The development of a Simplified, Effective, Labour Monitoring-to-Action (SELMA) tool for Better Outcomes in Labour Difficulty (BOLD): study protocol

BACKGROUND: The partograph is currently the main tool available to support decision-making of health professionals during labour. However, the rate of appropriate use of the partograph is disappointingly low. Apart from limitations that are associated with partograph use, evidence of positive impact on labour-related health outcomes is lacking. The main goal of this study is to develop a Simplified, Effective, Labour Monitoring-to-Action (SELMA) tool. The primary objectives are: to identify the essential elements of intrapartum monitoring that trigger the decision to use interventions aimed at preventing poor labour outcomes; to develop a simplified, monitoring-to-action algorithm for labour management; and to compare the diagnostic performance of SELMA and partograph algorithms as tools to identify women who are likely to develop poor labour-related outcomes. METHODS/DESIGN: A prospective cohort study will be conducted in eight health facilities in Nigeria and Uganda (four facilities from each country). All women admitted for vaginal birth will comprise the study population (estimated sample size: 7,812 women). Data will be collected on maternal characteristics on admission, labour events and pregnancy outcomes by trained research assistants at the participating health facilities. Prediction models will be developed to identify women at risk of intrapartum-related perinatal death or morbidity (primary outcomes) throughout the course of labour. These predictions models will be used to assemble a decision-support tool that will be able to suggest the best course of action to avert adverse outcomes during the course of labour. To develop this set of prediction models, we will use up-to-date techniques of prognostic research, including identification of important predictors, assigning of relative weights to each predictor, estimation of the predictive performance of the model through calibration and discrimination, and determination of its potential for application usi