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.
Pregnancies in patients with systemic lupus erythematosus (SLE) are at increased risk for poor outcomes, such as very premature delivery, fetal death or growth restriction, yet there are no validated instruments to predict pregnancy outcomes in individual patients and focus care on those most likely to have serious complications. Our proposal is to develop and validate a prediction model that can be used to identify, early in pregnancy, SLE patients at high risk for adverse outcomes. This tool will significantly enhance the capacity to manage these patients, optimize allocation of healthcare resources, and design and conduct trials of new treatments to prevent the placental insufficiency that can result in poor outcomes.
