Objective Screening tools, including the Systemic Inflammatory Response Syndrome (SIRS) criteria and Sequential Organ Failure Assessment (SOFA) criteria, have not been validated in the pregnant population. We aimed to determine if pregnancy-specific modifications to the quick SOFA (qSOFA) can improve prediction of severe maternal morbidity in pregnant women with serious infections. Methods We performed a retrospective cohort study of pregnant patients with severe infections admitted to a single institution from January 1, 2011, through December 31, 2017. The primary outcome was severe maternal morbidity, defined as a composite of adverse maternal outcomes: intensive care unit (ICU) admission for >48 hours, need for invasive monitoring (central line or arterial line), intubation, pharmacologic hemodynamic support (intravenous vasopressors or inotropes), and/or maternal death. A logistic regression was then applied and the resulting predictors were analyzed individually and in combination with receiver operating characteristic (ROC) curves to modify qSOFA for pregnancy, that is, qSOFA-P. Results Analysis of 104 pregnant patients with severe infections found that the standard qSOFA did not accurately predict severe maternal morbidity (ROC area under the curve [AUC] = 0.54, p = 0.49, sensitivity = 0.38, and specificity = 0.70). Pregnancy-specific modifications or “qSOFA-P” (respiratory rate [RR] ≥ 35 breaths/minute and systolic blood pressure [SBP] ≤ 85 mm Hg) significantly improved prediction of severe maternal morbidity (AUC = 0.77, p < 0.001, sensitivity = 0.79, and specificity = 0.74). Conclusion The standard qSOFA is a poor screening tool in the prediction of severe maternal morbidity in pregnant patients with infections. A pregnancy-specific screening system, qSOFA-P, improved prediction of severe maternal morbidity in pregnant women with severe infections. Further prospective and large multicenter studies are needed to validate this scoring system in pregnant women. Key Points

Improved recognition of pregnancy-associated deaths can be achieved with modern data analytics. As we continue to combat the rising tide of maternal mortality in the United States, we can advocate for clinical care strategies, community support structures, and economic development plans that address the full spectrum of factors contributing to the broadest spectrum of maternal mortality.