Constructing evidence-based clinical intrapartum care algorithms for decision-support tools.

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.

Cervical dilatation over time is a poor predictor of severe adverse birth outcomes: a diagnostic accuracy study.

OBJECTIVE: To assess the accuracy of the World Health Organization (WHO) partograph alert line and other candidate predictors in the identification of women at risk of developing severe adverse birth outcomes. DESIGN: A facility-based, multicentre, prospective cohort study. SETTING: Thirteen maternity hospitals located in Nigeria and Uganda. POPULATION: A total of 9995 women with spontaneous onset of labour presenting at cervical dilatation of ≤6 cm or undergoing induction of labour. METHODS: Research assistants collected data on sociodemographic, anthropometric, obstetric, and medical characteristics of study participants at hospital admission, multiple assessments during labour, and interventions during labour and childbirth. The alert line and action line, intrapartum monitoring parameters, and customised labour curves were assessed using sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and the J statistic. OUTCOMES: Severe adverse birth outcomes. RESULTS: The rate of severe adverse birth outcomes was 2.2% (223 women with severe adverse birth outcomes), the rate of augmentation of labour was 35.1% (3506 women), and the caesarean section rate was 13.2% (1323 women). Forty-nine percent of women in labour crossed the alert line (4163/8489). All reference labour curves had a diagnostic odds ratio ranging from 1.29 to 1.60. The J statistic was less than 10% for all reference curves. CONCLUSIONS: Our findings suggest that labour is an extremely variable phenomenon, and the assessment of cervical dilatation over time is a poor predictor of severe adverse birth outcomes. The validity of a partograph alert line based on the 'one-centimetre per hour' rule should be re-evaluated. FUNDING: Bill & Melinda Gates Foundation, United States Agency for International Development (USAID), UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), and WHO (A65879). TWEETABLE ABSTRACT: The alert line in check: results from a WHO study.

A global reference for caesarean section rates (C-Model): a multicountry cross-sectional study.

OBJECTIVE: To generate a global reference for caesarean section (CS) rates at health facilities. DESIGN: Cross-sectional study. SETTING: Health facilities from 43 countries. POPULATION/SAMPLE: Thirty eight thousand three hundred and twenty-four women giving birth from 22 countries for model building and 10,045,875 women giving birth from 43 countries for model testing. METHODS: We hypothesised that mathematical models could determine the relationship between clinical-obstetric characteristics and CS. These models generated probabilities of CS that could be compared with the observed CS rates. We devised a three-step approach to generate the global benchmark of CS rates at health facilities: creation of a multi-country reference population, building mathematical models, and testing these models. MAIN OUTCOME MEASURES: Area under the ROC curves, diagnostic odds ratio, expected CS rate, observed CS rate. RESULTS: According to the different versions of the model, areas under the ROC curves suggested a good discriminatory capacity of C-Model, with summary estimates ranging from 0.832 to 0.844. The C-Model was able to generate expected CS rates adjusted for the case-mix of the obstetric population. We have also prepared an e-calculator to facilitate use of C-Model (www.who.int/reproductivehealth/publications/maternal_perinatal_health/c-model/en/). CONCLUSIONS: This article describes the development of a global reference for CS rates. Based on maternal characteristics, this tool was able to generate an individualised expected CS rate for health facilities or groups of health facilities. With C-Model, obstetric teams, health system managers, health facilities, health insurance companies, and governments can produce a customised reference CS rate for assessing use (and overuse) of CS. TWEETABLE ABSTRACT: The C-Model provides a customized benchmark for caesarean section rates in health facilities and systems.