Recurrent pregnancy loss: risk factors and predictive modeling approaches

Abstract

Purpose: This review aims to identify and analyze the risk factors associated with recurrent pregnancy loss (RPL) and to evaluate the effectiveness of various predictive models in estimating the risk of RPL. The review also explores recent advancements in machine learning algorithms that can enhance the accuracy of these predictive models. The ultimate goal is to provide a comprehensive understanding of how these tools can aid in the personalized management of women experiencing RPL.

Materials and methods: The review synthesizes current literature on RPL, focusing on various risk factors such as chromosomal abnormalities, autoimmune conditions, hormonal imbalances, and structural uterine anomalies. It also analyzes different predictive models for RPL risk assessment, including genetic screening tools, risk scoring systems that integrate multiple clinical parameters, and machine learning algorithms capable of processing complex datasets. The effectiveness and limitations of these models are critically evaluated to provide insights into their clinical application.

Results: Key risk factors for RPL were identified, including chromosomal abnormalities (e.g. translocations and aneuploidies), autoimmune conditions (e.g. antiphospholipid syndrome), hormonal imbalances (e.g. thyroid dysfunction and luteal phase defects), and structural uterine anomalies (e.g. septate or fibroid-affected uteri). Predictive models such as genetic screening tools and risk scoring systems were shown to be effective in estimating RPL risk. Recent advancements in machine learning algorithms demonstrate potential for enhancing predictive accuracy by analyzing complex datasets, which may lead to improved personalized management strategies.

Conclusions: The integration of risk factors and predictive modeling offers a promising approach to improving outcomes for women affected by RPL. A comprehensive understanding of these factors and models can aid clinicians and researchers in refining risk assessment and developing targeted interventions. The review underscores the need for further research into specific pathways involved in RPL and the potential of novel treatments aimed at mitigating risk.

Keywords: Recurrent pregnancy loss; machine learning algorithms; personalized management; prognostic model; risk factor.