BJOG : an international journal of obstetrics and gynaecology · 2022
Constructing evidence-based clinical intrapartum care algorithms for decision-support tools.
СтатьяОбзорПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Bonet M, Ciabati L, De Oliveira L L, Souza R, Browne J L, Rijken M, Fawcus S, Hofmeyr G J, Liabsuetrakul T, Gülümser Ç, Blennerhassett A, Lissauer D, Meher S, Althabe F, Oladapo O T, WHO Intrapartum Care Algorithms Working Group
Аннотация
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.
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.