Machine Learning Can Be Used to Improve the Ability to Predict Adverse Pregnancy Outcomes in Women with Lupus
Nearly 20% of pregnancies in people with lupus result in an adverse pregnancy outcome (APO). In a new study, scientists were able to improve prediction accuracy of APOs using machine learning. Machine learning refers to the process by which a computer is able to improve its own performance by continuously incorporating new data into an existing statistical model.
Using a previously developed APO prediction model utilizing data from a larger multi-center, multi-ethnic study of lupus pregnancies known as the Predictors of pRegnancy Outcome: bioMarkers In Antiphospholid Antibody Syndrome and the Systemic Lupus Erythematosus (PROMISSE) study, and statistical analysis coupled with machine learning, researchers analyzed data from 385 women in their first trimester of pregnancy. They identified lupus anticoagulant positivity, disease assessment score, diastolic blood pressure or resting heartbeat, current use of antihypertension medication, and platelet count as significant baseline predictors of APO.
Researchers suggest that the ability to identify, lupus patients at high risk of APO early in pregnancy, could enhance the capacity to manage these patients and conduct trials of new treatments to prevent pre-eclampsia and placental insufficiency.
Further studies to identify new biomarkers and risk factors for APO are still needed. The Lupus Foundation of America provided the study author, Jane Salmon, MD, with a three-year grant for her IMPACT study, the first trial of a biologic therapy to prevent adverse pregnancy outcomes in high-risk pregnancies in patients with antiphospholipid syndrome (APS) with or without systemic lupus erythematosus (SLE), which also helped support this new research. Learn more about lupus and pregnancy.
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