Prof. Dr. Matthias Schmid
Institute of medical Biometry, Computer Science and Epidemiology
sekretariat@imbie.uni-bonn.de View member: Prof. Dr. Matthias Schmid
Archives of gynecology and obstetrics
OBJECTIVE: To evaluate the association of short-term variation (STV) and Doppler parameters with adverse perinatal outcome in small-for-gestational-age (SGA) fetuses at term.
METHODS: In this retrospective single-center study 97 patients with singleton SGA fetuses at term (≥ 37 + 0 weeks' gestation) were examined. Inclusion criteria were a birth weight < 10th centile, cephalic presentation and planned vaginal birth. Only cases with available Doppler measurements of umbilical artery (UA) and middle cerebral artery (MCA) with calculated cerebroplacental ratio (CPR) in combination with a computerized CTG (cCTG) and STV 72 h prior to delivery were eligible for analysis. Pulsatility indices (PI) were converted into multiples of median (MoM), adjusted for gestational age. The association between Doppler indices and STV values with mode of delivery [secondary cesarean delivery (CD), operative vaginal delivery (OVD), as well as secondary CD and OVD due to fetal distress] and neonatal outcome [UA blood pH ≤ 7.15 and the need of admission to the neonatal intensive care unit (NICU)] was analyzed using logistic regression analysis.
RESULTS: There was a significant association between UA PI MoM and the rate of CD. CD due to fetal distress, OVD and OVD due to fetal distress did not show a correlation with the evaluated Doppler parameters. Furthermore, we did not find an association between low UA birth pH and Doppler parameters while neonates with the need of admission to NICU had significant higher UA PI MoM and significant lower MCA PI MoM and CPR MoM. Regarding STV, a significant effect of low STV on NICU admission was found while none of the other assessed outcome parameters were significantly associated with STV.
CONCLUSION: STV and Doppler parameters in SGA fetuses at term are significantly associated to the rate of NICU admission.
PMID: 31214775
Institute of medical Biometry, Computer Science and Epidemiology
sekretariat@imbie.uni-bonn.de View member: Prof. Dr. Matthias Schmid