Trajectory analysis for postoperative pain using electronic health records: A nonparametric method with robust linear regression and K-medians cluster analysis

Identifying Weekly Trajectories of Pain Severity Using Daily Data From an mHealth Study: Cluster Analysis

BACKGROUND

People with chronic pain experience variability in their trajectories of pain severity. Previous studies have explored pain trajectories by clustering sparse data; however, to understand daily pain variability, there is a need to identify clusters of weekly trajectories using daily pain data. Between-week variability can be explored by quantifying the week-to-week movement between these clusters. We propose that future work can use clusters of pain severity in a forecasting model for short-term (eg, daily fluctuations) and longer-term (eg, weekly patterns) variability. Specifically, future work can use clusters of weekly trajectories to predict between-cluster movement and within-cluster variability in pain severity.

OBJECTIVE

This study aims to understand clusters of common weekly patterns as a first stage in developing a pain-forecasting model.

METHODS

Data from a population-based mobile health study were used to compile weekly pain trajectories (n=21,919) that were then clustered using a k-medoids algorithm. Sensitivity analyses tested the impact of assumptions related to the ordinal and longitudinal structure of the data. The characteristics of people within clusters were examined, and a transition analysis was conducted to understand the movement of people between consecutive weekly clusters.

RESULTS

Four clusters were identified representing trajectories of no or low pain (1714/21,919, 7.82%), mild pain (8246/21,919, 37.62%), moderate pain (8376/21,919, 38.21%), and severe pain (3583/21,919, 16.35%). Sensitivity analyses confirmed the 4-cluster solution, and the resulting clusters were similar to those in the main analysis, with at least 85% of the trajectories belonging to the same cluster as in the main analysis. Male participants spent longer (participant mean 7.9, 95% bootstrap CI 6%-9.9%) in the no or low pain cluster than female participants (participant mean 6.5, 95% bootstrap CI 5.7%-7.3%). Younger people (aged 17-24 y) spent longer (participant mean 28.3, 95% bootstrap CI 19.3%-38.5%) in the severe pain cluster than older people (aged 65-86 y; participant mean 9.8, 95% bootstrap CI 7.7%-12.3%). People with fibromyalgia (participant mean 31.5, 95% bootstrap CI 28.5%-34.4%) and neuropathic pain (participant mean 31.1, 95% bootstrap CI 27.3%-34.9%) spent longer in the severe pain cluster than those with other conditions, and people with rheumatoid arthritis spent longer (participant mean 7.8, 95% bootstrap CI 6.1%-9.6%) in the no or low pain cluster than those with other conditions. There were 12,267 pairs of consecutive weeks that contributed to the transition analysis. The empirical percentage remaining in the same cluster across consecutive weeks was 65.96% (8091/12,267). When movement between clusters occurred, the highest percentage of movement was to an adjacent cluster.

CONCLUSIONS

The clusters of pain severity identified in this study provide a parsimonious description of the weekly experiences of people with chronic pain. These clusters could be used for future study of between-cluster movement and within-cluster variability to develop accurate and stakeholder-informed pain-forecasting tools.

Development of a Knowledge Graph Embeddings Model for Pain

Pain is a complex concept that can interconnect with other concepts such as a disorder that might cause pain, a medication that might relieve pain, and so on. To fully understand the context of pain experienced by either an individual or across a population, we may need to examine all concepts related to pain and the relationships between them. This is especially useful when modeling pain that has been recorded in electronic health records. Knowledge graphs represent concepts and their relations by an interlinked network, enabling semantic and context-based reasoning in a computationally tractable form. These graphs can, however, be too large for efficient computation. Knowledge graph embeddings help to resolve this by representing the graphs in a low-dimensional vector space. These embeddings can then be used in various downstream tasks such as classification and link prediction. The various relations associated with pain which are required to construct such a knowledge graph can be obtained from external medical knowledge bases such as SNOMED CT, a hierarchical systematic nomenclature of medical terms. A knowledge graph built in this way could be further enriched with real-world examples of pain and its relations extracted from electronic health records. This paper describes the construction of such knowledge graph embedding models of pain concepts, extracted from the unstructured text of mental health electronic health records, combined with external knowledge created from relations described in SNOMED CT, and their evaluation on a subject-object link prediction task. The performance of the models was compared with other baseline models.

Clusters of pain trajectories among patients with sickle cell disease hospitalized for vaso-occlusive crisis: a data-driven approach

BACKGROUND

Vaso-occlusive crises (VOC) are the hallmark of sickle cell disease (SCD), with higher severity among hospitalized patients. Clustering hospitalizations with similar pain trajectories could identify vulnerable patient subgroups. Aims were to (1) identify clusters of hospitalizations based on pain trajectories; (2) identify factors associated with these clusters; and (3) determine the association between these clusters and 30-day readmissions.

METHODS

We retrospectively included 350 VOC hospitalizations from 2013-2016 among 59 patients. Finite mixture modeling identified clusters of hospitalizations from intercepts and slopes of pain trajectories during the hospitalization. Generalized estimating equations for multinomial and logistic models were used to identify factors associated with clusters of hospitalizations based on pain trajectories and 30-day readmissions, respectively, while accounting for multiple hospitalizations per patient.

RESULTS

Three clusters of hospitalizations based on pain trajectories were identified: slow (n=99), moderate (n=207), and rapid (n=44) decrease in pain scores. In multivariable analysis, SCD complications, female gender, and affective disorders were associated with clusters with slow or moderate decrease in pain scores (compared to rapid decrease). Although univariate analysis found that the cluster with moderate decrease in pain scores was associated with lower odds of 30-day readmissions compared to the cluster with slow decrease, it was non-significant in multivariable analysis. SCD complications were associated with higher odds of 30-day readmissions and older age was associated with lower odds of 30-day readmissions.

CONCLUSIONS

Our results highlight variability in pain trajectories among patients with SCD experiencing VOC and provide a novel approach for identifying subgroups of patients that could benefit from more intensive follow-up.