Objective: Severe maternal morbidity has increased in the United States over the past two decades by approximately 200%, to 144 cases per 10,000 delivery hospitalizations. There are limited data available to assist in identifying at-risk women before parturition. We sought to evaluate risk factors associated with maternal admission to an intensive care unit (ICU).
Methods: We conducted a population-based cohort study of all live births delivered between 20 and 44 weeks of gestation in the United States during 2012-2016. Our primary objective was to identify prenatal factors associated with increased risk of maternal ICU admission to build a multivariable predictive model to estimate the association of these factors with ICU admission risk. We performed k-fold cross-validation for internal validation and then externally validated the model on a separate live birth cohort (2006-2011, n=856,255).
Results: There were 18,745,615 live births in the United States between 2012 and 2016. Among the mothers of these live newborns, 27,602 (0.15%) were admitted to the ICU in the peripartum period. Fourteen variables were selected for inclusion in the predictive model for maternal ICU admission. The predicted minimal and maximal risk for ICU admission ranged 0-25%. The receiver operating characteristic curve for these 14 variables achieved an area under the curve (AUC) of 0.81 (95% CI 0.79-0.81). External validation with a separate live birth cohort demonstrated a consistent measure of discrimination with an AUC of 0.83 (95% CI 0.82-0.84). Using a relatively high cut point of 5.0% or more predicted risk for ICU admission, achieved a positive predictive value (PPV) of only 4.0%.
Conclusion: This model provides insight as to the cumulative effect of multiple risk factors on maternal ICU admission risk. The predictive model achieves an AUC of 0.81, discriminating women with significantly increased risk (30-fold) for ICU admission. Nonetheless, because of the low frequency of maternal ICU admission, the PPV of the model was low and therefore whether models such as ours may be beneficial in future efforts to reduce the prevalence and burden of maternal morbidity is uncertain.