Patients Presenting to the Emergency Department with Bleeding in Early Pregnancy: Comparing Different Models to Predict Pregnancy Success

Objectives

Bleeding in early pregnancy is a common obstetric presentation in the emergency department (ED), and the outcome is difficult to predict. We developed and compared random forest machine learning (Live Birth Risk Score [LiBRisk]) and nomogram models for predicting the likelihood of a live birth among women presenting at 3 Canadian EDs with bleeding in early pregnancy.

Methods

Data were prospectively collected on 200 patients with bleeding in early pregnancy using a structured questionnaire, medical record review, and administrative databases. We developed the nomogram with variables selected via multivariable logistic regression analysis. LiBRisk was built using the Shapley variable importance cloud (ShapleyVIC) to derive a simple point-based clinical risk scoring system.

Results

Overall, 115 (55%) patients experienced a miscarriage. We excluded duration of vaginal bleeding and pain score, which did not enhance model performance, and constructed LiBRisk with the 8 most important variables (β-human chorionic gonadotrophin level, age, gestational age, gravidity, parity, proportionality of uterine size to gestational age, abdominal cramping, and number of prior spontaneous abortions). All 10 variables were included in the nomogram. The area under the receiver operating characteristic curve of LiBRisk in the test and validation sets were 0.913 (95% CI 0.907−0.919) and 0.900 (95% CI 0.887−0.913), respectively. The C-index of the nomogram was 0.720 (95% CI 0.714−0.726) and 0.860 (95% CI 0.853−0.867) in the test and validation sets, respectively. LiBRisk outperformed the nomogram in all metrics.

Conclusions

We developed and compared LiBRisk and nomogram models for determining the probability of eventual pregnancy success/failure in women presenting to the ED with bleeding in early pregnancy. LiBRisk was more parsimonious, incorporating only 8 variables, and outperformed the nomogram in all metrics. Given these promising results, further testing seems warranted.