# Interpretable machine-learning-based prediction of postpartum haemorrhage in normal vaginal births in Shanghai, China

## TL;DR

This study develops a machine-learning model to predict postpartum haemorrhage in vaginal births, aiming to improve maternal health outcomes in Shanghai, China.

## Contribution

The novel contribution is an interpretable machine-learning model for predicting postpartum haemorrhage with actionable insights for midwives.

## Key findings

- The eXtreme Gradient Boosting model showed the best performance in predicting postpartum haemorrhage.
- Midwife years of service and companionship during childbirth reduced the impact of risk factors like childbirth fear and prolonged labor.
- Thirteen predictive variables were identified, including labor duration and episiotomy, as significant contributors to postpartum haemorrhage risk.

## Abstract

Postpartum haemorrhage is the most common complication associated with vaginal birth and a principal cause of maternal mortality. While clinical guidelines suggest that the majority of postpartum haemorrhage cases can be averted through precise prediction and scientific management that utilise assessment tools, existing tools for predicting postpartum haemorrhage in vaginal births have demonstrated inadequacies.

To develop a predictive model for postpartum haemorrhage in vaginal births based on machine-learning algorithms.

We selected pregnant women who gave birth vaginally at a tertiary-level obstetrics and gynaecology hospital in Shanghai, China, from July 2023 to August 2024. Multidimensional data were collected on demographic factors of pregnant women and midwives, along with their antenatal factors (e.g., previous medical history, current medical history, laboratory indicators, and psychosocial factors) and intrapartum factors (e.g., induction techniques; the first, second, and third stages of labour; and other factors). Five predictive models were constructed using machine-learning algorithms, and these models were subsequently validated and evaluated for performance. We applied the SHapley Additive exPlanations tool to conduct an interpretative analysis of the optimal model.

A total of 1,225 women who underwent vaginal births were included in our final analysis, and following univariate analysis and least absolute shrinkage and selection operator regression, 13 predictive variables were incorporated into the model. The eXtreme Gradient Boosting model exhibited the most superior performance. A midwife’s years of service, degree of a woman’s fear of childbirth, parity, duration of the second stage of labour, episiotomy, and companionship during labour and childbirth were identified as significant predictive factors. Moreover, the midwife’s years of service and their companionship during childbirth had a moderating effect, which could effectively reduce the impact of childbirth fear and prolonged labour on the risk of postpartum haemorrhage.

The postpartum haemorrhage prediction model for vaginal births developed in this study will furnish clinical midwives with a scientific and objective tool for assessing the risk of postpartum haemorrhage, thereby supporting timely risk stratification and management in the immediate postpartum period.