Evidence on the links between patient experience and safety: a systematic review.

Understanding the interrelationships among co-occurring symptoms as well as the dynamic interplay between patient characteristics (e.g., clinical features, time since injury, co-morbidities) and clusters of symptoms has tremendous potential to transform how clinicians treat the overall symptom burden of spinal cord injury (SCI) and improve health-related quality of life (HRQOL) in the long term. Clinicians often address discrete symptoms and may fail to identify the interrelation between the symptoms and their root cause(s) [, ]. Recent calls for an increased focus on symptom science in clinical research [, ] aim to help clinicians identify related groups or “clusters” of symptoms and limitations. is a conceptual and methodological approach defined by the analysis of two or more concurrent, interrelated symptoms, where the interaction of these symptoms may be synergistic [–]. Symptom clustering can personalize clinical care by revealing underlying connections between symptoms, allowing for tailoring of interventions. Further, identifying clusters multiple conditions as well as those which present uniquely or differentially within specific conditions such as SCI is an aim of current symptom cluster efforts and a prerequisite for constructing holistic treatment strategies [, ]. Symptom clusters research has shown promise in oncology populations [–], and evidence is also emerging within cardiac [–], gastrointestinal [], nephrological [], musculoskeletal [], and mental health populations [–]. However, symptom science remains nascent in rehabilitation settings, particularly for SCI. Some exceptions include Widerström-Noga et al.’s [] cluster analysis of neuropathic pain phenotypes in SCI, which revealed two distinct clusters of individuals differentiated by residual spinothalamic tract function and pain catastrophizing that may necessitate different approaches to pain management. Tanadini and colleagues [] developed data-driven approaches to stratify patients into clinically homogeneous subgroups to reduce ambiguity in the evaluation of treatment effects in clinical trials. Ehrmann et al. [] used graphical modeling to identify four general domains of function in SCI among a large set of symptoms and functional complaints including bodily function, independence, mental health, and social participation. Within the domain of bodily function, researchers identified distinct profiles of patients differentiated primarily by impairment severity []. However, current work on symptom clusters in SCI is limited in its typical focus on one or two very closely related domains; further research is needed to identify related symptoms across physical, emotional, and social domains. Tulsky and colleagues recently conducted a large study examining symptoms and profiles of participants in a sample of 755 individuals with spinal cord injury, traumatic brain injury, stroke, or major extremity injury/loss. Using confirmatory factor analysis (CFA) on scores from 23 patient-reported outcome measures (PROMs) assessing aspects of physical, emotional, and social health and 3 performance-based measures of cognition, the research team identified 10 factors (or clusters) of physical, emotional, and social issues, symptoms and limitations affecting HRQOL []. Latent profile analysis (LPA) was then used to identify four patient profiles that were largely condition-agnostic []. Each of these techniques is designed to reduce multiple variables into a more coherent and unified unit with greater reliability and clinical utility–i.e., a symptom cluster (group of interrelated symptoms) or symptom profile (group of patients with similar symptom patterns). However, results of both the factor and profile analyses identified variations in the dimensions across injury conditions that needed further work to interpret these clusters for individuals with SCI. Collinearity was observed between two emotional health factors–psychological adjustment and negative affect – suggesting that the structure of emotional well-being is nuanced and should be explored further within diagnostic groups. Additionally, variance was detected between groups on the physical function factor. Considering our prior qualitative [] and quantitative [, ] work evaluating physical function in individuals with SCI, we expected a more refined model of this cluster to be needed (i.e., comprised of separate sub-constructs such as basic mobility, fine-motor function, and wheelchair mobility). Furthermore, the selection of variables applicable to four diagnostic groups resulted in the omission of key domains relevant to individuals with SCI (e.g., bowel and bladder management difficulties). Analyses in a large sample of individuals with SCI could determine whether and where these subjectively important symptoms fit in the overall structure of HRQOL after SCI and inform the most useful cluster solution for this population.