TY - JOUR
T1 - Clinical algorithms for identification and management of delay in the progression of first and second stage of labour
AU - Pasquale, J.
AU - Chamillard, M.
AU - Diaz, V.
AU - Gialdini, C.
AU - Bonet, M.
AU - Oladapo, O. T.
AU - Abalos, E.
AU - Algorithms Working Group, for the W. HO Intrapartum Care
AU - Ciabati, Livia
AU - de Oliveira, Lariza Laura
AU - Browne, Joyce
AU - Rijken, Marcus
AU - Fawcus, Sue
AU - Hofmeyr, Justus
AU - Liabsuetrakul, Tippawan
AU - Gülümser, Çağri
AU - Blennerhassett, Anna
AU - Lissauer, David
AU - Meher, Shireen
AU - Althabe, Fernando
AU - Bonet, Mercedes
AU - Metin Gülmezoglu, A.
AU - Oladapo, Olufemi
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 (WHO). The funders had no role in design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: © 2022 John Wiley & Sons Ltd.
PY - 2022
Y1 - 2022
N2 - Aim: To develop clinical algorithms for the assessment and management of slow progress of labour. Population: Low-risk singleton, term, pregnant women in labour. Setting: Institutional births in low- and middle-income countries. Search strategy: We systematically reviewed the literature on normal labour progression, and guidance on clinical management of abnormally slow progression from 1 December 2015 to 1 October 2020 from relevant international guidelines, Cochrane reviews and primary research studies in PubMed by searching for international and national guidance documents, electronic databases and published systematic reviews using relevant keywords. Case scenarios: We developed two clinical algorithms: one for abnormally slow labour progression and arrest during first stage and one for the second stage. The algorithms provide definitions of suspected and confirmed slow progress of labour or arrest, initial assessment and ongoing monitoring, differential diagnosis, and management of the abnormalities, as well as links to other algorithms for labour management. Conclusions: Identifying abnormal progress of labour is often challenging. These algorithms may help healthcare providers identify abnormal labour progress and institute prompt management or referral where needed but also reduce misdiagnosis and unnecessary use of interventions to accelerate labour. Tweetable abstract: Evidence-based clinical algorithms may help and standardize early identification and management of abnormally slow labour progress or arrest.
AB - Aim: To develop clinical algorithms for the assessment and management of slow progress of labour. Population: Low-risk singleton, term, pregnant women in labour. Setting: Institutional births in low- and middle-income countries. Search strategy: We systematically reviewed the literature on normal labour progression, and guidance on clinical management of abnormally slow progression from 1 December 2015 to 1 October 2020 from relevant international guidelines, Cochrane reviews and primary research studies in PubMed by searching for international and national guidance documents, electronic databases and published systematic reviews using relevant keywords. Case scenarios: We developed two clinical algorithms: one for abnormally slow labour progression and arrest during first stage and one for the second stage. The algorithms provide definitions of suspected and confirmed slow progress of labour or arrest, initial assessment and ongoing monitoring, differential diagnosis, and management of the abnormalities, as well as links to other algorithms for labour management. Conclusions: Identifying abnormal progress of labour is often challenging. These algorithms may help healthcare providers identify abnormal labour progress and institute prompt management or referral where needed but also reduce misdiagnosis and unnecessary use of interventions to accelerate labour. Tweetable abstract: Evidence-based clinical algorithms may help and standardize early identification and management of abnormally slow labour progress or arrest.
KW - Abnormal
KW - active first stage
KW - delay
KW - duration
KW - dystocia
KW - labour
KW - length
KW - prolonged labour
KW - protracted
KW - second stage of labour
UR - http://www.scopus.com/inward/record.url?scp=85127955769&partnerID=8YFLogxK
U2 - https://doi.org/10.1111/1471-0528.16775
DO - https://doi.org/10.1111/1471-0528.16775
M3 - Review article
SN - 1470-0328
JO - BJOG: An International Journal of Obstetrics and Gynaecology
JF - BJOG: An International Journal of Obstetrics and Gynaecology
ER -