Abstract
In this study, a new model for predicting preterm delivery (PD) was proposed. The primary model was constructed using ten selected variables, as previously defined in seventeen different studies. The ability of the model to predict PD was evaluated using the combined measurement from these variables. Therefore, a prospective investigation was performed by enrolling 130 pregnant patients whose gestational ages varied from 17+0 to 28+6 weeks. The patients underwent epidemiological surveys and ultrasonographic measurements of their cervixes, and cervicovaginal fluid and serum were collected during a routine speculum examination performed by the managing gynecologist. The results showed eight significant variables were included in the present analysis, and combination of the positive variables indicated an increased probability of PD in pregnant patients. The accuracy for predicting PD were as follows: one positive – 42.9%; two positives – 75.0%; three positives – 81.8% and four positives – 100.0%. In particular, the combination of ≥2× positives had the best predictive value, with a relatively high sensitivity (82.6%), specificity (88.1%) and accuracy rate (79.2%), and was considered the cut-off point for predicting PD. In conclusion, the new model provides a useful reference for evaluating the risk of PD in clinical cases.
Highlights
Preterm delivery (PD) remains a global problem associated with perinatal morbidity, including low birth weight, growth retardation and irreversible damage to the nervous system[1]
These studies have resulted in improved prediction of preterm delivery (PD) and a decrease in the number of premature births, at present, the accuracy of predicting preterm births is still a puzzle because of many factors that contribute to the outcome of PD
These factors include a previous history of PD, gestational age, pregnancy complications, psychological and genetic factors[7], maternal obesity[8, 9], placenta previa[10], fat-to-placenta strain ratio value[11], serum relaxin[12], insulin-like growth factor-binding protein-113, interleukin-1β (IL-1β)[14], thioredoxin and interleukin 1 receptor antagonist[15], and fetal fibronectin levels and cervical length measurements[16, 17]
Summary
Preterm delivery (PD) remains a global problem associated with perinatal morbidity, including low birth weight, growth retardation and irreversible damage to the nervous system[1]. These studies have resulted in improved prediction of PD and a decrease in the number of premature births, at present, the accuracy of predicting preterm births is still a puzzle because of many factors that contribute to the outcome of PD These factors include a previous history of PD, gestational age, pregnancy complications, psychological and genetic factors[7], maternal obesity[8, 9], placenta previa[10], fat-to-placenta strain ratio value[11], serum relaxin[12], insulin-like growth factor-binding protein-113, interleukin-1β (IL-1β)[14], thioredoxin and interleukin 1 receptor antagonist[15], and fetal fibronectin levels and cervical length measurements[16, 17]. Seeking to improve the sensitivity and specificity for predicting PD, the present study proposes a new prediction model for premature birth
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