Journal Article

In simulated data and health records, latent class analysis was the optimum multimorbidity clustering algorithm

Barrett, Jessica, Griffin, Simon, Yau, Christopher, Edwards, Duncan, Marshall, Tom, Kirk, Paul, Nirantharakumar, Krishnarajah, Richardson, Sylvia, Kiddle, Steven, Nichols, Linda, Crowe, Francesca, Taverner, Tom

2022

Overview

To investigate the reproducibility and validity of latent class analysis (LCA) and hierarchical cluster analysis (HCA), multiple correspondence analysis followed by k-means (MCA-kmeans) and k-means (kmeans) for multimorbidity clustering. We first investigated clustering algorithms in simulated datasets with 26 diseases of varying prevalence in predetermined clusters, comparing the derived clusters to known clusters using the adjusted Rand Index (aRI). We then them investigated the medical records of male patients, aged 65 to 84 years from 50 UK general practices, with 49 long-term health conditions. We compared within cluster morbidity profiles using the Pearson correlation coefficient and assessed cluster stability using in 400 bootstrap samples. In the simulated datasets, the closest agreement (largest aRI) to known clusters was with LCA and then MCA-kmeans algorithms. In the medical records dataset, all four algorithms identified one cluster of 20–25% of the dataset with about 82% of the same patients across all four algorithms. LCA and MCA-kmeans both found a second cluster of 7% of the dataset. Other clusters were found by only one algorithm. LCA and MCA-kmeans clustering gave the most similar partitioning (aRI 0.54). LCA achieved higher aRI than other clustering algorithms.

• Publisher: Elsevier Inc,Elsevier Limited
• Subject: Algorithms / Cluster Analysis / Clustering / Clustering methods / Comorbidity / Correlation coefficient / Correlation coefficients / Datasets / Disease / Electronic medical records / Epidemiology / Hierarchical cluster analysis / Humans / Internal Medicine / K-means / Latent Class Analysis / Male / Medical records / Methods / Morbidity / Multimorbidity / Multiple correspondence analysis / Patients / Primary care / Reproducibility / Reproducibility of Results / Sensitivity analysis / Simulation / Stability analysis