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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. Evidence‐based clinical algorithms may help support respectful, high quality intrapartum care. [Correction added on 23 August 2024, after first online publication: The Abstract has been updated to structure layout in this version.] [ABSTRACT FROM AUTHOR]

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. Evidence‐based clinical algorithms may help and standardize early identification and management of abnormally slow labour progress or arrest. Evidence‐based clinical algorithms may help and standardize early identification and management of abnormally slow labour progress or arrest. [ABSTRACT FROM AUTHOR]

Objective: To construct algorithms with a sequential decision analysis pathway for monitoring of the fetal heart rate and managing fetal heart rate bradycardia, late decelerations and tachycardia during labour. Population: Low‐risk pregnant women in labour with singleton cephalic term pregnancies. Setting: Institutional births in low‐ and middle‐income countries. Search strategy: We sought relevant published clinical algorithms, guidelines and randomised trials/reviews by searching the Cochrane Library, PubMed and Google on the terms: "fetal AND heart AND rate AND algorithm AND (labour OR intrapartum)", up to March 2020. Case scenarios: The two scenarios included were fetal heart rate bradycardia or late decelerations (potentially related to uterine rupture, placental abruption, cord prolapse, maternal hypotension, uterine hyperstimulation or unexplained) and fetal heart rate tachycardia (potentially related to maternal hyperthermia, infection, dehydration or unexplained). The algorithms provide pathways for definition, assessment, diagnosis, interventions to correct the abnormalities and ongoing monitoring leading to mode of birth, and linking to other algorithms in the series. Conclusions: The algorithms provide a framework for monitoring and managing fetal heart rate bradycardia, late decelerations and tachycardia during labour. We emphasise the inherent diagnostic inaccuracy of fetal heart rate monitoring, the tendency to over‐diagnose fetal compromise, the need to consider fetal heart rate information in the context of other clinical features and the need to engage in informed, shared, family‐centred decision‐making. We note the need for further research on methods of fetal assessment during labour including clinical fetal arousal testing and the rapid biophysical profile test. Decision analysis algorithms for fetal bradycardia, late decelerations and tachycardia highlight diagnostic limitations. Decision analysis algorithms for fetal bradycardia, late decelerations and tachycardia highlight diagnostic limitations. [ABSTRACT FROM AUTHOR]

Aim: The development of an evidence‐based algorithm for the clinical management of deviations in maternal temperature during labour and childbirth. Population: Pregnant women at any stage of labour, with singleton, term (37–42 weeks) pregnancies at low risk of developing complications. Setting: Health facilities in low‐ and middle‐income countries. Search strategy: We searched for international guidelines and prioritised WHO guidelines. In addition, we searched for other sources of evidence in the Cochrane Database of Systematic Reviews, EMBASE, MEDLINE and CINAHL until June 2020. Studies were prioritised according to the hierarchy of evidence. Case scenarios: Two case scenarios were identified: maternal hyperthermia and hypothermia. We developed a single algorithm including both, due to commonalities in diagnosis, monitoring and management of underlying causes. The underlying conditions covered in the pathway include maternal sepsis and infection, chorioamnionitis, pyelonephritis, lower urinary tract and respiratory infections. Key decision points in the algorithm are suspicion of condition, definition, differential diagnosis, monitoring and management. Conclusions: We present an evidence‐based algorithm to assist healthcare professionals in making decisions about appropriate clinical management of deviations in maternal temperature. Research is needed to assess the views of healthcare professionals and women accessing healthcare on the feasibility of implementing the algorithm. An evidence‐based intrapartum care algorithm to support management of deviations in maternal temperature in labour and childbirth. #sepsis #maternitycare. An evidence‐based intrapartum care algorithm to support management of deviations in maternal temperature in labour and childbirth. #sepsis #maternitycare. [ABSTRACT FROM AUTHOR]

Aims: To develop algorithms for identifying, managing and monitoring postpartum haemorrhage (PPH) and other third stage of labour abnormalities after vaginal delivery. Population: Women with low‐risk singleton term pregnancies who have had a vaginal delivery. Setting: Hospital settings with a particular focus on healthcare facilities in low‐ and middle‐income countries (LMICs). Search strategy: Searches for international and national guidance documents, research databases (Cochrane, Medline and CINAHL) and published systematic reviews. Searches were limited to work published in English between 1 January 2008 and 31 December 2018. Case scenarios: Four interlinked case scenarios were identified for algorithm development: (1) an approach to PPH after vaginal delivery, (2) uterine atony, (3) genital tract trauma and (4) retained placenta/placental products. Conclusions: The development of clear approaches to the assessment, resuscitation, treatment and monitoring of the four case scenarios are presented as algorithms, based on available evidence. They need to be field tested and evaluated for effectiveness, and may be adapted for electronic decision support tools using artificial intelligence in different settings. Further research is needed around multimodal sequential packages of care for PPH, conservative surgical measures, resuscitation in LMICs, and how a respectful maternity care focus can be incorporated into the algorithms. Algorithm development for standardised approaches to managing PPH in low‐resource settings. Algorithm development for standardised approaches to managing PPH in low‐resource settings. [ABSTRACT FROM AUTHOR]

Aim: To construct evidence‐based algorithms for the assessment and management of common amniotic fluid abnormalities detected during labour. Population: Low‐risk singleton, term pregnant women in labour. Setting: Birth facilities in low‐ and middle‐income countries. Search Strategy: We searched international guidelines published by the American College of Obstetricians and Gynecologists (ACOG), the National Institute for Health and Care Excellence (NICE), the Royal Australian and New Zealand College of Obstetricians and Gynaecologists (RANZCOG), the Royal College of Obstetricians and Gynaecologists (RCOG), the Society of Obstetrics and Gynaecology (SOGC) and the World Health Organization (WHO). We also searched The Cochrane Library and MEDLINE up to 20 January 2020 using keywords for relevant systematic reviews and randomised trials. Case scenarios: We developed evidence‐based intrapartum care algorithms for four case scenarios: oligohydramnios; meconium‐stained amniotic fluid; bloody amniotic fluid or vaginal bleeding; and purulent amniotic fluid or discharge. These conditions may be associated with fetal and /or maternal morbidity. Differential diagnosis includes uteroplacental insufficiency, fetal growth restriction, fetal distress, abruption, placenta or vasa praevia, uterine rupture and intra‐amniotic infection, respectively. Algorithms include how to assess for, diagnose and manage these conditions. Conclusions: Four algorithms are presented, to provide a systematic approach and guidance on the clinical management for the following amniotic fluid abnormalities: oligohydramnios; meconium‐stained liquor; bloody amniotic fluid or vaginal bleeding; and purulent amniotic fluid or discharge. These algorithms may be beneficial in supporting clinical decision making, particularly in low‐resource settings. Evidence based algorithms for management of common amniotic fluid abnormalities seen during labour. Evidence based algorithms for management of common amniotic fluid abnormalities seen during labour. [ABSTRACT FROM AUTHOR]

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. An evidence‐based algorithm to support clinical management of abnormal uterine activity during labour. An evidence‐based algorithm to support clinical management of abnormal uterine activity during labour. [ABSTRACT FROM AUTHOR]

Aim: To develop evidence‐based clinical algorithms for management of common intrapartum urinary abnormalities. Population: Women with singleton, term pregnancies in active labour and immediate postnatal period, at low risk of complications. Setting: Healthcare facilities in low‐ and middle‐income countries. Search strategy: A systematic search and review were conducted on the current guidelines from WHO, NICE, ACOG and RCOG. Additional search was done on PubMed and The Cochrane Database of Systematic Reviews up to May 2020. Case scenarios: Four common intrapartum urinary abnormalities were selected: proteinuria, ketonuria, glycosuria and oliguria. Using reagent strip testing, glycosuria was defined as ≥2+ on one occasion or of ≥1+ on two or more occasions. Proteinuria was defined as ≥2+ and presence of ketone indicated ketonuria. Oliguria was defined as hourly urine output ≤30 ml. Thorough initial assessment using history, physical examination and basic investigations helped differentiate most of the underlying causes, which include diabetes mellitus, dehydration, sepsis, pre‐eclampsia, shock, anaemia, obstructed labour, underlying cardiac or renal problems. A clinical algorithm was developed for each urinary abnormality to facilitate intrapartum management and referral of complicated cases for specialised care. Conclusions: Four simple, user‐friendly and evidence‐based clinical algorithms were developed to enhance intrapartum care of commonly encountered maternal urine abnormalities. These algorithms may be used to support healthcare professionals in clinical decision‐making when handling normal and potentially complicated labour, especially in low resource countries. Evidence‐based clinical algorithms developed to guide intrapartum management of commonly encountered urinary abnormalities. An evidence‐based clinical algorithm was developed to enhance intrapartum management of commonly encountered urinary abnormalities. [ABSTRACT FROM AUTHOR]