Breathing Movements of Fetus, Length of Cervix and Vaginal Secretion Positive Fibronectin, As Tools to Predict Chances of Preterm Labour
DOI:
https://doi.org/10.37018/JMNT5842Keywords:
Gestational age, preterm labor, cervical length, assessmentAbstract
Background: The leading cause of newborn health problems and death results from preterm labour. Early predictions about developing preterm labour need to be exact to enable prompt medical assistance. This study evaluates fetal breathing movements together with cervical length measurements and vaginal fibronectin tests to determine their accuracy in predicting preterm labour whether used separately or in combination.
Methods: This was cross sectional study took place within a Lady Willingdon Hospital with n=110 pregnant women participating who were at gestational weeks 24 to 34. Three tests were utilized to evaluate fetal breathing movements and cervical length measurements using transvaginal ultrasound while fibronectin levels required vaginal secretion analysis. The study measured either preterm labour or term delivery as its primary outcome. The analysis used Sensitivity, Specificity, Predictive Values and Logistic Regression techniques.
Results: The occurrence of preterm labour developed in 50% (55/110) of all participants. Fetal breathing movements measured at 5.3 ± 1.1 movements were lower than the 7.2 ± 0.9 movements seen in control subjects with a statistically significant difference (p<0.05). Preterm women exhibited cervical lengths measuring 2.1 ± 0.6 cm that were significantly shorter than the term-group values of 3.4 ± 0.5 cm (p<0.01). The group of preterm subjects showed higher fibronectin concentrations than term subjects along with measurements of 50.2 ± 20.5 ng/mL versus 10.1 ± 8.4 ng/mL (p<0.01). Multi-parameter assessment generated the most accurate results through a combined logistic model providing an odds ratio of 0.38 (95% CI 0.22–0.64, p<0.001).
Conclusion: The assessment of fetal breathing movements plus cervical length along with fibronectin levels works effectively as preterm labour risk predictors. Prediction accuracy increases when different clinical parameters are combined into a single assessment approach for potential clinical implementation.
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