Establishment of a new classification system for chronic inflammatory demyelinating polyneuropathy based on unsupervised machine learning

摘要

Abstract Introduction/Aims A model for predicting responsiveness to immunotherapy in patients with chronic inflammatory demyelinating polyneuropathy (CIDP) has not been well established. We aimed to establish a new classifier for CIDP patients based on clinical characteristics, laboratory findings, and electrophysiological features. Methods The clinical, laboratory, and electrophysiological features of 172 treatment‐na?ve patients with CIDP between 2003 and 2019 were analyzed using an unsupervised hierarchical clustering. The identified pivotal features were used to establish simple classifications using a tree‐based model. Results Three clusters were identified: 1, n?=?65; 2, n?=?70; and 3, n?=?37. Patients in Cluster 1 scored lower on the disability assessment score before treatment. More patients in Clusters 2 (90.0%) fulfilled demyelinating criteria than patients in Cluster 1 (30.8%, p?