TY - JOUR
T1 - Constructing evidence-based clinical intrapartum care algorithms for decision-support tools
AU - Bonet, M.
AU - Ciabati, L.
AU - de Oliveira, L. L.
AU - Souza, R.
AU - Browne, J. L.
AU - Rijken, M.
AU - Fawcus, S.
AU - Hofmeyr, G. J.
AU - Liabsuetrakul, T.
AU - Gülümser, Çağri
AU - Blennerhassett, A.
AU - Lissauer, D.
AU - the WHO Intrapartum Care Algorithms Working Group
AU - Meher, S.
AU - Althabe, F.
AU - Oladapo, O.
N1 - Funding Information: This work was funded by the Bill & Melinda Gates Foundation (Grant #OPP1084318); The United States Agency for International Development (USAID); and the UNDP‐UNFPA‐UNICEF‐WHO‐World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), a co‐sponsored programme executed by the World Health Organization. The funders had no role in design, decision to publish or preparation of the manuscript. Funding Information: Dr Renato T Souza received funding from the HRP Alliance, part of the UNDP‐UNFPA‐UNICEF‐WHO‐World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), a co‐sponsored programme executed by WHO, to complete his studies. Other co‐authors have no conflicts of interest to declare. Full disclosure of interests available to view online as supporting information. Publisher Copyright: © 2022 The World Health Organization. The World Health Organization retains copyright and all other rights in the manuscript of this article as submitted for publication.
PY - 2022
Y1 - 2022
N2 - Aim: To describe standardised iterative methods used by a multidisciplinary group to develop evidence-based clinical intrapartum care algorithms for the management of uneventful and complicated labours. Population: Singleton, term pregnancies considered to be at low risk of developing complications at admission to the birthing facility. Setting: Health facilities in low- and middle-income countries. Search strategy: Literature reviews were conducted to identify standardised methods for algorithm development and examples from other fields, and evidence and guidelines for intrapartum care. Searches for different algorithm topics were last updated between January and October 2020 and included a combination of terms such as ‘labour’, ‘intrapartum’, ‘algorithms’ and specific topic terms, using Cochrane Library and MEDLINE/PubMED, CINAHL, National Guidelines Clearinghouse and Google. Case scenarios: Nine algorithm topics were identified for monitoring and management of uncomplicated labour and childbirth, identification and management of abnormalities of fetal heart rate, liquor, uterine contractions, labour progress, maternal pulse and blood pressure, temperature, urine and complicated third stage of labour. Each topic included between two and four case scenarios covering most common deviations, severity of related complications or critical clinical outcomes. Conclusions: Intrapartum care algorithms provide a framework for monitoring women, and identifying and managing complications during labour and childbirth. These algorithms will support implementation of WHO recommendations and facilitate the development by stakeholders of evidence-based, up to date, paper-based or digital reminders and decision-support tools. The algorithms need to be field tested and may need to be adapted to specific contexts. Tweetable abstract: Evidence-based intrapartum care clinical algorithms for a safe and positive childbirth experience.
AB - Aim: To describe standardised iterative methods used by a multidisciplinary group to develop evidence-based clinical intrapartum care algorithms for the management of uneventful and complicated labours. Population: Singleton, term pregnancies considered to be at low risk of developing complications at admission to the birthing facility. Setting: Health facilities in low- and middle-income countries. Search strategy: Literature reviews were conducted to identify standardised methods for algorithm development and examples from other fields, and evidence and guidelines for intrapartum care. Searches for different algorithm topics were last updated between January and October 2020 and included a combination of terms such as ‘labour’, ‘intrapartum’, ‘algorithms’ and specific topic terms, using Cochrane Library and MEDLINE/PubMED, CINAHL, National Guidelines Clearinghouse and Google. Case scenarios: Nine algorithm topics were identified for monitoring and management of uncomplicated labour and childbirth, identification and management of abnormalities of fetal heart rate, liquor, uterine contractions, labour progress, maternal pulse and blood pressure, temperature, urine and complicated third stage of labour. Each topic included between two and four case scenarios covering most common deviations, severity of related complications or critical clinical outcomes. Conclusions: Intrapartum care algorithms provide a framework for monitoring women, and identifying and managing complications during labour and childbirth. These algorithms will support implementation of WHO recommendations and facilitate the development by stakeholders of evidence-based, up to date, paper-based or digital reminders and decision-support tools. The algorithms need to be field tested and may need to be adapted to specific contexts. Tweetable abstract: Evidence-based intrapartum care clinical algorithms for a safe and positive childbirth experience.
KW - Algorithms
KW - childbirth
KW - first stage of labour
KW - intrapartum care
KW - labour complications
KW - second stage of labour
KW - third stage of labour
UR - http://www.scopus.com/inward/record.url?scp=85127980050&partnerID=8YFLogxK
U2 - https://doi.org/10.1111/1471-0528.16958
DO - https://doi.org/10.1111/1471-0528.16958
M3 - Article
C2 - 35411684
SN - 1470-0328
JO - BJOG: An International Journal of Obstetrics and Gynaecology
JF - BJOG: An International Journal of Obstetrics and Gynaecology
ER -