Developing a refined comorbidity index for use in obstetric patients

开发用于产科患者的精细合并症指数

• 批准号: 10719480
• 负责人: Andreea Alina Creanga
• 金额: $ 57.04万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10719480
• 关键词: Address; Admission activity; Affect; Algorithms; American College of Obstetricians and Gynecologists; Area; Area Under Curve; Birth; Calibration; Caring; Centers for Disease Control and Prevention (U.S.); Certification; Characteristics; Clinical; Communities; Country; Data; Data Analyses; Data Sources; Diagnosis; Discipline of obstetrics; Discrimination; Effectiveness; Electronic Health Record; Evaluation; Event; Goals; Guidelines; Health Care Costs; Health system; Hospitals; Individual; Inpatients; Intensive Care Units; International Classification of Disease Codes; Investments; Machine Learning; Maryland; Maternal Health; Maternal Health Services; Maternal Mortality; Maternal-fetal medicine; Measures; Medical; Monitor; Outcome; Outcome Measure; Patients; Peer Review; Performance; Perinatal Care; Pregnancy; Process; Professional Organizations; Prognosis; Program Evaluation; Property; Psychometrics; Publishing; Receiver Operating Characteristics; Recommendation; Reporting; Research; Resources; Review Literature; Risk; Risk Adjustment; Sampling; Societies; Structure; System; Techniques; Testing; Time; Transfusion; Triage; United States; Work; blood product; clinical practice; comorbidity; comorbidity Index; data standards; health care delivery; health data; high risk; improved; indexing; innovation; meetings; patient population; patient safety; predictive modeling; severe maternal morbidity; sociodemographic group; trend; virtual

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.

项目摘要 解决孕产妇死亡率和严重孕产妇发病率上升的趋势是#年的一个关键优先事项。 美国。约有一半的不良孕产妇健康后果可归因于可预防的 临床实践和提供围产期护理的制度产生的伤害或意外后果。 目前正在投入大量资源来实施分娩质量改进(QI)计划 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。几乎所有分娩医院的QI举措 使用SMM作为结果衡量标准，但他们的评估因需要对SMM比率进行风险调整而受到阻碍 控制医院内和医院之间患者构成的差异。到目前为止，有3个不同的研究小组 提出的产科共病指数，但都有明显的局限性。这项研究的首要目标是 为产科患者开发和验证精细化的共病指数，以允许SMM比率比较 跨医院和充分监测产科的QI举措。我们将使用马里兰州独一无二的黄金- 标准的、基于医院的、具有州代表性的SMM监督和审查数据，以确定全面的 SMM事件患者的合并症列表。使用约翰·霍普金斯大学的电子健康记录数据 ，我们将使用机器学习技术的可变重要性评估来开发 共病指数。随后，我们将利用接收器的工作特性来确定其准确性 (ROC)/精确检索(PR)曲线和曲线下面积(AUC)，用于结果区分和低- 平滑的校准曲线图。此外，我们还将比较改进的共病指数的性能以预测 SMM与先前发表的共病指数相比较。为了进一步验证我们改进的共病指数 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 医院出院数据来自医疗保健成本和利用项目的全国住院患者样本。一个 由临床医生、社区合作伙伴、患者安全专家和经认证的 医疗编码员将每季度召开一次数据解释会议。在研究结束时，我们预计将有一个 在黄金标准数据中开发的精细化共病指数，具有优于 以前发表的共病指数，并在电子病历和国家医院出院数据中得到验证。我们的 结果将在同行评议的文献中传播，并通过在科学会议上的陈述来传播。