Dynamic prediction of bleeding risk in thrombocytopenic preterm neonates

Susanna F Fustolo-Gunnink, Karin Fijnvandraat, Hein Putter, Isabelle M Ree, Camila Caram-Deelder, Peter Andriessen, Esther J d'Haens, Christian V Hulzebos, Wes Onland, André A Kroon, Daniël C Vijlbrief, Enrico Lopriore, Johanna G van der Bom

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14 Citations (Scopus)

Abstract

Over 75% of severely thrombocytopenic neonates receive platelet transfusions, though little evidence supports this practice, and only 10% develop major bleeding. In a recent randomized trial, giving platelet transfusions at a threshold platelet count of 50x109/L compared to a threshold of 25x109/L was associated with an increased risk of major bleeding or mortality. This finding highlights the need for improved and individualized guidelines on neonatal platelet transfusion, which require accurate prediction of bleeding risk. Therefore, the objective of this study was to develop a dynamic prediction model for major bleeding in thrombocytopenic preterm neonates. This model allows for calculation of bleeding risk at any time-point during the first week after the onset of severe thrombocytopenia. In this multicenter cohort study, we included neonates with a gestational age <34 weeks, admitted to a neonatal intensive care unit, who developed severe thrombocytopenia (platelet count <50x109/L). The study endpoint was major bleeding. We obtained predictions of bleeding risk using a proportional baselines landmark supermodel. Of 640 included neonates, 71 (11%) had a major bleed. We included the variables gestational age, postnatal age, intrauterine growth retardation, necrotizing enterocolitis, sepsis, platelet count and mechanical ventilation in the model. The median cross-validated c-index was 0.74 (interquartile range, 0.69-0.82). This is a promising dynamic prediction model for bleeding in this population that should be explored further in clinical studies as a potential instrument for supporting clinical decisions. The study was registered at www.clinicaltrials.gov (NCT03110887).

Original languageEnglish
Pages (from-to)2300-2306
Number of pages7
JournalHaematologica
Volume104
Issue number11
DOIs
Publication statusPublished - Nov 2019

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