Using graph learning to understand adverse pregnancy outcomes and stress pathways

Abstract

To identify pathways between stress indicators and adverse pregnancy outcomes, we applied a nonparametric graph-learning algorithm, PC-KCI, to data from an observational prospective cohort study. The Measurement of Maternal Stress study (MOMS) followed 744 women with a singleton intrauterine pregnancy recruited between June 2013 and May 2015. Infant adverse pregnancy outcomes were prematurity (<37 weeks' gestation), infant days spent in hospital after birth, and being small for gestational age (percentile gestational weight at birth). Maternal adverse pregnancy outcomes were pre-eclampsia, gestational diabetes, and gestational hypertension. PC-KCI replicated well-established pathways, such as the relationship between gestational weeks and preterm premature rupture of membranes. PC-KCI also identified previously unobserved pathways to adverse pregnancy outcomes, including 1) a link between hair cortisol levels (at 12-21 weeks of pregnancy) and pre-eclampsia; 2) two pathways to preterm birth depending on race, with one linking Hispanic race, pre-gestational diabetes and gestational weeks, and a second pathway linking black race, hair cortisol, preeclampsia, and gestational weeks; and 3) a relationship between maternal childhood trauma, perceived social stress in adulthood, and low weight for gestational age. Our approach confirmed previous findings and identified previously unobserved pathways to adverse pregnancy outcomes. It presents a method for a global assessment of a clinical problem for further study of possible causal pathways.

Fig 1

Type I error rate (A) & Type II error rate (B) for the KCI test varying sample size (200 and 400) and number of variables (2–5).

Fig 2. Relationship between graph structure and regression results.

Note: The left-hand side shows the true underlying causal structure of a fictitious dataset with enough observations to detect significance. For example, a and b jointly influence d; d and e jointly influence the outcome. Further, there are no unmeasured variables affecting two or more variables in the data collected, variables a through f. The right-hand side shows the expected regression results given the underlying causal structure. Model 1 regresses the outcome on all variables, a through f. Notice that while a, b, and c are, in fact, indirect causes of the outcome through d and e, Model 1 renders these associations not significant because it is controlling for the mediating variables, d and e. Model 2 regresses the outcome on all variables except d, which is left out of the model. Without d, the model finds new associations with a, b, and f because that pathway is no longer blocked by d.

Fig 3. Graphical output from PC-KCI algorithm, identifying potential pathways from stress variables to adverse pregnancy outcomes for 4 US birth cohorts, 2013–2015.

Note: Solid lines indicate p < .01 associations, while dashed lines indicate p < .05. Blue dots indicate an example pathway missed by PC-KCI (false negative results).