Adverse pregnancy outcomes in women with systemic lupus erythematosus: improving and validating risk prediction

系统性红斑狼疮女性的不良妊娠结局：改进和验证风险预测

• 批准号: 10248288
• 负责人: Mimi Y Kim
• 金额: $ 18.53万
• 依托单位: HOSPITAL FOR SPECIAL SURGERY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10248288
• 关键词: Adopted; Affect; Angiogenic Factor; Antihypertensive Agents; Antiphospholipid Antibodies; Antiphospholipid Syndrome; Autoantibodies; Biological Markers; Biometry; Blood Platelets; Caring; Characteristics; Clinical; Clinical Management; Clinical Trials; Complement Activation; Complex; Conceptions; Country; Data; Data Set; Development; Discipline of obstetrics; Disease; Emotional; Ethnic Origin; Europe; Face; Failure; Fetal Death; Fetal Growth Retardation; Fetal health; Fetus; Financial Hardship; Financial cost; First Pregnancy Trimester; France; Future; Germany; Goals; Growth; Healthcare; International; Intervention; Investigation; Laboratories; Lupus; Lupus Coagulation Inhibitor; Maternal Health; Modeling; Monitor; Morbidity - disease rate; Mothers; Norway; Outcome; Patient Focused Care; Patients; Pharmaceutical Preparations; Physicians; Placenta; Placental Insufficiency; Pre-Eclampsia; Pregnancy; Pregnancy Outcome; Premature Birth; Prevention trial; Procedures; Prospective cohort; Race; Research; Research Personnel; Resources; Risk; Risk Factors; Salmon; Schedule; Societies; Socioeconomic Status; South Carolina; Statistical Models; Systemic Lupus Erythematosus; Testing; Vascularization; Woman; adverse outcome; adverse pregnancy outcome; child bearing; clinical care; clinical decision-making; clinical practice; cohort; cost; design; fetal; high risk; hypoperfusion; improved; individual patient; instrument; machine learning algorithm; machine learning method; maternal risk; model development; mortality; multi-ethnic; multi-racial; multidisciplinary; neonatal death; novel; outcome prediction; patient population; predictive modeling; pregnant; prevent; prospective; racial and ethnic; random forest; recruit; reproductive; risk prediction; risk stratification; socioeconomics; study population; tool; user-friendly

ABSTRACT: Systemic lupus erythematosus (SLE) predominantly affects women during reproductive years, raising concerns regarding maternal and fetal health during pregnancy. Although physicians no longer uniformly discourage women with SLE from childbearing, patients face 20% likelihood of adverse pregnancy outcomes (APO), including preeclampsia, fetal and neonatal death, growth restriction, and preterm delivery, even during clinical disease quiescence. Because there are no established instruments to predict APO in individual patients, SLE pregnancies are intensely monitored at an emotional cost to patients and financial burden to society. The ability to identify, early in pregnancy, patients at high risk of APO would significantly enhance our capacity to clinically manage patients. Furthermore, validated risk stratification models are needed to design and execute trials to prevent APOs. In PROMISSE, the largest multi-center, multi-ethnic and multi-racial study of pregnant SLE patients to date, several risk factors were identified as significant predictors of APO. Although a major advance, these results have neither been externally validated nor shown to generalize to independent study populations. Moreover, risk factors were identified using standard statistical models that did not fully account for complex effects of multiple predictor variables. In this project, an international team of SLE, obstetric and biostatistics researchers, led by PROMISSE investigators, will rigorously develop and externally validate an APO prediction model by leveraging data from PROMISSE (N=447), as well as five independent cohorts of lupus patients from different countries (total N = 979). In Aim 1, powerful machine learning algorithms will be applied to PROMISSE data to create an accurate and clinically useful model to predict APOs in SLE patients. To maximize utility of this model in the real world, only clinical and laboratory features that are routinely and accurately assessed on SLE patients during clinical care will be considered as potential predictors. In Aim 2, the APO model will be externally validated in prospective cohorts of pregnant lupus patients from Europe (France: N=246; Germany: N=180; Norway: N=349) and regions in the US, not included in PROMISSE (South Carolina: N=82; Bronx, NY: N=122). These cohorts are heterogeneous with respect to race, ethnicity, socioeconomic strata, and SLE disease activity, allowing for a thorough investigation into generalizability and transportability of the APO model to diverse lupus patient populations. In each cohort, detailed baseline and longitudinal clinical, laboratory and pregnancy outcome data have been obtained using procedures similar to those in PROMISSE. The overarching goal is development of an online risk calculator that will significantly improve real world clinical decision making and enable risk stratification for future APO prevention trials. Impact: An accurate, validated, and user-friendly prediction model for APO is necessary for effective clinical care of pregnant lupus patients, optimal allocation of healthcare resources, and the design of and recruitment to future clinical trials of experimental interventions to prevent APO.

摘要：系统性红斑狼疮（SLE）主要影响育龄期的女性， 引起人们对怀孕期间母婴健康的担忧。虽然医生不再统一 阻止患有 SLE 的女性生育，患者面临 20% 的不良妊娠结局的可能性 (APO)，包括先兆子痫、胎儿和新生儿死亡、生长受限和早产，甚至在怀孕期间 临床疾病静止。由于没有成熟的工具来预测个体患者的 APO， 系统性红斑狼疮妊娠受到严格监测，这给患者带来了情感成本，给社会带来了经济负担。这 在怀孕早期识别 APO 高风险患者的能力将显着增强我们的能力 临床管理患者。此外，需要经过验证的风险分层模型来设计和执行 预防 APO 的试验。在PROMISSE中，最大的多中心、多民族、多种族孕妇研究 迄今为止，SLE 患者中的几个危险因素被确定为 APO 的重要预测因子。虽然是大专 提前，这些结果既没有经过外部验证，也没有被证明可以推广到独立研究 人口。此外，使用标准统计模型确定了风险因素，但该模型并未充分考虑 多个预测变量的复杂影响。在这个项目中，一个由 SLE、产科和 由 PROMISSE 研究人员领导的生物统计学研究人员将严格开发 APO 并进行外部验证 利用 PROMISSE (N=447) 以及五个独立狼疮队列的数据建立预测模型 来自不同国家的患者（总数 = 979）。在目标1中，将应用强大的机器学习算法 PROMISSE 数据创建一个准确的、临床上有用的模型来预测 SLE 患者的 APO。到 最大化该模型在现实世界中的效用，只有常规和实验室特征 在临床护理期间对 SLE 患者进行的准确评估将被视为潜在的预测因素。在目标 2 中， APO 模型将在欧洲妊娠狼疮患者前瞻性队列中进行外部验证（法国： N=246；德国：N=180；挪威：N=349）和美国未包含在 PROMISSE 中的地区（南卡罗来纳州： N=82；纽约州布朗克斯：N=122）。这些群体在种族、民族、社会经济方面具有异质性 地层和 SLE 疾病活动，从而可以彻底调查其普遍性和可移植性 APO 模型适用于不同的狼疮患者群体。在每个队列中，详细的基线和纵向临床， 实验室和妊娠结局数据已使用类似于 PROMISSE 中的程序获得。 总体目标是开发在线风险计算器，该计算器将显着改善现实世界的临床 决策并为未来 APO 预防试验进行风险分层。影响：准确、经过验证、 用户友好的 APO 预测模型对于妊娠狼疮患者的有效临床护理是必要的， 医疗资源的优化配置，以及未来临床试验的设计和招募 预防 APO 的实验干预措施。