflatoxin control measures. The report emphasizes the need of capacity building, resource allocation, and monitoring systems in ensuring the efficacy of these initiatives. Thus, Cameroon can apply the PACA concept and the mentioned measures to decrease the level of aflatoxin contamination. Therefore, enhance the conditions for food safety and decrease the level of health risks. Consequently, this research provides useful information and suggestions to policymakers, researchers, and any party interested in Cameroon’s aflatoxin-related programs. This paper emphasizes the need for capacity enhancement, resource mobilization, and the proper monitoring and implementation of the strategies. Through the application of the PACA approach and the above-outlined strategies, Cameroon can reduce the incidence of aflatoxin contamination and its disturbing impacts on consumer health and food security. In conclusion, the findings and recommendations of this paper are highly informative to policymakers, researchers, and other interested stakeholders in Cameroon’s aflatoxin management processes.

Resource Allocation and Security Threat in Cloud Computing: A Survey

Cloud computing is a cutting-edge technology with enormous commercial and enterprise potential. Apps and related data can be accessed from any location thanks to clouds. Companies can drastically lower the cost of their infrastructure by renting resources from the cloud for storage and other processing needs. They can also use pay-as-you-go application access available to the entire firm. Therefore, obtaining licenses for specific products is not necessary. However, allocating resources as efficiently as possible is one of the main challenges in cloud computing. Because the model is unique, resource allocation is done to lower its expenses. Meeting application and customer requirements and consumer needs presents additional issues in resource allocation. This study provides a detailed discussion of several resource allocation systems, security threats, and the difficulties they face. This work is anticipated to help researchers and cloud users overcome obstacles.

An intelligent parking allocation framework for digital society 5.0

In recent smart city innovations, parking lot location has garnered a lot of focus. The issue of where to put cars has been the subject of a lot of literature. However, these efforts rely heavily on algorithms built on centralized servers using historical data as their basis. In this study, we propose a smart parking allocation system by fusing k-NN, decision trees, and random forests with the boosting techniques Adaboost and Catboost. Implementing the recommended intelligent parking distribution technique in Smart Society 5.0 offers promise as a practical means of handling parking in contemporary urban settings. Users will be given parking spots in accordance with their preferences and present locations as recorded in a centralized database using the proposed system’s hybrid algorithms. The evaluation of performance considers the effectiveness of both the ML classifier and the boosting technique, and it finds that the combination of Random Forest and Adaboost achieves 98% accuracy. Users and operators alike can benefit from the suggested method’s optimised parking allocation and pricing structure, which in turn provides more convenient and efficient parking options.

First consensus document of waiting list prioritization for liver transplantation by the Spanish Society of Liver Transplantation (SETH).

Spain is worldwide leader in deceased donation rates per million habitants and count on a strong network of twenty-five liver transplant institutions. Although the access to liver transplantation is higher than in other countries, approximately 10% of patients qualifying for liver transplantation in Spain will die in the waiting list or would be excluded due to clinical deterioration. A robust waiting list prioritization system is paramount to grant the sickest patients with the first positions in the waiting list for an earlier access to transplant. In addition, the allocation policy may not create or perpetuate inequities, particularly in a public and universal healthcare system. Hitherto, Spain lacks a unique national allocation system for elective liver transplantation. Most institutions establish their own rules for liver allocation and only two autonomous regions, namely Andalucía and Cataluña, share part of their waiting list within their territory to provide regional priority to patients requiring more urgent transplantation. This heterogeneity is further aggravated by the recently described sex-based disparities for accessing liver transplantation in Spain, and by the expansion of liver transplant indications, mainly for oncological indications, in absence of clear guidance on the optimal prioritization policy. The present document contains the recommendations from the first consensus of waiting list prioritization for liver transplantation issued by the Spanish Society of Liver Transplantation (SETH). The document was supported by all liver transplant institutions in Spain and by the Organización Nacional de Trasplantes (ONT). Its implementation will allow to homogenize practices and to improve equity and outcomes among patients with end-stage liver disease.

Use of intermediate-resolution deceased donor molecular typing in organ allocation systems could increase the fraction of HLA-compatible donor offers

Use of intermediate-resolution deceased donor molecular typing in organ allocation systems could increase the fraction of HLA-compatible donor offers

A Machine Learning Method for Allocating Scarce COVID-19 Monoclonal Antibodies

During the COVID-19 pandemic, the effective distribution of limited treatments became a crucial policy goal. Yet, limited research exists using electronic health record data and machine learning techniques, such as policy learning trees (PLTs), to optimize the distribution of scarce therapeutics. To evaluate whether a machine learning PLT-based method of scarce resource allocation optimizes the treatment benefit of COVID-19 neutralizing monoclonal antibodies (mAbs) during periods of resource constraint. This retrospective cohort study used electronic health record data from October 1, 2021, to December 11, 2021, for the training cohort and data from June 1, 2021, to October 1, 2021, for the testing cohort. The cohorts included patients who had positive test results for SARS-CoV-2 and qualified for COVID-19 mAb therapy based on the US Food and Drug Administration's emergency use authorization criteria, ascertained from the patient electronic health record. Only some of the qualifying candidates received treatment with mAbs. Data were analyzed between from January 2023 to May 2024. The primary outcome was overall expected hospitalization, assessed as the potential reduction in overall expected hospitalization if the PLT-based allocation system was used. This was compared to observed allocation using risk differences. Among 9542 eligible patients in the training cohort (5418 female [56.8%]; age distribution: 18-44 years, 4151 [43.5%]; 45-64 years, 3146 [33.0%]; and ≥65 years, 2245 [23.5%]), a total of 3862 (40.5%) received mAbs. Among 6248 eligible patients in the testing cohort (3416 female [54.7%]; age distribution: 18-44 years, 2827 [45.2%]; 45-64 years, 1927 [30.8%]; and ≥65 years, 1494 [23.9%]), a total of 1329 (21.3%) received mAbs. Treatment allocation using the trained PLT model led to an estimated 1.6% reduction (95% CI, -2.0% to -1.2%) in overall expected hospitalization compared to observed treatment allocation in the testing cohort. The visual assessment showed that the PLT-based point system had a larger reduction in 28-day hospitalization compared with the Monoclonal Antibody Screening Score (maximum overall hospitalization difference, -1.0% [95% CI, -1.3% to -0.7%]) in the testing cohort. This retrospective cohort study proposes and tests a PLT method, which can be linked to a electronic health record data platform to improve real-time allocation of scarce treatments. Use of this PLT-based allocation method would have likely resulted in fewer hospitalizations across a population than were observed in usual care, with greater expected reductions than a commonly used point system.

Highly Sensitized Kidney Transplant Outcomes After the 2014 Kidney Allocation System Change.

Introduction: Kidney Allocation System (KAS) was implemented by United Network for Organ Sharing in 2014 to reduce allocation disparities. Research Questions: Outcomes of highly sensitized patients (calculated panel reactive antibody (cPRA) ≥ 97%) before and after KAS were compared to low-risk recipients (cPRA <10%) in the post-KAS era were examined. The impact on racial disparities was determined. Design: This was a retrospective study of national registry data. Two cohorts of adult candidates waitlisted for deceased donor transplantation during 3-year periods before and after KAS were identified. Results: Highly sensitized patients (N = 1238 and 4687) received a deceased donor kidney transplant between January 1, 2011 and December 31, 2013 and between January 1, 2015 and December, 31, 2017. Racial disparity for highly sensitized patients improved, yet remained significant (P < 0.001), with Black patients comprising 40% and 41% of the highly sensitized candidates and 28% and 34% of the recipients pre- and post-KAS. While posttransplant death-censored graft failure for highly sensitized recipients was similar overall, post-KAS was associated with improved graft survival in the first year after transplant (HR 0.56, 95% CI 0.40-0.78). When compared to contemporaneous lowrisk recipients, both death-censored and all-cause graft failure were similar for highly sensitized recipients and was associated with increased risk for death-censored graft failure beyond the first year (HR 1.39, 95% CI 1.11-1.73). Conclusion: The allocation system led to an increase in transplantation in highly sensitized candidates without compromising outcomes. Although KAS has led to more balanced transplant rates between highly sensitized Black and White patients, racial inequalities persist.