# A study protocol for a predictive model to assess population‑based risk of adverse pregnancy outcomes: The Adverse Pregnancy Outcomes Population Risk Tool (PregPoRT)

## TL;DR

This paper outlines a new tool called PregPoRT to predict risks of adverse pregnancy outcomes using a wide range of health and social data in Canada.

## Contribution

PregPoRT is the first Canadian model integrating social, behavioral, and environmental factors into predicting adverse pregnancy outcomes.

## Key findings

- PregPoRT will use a Weibull accelerated failure time model with LASSO variable selection to estimate APO risk.
- The model will incorporate data from the Canadian Community Health Survey and administrative databases.
- Validation will include split-sample, bootstrap, and temporal methods to ensure robustness.

## Abstract

Adverse pregnancy outcomes (APOs), such as gestational diabetes, preeclampsia, and placental abruption, are major contributors to maternal and fetal morbidity and mortality, with implications for individual long-term health and health system performance. Existing prediction models for APOs rely primarily on clinical or biomarker data, with few incorporating social, behavioral, or environmental determinants that are critical for shaping perinatal outcomes. This study describes the development and validation protocol for the Adverse Pregnancy Outcomes Population Risk Tool (PregPoRT), a novel, population-based prediction model designed to estimate APO risk using population-based and routinely collected survey and administrative data in Canada.

PregPoRT will be developed using a retrospective cohort of female-identifying individuals, aged 15–49, who participated in the Canadian Community Health Survey (CCHS) between 2000 and 2017, and had a subsequent delivery hospitalization within two years recorded in the Discharge Abstract Database (DAD). Pre-pregnancy predictors were selected according to a health equity-informed framework by Kramer and colleagues (2019), and include biomedical, behavioral, social, and environmental variables from the CCHS, the Canadian Marginalization Index (CAN-Marg), the Canadian Urban Environmental Health Research Consortium (CANUE), and the Canadian Active Living Environments (Can-ALE) dataset. The primary outcome is a composite measure of APOs (gestational diabetes, preeclampsia, or placental abruption), identified using validated ICD codes. A Weibull accelerated failure time model will be used to estimate the risk of experiencing an APO. Continuous variables will be modeled with restricted cubic splines. Variable selection will be performed using the Least Absolute Shrinkage and Selection Operator (LASSO), and model performance will be assessed via discrimination, calibration, and overall accuracy. Validation strategies include split-sample, bootstrap, and temporal validation using later CCHS cycles. Survey weights will be applied throughout to ensure national representativeness.

PregPoRT will be the first Canadian prediction model for APOs that leverages nationally representative, linked survey and administrative data and explicitly integrates social, behavioral, and environmental determinants of health, domains that have been largely absent from prior models. By incorporating modifiable and socially patterned risk factors, the tool is designed to support public health planning, resource allocation, and maternal health equity monitoring.