Identifying Predictor Variables for a Composite Risk Prediction Tool for Gestational Diabetes and Hypertensive...

Area of Science:

• Obstetrics and Gynecology
• Cardiology
• Endocrinology

Background:

• Cardiometabolic risk during pregnancy, including gestational diabetes mellitus and hypertensive disorders of pregnancy, necessitates early identification for effective intervention.
• A composite risk prediction tool for the first trimester can systematically identify at-risk women.

Purpose of the Study:

• To identify and select predictor variables for a composite cardiometabolic risk prediction tool for use in the first trimester of pregnancy.
• To develop a tool for early screening and intervention of gestational diabetes mellitus and/or hypertensive disorders of pregnancy.

Main Methods:

• A two-round modified online Delphi study involving multidisciplinary experts (n=31).
• A systematic literature review to generate initial predictor variables.
• Online surveys and meetings to assess clinical importance, feasibility, and acceptability of variables.

Main Results:

• Twenty-four variables were considered, with 15 ultimately selected for the prediction tool.
• Selected variables include maternal demographics, pre-pregnancy history, obstetric history, biochemical, and hemodynamic measures.
• The final 15 variables are evidence-based, clinically relevant, and routinely collected.

Conclusions:

• The selected 15 variables will inform the development of a composite cardiometabolic risk prediction tool for early pregnancy.
• This tool aims to facilitate systematic identification and early intervention for women at increased risk.
• The focus on routinely collected data enhances the tool's practical applicability in clinical settings.