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Clinical management of uterine contraction abnormalities; an evidence-based intrapartum care algorithm.
Gülümser, C; Yassa, M.
Afiliação
  • Gülümser C; Department of Obstetrics and Gynaecology, Yuksek Ihtisas University School of Medicine, Ankara, Turkey.
  • Yassa M; Department of Obstetrics and Gynaecology, Bahcesehir University Medical Park Maltepe Hospital, Istanbul, Turkey.
BJOG ; 2022 Apr 12.
Article em En | MEDLINE | ID: mdl-35415963
ABSTRACT

AIM:

To develop algorithms as decision support tools for identifying, managing and monitoring abnormal uterine activity during labour. POPULATION Women with singleton, term (37-42 weeks) pregnancies in active labour at admission.

SETTING:

Institutional birth settings in low- and middle-income countries (the algorithm may be applicable to any health facility). SEARCH STRATEGY PubMed was searched up to January 2020 using keywords. We also searched The Cochrane Library, and international guidelines from World Health Organization (WHO), National Institute for Health and Care Excellence (NICE), American College of Obstetricians and Gynecologists (ACOG) and French College of Gynaecologists and Obstetricians (CNGOF). CASE SCENARIOS Algorithms were developed for two case scenarios uterine hypoactivity and excessive uterine contractions. Key themes in the algorithm are diagnosis, identification of probable causes, assessment of maternal and fetal condition and labour progress, monitoring and management.

CONCLUSION:

The algorithms for uterine hypoactivity and excessive uterine contractions have been developed to facilitate safe and effective management of abnormal uterine activity during labour. Research is needed to assess the views of healthcare professionals and women accessing healthcare to explore the feasibility of implementing these algorithms, and impact on labour outcomes. TWEETABLE ABSTRACT An evidence-based algorithm to support clinical management of abnormal uterine activity during labour.
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Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 / 2_ODS3 Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Revista: BJOG Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 / 2_ODS3 Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Revista: BJOG Ano de publicação: 2022 Tipo de documento: Article