Integrating Microbiome and Clinical Data for Translational Research: Developing a Risk Prediction Model for Late-Onset Sepsis in Preterm Infants

• Applicant: Dr. Rebecca Knoll
• Subject Area: Pediatric and Adolescent Medicine
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 551589343

Background: The early stages of life offer a crucial opportunity for cultivating a healthy microbiome, thereby exerting a lasting impact on future health outcomes. For preterm infants, this developmental window is particularly significant, as their microbiome maturation is notably delayed compared to that of full-term infants. Rationale: Neonatal sepsis remains a significant contributor to mortality among preterm infants in Germany. The disruption of microbial balance has been identified as a contributing factor in heightening vulnerability to late-onset sepsis (LOS). Despite the availability of risk assessment tools for early-onset sepsis, predictive measures for LOS are lacking. This study pioneers an innovative strategy by merging microbiome profiling with clinical data to evaluate LOS risk in preterm infants, bridging the gap between microbiome research and clinical application. Objectives: This research encompasses several key objectives: 1. Comprehensive Preterm Microbiome Catalogue: By constructing a comprehensive preterm microbiome catalogue through global data integration, this study seeks to harmonize diverse datasets, overcoming disparities inherent to different studies. The resulting catalogue will serve as the foundational framework for achieving objectives 2 and 3, enabling the derivation of robust and meaningful conclusions. 2. Characterization of preterm microbiome development: By comprehensively analyzing preterm microbiome development alongside clinical data, the study aims to identify key factors. This will enable the recognition of a core microbiome linked to favorable clinical outcomes, establishing a reference for a “healthy“ preterm microbiome. 3. Development of a late-onset sepsis risk prediction score: Employing machine-learning algorithms, this study aims to pinpoint microbiome and clinical risk factors, creating a predictive scoring model for LOS risk assessment. The preterm microbiome catalogue and the LOS risk prediction score will be made readily accessible to both clinicians and the research community through a dedicated online platform. Impact: This project holds immense potential for significantly improving the health trajectory of preterm infants, reducing the burden of preterm sequelae, and advancing our comprehension of the intricate interplay between the microbiome and neonatal health. The integration of microbiome data into clinical decision-making processes may usher in point-of-care diagnostics and integration into clinical information systems. The successful implementation of the LOS risk prediction score could potentially lead to a reduction in invasive diagnostics and the judicious use of antibiotics, thereby enhancing personalized healthcare for preterm infants while also promoting antibiotic stewardship.

• DFG Programme: WBP Fellowship
• International Connection: USA
• Host: Professorin Susan Lynch