Developing a refined comorbidity index for use in obstetric patients

项目摘要 应对孕产妇死亡率和孕产妇严重发病率上升的趋势是2010年的一个关键优先事项， 美国的大约一半的不良孕产妇健康结果被认为是可预防的 由临床实践和提供围产期护理系统引起的伤害或意外后果。 目前正在投入大量资源，以实施分娩质量改进（QI）举措 全国各地的医院。非常需要评估这些努力并证明其有效性 减少可预防的SMM和孕产妇死亡的负担。分娩医院的几乎所有QI计划 使用SMM作为结果衡量标准，但由于需要对SMM率进行风险调整， 控制医院内部和医院之间病人构成的差异。迄今为止，三个不同的研究小组 建议的产科合并症指数，但都有显着的局限性。本研究的总体目标是 开发和验证一个完善的产科患者合并症指数，允许SMM率比较 并充分监测产科的QI计划。我们会用马里兰州独一无二的黄金- 标准的、以医院为基础的、具有州代表性的SMM监督和审查数据，以确定一个全面的 发生SMM事件患者的合并症列表。使用约翰霍普金斯的电子健康记录数据 卫生系统，我们将采用变量重要性估计与机器学习技术来开发 comorbidity index.随后，我们将使用接收机工作特性来确定其准确性 (ROC)/用于结果区分的精确-召回（PR）曲线和曲线下面积（AUC）， 平滑校准图。此外，我们还将比较改进后的共患病指数的表现，以预测 SMM与先前公布的comoreconomy指数的比较。为了进一步验证我们改进后的共患病指数， 并评估其在不同社会人口群体中的表现一致性，我们将使用国家 出院数据来自医疗保健成本和利用项目的国家住院病人样本。一 技术咨询小组由临床医生、社区合作伙伴、患者安全专家和认证专家组成， 医学编码员将每季度举行一次数据解读会议。在研究结束时，我们希望有一个 在金标准数据中开发的精炼的共病指数，具有上级 先前发表的合并症指数，并在EHR和国家医院出院数据中得到验证。我们 研究结果将在同行审查的文献中传播，并通过在科学会议上的介绍加以传播。

Project Summary Addressing the rising trends in maternal mortality and severe maternal morbidity (SMM) is a critical priority in the United States. About half of adverse maternal health outcomes were found to be attributable to preventable harm or unintended consequences arising from clinical practice and the system of delivering perinatal care. Significant resources are currently being invested to implement quality improvement (QI) initiatives in birthing hospitals across the country. There is great need to evaluate these efforts and demonstrate their effectiveness to reducing the burden of preventable SMM and maternal deaths. Virtually all QI initiatives in birthing hospitals use SMM as an outcome measure, but their evaluation is hindered by the need to risk-adjust SMM rates to control for differences in patient composition within and between hospitals. To date, 3 different research groups proposed obstetric comorbidity indices, yet all have significant limitations. The overarching goal of this study is to develop and validate a refined comorbidity index for obstetric patients that allows SMM rate comparisons across hospitals and adequate monitoring of QI initiatives in obstetrics. We will use Maryland’s unique, gold- standard, hospital-based, state-representative SMM Surveillance and Review data to identify a comprehensive list of comorbidities in patients with SMM events. Using electronic health record data from the Johns Hopkins Health System, we will employ variable importance estimation with machine learning techniques to develop the comorbidity index. Subsequently, we will ascertain its accuracy using receiver operating characteristic (ROC)/precision-recall (PR) curves and areas under the curve (AUC) for outcome discrimination and lowess- smoothed calibration plots. Also, we will compare the performance of the refined comorbidity index to predict SMM against that of previously published comorbidity indices. To further validate our refined comorbidity index and assesses its performance consistency across various sociodemographic groups, we will use national hospital discharge data from the Healthcare Cost and Utilization Project’s National Inpatient Sample. A Technical Advisory Group comprised of clinicians, community partners, patient safety experts, and certified medical coders will meet quarterly for data interpretation sessions. At the end of the study, we expect to have a refined comorbidity index developed in gold-standard data, with superior psychometric properties than the previously published comorbidity indices and validated in both EHR and national hospital discharge data. Our results will be disseminated in the peer-reviewed literature and through presentations at scientific meetings.