Clustering of adverse perinatal outcomes in women with multiple sensitising events: a data-driven approach using clinical and immunohematologic profiles.

Background: Red-cell alloimmunisation is a preventable driver of haemolytic disease of the foetus and newborn, yet most risk scores rely on single-parameter thresholds and overlook clinically important heterogeneity.

Objective: To uncover latent phenotypes among sensitised pregnancies by clustering routinely collected clinical and immunohaematologic variables.

Methods: We retrospectively analysed 2084 antenatal records (2020-2021). Five variables-maternal antibody status, haemoglobin (Hb) concentration, cumulative number of sensitising events, gestational age at first positive antibody screen, and parity-were multiply imputed and scaled. A rule-based approximation of Gaussian mixture modelling and HDBSCAN assigned women to five clusters. Internal validity was assessed with the silhouette coefficient (0.41) and Davies-Bouldin index (0.88). Multivariable logistic regression evaluated the association between cluster membership and a composite adverse perinatal outcome, adjusting for maternal age and comorbidities.

Results: Five clinically coherent clusters emerged (Cluster 1 = 13, Cluster 2 = 848, Cluster 3 = 26, Cluster 4 = 36, Cluster 5 = 513; 648 records lacked sufficient data for assignment). Cluster 1 combined antibody positivity with marked anaemia (mean Hb 8.6 ± 1.3 g/dL) and showed the highest risk of adverse outcome (adjusted OR 4.3, 95 % CI 2.7-6.8, p < 0.001). Cluster 3-seronegative women with preserved Hb (≥12 g/dL) but neonatal depression (1-min Apgar < 7 in 100 % of cases)-represented an unexpected high-risk phenotype. Cluster 5 (antibody-negative, Hb ≥ 12 g/dL, 1-min Apgar ≥ 7) served as the low-risk reference.

Conclusion: Unsupervised clustering of simple antenatal parameters reveals hidden risk profiles that outperform single-threshold screening. This data-driven phenotyping could refine surveillance intensity and transfusion strategies in sensitised pregnancies.