Mode of delivery and maternal vitamin D deficiency: an optimized intelligent Bayesian network algorithm analysis of a stratified randomized controlled field trial

Author:

Amiri Mina,Rostami Maryam,Sheidaei Ali,Fallahzadeh Aida,Ramezani Tehrani Fahimeh

Abstract

AbstractThis study aimed to elucidate the algorithm of various influential factors relating to the association between 25-hydroxyvitamin D (25(OH)D) concentration at delivery and mode of delivery. The investigation constituted a secondary analysis using data collected as part of the Khuzestan Vitamin D Deficiency Screening Program in Pregnancy, which is a stratified randomized vitamin D supplementation-controlled trial comprising 1649 eligible pregnant women. The Bayesian Network (BN) method was utilized to determine the association algorithm between diverse influential factors associated with maternal vitamin D and mode of delivery. The optimized intelligent BN algorithm revealed that women presenting with moderate (35.67%; 95% CI: 33.36–37.96) and severe vitamin D deficiency (47.22%; 95% CI: 44.81–49.63) at delivery were more likely to undergo cesarean section than those presenting with normal concentrations of this nutritional hormone (18.62%; 95% CI: 16.74–20.5). The occurrence probabilities of preeclampsia in mothers with normal, moderate, and severe vitamin D deficiency at delivery were (1.5%; 95% CI: 0.92–2.09), (14.01%; 95% CI: 12.33–15.68), and (26.81%; 95% CI: 24.67–28.95), respectively. Additionally, mothers with moderate (11.81%; 95% CI: 10.25–13.36) and severe (27.86%; 95% CI: 25.69–30.02) vitamin D deficiency exhibited a higher probability of preterm delivery in comparison to those presenting with normal concentrations (1.12%; 95% CI: 0.62–1.63). This study demonstrated that the vitamin D status of pregnant women at delivery could directly affect the mode of delivery and indirectly through maternal complications, such as preeclampsia and preterm delivery, leading to a higher occurrence probability of cesarean section.

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

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