Prebic Working Group 2026

“Innovation for advancing prematurity care.” The group agreed that innovation extends beyond technology to include collaboration, data sharing, research methods, and knowledge synthesis.

Diverse electronic health records across regions remain unharmonized, and no global data-sharing platform currently exists. Cross-border data sharing is constrained at the government level in key countries, and no operational, neutral third-party mechanism exists to broker access to, or ingest, positive and negative findings across research groups. AI tools are trained predominantly on high-income country data, underrepresenting Sub-Saharan Africa and South/Southeast Asia regions. A substantial portion of historical literature predates digitization, creating structural gaps in any AI knowledge base, and publication bias against Low- and Middle- Income Countries (LMIC) research compounds this problem.

A federated data sharing approach was proposed as a key innovation: This model allows countries to keep raw data locally while sharing only analysis results, avoiding government barriers and new international agreements. Entry points discussed included Medical Research Councils, the African Academy of Science, and major referral hospitals, with in-country researchers driving participation and PREBIC in a facilitative role. A multilevel research ambassador network was suggested for each country to build a compendium of resources.

Innovation in Biospecimens
Low-resource settings lack specialist facilities and storage capabilities, limiting biological sample collection at scale. Dried blood spots offer a feasible alternative, and OLINK technology supports proteomics and genotyping without freezing. Each country's biospecimen platform capacity remains undetermined and requires assessment

The meeting focused on current therapeutic approaches for the prevention of preterm birth (PTB), limitations of existing interventions, and future strategies including precision medicine and AI-based prediction models. Participants critically evaluated whether current obstetric interventions truly improved neonatal outcomes or merely prolonged gestation. The conceptual distinction between prevention and intervention in PTB research was discussed. While all preventive approaches may be considered interventions, not all interventions represent prevention. Current obstetric therapies often intervene after pathological pathways have already begun rather than preventing PTB itself.

The group emphasized that prolongation of gestation alone should not be the primary endpoint of PTB prevention studies. Improvement of neonatal morbidity and mortality should be considered the ultimate therapeutic objective.

Participants emphasized the need to shift attention from tertiary intervention toward primary and secondary prevention, including earlier risk identification and intervention before irreversible inflammatory or cervical pathways develop.

The future role of AI, multi-omics, predictive analytics, and translational science in improving individualized prediction models and therapeutic development was discussed. Limited pharmaceutical investment, regulatory difficulties in pregnancy research, and high safety requirements for maternal-fetal therapeutics are some of the identified barriers to therapeutic development.

The group emphasized that the clinical community must begin to regard PTB as a preventable condition rather than an unavoidable obstetric complication. It was concluded that although current interventions remain limited, neonatal outcomes should be prioritized over gestational prolongation alone, and greater focus on prevention, biomarkers, AI, and translational collaboration is needed.

Preterm birth (PTB) remains a leading contributor to neonatal morbidity and mortality worldwide, with the highest burden concentrated in low- and middle-income countries (LMICs). However, current global estimates often rely on single national or regional PTB rates, which obscure important within-country differences in preterm birth risk and disparities. This is particularly true regarding factors such as socioeconomic position and other social determinants of health, which may indirectly be driven by population insecurity and displacement in the context of armed conflict.

The relationship between these factors and preterm birth is very deserving of focused study, especially in regions with a high burden of PTB, including Southern Asia, Sub-Saharan Africa, North Africa, and Western Asia, the top LMIC regions with the highest PTB rates (9-13%). Findings from such innovative studies will document the existence of vulnerable groups. They will further delineate the extent of evidence gaps in LMICs and support future prospective studies designed to generate more detailed, within-country epidemiologic data on PTB disparities and risk patterns.

The PREBIC working group on Mechanisms of Preterm Birth: Biology of Parturition, Maternal/Fetal Interface, Uterine Physiology and Tissue-Level Inflammation, and Hormonal (Endocrine/Paracrine) Control identified several critical gaps limiting progress in preterm birth (PTB) research and clinical translation. A major challenge is the lack of coordinated global research infrastructure, particularly around access to biorepositories, harmonized clinical datasets, and shared biological samples. To address this, the group launched a PREBIC registry of PTB biorepositories prior to the conclusion of the workshop, creating an initial framework to support collaboration, protocol sharing, sample exchange, and future multi-site funding initiatives.

Another key gap identified was the absence of standardized laboratory protocols for pregnancy tissue collection and analysis. Variability in tissue isolation and processing methods across laboratories continues to limit reproducibility and cross-study comparisons. To address this need, the group proposed developing a collaborative manuscript focused on standardized protocols for decidual, placental, membrane, and related pregnancy tissues, with the goal of improving consistency and accelerating mechanistic discoveries in PTB research.

The workshop also emphasized the need to modernize current scientific frameworks defining PTB mechanisms and outcomes. Existing definitions based primarily on gestational age do not adequately reflect biological complexity or long-term neonatal outcomes. Participants discussed developing updated models that incorporate fetal maturity, neonatal morbidity, developmental trajectories, and mechanistic PTB subtypes to better guide research, clinical trials, and therapeutic development.

Across all themes, the workshop reinforced the importance of building an integrated international research community capable of advancing shared resources, standardized methods, and innovative translational approaches to PTB prevention.

We identified this as a pivotal moment in preterm birth research, emphasizing that, despite significant scientific advances, the translational impact has been limited. Preterm birth is currently recognized as a heterogeneous “syndrome of syndromes” resulting from diverse and overlapping biological and pathophysiological mechanisms, such as inflammation, infection, vascular dysfunction, and endocrine signaling.

The field continues to encounter three primary challenges: insufficient alignment between clinical phenotypes and underlying mechanisms, dependence on associative rather than causal findings, and limited translation of research discoveries into effective interventions. Key structural barriers include fragmented data infrastructure, inconsistent biospecimen and clinical data standards, limited global representation, and unequal access to technologies that hinder education and translation in our field. Addressing these challenges will require harmonized international collaborations, standardized protocols, shared data platforms, and capacity-building in regions with the highest burden.

We determined that advancing the field further requires a transition toward precision-oriented research that integrates longitudinal cohort designs, multi-omics data, environmental exposures, and social-demographic determinants of health. Epidemiological approaches should move beyond static risk-factor analyses to adopt dynamic, trajectory-based models of pregnancy. Concurrently, basic science should prioritize mechanistic understanding within complex translational systems and establish more robust pathways for clinical and policy makers.

Importantly, the concept of translation is defined broadly to encompass both novel discoveries and the implementation of established evidence-based practices. There is a huge need to engage patients, clinicians, policymakers, funding agencies, and global partners to enhance translational impact across the field of preterm birth. Organizations such as PREBIC can serve as central facilitators of collaboration, education, and equitable dissemination, aiming to transform scientific complexity into actionable strategies that improve birth outcomes worldwide.

The PREBIC 2026 Committee on Preterm Birth Risk Prediction identified several critical gaps in knowledge, resources, and implementation that hinder progress in reducing preterm birth globally. A major limitation is the continued definition of preterm birth primarily by gestational age, without adequate recognition of distinct biological and clinical phenotypes. The group emphasized the need for phenotype-based classification systems to improve prediction, prevention, and personalized interventions.

Existing biomarkers show limited predictive accuracy, particularly in low-risk populations, and often fail to distinguish symptomatic from asymptomatic women. Most studies have been conducted in high-income settings, with insufficient validation across diverse populations and low- and middle-income countries (LMICs). Delayed antenatal care access, high costs, and limited laboratory infrastructure further restrict implementation in resource-limited regions.

The committee highlighted the need for dynamic longitudinal screening throughout pregnancy, especially during the first and second trimesters, recognizing that risk profiles evolve over time. Future predictive models should integrate clinical, biological, microbiome, environmental, and biomedical engineering data, while incorporating artificial intelligence and machine learning approaches where appropriate.

Priority areas for future work include development of standardized core datasets for preterm birth prediction, harmonized phenotyping strategies, inclusion of LMIC populations in multicenter studies, and validation of affordable home-based predictive tools.

Key concepts and literature areas include preterm birth phenotyping, precision medicine, longitudinal biomarker profiling, microbiome-associated risk prediction, and AI-driven predictive modeling in obstetrics.