Untangling profiles of postthrombotic syndrome using unsupervised machine learning.

ABSTRACT:

Abstract

Postthrombotic syndrome (PTS) is a chronic condition that can develop after deep vein thrombosis (DVT) and is diagnosed using the Villalta scale. This study applied unsupervised machine learning to investigate the heterogeneity of PTS among patients and within the Villalta scale. In 818 patients from the IDEAL DVT study, clustering identified 4 clinical profiles: (1) younger patients with provoked DVT, (2) women with joint pain, (3) men with isolated popliteal DVT, and (4) older men with diabetes and femoral vein involvement. Clustering of Villalta items revealed a distinction between signs and symptoms. Sign scores increased with older age, male sex, higher body mass index (BMI), and DVT extent, whereas symptom scores increased with younger age, female sex, higher BMI, an

SUBMITTER: Iding AFJ 

PROVIDER: S-EPMC12302148 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature