Clinical utility of novel biomarkers for prediction of early pregnancy failure

新型生物标志物预测早期妊娠失败的临床应用

• 批准号: 10563608
• 负责人: Kurt T Barnhart
• 金额: $ 71.84万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10563608
• 关键词: Affect; Area; Biological; Biological Markers; Caring; Cause of Death; Cessation of life; Characteristics; Clinical; Clinical Management; Clinical Trials; Complement; Consumption; Data; Development Plans; Diagnosis; Diagnostic; Discipline of obstetrics; Discrimination; Disease; Ectopic Pregnancy; Ethnic Origin; First Pregnancy Trimester; Funding; Gestational Age; Goals; Gynecologic; Hemorrhage; Human Chorionic Gonadotropin; Iatrogenesis; Interruption; Location; Machine Learning; Mammalian Oviducts; Medical; Medical Care Team; Methodology; Modeling; Morbidity - disease rate; National Institute of Child Health and Human Development; Pain; Pathway interactions; Patients; Performance; Physiology; Population; Predictive Value; Predictive Value of Tests; Pregnancy; Pregnancy Outcome; Procedures; Process; Productivity; Prospective Studies; Prospective cohort; Proteomics; Race; Recording of previous events; Research; Research Priority; Resources; Risk; Rupture; Sampling; Serum; Signs and Symptoms; Spontaneous abortion; Stress; Subgroup; Symptoms; System; Testing; Time; Uncertainty; United States National Institutes of Health; Uterus; Validation; Woman; Work; biomarker development; biomarker panel; biomarker performance; classification algorithm; clinical care; companion diagnostics; design; diagnostic tool; early pregnancy; early pregnancy loss; feature selection; health care delivery; improved; interest; machine learning algorithm; model building; novel; novel marker; pain symptom; population based; predictive marker; pregnancy failure; random forest; regression trees; screening; ultrasound

Abstract: The diagnosis and management for women who are at risk for ectopic pregnancy (EP) and spontaneous abortion (SAB), has not changed substantially in decades. While ultrasound can diagnose a significant portion of women at presentation, because of its limited accuracy in abnormal and early gestation, a large number of women need serial tests and procedures to determine the final location and viability of an early pregnancy. Misdiagnosis and iatrogenic complications during this time are still too common. This conundrum results in great stress and uncertainty for women undergoing clinical care and their health care teams. To date, we have demonstrated (and validated) that multiplexed biomarkers from divergent biological pathways can be used to minimize both false positive and false negative discrimination (rather than balancing the two errors with lower accuracy). In parallel, we have discovered and screened more than 54 novel biomarkers to obtain 11 candidates that can predict early pregnancy outcome. Using machine learning we have demonstrated that as few as 3 of these markers can predict the location of an early gestation (IUP vs EP or SAB) with 95% accuracy. Additionally, an overlapping group of 3 markers can predict the viability of a gestation (IUP vs SAB or EP) with 94% accuracy. When both tests are used in combination the accuracy is 96.9%. We now plan to externally validate these companion diagnostic(s) in a separate population-based prospective study and to determine the optimal conditions of use. We will optimize performance in women presenting with a pregnancy of unknown location as well as assess predictive values for all women presenting at risk for early pregnancy loss using an independent population-based prospective study (SA1). We will determine if accuracy can be improved with the combination of presenting signs, symptoms, and ultrasound findings (SA2). Moreover, we will validate our biomarker tests in two distinct populations of interest: women with high and low risk for EP: a) asymptomatic women prior to presentation for care (EAGeR Trial) and b) women diagnosed with a persistent pregnancy of unknown location (ACTorNOT Trial) (SA3). Developing novel biomarkers that distinguish normal physiology from the presence of gynecologic disorders is an NICHD research priority area. Our productive and established team is proposing rigorous cross disciplinary, state of the art, and novel research with great scientific impact that has the potential to produce a paradigm shift in clinical care models for the diagnosis and management of women in early pregnancy. Our detailed plan for biomarker development is iterative and nimble and, importantly, includes validation. Our development plan is informed by methodology from successful biomarker development, is designed to minimize known pitfalls, and is developed with FDA guidance.

翻译后摘要：诊断和管理的妇女谁是在危险的异位妊娠（EP）和 自然流产（SAB），几十年来没有发生实质性变化。虽然超声波可以诊断 由于其对异常妊娠和早期妊娠的准确性有限， 大量的妇女需要一系列的测试和程序，以确定最终的位置和可行性的早期 怀孕误诊和医源性并发症在这段时间仍然太常见。这个难题 这给接受临床护理的妇女及其卫生保健团队带来了巨大的压力和不确定性。到目前为止， 我们已经证明（并验证），来自不同生物途径的多重生物标志物可以 用于最小化假阳性和假阴性鉴别（而不是用 精度低）。与此同时，我们发现并筛选了超过54种新的生物标志物， 可以预测早期妊娠结果的候选人。使用机器学习，我们已经证明， 这些标记物中的几个可以预测早期妊娠的位置（IUP vs EP或SAB），95% 精度此外，一组重叠的3个标记物可以预测妊娠的生存能力（IUP vs SAB 或EP），准确率为94%。当两种测试结合使用时，准确度为96.9%。我们现在计划 在单独的基于人群的前瞻性研究中对这些伴随诊断进行外部验证， 确定最佳使用条件。我们将优化妊娠妇女的性能 以及评估所有存在早孕风险的女性的预测值 使用独立的基于人群的前瞻性研究（SA1）。我们将确定准确性是否可以 结合体征、症状和超声检查结果改善（SA2）。而且我们 将在两个不同的感兴趣的人群中验证我们的生物标志物测试：EP高风险和低风险的女性：a） b）在就诊前无症状的女性（EAGeR试验）和B）诊断为持续性 未知部位妊娠（ACTorNOT试验）（SA3）。开发新的生物标志物来区分正常的 从妇科疾病的存在生理是NICHD的研究优先领域。我们的生产力和 一个成熟的团队正在提出严格的跨学科，最先进的，新颖的研究， 科学的影响，有可能产生一个范式转变的临床护理模式的诊断和 妇女在怀孕早期的管理。我们的生物标志物开发详细计划是迭代的， 灵活，重要的是，包括验证。我们的发展计划是根据以下方法制定的： 成功的生物标志物开发，旨在最大限度地减少已知的陷阱，并与FDA一起开发 指导