Clinical algorithms for the monitoring and management of spontaneous, uncomplicated labour and childbirth

Julia Pasquale*, Celina Gialdini, Mónica Chamillard, Virginia Diaz, Marcus J. Rijken, Joyce L. Browne, Mimi Tin Yan Seto, Ka Wang Cheung, Mercedes Bonet,

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

Aim: To develop evidence-based clinical algorithms for the assessment and management of spontaneous, uncomplicated labour and vaginal birth. Population: Pregnant women at any stage of labour, with singleton, term pregnancies considered to be at low risk of developing complications. Setting: Health facilities in low- and middle-income countries. Search Strategy: We searched for relevant published algorithms, guidelines, systematic reviews and primary research studies on Cochrane Library, PubMed, and Google on terms related to spontaneous, uncomplicated labour and childbirth up to 01 June 2023. Case scenarios: Three case scenarios were developed to cover assessments and management for spontaneous, uncomplicated first, second and third stage of labour. The algorithms provide pathways for definition, assessments, diagnosis, and links to other algorithms in this series for management of complications. Conclusions: We have developed three clinical algorithms to support evidence-based decision making during spontaneous, uncomplicated labour and vaginal birth. These algorithms may help guide health care staff to institute respectful care, appropriate interventions where needed, and potentially reduce the unnecessary use of interventions during labour and childbirth.

Original languageEnglish
Article numberdoi.org/10.1111/1471-0528.17895
Pages (from-to)17-27
Number of pages11
JournalBJOG: An International Journal of Obstetrics and Gynaecology
Volume131
Issue numberS2
Early online date10 Jul 2024
DOIs
Publication statusPublished - Aug 2024

Keywords

  • childbirth
  • labour
  • normal
  • spontaneous
  • uncomplicated

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