Unsupervised machine learning algorithms identify expected haemorrhage relationships but define unexplained coagulation profiles mapping to thrombotic phenotypes in hereditary haemorrhagic telangiectasia

Abstract

Hereditary haemorrhagic telangiectasia (HHT) can result in challenging anaemia and thrombosis phenotypes. Clinical presentations of HHT vary for relatives with identical casual mutations, suggesting other factors may modify severity. To examine objectively, we developed unsupervised machine learning algorithms to test whether haematological data at presentation could be categorised into sub-groupings and fitted to known biological factors. With ethical approval, we examined 10 complete blood count (CBC) variables, four iron index variables, four coagulation variables and eight iron/coagulation indices combined from 336 genotyped HHT patients (40% male, 60% female, 86.5% not using iron supplementation) at a single centre. T-SNE unsupervised, dimension reduction, machine learning algorithms assigned each high-dimensional datapoint to a location in a two-dimensional plane. k-Means clustering algorithms grouped into profiles, enabling visualisation and inter-profile comparisons of patients' clinical and genetic features. The unsupervised machine learning algorithms using t-SNE and k-Means identified two distinct CBC profiles, two iron profiles, four clotting profiles and three combined profiles. Validating the methodology, profiles for CBC or iron indices fitted expected patterns for haemorrhage. Distinct coagulation profiles displayed no association with age, sex, C-reactive protein, pulmonary arteriovenous malformations (AVMs), ENG/ACVRL1 genotype or epistaxis severity. The most distinct profiles were from t-SNE/k-Means analyses of combined iron-coagulation indices and mapped to three risk states - for venous thromboembolism in HHT; for ischaemic stroke attributed to paradoxical emboli through pulmonary AVMs in HHT; and for cerebral abscess attributed to odontogenic bacteremias in immunocompetent HHT patients with right-to-left shunting through pulmonary AVMs. In conclusion, unsupervised machine learning algorithms categorise HHT haematological indices into distinct, clinically relevant profiles which are independent of age, sex or HHT genotype. Further evaluation may inform prophylaxis and management for HHT patients' haemorrhagic and thrombotic phenotypes.

Keywords: anaemia; genetic disorders; haemorrhage; high‐dimensional data; t‐SNE.

FIGURE 1

Complete blood count indices. Two‐dimensional scatter plot of the t‐SNE embeddings and k‐Means clusters for CBC indices (n = 336). Each datapoint represents a patient. k‐Means clusters are displayed by colour. Distances between datapoints representative of similarity.

FIGURE 2

Iron indices. Two‐dimensional scatter plot of the t‐SNE embeddings and k‐Means clusters for CBC indices (n = 218). Each datapoint represents a patient. k‐Means clusters are displayed by colour. Distances between datapoints representative of similarity.

FIGURE 3

Clotting indices. Two‐dimensional scatter plot of the t‐SNE embeddings and k‐Means clusters for CBC indices (n = 309). Each datapoint represents a patient. k‐Means clusters are displayed by colour. Distances between datapoints representative of similarity.

FIGURE 4

Combined iron and clotting indices. Two‐dimensional scatter plot of the t‐SNE embeddings and k‐Means clusters for iron and clotting indices (n = 196). Each datapoint represents a patient. k‐Means clusters are displayed by colour.