Longitudinal K-means approaches to clustering and analyzing EHR opioid use trajectories for clinical subtypes

Abstract

Identification of patient subtypes from retrospective Electronic Health Record (EHR) data is fraught with inherent modeling issues, such as missing data and variable length time intervals, and the results obtained are highly dependent on data pre-processing strategies. As we move towards personalized medicine, assessing accurate patient subtypes will be a key factor in creating patient specific treatment plans. Partitioning longitudinal trajectories from irregularly spaced and variable length time intervals is a well-established, but open problem. In this work, we present and compare k-means approaches for subtyping opioid use trajectories from EHR data. We then interpret the resulting subtypes using decision trees, examining how each subtype is influenced by opioid medication features and patient diagnoses, procedures, and demographics. Finally, we discuss how the subtypes can be incorporated in static machine learning models as features in predicting opioid overdose and adverse events. The proposed methods are general, and can be extended to other EHR prescription dosage trajectories.

Keywords: Electronic health records; Longitudinal k-means clustering; Opioids; Patient subtypes; Trajectory analysis.

Figure 1.

Raw Patient Morphine Milligram Equivalent (MME) Trajectories for 50 Randomly Sampled Patients

Figure 2.

Selecting optimal k using cross-validation and method suggested internal criterion. The plots show the mean criterion value with error bars across the five folds. In (a), the KML method shows all criterion (Calinski-Harabasz, Davies-Bouldin, and Ray and Turi) maximized for k=3 clusters. For B-spline (b), the BIC is minimized for k=7 clusters and AIC plateaus at k=7 as well. Finally, (c) shows the silhouette score is highest for k=7 clusters in VRAE.

Figure 3.

Profile analysis of 5-fold cross validation for clusters selected by method criterion. Other than for the highest, smallest, and most erratic clusters (represented by the top curves in all plots), the profile analysis shows stable clusters across all folds for each method.

Figure 4.

Profile analysis and trajectories found using the three k-means methods on the full 70% training set (n=2,846) compared to the 30% testing set (n=1,151).

Figure 5.

Decision tree analysis for KML and B-spline extracted k-means clusters

Figure 6.

Proportion of cases for clinical features by cluster. For kml (a), since the majority of patients have clustered to cluster 1 (89.6%), they also make up a large portion of the clinical features. Cluster 2 has a high proportion of opioid treatment patients. For B-spline (b), clusters 3 and 5 contain a large portion of patients on buprenorphine. For VRAE (c), the majority of buprenorphine patients come from clusters 6 and 7.