Development and validation of a predictive model to identify the active phase of labor.
Active labor
Active phase
Maternal behavior
Midwifery diagnosis
Journal
BMC pregnancy and childbirth
ISSN: 1471-2393
Titre abrégé: BMC Pregnancy Childbirth
Pays: England
ID NLM: 100967799
Informations de publication
Date de publication:
15 Aug 2022
15 Aug 2022
Historique:
received:
18
02
2022
accepted:
25
07
2022
entrez:
15
8
2022
pubmed:
16
8
2022
medline:
18
8
2022
Statut:
epublish
Résumé
The diagnosis of the active phase of labor is a crucial clinical decision, thus requiring an accurate assessment. This study aimed to build and to validate a predictive model, based on maternal signs and symptoms to identify a cervical dilatation ≥4 cm. A prospective study was conducted from May to September 2018 in a II Level Maternity Unit (development data), and from May to September 2019 in a I Level Maternity Unit (validation data). Women with singleton, term pregnancy, cephalic presentation and presence of contractions were consecutively enrolled during the initial assessment to diagnose the stage of labor. Women < 18 years old, with language barrier or induction of labor were excluded. A nomogram for the calculation of the predictions of cervical dilatation ≥4 cm on the ground of 11 maternal signs and symptoms was obtained from a multivariate logistic model. The predictive performance of the model was investigated by internal and external validation. A total of 288 assessments were analyzed. All maternal signs and symptoms showed a significant impact on increasing the probability of cervical dilatation ≥4 cm. In the final logistic model, "Rhythm" (OR 6.26), "Duration" (OR 8.15) of contractions and "Show" (OR 4.29) confirmed their significance while, unexpectedly, "Frequency" of contractions had no impact. The area under the ROC curve in the model of the uterine activity was 0.865 (development data) and 0.927 (validation data), with an increment to 0.905 and 0.956, respectively, when adding maternal signs. The Brier Score error in the model of the uterine activity was 0.140 (development data) and 0.097 (validation data), with a decrement to 0.121 and 0.092, respectively, when adding maternal signs. Our predictive model showed a good performance. The introduction of a non-invasive tool might assist midwives in the decision-making process, avoiding interventions and thus offering an evidenced-base care.
Sections du résumé
BACKGROUND
BACKGROUND
The diagnosis of the active phase of labor is a crucial clinical decision, thus requiring an accurate assessment. This study aimed to build and to validate a predictive model, based on maternal signs and symptoms to identify a cervical dilatation ≥4 cm.
METHODS
METHODS
A prospective study was conducted from May to September 2018 in a II Level Maternity Unit (development data), and from May to September 2019 in a I Level Maternity Unit (validation data). Women with singleton, term pregnancy, cephalic presentation and presence of contractions were consecutively enrolled during the initial assessment to diagnose the stage of labor. Women < 18 years old, with language barrier or induction of labor were excluded. A nomogram for the calculation of the predictions of cervical dilatation ≥4 cm on the ground of 11 maternal signs and symptoms was obtained from a multivariate logistic model. The predictive performance of the model was investigated by internal and external validation.
RESULTS
RESULTS
A total of 288 assessments were analyzed. All maternal signs and symptoms showed a significant impact on increasing the probability of cervical dilatation ≥4 cm. In the final logistic model, "Rhythm" (OR 6.26), "Duration" (OR 8.15) of contractions and "Show" (OR 4.29) confirmed their significance while, unexpectedly, "Frequency" of contractions had no impact. The area under the ROC curve in the model of the uterine activity was 0.865 (development data) and 0.927 (validation data), with an increment to 0.905 and 0.956, respectively, when adding maternal signs. The Brier Score error in the model of the uterine activity was 0.140 (development data) and 0.097 (validation data), with a decrement to 0.121 and 0.092, respectively, when adding maternal signs.
CONCLUSION
CONCLUSIONS
Our predictive model showed a good performance. The introduction of a non-invasive tool might assist midwives in the decision-making process, avoiding interventions and thus offering an evidenced-base care.
Identifiants
pubmed: 35971093
doi: 10.1186/s12884-022-04946-y
pii: 10.1186/s12884-022-04946-y
pmc: PMC9377074
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
641Informations de copyright
© 2022. The Author(s).
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