Uncertainty aware virtual treatment planning for peripheral pulmonary artery stenosis
Uncertainty aware virtual treatment planning for peripheral pulmonary artery stenosis
批准号:
10734008
负责人:
Jeffrey A. Feinstein
金额:
$74.23万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-07-01 至 2028-06-30
关键词:
AffectAftercareAgeAlagille SyndromeAngioplastyArteriesAwarenessBalloon AngioplastyBasic ScienceBayesian MethodBayesian ModelingBiologicalBiomechanicsBiomedical EngineeringBlood VesselsBlood flowCardiac Catheterization ProceduresCardiovascular systemCathetersCessation of lifeCharacteristicsChildhoodClinicalClinical DataComplexComplicationComputer ModelsCouplingDataData CollectionDiseaseDisease ProgressionDistalEngineeringEnsureFormulationFoundationsGoalsGrowthHistologicHistologyHourHumanHypertensionInfant MortalityInstitutionInterventionLesionLiquid substanceLiteratureLobarLungMachine LearningMechanicsMembraneMethodsModelingMorbidity - disease rateMorphologyOperative Surgical ProceduresOutcomePathologicPatient-Focused OutcomesPatientsPediatric cardiologyPeripheralPhysicsPhysiologic intraventricular pressurePhysiologicalPopulationPostoperative PeriodPrediction of Response to TherapyProceduresPropertyPulmonary Valve StenosisPulmonary arterial remodelingPulmonary artery structureReconstructive Surgical ProceduresSeveritiesSolidStatistical ModelsStenosisStentsStructureTestingTetanus Helper PeptideTetralogy of FallotTimeTissue ModelTissuesTrainingTreatment outcomeTreesUncertaintyUnnecessary ProceduresVentricularWorkartery stenosisclinical decision supportclinical decision-makingclinically relevantcongenital heart disorderdigital twinexperimental studyhemodynamicshomografthuman datahuman diseaseimprovedimproved outcomeindividual patientintervention effectmechanical propertiesmortalitymulti-scale modelingnoveloptimal treatmentspatient populationpediatric patientspersonalized medicinepost interventionpredictive modelingpulmonary arterial pressurereconstructionright ventricular failuresimulationsoft tissuestandard of caresuccesssupport toolstheoriestreatment comparisontreatment optimizationtreatment planningtreatment strategyvascular abnormalityvirtual modelvirtual therapy
中文摘要
先天性心脏病(CHD)影响着1/100的婴儿,是美国婴儿死亡的主要原因。
肺动脉(PA)狭窄在冠心病患者中很常见,当发生时尤其具有挑战性。
在PA树的外围。周围性肺狭窄(PPS),通常由多条血管组成
近端和远端分叉节段狭窄可导致持续性右室高血压、右室衰竭,甚至
死亡。大多数机构对PPS患者的治疗仅限于近端(中央和肺叶)的支架和血管成形术。
仅限通行证。然而,这些以导管为基础的介入治疗在缩小右心室方面往往无效。
压力,并与糟糕和不可预测的结果相关联。全面的外科重建,
涉及所有狭窄(中央、肺叶、节段性狭窄)的补片增强,可以实现长期的RV
减压,发病率和死亡率低,但需要10小时的程序和专业知识
仅在选定的机构提供。因为治疗策略继续在全国范围内辩论,而且
结果仍然很差,对新的临床决策支持工具的迫切需求尚未得到满足。我们的目标是
开发两种互补的建模方法来支持冠心病患者的临床决策
PPS:1)肺液固相生长熔融流体结构的多尺度力学模型
交互(FSI)和血管生长和重塑(G&R),以及2)实时不确定性感知数字
虚拟治疗计划的孪生模型,以帮助临床医生确定最佳治疗策略。至
为了实现这些目标,我们提出了三个具体目标:(1)表征机械和免疫组织化学
通过双轴测试和组织学研究PPS患者的PA组织特性;(2)建立和验证
能够预测治疗后的计算建模框架(融合血流动力学和G&R)
PPS中的血流动力学;以及(3)开发和验证快速、交互的贝叶斯建模框架
不确定性下的治疗计划有助于PPS的近乎实时的临床决策,利用减少的
订购模型。我们提出的研究将把建模和实验紧密结合起来,以提高生理保真度
在研究不足的患者群体中,患者特定模型的临床相关性。双轴机械
儿科人PA组织的特征将提供关于CHD组织特性的迫切需要的数据
目前都没有出现在文献中。该项目汇集了一个跨学科的工程师团队,
擅长血流动力学建模、心血管生物力学、生物力学的力学特性
组织和不确定性量化,以及在儿科心脏病学/肺血管方面有专长的临床医生
畸形、心胸外科手术和心导管置入术。我们的翻译目标是:(1)
系统地比较PPS的治疗方案,从而挑战当前的护理标准,以及,(2)
最终优化对个别患者的治疗,从而减少不必要的程序和潜在的伤害
并改善这一复杂的儿科人群的长期结果。
英文摘要
Congenital heart disease (CHD) affects 1/100 babies and is the leading cause of infant mortality in the U.S.
Pulmonary artery (PA) stenosis is common in CHD patients and is particularly challenging to treat when occurring
in the periphery of the PA tree. Peripheral pulmonary stenosis (PPS), often consisting of numerous vessel
narrowings at proximal and distal bifurcation levels, can lead to persistent RV hypertension, RV failure, and even
death. Most institutions treat PPS patients with stenting and angioplasty limited to the proximal (central and lobar)
PAs only. These catheter-based interventions, however, are often ineffective at reducing right ventricular
pressures and are associated with poor and unpredictable outcomes. Comprehensive surgical reconstruction,
involving patch augmentation of ALL stenoses (central, lobar, segmental PAs), can achieve long-term RV
pressure reduction with low morbidity and mortality, but requires >10-hour procedures and specialized expertise
available only at select institutions. Because treatment strategies continue to be debated nationally, and
outcomes remain poor, there is a pressing unmet need for novel clinical decision support tools. We aim to
develop two complementary modeling methods to support clinical decision making in CHD patients with
PPS: 1) a mechanistic multiscale model of pulmonary fluid solid growth melding fluid structure
interaction (FSI) and vascular growth and remodeling (G&R), and 2) a real-time uncertainty-aware digital
twin model for virtual treatment planning to aid clinicians in identifying optimal treatment strategies. To
accomplish these goals, we propose three specific aims: (1) Characterize mechanical and immunohistochemical
properties of PA tissue in human PPS patients via biaxial testing and histology; (2) Develop and validate a
computational modeling framework (melding hemodynamics and G&R) capable of predicting post-treatment
hemodynamics in PPS; and (3) Develop and validate a fast, interactive Bayesian modeling framework for virtual
treatment planning under uncertainty to aid near real-time clinical decision making for PPS, leveraging reduced
order models. Our proposed study will tightly integrate modeling and experiments to improve physiological fidelity
and clinical relevance of patient-specific models in an understudied patient population. The biaxial mechanical
characterization of pediatric human PA tissue will provide much needed data on tissue properties in CHD which
are currently absent from the literature. This project assembles an interdisciplinary team of engineers with
expertise in hemodynamics modeling, cardiovascular biomechanics, mechanical characterization of biological
tissues, and uncertainty quantification, and clinicians with expertise in pediatric cardiology/pulmonary vascular
abnormalities, cardiothoracic surgery, and cardiac catheterization. Our translational objectives are to: (1)
systematically compare treatment options for PPS and thus challenge the current standard of care and, (2)
ultimately optimize treatments for individual patients, thus reducing unnecessary procedures and potential harm
and improving long-term outcomes in this complex pediatric population.
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