New model for predicting preterm delivery during the second trimester of pregnancy

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.

Systematic Review of Prediction Models for Preterm Birth Using CHARMS

“…Of the 16 studies that included model development, five studies collected data prospectively (Allouche et al, 2011; Gioan et al, 2018; Mercer et al, 1996; Misra et al, 2001; Zhu et al, 2017), 10 studies collected data retrospectively (Baer et al, 2018; de Caunes et al, 1990; Goyal et al, 2015; Hueston, 1998; Lee et al, 2020; Luo et al, 2016; Morken et al, 2014; Tan et al, 2007; Weber et al, 2018; Woolery, 1992), and one study combined prospective and retrospective designs (Takagi et al, 2012). Of the five studies that only conducted validation, three studies collected data prospectively (R. K. Creasy et al, 1980; Goodfellow et al, 2019; Holbrook et al, 1989), one study collected data retrospectively (Edenfield et al, 1995), and one study combined both designs (Wilson & Sill, 1973) (Table 2).…”

Evaluation of transvaginal ultrasonography, power Doppler indices, electromyography and serum protein 53 level in prediction for preterm labour

“…Despite the recent advances in antenatal care, preterm delivery has remained a major cause of perinatal morbidity and mortality, furthermore, it is associated with a high incidence of severe neurological deficits and developmental disabilities [10] . The exact mechanism of preterm labor is largely unknown , but it is believed to include many factors including genetics and environmental ones [1] . Several different approaches have been proved to be clinically useful screening methods for the early prediction of preterm birth, but of these, some methods have very low sensitivity and PPV such as tocodynamometry and others have several major drawbacks.…”

“…Preterm delivery remains a worldwide problem, it is defined as delivery at less than 37 weeks of gestation and it is associated with obvious increase in perinatal morbidity (low birth weight, growth debilitation and damage of the nervous system) and increased risk of developing respiratory distress syndrome, intraventricular hemorrhage, bronchopulmonary dysplasia, sepsis, necrotizing enterocolitis, patent ductus arteriosus and further, it is the second leading cause of perinatal mortality (nearly 30% of all neonatal deaths) [1] . Globally, its incidence ranges from 5:15% (about of 15 million babies annually), making this disorder has overall clinical, social, and economic burden [2] .…”

“…Nowadays, prediction and prevention of preterm delivery stay of the great challenges. The etiology of preterm delivery and its exact mechanism is largely unclear, it is complex and influenced by many factors such as genetics, gestational age, history of PTL, psychological and pregnancy complications, placenta previa, cervical length measurements and obesity [1] . Many different methods were used for prediction of preterm birth ; of these, cervical morphology and biometry, biomarkers such as fetal fibronectin "FFN", salivary estriol, cervicovaginal intracellular adhesion molecule-1 "ICAM-1", maternal serum alpha fetoprotein "MS-AFP", cervicovaginal beta-human chorionic gonadotropin "β-hCG", phosphorylated insulin-like growth factor binding protein-1 "phIGFBP-1" [4] , in addition to hospital tocodynamometry for monitoring uterine contractions to evaluate preterm labor [5] .…”

Neuroprotection of the preterm brain

“…However this testing is not effective for low risk women (133). In addition, multi-modal screening tools have been designed that have excellent specificity and sensitivity, but these also rely on multiple invasive procedures and have lesser efficacy in low risk populations (134). The importance of identifying women who will go on to deliver preterm cannot be understated as preventing rather than treating an injury is always the preferred approach.…”