Recent reports of increased maternal mortality have put pregnancy care in the center of the public’s attention. The World Health Organization defines maternal death as the death of a woman while pregnant or within 42 days of termination of pregnancy from any cause related to or aggravated by the pregnancy or its management ( Alarmingly, in the United States, 1,205 women died of pregnancy-related causes in 2021, a 40% increase compared to 2020 and 60% increase compared to 2019 (NCHS Health E-Stats 2023). A stark racial and ethnic disparity in maternal mortality persists, with non-Hispanic Black mothers being 2.6 times more likely to die compared to non-Hispanic White individuals. Luckily, maternal mortality is relatively rare, but for every maternal death, another 20 women suffer serious complications, referred to as severe maternal morbidity (SMM). Racial and ethnic disparities in SMM have also been identified. Reducing maternal morbidity and mortality has been the focus of intense public health campaigns; yet the problem is worsening. Despite these grim statistics, there is significant hope for improvement, given that the majority of maternal deaths and cases of severe maternal morbidity are felt to be preventable. Therefore, new strategies are needed to address an urgent problem.

As pioneers in patient safety, anesthesiologists have spearheaded multiple safety initiatives, and now we can step up to improve maternal care. Identifying patients at risk for adverse outcomes would allow timely intervention, escalation of care, and mobilization of resources. A few risk prediction tools are currently available, and while not universally implemented, those tools are showing promising results. For example, the obstetric comorbidity index (OB-CMI) is calculated using 20 weighted maternal conditions and age, and for every 1-point increase in score, patients experienced a 1.55 times increase in severe maternal morbidity (Am J Obstet Gynecol 2019;221:271.e1-10). Digital technologies, such as machine learning methods, can aid precise outcome prognostication and may improve maternal morbidity prediction. A machine learning tool to estimate the risk of postpartum hemorrhage demonstrated highly accurate predictions (Obstet Gynecol 2020;135:935-44). We developed a machine learning tool to predict preeclampsia on admission using routinely available clinical data, such as maternal age, demographics, clinical diagnoses, and vital signs, that performs better than the current standard of care (medRxiv 2023). This technology can be implemented as part of the electronic health record and automated to support clinical decision-making. Using this approach, individual risk can be assessed on admission, and, if needed, heightened monitoring, specialist consultation, or transfer to a tertiary care facility can be promptly initiated.

In addition to clinical factors, genetics can also offer actionable insights. With the advancement of population genetics, and as genetic testing becomes more accessible, we will likely see clinical implementation in the near future. Using a polygenic risk score, a method to summarize the effect of many genes, our team found that we can predict which individual may be at risk for preeclampsia, even before they become pregnant (medRxiv 2023). While much work remains to be done, especially since most genomic research historically was performed in White individuals, our early results show the potential of this new approach and highlight the need for more research. Integrating clinical and genetic risk factors can aid personalized care, monitoring, and interventions to individuals at increased risk, thus significantly enhancing maternal safety.

It has become clear from maternal mortality reviews that most maternal deaths do not occur in the hospital, but rather before and after delivery. Therefore, while we can, and should, continue to improve the quality of the in-hospital care we provide, we anesthesiologists should use our unique vantage point as perioperative physicians to mitigate the risk of adverse outcomes. One condition that contributes to a significant portion of maternal morbidity and mortality in the U.S. is postpartum hemorrhage (PPH). While PPH itself is largely not preventable, poor outcomes, particularly maternal morbidity and mortality from hemorrhage, have been deemed to be highly preventable, with 67%-90% of deaths being deemed preventable in maternal mortality reviews (Obstet Gynecol 2005;106:1228-34; Am J Obstet Gynecol 2014;211:698.e1-11).

Recommendations to improve PPH outcomes largely fall into three categories: 1) identification of women at high risk for PPH, 2) improved recognition of PPH, and 3) timely management of bleeding (Obstet Gynecol 2005;106:1228-34; Am J Obstet Gynecol 2014;211:698.e1-11; Am J Obstet Gynecol 2008;199:36.e1-5; Obstet Med 2008;1:54; Anaesthesia 1999;54:207-9; BJOG 2015;122:e1). Women who are anemic and hemorrhage are at increased risk for blood transfusions, which carry significant morbidity and risks to the patient, such as 1) infections; 2) thrombotic events (deep venous thrombosis, pulmonary embolus, or disseminated intravascular coagulation); 3) renal injuries; 4) respiratory events; and 5) ischemic events (myocardial infarction, transient ischemic attack, or cerebrovascular accident) (Anesthesiology 2016;124:387-95). Therefore, one strategy to reduce risk is to address anemia prior to delivery. Iron deficiency anemia is easily corrected, yet many women fail to respond because they do not adhere to the medication regimen due to intolerance of the side effects, or they present late in pregnancy with insufficient time for oral supplementation to be effective (BJOG 2006;113:1248-52; Obstet Gynecol 2005;106:1335-40). Despite the frequency of iron deficiency anemia in the pregnant population, treatment protocols to guide peripartum management of it are scarce. Our team has completed two qualitative studies that evaluated patient awareness of peripartum anemia, management options, and barriers to treatment, as well as our understanding of anemia and management strategies. Our goal is to develop – and ultimately implement – a patient-centered algorithm that will decrease the number of women with untreated iron deficiency anemia, so we can reduce the number of women who are anemic at the time of delivery. This is just one example of how anesthesiologists, in partnership with a multidisciplinary team of obstetricians, nurses, hematologists, and patient partners, can look for new opportunities to reduce harm.

Anesthesiologists have made significant strides in improving patient safety. We should apply the principles that have led to our past successes in patient safety to address other important public health issues, such as maternal morbidity and mortality. To make the most impact, the research community must now take this framework, test interventions, and disseminate and scale what is most effective. Together, we can move the needle and improve the health of all our patients.