Development of Machine Learning Composite Measures for Graft Outcome Selection in Pediatric Liver Transplantation
开发用于小儿肝移植移植结果选择的机器学习综合措施
基本信息
- 批准号:10484107
- 负责人:
- 金额:$ 24.94万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-05-15 至 2022-11-15
- 项目状态:已结题
- 来源:
- 关键词:AbdomenAdoptionAdultAlgorithmsArtificial IntelligenceAwarenessCessation of lifeChildChildhoodClinicalClinical assessmentsCommunicationComplexComputer softwareCountryDataData ScienceData SetDecision MakingDevelopmentDonor personEffectivenessFoundationsFutureGeographyGoalsGraft SurvivalHealth Insurance Portability and Accountability ActHourHumanIndividualIndustryJournalsKidney TransplantationLinkLiverLiving Donor Liver TransplantationLiving DonorsLogisticsMachine LearningManuscriptsMeasuresMedical centerMethodsModelingMorbidity - disease rateOperative Surgical ProceduresOrganOrgan DonationsOrgan DonorOrgan TransplantationOutcomeOutcome MeasurePatient-Focused OutcomesPatientsPerformancePhasePoliciesPositioning AttributeProblem SolvingProductivityProviderPublicationsQuality of lifeRecoveryReportingRiskSmall Business Innovation Research GrantSoftware ToolsTelephoneTestingTimeTrainingTransplant RecipientsTransplantationUniversitiesValidationVariantVulnerable PopulationsWaiting ListsWorkbaseclinical decision supportcohesiondata registrydeep learningdesignexhaustexperiencehazardhealth information technologyimprovedliver transplantationmachine learning algorithmmachine learning methodmobile applicationmortalitymulti-site trialnovelorgan allocationpost-transplantpredictive modelingpreventprogramssimulationsuccesssupervised learningsupport toolssurvival outcomesurvival predictiontooltransplant centers
项目摘要
ABSTRACT
Outcomes in pediatric liver transplantation (pLT) are not limited by the donated organ supply. Kids are dying
waiting for organs even when these deaths are completely preventable through proper organ selection. Instead of
dying, children can live a full and active lifetime with a properly selected liver graft for transplant. Critical to
achieving zero waitlist mortality and long-term transplant benefit is the capacity to intervene in a timely manner
with a suitable organ and graft type. Decisions to proceed with pLT are complicated, ultimately based on the
alignment of transplant team experience, clinical assessment, and organ availability. In an era of organ shortages,
the use of technical variant (TV) grafts, including split liver transplantation and living donor liver transplant, has
the potential to expand graft choice and enable timelier surgical intervention. Most transplant programs that have
prior experience with TV grafts have low patient mortality and excellent transplant outcomes. However, some
transplant programs that have limited prior experience with TV grafts have reported many poor outcomes for
patients receiving TV transplants. Despite improvements in overall outcomes, national registry data have
confirmed significant variation among transplant centers in waitlist mortality, TV graft use, and post-transplant
outcomes. Integrally linked to this variation is the intricacy of transplant decision making. Collectively, donor
and graft acceptance, prioritization of candidates, and allocation policies depict a complex scenario. More than
100 variables can be considered in a single donor-recipient ‘‘best matching’’ decision, with a risk of subjectivity
and mismatch because of human limitations that should not be underestimated. Recognizing these limitations,
artificial intelligence classifiers, including machine learning and deep learning, have been recognized for their
potential to support or confirm decision making within the field of transplantation. Still, overall data-driven
support for optimal graft selection and dissemination of graft decision support is lacking. Opportunities for, and
the impact of, discovery are high. This project will result in a composite decision support software tool that uses
machine learning to predict and model the best survival for the patient using pre-transplant mortality, post-
transplant outcomes, and prior center experience. The decision support tool can be established to supplement
current graft selection practices in pLT. We anticipate that modeling based on composite measures will
demonstrate equivalent outcomes in recipients of TV grafts. We will develop an algorithm for optimal pediatric
graft-type selection that will be commercialized for use through the Starzl Network for Excellence in Pediatric
Transplantation and after further multi-center validation it will be available for all pediatric transplant programs.
We will accomplish our objective through the following three aims. One, determine the optimal feature space for
predictive variables for patient and pLT graft survival. Two, develop survival prediction models, “PSELECT,” for
remaining on the waitlist or receiving various graft types. Three, demonstrate the simulated technical feasibility
to eliminate the waitlist mortality based on the PSELECT performance on previously held-out data.
摘要
小儿肝移植(pLT)的结局不受捐献器官供应的限制。孩子们都快死了
即使这些死亡是完全可以通过适当的器官选择来预防的。而不是
在生命垂危时,儿童可以通过适当选择的肝移植物活出充实而活跃的一生。的关键
实现零等待名单死亡率和长期移植效益是及时干预的能力
合适的器官和移植类型进行pLT的决定是复杂的,最终取决于
移植团队经验、临床评估和器官可用性的一致性。在器官短缺的时代,
包括劈离式肝移植和活体供肝移植在内的技术变体(TV)移植物的使用,
扩大移植物选择的可能性,并使手术干预更及时。大多数移植项目
以前使用TV移植物的经验表明,患者死亡率低,移植结果良好。但也有
以前使用TV移植物的经验有限的移植程序报告了许多不良结果,
接受电视移植的患者。尽管总体结果有所改善,但国家登记册数据
证实了移植中心之间在等待名单死亡率、TV移植物使用和移植后
成果。与这种变化紧密相关的是移植决策的复杂性。捐助者集体
并且嫁接接受、候选人的优先级以及分配策略描绘了复杂的情况。超过
在单个捐赠者-受援者“最佳匹配”决策中可以考虑100个变量,存在主观性风险
和不匹配,因为人类的局限性,不应该被低估。认识到这些局限性,
人工智能分类器,包括机器学习和深度学习,已被公认为
支持或确认移植领域决策的潜力。尽管如此,整体数据驱动
缺乏对最佳嫁接选择和嫁接决策支持传播的支持。机会和
发现的影响是很大的。该项目将产生一个复合决策支持软件工具,
机器学习使用移植前死亡率、移植后死亡率和移植后死亡率来预测和建模患者的最佳生存率。
移植结果和先前的中心经验。可以建立决策支持工具来补充
目前pLT中的移植物选择实践。我们预计,基于复合测量的建模将
在TV移植物的接受者中表现出等同的结果。我们将开发一种算法,
将通过Starzl儿科卓越网络进行商业化使用的移植物类型选择
在进一步多中心验证后,它将可用于所有儿科移植项目。
我们将通过以下三个目标来实现我们的目标。第一,确定最佳特征空间,
患者和pLT移植物存活的预测变量。第二,开发生存预测模型“PSELECT”,
仍在等待名单上或接受各种移植类型。三、论证模拟技术的可行性
基于PSELECT对先前保留数据的性能来消除等待列表死亡率。
项目成果
期刊论文数量(0)
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George V Mazariegos其他文献
George V Mazariegos的其他文献
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{{ truncateString('George V Mazariegos', 18)}}的其他基金
SNEPT and SPLIT implementation of a QoL measure in pediatric transplant recipients
SNEPT 和 SPLIT 在儿科移植受者中实施 QoL 测量
- 批准号:
10247314 - 财政年份:2021
- 资助金额:
$ 24.94万 - 项目类别:
SNEPT and SPLIT implementation of a QoL measure in pediatric transplant recipients
SNEPT 和 SPLIT 在儿科移植受者中实施 QoL 测量
- 批准号:
10457344 - 财政年份:2021
- 资助金额:
$ 24.94万 - 项目类别:
SNEPT and SPLIT implementation of a QoL measure in pediatric transplant recipients
SNEPT 和 SPLIT 在儿科移植受者中实施 QoL 测量
- 批准号:
10668296 - 财政年份:2021
- 资助金额:
$ 24.94万 - 项目类别:
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