European journal of obstetrics, gynecology, and reproductive biology · 2013
Prediction of spontaneous preterm delivery in the first trimester of pregnancy.
СтатьяНазвание и аннотация: Перевод в очереди
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Sananes Nicolas, Meyer Nicolas, Gaudineau Adrien, Aissi Germain, Boudier Eric, Fritz Gabrielle, Viville Brigitte, Nisand Israël, Langer Bruno, Favre Romain
Аннотация
OBJECTIVE: To develop a model for predicting premature delivery before 37 weeks' gestation based on maternal factors, obstetric history and biomarkers in the first trimester of pregnancy.
STUDY DESIGN: Cohort study based on data collected prospectively between 1 January 2000 and 30 November 2011. Multivariate logistic regression was used to construct a model of the risk of premature delivery.
RESULTS: 31,834 pregnancies were included, of which 1188 cases were spontaneous premature deliveries before 37 weeks (3.7%). We built a predictive model based on maternal age, body mass index, smoking status and previous obstetric history. This could identify 23.3% of premature deliveries in our study population, with a false positive rate of 10%. In the group of patients who had already had at least one pregnancy at or beyond 16 weeks, the detection level increased to 29.7%. The positive predictive value was 7.4 and 7.3% respectively, while negative predictive value was 97.2 and 97.9%.
CONCLUSIONS: Predicting preterm delivery on the basis of maternal characteristics and obstetric history needs to be further improved. PAPP-A levels and ultrasonographic measurement of cervical length could not be integrated in the model but require further investigations.
STUDY DESIGN: Cohort study based on data collected prospectively between 1 January 2000 and 30 November 2011. Multivariate logistic regression was used to construct a model of the risk of premature delivery.
RESULTS: 31,834 pregnancies were included, of which 1188 cases were spontaneous premature deliveries before 37 weeks (3.7%). We built a predictive model based on maternal age, body mass index, smoking status and previous obstetric history. This could identify 23.3% of premature deliveries in our study population, with a false positive rate of 10%. In the group of patients who had already had at least one pregnancy at or beyond 16 weeks, the detection level increased to 29.7%. The positive predictive value was 7.4 and 7.3% respectively, while negative predictive value was 97.2 and 97.9%.
CONCLUSIONS: Predicting preterm delivery on the basis of maternal characteristics and obstetric history needs to be further improved. PAPP-A levels and ultrasonographic measurement of cervical length could not be integrated in the model but require further investigations.