AI-Driven Model Predicts Which Preterm Infants Benefit From Platelet Transfusions?
Netherlands: A multicenter study has described the development of a dynamic prediction tool that helps tailor platelet transfusion decisions for preterm infants with severe thrombocytopenia, showing that the potential benefit or harm of prophylactic transfusion can vary widely based on the infant’s real-time clinical status.
Published in JAMA, the research highlights that using this individualized model could guide clinicians in balancing the risk of major bleeding against the possibility of unnecessary transfusions.
Led by Hilde van der Staaij of the Department of Clinical Epidemiology at Leiden University Medical Center, the Netherlands, the investigators addressed a longstanding challenge in neonatal care: identifying which critically ill preterm infants genuinely benefit from early platelet transfusions. Severe thrombocytopenia—defined as a platelet count below 50 × 10⁹/L—is common in extremely premature babies, but routine prophylactic transfusions have uncertain advantages and may introduce new complications.
To create the prediction model, the team analyzed data from an international cohort of 1,042 infants admitted to 14 neonatal intensive care units across the Netherlands, Sweden, and Germany between 2017 and 2021. All infants were born before 34 weeks of gestation and experienced severe thrombocytopenia. The researchers compared two strategies at repeated two-hour intervals during the first week after onset of thrombocytopenia: administering a platelet transfusion within six hours (prophylaxis) versus withholding transfusion for three days (no prophylaxis). The main outcome was the three-day risk of major bleeding or death.
The model incorporated a broad set of predictors, including gestational and postnatal age, growth restriction, presence of necrotizing enterocolitis or sepsis, need for mechanical ventilation or vasoactive medications, platelet count trends, and prior transfusions. This “landmarking” approach, combined with a clone-censor-weight method, allowed for dynamic updates of each infant’s risk profile as their condition evolved.
Key Findings:
- Validation used a separate national cohort of 637 Dutch infants treated between 2010 and 2014.
- The median gestational age in this group was 28 weeks.
- The median birth weight was 900 g.
- Major bleeding or death occurred in about one in five infants in both the validation and development cohorts.
- Model performance was strong, with a time-dependent area under the receiver operating characteristic curve of 0.69 for the prophylactic transfusion strategy.
- The time-dependent area under the curve was 0.85 for the no-prophylaxis strategy, indicating good discriminatory ability and calibration.
Crucially, the predicted risks varied substantially depending on the infant’s immediate clinical state. Some babies were projected to gain clear protection from early transfusion, while others faced higher odds of harm or no measurable benefit. This heterogeneity underscores that a single platelet threshold is inadequate for guiding transfusion decisions in this vulnerable population.
The authors conclude that their individualized risk algorithm offers a promising step toward more precise, evidence-based management of severe thrombocytopenia in preterm infants. While prospective trials are needed to confirm clinical impact, the tool could soon help neonatologists move away from routine prophylactic transfusions and toward a personalized strategy that optimizes outcomes and minimizes unnecessary exposure to blood products.
Reference:
van der Staaij H, Prosepe I, Caram-Deelder C, et al. Individualized Prediction of Platelet Transfusion Outcomes in Preterm Infants With Severe Thrombocytopenia. JAMA. Published online September 15, 2025. doi:10.1001/jama.2025.14194
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