Clustering of Adverse Perinatal Outcomes in Women with Multiple Sensitizing Events: A Data-Driven Approach Using Clinical and Immunohematologic Profiles - 02/09/25
, Meenakshi Gothwal b, ⁎ , Garima Yadav b , Swati Asati c , Pratibha Singh b Cet article a été publié dans un numéro de la revue, cliquez ici pour y accéder
Highlights |
• | Cluster-guided surveillance: Women in Cluster 1 warrant expedited iron optimisation, serial fetal middle-cerebral-artery Dopplers, and early referral to a fetal-medicine unit. |
• | Hidden-risk Cluster 3: Seronegative women with high-normal Hb and multiparity may benefit from closer intrapartum monitoring and targeted placental function testing. |
• | Decision-support integration: Because the rule engine is transparent and computationally light, it can be embedded within (Electronic Medical Record System) EMRS to trigger automated risk flags without additional laboratory burden. |
• | This clustering framework, based on five routine antenatal parameters, identified two distinct and previously under-recognised high-risk phenotypes. Cluster 1: Classical antibody-positive pregnancies with significant anaemia and sensitisation. Cluster 3: Seronegative women with preserved haemoglobin, but poor neonatal adaptation (Apgar <7). Both clusters were strongly and independently associated with adverse perinatal outcomes, even after adjustment for maternal comorbidities. The model was robust across analytic specifications, suggesting that this phenotype-based approach may offer a superior alternative to current titre-based screening for personalised risk stratification in alloimmunised pregnancies. |
Abstract |
Background |
Red-cell alloimmunisation is a preventable driver of haemolytic disease of the fetus 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.
Le texte complet de cet article est disponible en PDF.Keywords : Alloimmunisation, haemoglobin, unsupervised clustering, silhouette coefficient, perinatal outcome, risk stratification
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