A digital algorithm using 24 patient characteristics identifies far more women who are likely to develop a postpartum hemorrhage than currently used tools to predict the risk for bleeding after delivery, according to a study published in the Journal of the American Medical Informatics Association.
About 1 in 10 of the roughly 700 pregnancy-related deaths in the United States are caused by postpartum hemorrhage, according to the U.S. Centers for Disease Control and Prevention. These deaths disproportionately occur among Black women, for whom studies show the risk of dying from a postpartum hemorrhage is fivefold greater than that of White women.
“Postpartum hemorrhage is a preventable medical emergency but remains the leading cause of maternal mortality globally,” the study’s senior author Li Li, MD, senior vice president of clinical informatics at Sema4, a company that uses artificial intelligence and machine learning to develop data-based clinical tools, told this news organization. “Early intervention is critical for reducing postpartum hemorrhage morbidity and mortality.”
Porous predictors
Existing tools for risk prediction are not particularly effective, Dr. Li said. For example, the American College of Obstetricians and Gynecologists’ (ACOG) Safe Motherhood Initiative offers checklists of clinical characteristics to classify women as low, medium, or high risk. However, 40% of the women classified as low risk based on this type of tool experience a hemorrhage.
ACOG also recommends quantifying blood loss during delivery or immediately after to identify women who are hemorrhaging, because imprecise estimates from clinicians may delay urgently needed care. Yet many hospitals have not implemented methods for measuring bleeding, said Dr. Li, who also is an assistant professor of genetics and genomic sciences at the Icahn School of Medicine at Mount Sinai, New York.
To develop a more precise way of identifying women at risk, Dr. Li and colleagues turned to artificial intelligence technology to create a “digital phenotype” based on approximately 72,000 births in the Mount Sinai Health System.
The digital tool retrospectively identified about 6,600 cases of postpartum hemorrhage, about 3.8 times the roughly 1,700 cases that would have been predicted based on methods that estimate blood loss. A blinded physician review of a subset of 45 patient charts – including 26 patients who experienced a hemorrhage, 11 who didn’t, and 6 with unclear outcomes – found that the digital approach was 89% percent accurate at identifying cases, whereas blood loss–based methods were accurate 67% of the time.
Several of the 24 characteristics included in the model appear in other risk predictors, including whether a woman has had a previous cesarean delivery or prior postdelivery bleeding and whether she has anemia or related blood disorders. Among the rest were risk factors that have been identified in the literature, including maternal blood pressure, time from admission to delivery, and average pulse during hospitalization. Five more features were new: red blood cell count and distribution width, mean corpuscular hemoglobin, absolute neutrophil count, and white blood cell count.
“These [new] values are easily obtainable from standard blood draws in the hospital but are not currently used in clinical practice to estimate postpartum hemorrhage risk,” Dr. Li said.
In a related retrospective study, Dr. Li and her colleagues used the new tool to classify women into high, low, or medium risk categories. They found that 28% of the women the algorithm classified as high risk experienced a postpartum hemorrhage compared with 15% to 19% of the women classified as high risk by standard predictive tools. They also identified potential “inflection points” where changes in vital signs may suggest a substantial increase in risk. For example, women whose median blood pressure during labor and delivery was above 132 mm Hg had an 11% average increase in their risk for bleeding.
By more precisely identifying women at risk, the new method “could be used to pre-emptively allocate resources that can ultimately reduce postpartum hemorrhage morbidity and mortality,” Dr. Li said. Sema4 is launching a prospective clinical trial to further assess the algorithm, she added.