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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要解决孕产妇死亡率和严重孕产妇发病率（SMM）不断上升的趋势是一项至关重要的优先事项美国大约一半的不良孕产妇健康结果可归因于可预防的问题。临床实践和提供围产期护理的系统引起的伤害或意外后果。目前正在投入大量资源来实施分娩质量改进（QI）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。减轻产科医院几乎所有 QI 举措中可预防的 SMM 和孕产妇死亡的负担。使用 SMM 作为结果衡量标准，但其评估因需要对 SMM 利率进行风险调整而受到阻碍迄今为止，有 3 个不同的研究小组控制了医院内部和医院之间患者构成的差异。提出的产科合并症指数，但都有很大的局限性。这项研究的总体目标是。开发并验证产科患者的精细合并症指数，以便进行 SMM 率比较我们将利用马里兰州独特的黄金质量监控措施。标准的、基于医院的、具有州代表性的 SMM 监测和审查数据，以确定全面的使用约翰霍普金斯大学的电子健康记录数据列出 SMM 事件患者的合并症列表。卫生系统，我们将采用机器学习技术的变量重要性估计来开发随后，我们将使用接收者操作特征来确定其准确性。（ROC）/精确召回（PR）曲线和曲线下面积（AUC）用于结果区分和低值- 此外，我们还将比较精炼的合并症指数的性能以进行预测。 SMM 与之前发布的合并症指数进行比较，以进一步验证我们改进的合并症指数。并评估其在不同社会人口群体中的表现一致性，我们将使用国家来自医疗保健成本和利用项目的全国住院患者样本 A 的出院数据。技术咨询小组由超级明星、社区合作伙伴、患者安全专家和经过认证的专家组成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，我们预计将举行一次会议。根据金标准数据开发的精细合并症指数，具有比标准数据更优越的心理测量特性之前发布的合并症指数并在 EHR 和国家医院出院数据中得到验证。结果将通过同行评审文献和科学会议上的演讲进行传播。