Neural differential equations enable early-stage prediction of preterm birth using vaginal microbiota

Abstract

AbstractPreterm births (PTBs), i.e., births before 37 weeks of gestation are completed, are one of the leading issues concerning infant health, and is a problem that plagues all parts of the world. Millions of infants are born preterm globally each year, resulting in developmental disorders in infants and increase in neonatal mortality. Although there are known risk factors for PTB, the current procedures used to assess PTB risk are effective only at the later stages of pregnancy, which reduces the impact of currently possible interventions administered to prevent PTB or mitigate its ill-effects. Vaginal microbial communities have recently garnered attention in the context of PTB, with the notion that a highly diverse microbiome is detrimental as far as PTB is concerned. Increased abundance or scarcity of certain microbial species belonging to specific genera has also been linked to PTB risk. Consequently, attempts have been made towards establishing a correlation between alpha-diversity indices associated with vaginal microbial communities, and PTB. However, the vaginal microbiome varies greatly from individual to individual, and this variation is more pronounced in racially, ethnically and geographically diverse populations, which diversity indices may not be able to overcome. Machine learning (ML)-based approaches have also previously been explored, however, the success of these approaches reported thus far has been limited. Additionally, microbial communities have been reported to evolve during the duration of the pregnancy, and capturing such a signature may require higher, more complex modeling paradigms. Thus, alternative approaches are necessary to identify signatures in these microbial communities that are capable of distinguishing PTB from a full-term pregnancy. In this study, we have highlighted the limitations of diversity indices for prediction of PTB in racially diverse cohorts. We applied Deep Learning (DL)-based methods to vaginal microbial abundance profiles obtained at various stages of pregnancy, and Neural Controlled Differential Equations (CDEs) are able to identify a signature in the temporally-evolving vaginal microbiome during trimester 2 and can predict incidences of PTB (mean test set ROC-AUC = 0.81, accuracy = 75%, F1-score = 0.71) significantly better than traditional ML classifiers such as Random Forests (mean test set ROC-AUC = 0.65, accuracy = 66%, F1-score = 0.42) and Decision Trees (mean test set ROC-AUC = 0.48, accuracy = 46%, F1-score = 0.40), thus enabling effective early-stage PTB risk assessment.GraphicalAbstract