Progression of the first stage of spontaneous labour: A prospective cohort study in two sub-Saharan African countries.

BACKGROUND: Escalation in the global rates of labour interventions, particularly cesarean section and oxytocin augmentation, has renewed interest in a better understanding of natural labour progression. Methodological advancements in statistical and computational techniques addressing the limitations of pioneer studies have led to novel findings and triggered a re-evaluation of current labour practices. As part of the World Health Organization's Better Outcomes in Labour Difficulty (BOLD) project, which aimed to develop a new labour monitoring-to-action tool, we examined the patterns of labour progression as depicted by cervical dilatation over time in a cohort of women in Nigeria and Uganda who gave birth vaginally following a spontaneous labour onset. METHODS AND FINDINGS: This was a prospective, multicentre, cohort study of 5,606 women with singleton, vertex, term gestation who presented at ≤ 6 cm of cervical dilatation following a spontaneous labour onset that resulted in a vaginal birth with no adverse birth outcomes in 13 hospitals across Nigeria and Uganda. We independently applied survival analysis and multistate Markov models to estimate the duration of labour centimetre by centimetre until 10 cm and the cumulative duration of labour from the cervical dilatation at admission through 10 cm. Multistate Markov and nonlinear mixed models were separately used to construct average labour curves. All analyses were conducted according to three parity groups: parity = 0 (n = 2,166), parity = 1 (n = 1,488), and parity = 2+ (n = 1,952). We performed sensitivity analyses to assess the impact of oxytocin augmentation on labour progression by re-examining the progression patterns after excluding women with augmented labours. Labour was augmented with oxytocin in 40% of nulliparous and 28% of multiparous women. The median time to advance by 1 cm exceeded 1 hour until 5 cm was reached in both nulliparous and multiparous women. Based on a 95th percentile threshold, nulliparous women may take up to 7 hours to progress from 4 to 5 cm and over 3 hours to progress from 5 to 6 cm. Median cumulative duration of labour indicates that nulliparous women admitted at 4 cm, 5 cm, and 6 cm reached 10 cm within an expected time frame if the dilatation rate was ≥ 1 cm/hour, but their corresponding 95th percentiles show that labour could last up to 14, 11, and 9 hours, respectively. Substantial differences exist between actual plots of labour progression of individual women and the 'average labour curves' derived from study population-level data. Exclusion of women with augmented labours from the study population resulted in slightly faster labour progression patterns. CONCLUSIONS: Cervical dilatation during labour in the slowest-yet-normal women can progress more slowly than the widely accepted benchmark of 1 cm/hour, irrespective of parity. Interventions to expedite labour to conform to a cervical dilatation threshold of 1 cm/hour may be inappropriate, especially when applied before 5 cm in nulliparous and multiparous women. Averaged labour curves may not truly reflect the variability associated with labour progression, and their use for decision-making in labour management should be de-emphasized

Examining the Applicability of Functional Principal Component Analysis by Conditional Expectation (PACE) in Cervical Dilation Data from the Labour Progression Study

Functional Data Analysis (FDA) is a method for extracting information from curves or functions based on time-related processes with discrete measures. This thesis explores the methodology for constructing and analysing curves studying labour progression, specifically in data containing the cervical dilation of 7277 primiparas. Identifying the trajectory functions of cervical dilation can support the development of tools used by health personnel to identify and monitor deviations from the normal range of cervical dilation and labour duration. Successful application of FDA can also enhance the understanding of potential explanatory variables that influence the rate and temporal pattern of cervical dilation and its variation among individuals. Data from digital vaginal exams that measure cervical dilation are complex due to sparse and irregular measurements for each participant and various forms of censoring, such as right censoring due to intrapartum cesarean section. The approach of Principal Analysis by Conditional Expectation (PACE) is explored to address these challenges. PACE is a Functional Principal Component Analysis (FPCA) algorithm that fits curves to sparse and irregular data. The trajectory functions for each participant are attempted to be recovered with corresponding estimates for the derivatives. However, PACE was unsuccessful in fitting curves to the cervical dilation data. Simulated data with known underlying distributions points to the limitations of PACE: When the data consist of multiple distributions and are sufficiently sparse, the method will produce an inaccurate estimation of mean and covariance functions. This means that PACE does not distinguish between different distributed groups; instead, it prioritises and directs the curve trajectories towards the weighted cross-sectional mean. The implications of PACE being inappropriate for fitting cervical dilation curves include failure to capture data variability and dynamics and the inability to perform functional regression and correlation analysis. The thesis concludes that FDA, in this case, is premature and calls for further development of methods that can handle the level of sparseness, irregularity, and censoring as seen in data from digital vaginal exams