Modern Analytics to Improve Quality & Outcome Assessments Following Congenital Heart Surgery

现代分析提高质量

• 批准号: 10419358
• 负责人: SHARON-LISE Teresa NORMAND
• 金额: $ 71万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10419358
• 关键词: Accounting; Address; Area; Bayesian learning; Birth; Calibration; Cardiac; Cardiac Surgery procedures; Case Mixes; Child; Childhood; Clinical; Clinical Data; Collaborations; Congenital Abnormality; Congenital Heart Defects; Critical Care; Data; Data Science; Data Set; Databases; Diagnosis; Discrimination; Family; Heart; Heart Transplantation; Hospitals; Hypoplastic Left Heart Syndrome; Lesion; Machine Learning; Measures; Methods; Modeling; Modernization; Morbidity - disease rate; Operative Surgical Procedures; Outcome; Outcome Assessment; PC4 Gene; Patient risk; Patient-Focused Outcomes; Patients; Pediatric Hospitals; Physiological; Probability; Registries; Reporting; Research; Resources; Risk; Risk Adjustment; Risk Factors; Sample Size; Societies; Source; Testing; Thoracic Surgeon; Time; Uncertainty; Variant; analytical method; base; clinically relevant; density; diverse data; high dimensionality; improved; improved outcome; interest; mortality; operation; patient subsets; primary outcome; programs; regression trees; response; secondary outcome; surgery outcome; virtual

Congenital heart defects (CHD) are the most common birth defects, impacting ~1 in every 100 births. While surgical outcomes have improved over time, morbidity and mortality remain high for certain lesions, and significant variation exists across the ~120 U.S. congenital heart centers. This has heightened the scrutiny of congenital heart programs, with several national high-profile reports leading to program closure and several quality improvement initiatives to improve national outcomes. All such initiatives depend upon rigorous measures of program quality, but how best to measure and report quality remains controversial and understudied. Several features of CHD data and current analytic methods complicate quality assessments. 1) CHD is a widely heterogeneous, sparse mix of different lesions. Current approaches combine all CHD surgeries together for quality reporting and adjust for risk based only on operation type and few general factors, despite the important influence of other routinely collected variables. Machine learning approaches are better able to incorporate these high-dimensional clinical data but remain understudied, and current methods rely upon outdated regression. 2) Preliminary studies suggest wide variability in the types of CHD treated across hospitals, yet differences in hospital case-mix have not been quantified and are not used in quality reporting. Approaches to assess hospital case-mix distributions could provide an improved understanding of which hospitals treat comparable patients and which patient subsets are large enough to allow meaningful quality assessments but have not been studied. 3) Current methods support only indirect assessments of CHD program quality, prohibiting direct comparisons of interest and ignore important sources of uncertainty, leading to inaccuracies in the quality estimates. A causal inference framework could support more direct and clinically relevant quality comparisons across hospitals but has not been investigated. In response to NOT-HL-19-712, we propose to apply contemporary statistical approaches to address these challenges. Aim 1 will optimize risk- adjustment using machine learning approaches to characterize operative morbidity and mortality. Aim 2 will identify overlapping and unique CHD patient subgroups across hospitals to improve characterization of hospital case-mix and enable valid hospital comparisons. Aim 3 will develop robust hospital quality estimates that will support direct and clinically relevant quality comparisons across hospitals using causal inference approaches, focusing on areas of overlap, and including hospital random effects to account for currently ignored sources of uncertainty. Our methods will be applied within the STS Congenital Heart Surgery Database which captures clinical information >90% of U.S. congenital heart hospitals and validated in an external source, the Pediatric Cardiac Critical Care Consortium (PC4) Registry. These advancements will support national initiatives to improve quality and outcomes for children and families impacted in CHD and will also provide a framework that can be applied broadly to improve quality assessment across other fields.

先天性心脏缺陷（CHD）是最常见的先天缺陷，每100个出生就会影响约1个。尽管 随着时间的流逝，手术结果有所改善，某些病变的发病率和死亡率仍然很高，并且 在约120个美国先天性心脏中心之间存在显着差异。这加深了审查 先天性心脏计划，还有几份全国性的备受瞩目的报告，导致计划关闭和几个 改善国家成果的质量改进计划。所有这些举措都取决于严格 计划质量的度量，但是如何最好地衡量和报告质量仍然有争议，并且 研究了。 CHD数据和当前分析方法的几个特征使质量评估复杂化。 1） 冠心病是一种广泛的异质性，稀疏的不同病变的混合物。当前方法结合了所有冠心 手术一起进行质量报告，并仅根据操作类型调整风险，很少 尽管其他常规收集的变量具有重要的影响，但因素。机器学习方法是 能够更好地纳入这些高维临床数据，但仍在研究中，并且当前方法 依靠过时的回归。 2）初步研究表明，经过治疗的CHD类型的差异很大 在整个医院，尚未量化医院病例的差异，质量尚未使用 报告。评估医院病例分布的方法可以提高人们对 哪些医院治疗可比的患者以及哪些患者子集足够大以允许有意义 质量评估，但尚未研究。 3）当前方法仅支持冠心病的间接评估 计划质量，禁止直接比较感兴趣的比较，而忽略了重要的不确定性来源，领先 质量估计值不准确。因果推理框架可以支持更直接和临床 相关质量比较，但尚未进行调查。为了响应非HL-19-712， 我们建议采用当代统计方法来应对这些挑战。 AIM 1将优化风险 - 使用机器学习方法调整以表征手术发病率和死亡率。 AIM 2意志 确定整个医院的重叠和独特的CHD患者子组，以改善医院的特征 病例混合并启用有效的医院比较。 AIM 3将制定强大的医院质量估计 使用因果推断方法，支持整个医院的直接和临床相关质量比较， 专注于重叠区域，并包括医院随机影响，以说明当前忽略的来源 不确定。我们的方法将应用于STS先天性心脏手术数据库中，该数据库捕获 临床信息> 90％的美国先天性心脏医院，并在外部来源（小儿）进行了验证 心脏重症监护联盟（PC4）注册表。这些进步将支持国家倡议 改善对CHD影响的儿童和家庭的质量和结果，还将提供一个框架 可以广泛应用以改善其他领域的质量评估。