Risk modeling and shared decision making for postpartum depression

产后抑郁症的风险建模和共同决策

基本信息

批准号：
10454932
负责人：
Michael B. Laskoff
金额：
$ 43.43万
依托单位：
IRIS OB HEALTH INC.
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-21 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10454932
关键词：
Address Affect Area Businesses Child Childbirth Clinical Clinical Data Complex Conceptions Conflict (Psychology)Data Decision Aid Depression screen Development Diagnosis Discipline of obstetrics Electronic Health Record Evaluation Evidence based intervention Family Feeling Feeling hopeless Funding Future Goals Group Health Insurance Gynecology Health Health Insurance Health Personnel Health Professional Health Sciences Health Services Accessibility Impairment Infant Mortality Intervention Iris Lead Licensing Life Machine Learning Medicine Mental Depression Mental Health Methods Mission Modeling Mothers Natural Language Processing New York City Outcome Patient risk Patient-Focused Outcomes Patients Phase Postpartum Depression Postpartum Period Pregnancy Pregnant Women Prevention strategy Preventive treatment Primary Prevention Process Provider Psychiatry Research Risk Secondary Prevention Site Small Business Technology Transfer Research Stress Symptoms Techniques Testing Woman child bearing clinical care clinical research site commercialization deep learning depression prevention depressive symptoms digital digital health disability effectiveness clinical trial evidence base experience heuristics high risk hospitalization rates improved machine learning algorithm motherhood novel patient stratification phase 1 study population health practice setting project-based learning prospective risk prediction model routine screening screening shared decision making social stigma software as a service support tools tool treatment choice usability user centered design

项目摘要

PROJECT ABSTRACT Postpartum depression (PPD) is a strikingly common and potentially life-threatening mental health condition, affecting 1 in 5 mothers in the US. PPD poses serious health concerns, not only to mothers but also their children and the family. PPD has been associated with increased infant mortality, higher rates of hospitalizations, impaired mother-child attachment, developmental problems in children, and increased stress within families. However, in reality, women who are at high risk for depression during the childbearing years are usually neither identified nor treated. In addition, even when depression is detected by health professionals, women rarely obtain assistance, despite the wide availability of treatment choices. In this project, we plan to leverage large-scale, integrated electronic health record and claims from the New York City Clinical Data Research Network to develop automated, scalable risk prediction models for PPD. We will also develop a digital shared decision-making (SDM) tool for PPD treatment by working collaboratively with healthcare providers and patients. The PPD risk model and the digital SDM tool for PPD treatment developed in this STTR Phase I project will be used to conduct a future large, prospective, multi-site clinical effectiveness trial to test their feasibility and utility in routine clinical care of women during the postpartum period.

项目摘要产后抑郁症（PPD）是一种非常常见且可能危及生命的心理健康状况，影响美国五分之一的母亲。 PPD 不仅对母亲而且对她们的孩子带来严重的健康问题和家人。 PPD 与婴儿死亡率增加、住院率增加、母子依恋受损、儿童发育问题以及家庭压力增加。然而，实际上，育龄期间患抑郁症的高风险女性通常两者都不是。已被识别或治疗。此外，即使卫生专业人员发现抑郁症，女性也很少获得治疗尽管有广泛的治疗选择可供选择。在这个项目中，我们计划利用大规模、整合电子健康记录和纽约市临床数据研究网络的索赔来开发自动化、可扩展的 PPD 风险预测模型。我们还将开发数字共享决策（SDM）通过与医疗保健提供者和患者合作来治疗 PPD 的工具。 PPD 风险模型 STTR一期项目开发的用于PPD治疗的数字SDM工具将用于进行未来大型、前瞻性、多中心临床有效性试验，以测试其在常规临床中的可行性和实用性女性产后期间的护理。