New labor curves of dilation and station to improve the accuracy of predicting labor progress.

BACKGROUND: The diagnosis of failure to progress, the most common indication for intrapartum cesarean delivery, is based on the assessment of cervical dilation and station over time. Labor curves serve as references of expected changes in dilation and fetal descent. The labor curves of Friedman, Zhang et al and others are based on time alone and derived from mothers with spontaneous labor onset. However, labor induction is now common, and clinicians also consider other factors when assessing labor progress. Labor curves that consider the use of induction and other factors that influence labor progress have the potential to be more accurate and closer to clinical decision-making.

OBJECTIVE: To compare the prediction errors of labor curves based on a single factor (time) or multiple clinically relevant factors using 2 modeling methods: mixed-effects regression, a standard statistical method, and Gaussian processes, a machine learning method.

STUDY DESIGN: This was a longitudinal cohort study of changes in dilation and station based on data from 8022 births in nulliparous women with a live, singleton, vertex presenting fetus at ≥35 weeks of gestation with a vaginal delivery. New labor curves of dilation and station were generated with 10-fold cross-validation. External validation was performed using a geographically independent group. Model variables included time from the first exam in the 20 hours before delivery; dilation, effacement and station recorded at the previous examination; cumulative contraction counts; and use of epidural anesthesia and labor induction. To assess model accuracy, we calculated the differences between each model's predicted value and its corresponding observed value. These prediction errors were summarized using mean absolute error and root mean squared error statistics.

RESULTS: (1) Dilation curves based on multiple parameters were more accurate than those derived from time alone. (2) The mean absolute error with the multifactor methods were better (lower) than those from the single-factor methods [0.826 cm (95% CI, 0.820-0.832) for the multifactor machine learning and 0.893 cm (95% CI, 0.885-0.901) for the multifactor mixed-effects method and 2.122 cm (95% CI, 2.108-2.136) for the single-factor methods; P<0.0001 for both comparisons]. (3) The root mean squared errors with the multifactor methods were also better (lower) than those from the single-factor methods [1.126 cm (95% CI, 1.118-1.133) P<0.0001 for the machine learning and 1.172cm (95% CI, 1.164-1.181) for the mixed-effects method and 2.504 cm (95% CI, 2.487-2.521) for the single-factor; P<0.0001 for both comparisons]. (4) The multifactor machine learning dilation models showed small but statistically significant improvements in accuracy compared to the mixed-effects regression models (P<0.0001). (5) The multifactor machine learning method produced a curve of descent with a mean absolute error of 0.512 cm (95% CI, 0.509-0.515) and a root mean squared error of 0.660 cm (95% CI, 0.655-0.666). (6) External validation using independent data produced similar findings.

CONCLUSIONS: (1) Cervical dilation models based on multiple clinically relevant parameters showed improved (lower) prediction errors compared to models based on time alone; (2) the mean prediction errors were reduced by more than 50%; and (3) a more accurate assessment of departure from expected dilation and station may help clinicians optimize intrapartum management.