<p>A Step Towards a Better Understanding of Pain Phenotypes: Latent Class Analysis in Chronic Pain Patients Receiving Multimodal Inpatient Treatment</p>

Abstract

Purpose: The number of non-responders to treatment among patients with chronic pain (CP) is high, although intensive multimodal treatment is broadly accessible. One reason is the large variability in manifestations of CP. To facilitate the development of tailored treatment approaches, phenotypes of CP must be identified. In this study, we aim to identify subgroups in patients with CP based on several aspects of self-reported health. Patients and Methods: A latent class analysis (LCA) was carried out in retrospective data from 411 patients with CP of different origins. All patients experienced severe physical and psychosocial consequences and were therefore undergoing multimodal inpatient pain treatment. Self-reported measures of pain (visual analogue scales for pain intensity, frequency, and impairment; Pain Perception Scale), emotional distress (Patient Health Questionnaire, PHQ-9; Generalized Anxiety Disorder Scale, GAD-7) and physical health (Short Form Health Survey; SF-8) were collected immediately after admission and before discharge. Instruments assessed at admission were used as input to the LCA. Resulting classes were compared in terms of patient characteristics and treatment outcome. Results: A model with four latent classes demonstrated the best model fit and interpretability. Classes 1 to 4 included patients with high (54.7%), extreme (17.0%), moderate (15.6%), and low (12.7%) pain burden, respectively. Patients in class 4 showed high levels of emotional distress, whereas emotional distress in the other classes corresponded to the levels of pain burden. While pain as well as physical and mental health improved in class 1, only the levels of depression and anxiety improved in patients in the other groups during multimodal treatment. Conclusion:The specific needs of these subgroups should be taken into account when developing individualized treatment programs. However, the retrospective design limits the significance of the results and replication in prospective studies is desirable.

Two birds with one stone.–Addressing depressive symptoms, emotional tension and worry improves tinnitus-related distress and affective pain perceptions in patients with chronic tinnitus

“…Similarly, a recent study reported that subgroups of patients with varying levels of affective pain experiences reported analogous levels of depressive difficulties. For sensory pain experiences, by contrast, respective mappings appeared more heterogeneous [ 66 ]. No changes in coping attitudes were observed.…”

Systems Medicine Approach for Tinnitus with Comorbid Disorders

“…In a first effort for phenotyping, a recent study used an analogous data-driven approach to identify multiple patterns of hearing loss in tinnitus patients [ 83 , 84 ]. Analyses using LCA on chronic pain patients revealed four classes separated by the severity of pain and affective symptoms [ 85 ]. While this previous work suffers from the limitations imposed by small datasets and a limited focus on the comorbid conditions, it delivers a data-driven proof of concept that a phenotyping approach is feasible.…”

“…In addition to such biomarkers, psychological markers are promising to understand complex phenotypes such as chronic pain. Using latent class analyses, Obbarius et al [ 85 ] demonstrated that depression, anxiety, and physical health were markers for the burden of pain. What is currently missing is an overarching approach combining biomarkers and psychosocial markers spanning comorbid disorders.…”

“…This approach identified four groups namely an “avoidance”, a “psychosomatic”, a “somatic”, and a “distress” group. Regarding chronic pain, Obbarius et al [ 85 ] used a similar approach and identified four phenotypes labelled as “high pain burden”, “extreme pain burden”, “moderate pain burden”, and “low pain burden”. Future research should map the different phenotypical groups with underlying biopsychological profiles of markers.…”

Insights from studying a large cohort of patients

“…Others, evaluating chronic or acute patient cohorts, also identified patterns of response to treatment. 2,5,[10][11][12]20 Thus, finding patterns of outcomes seems to be typical of patients in pain. However, the number and characteristics of the subgroups might be cohort specific, and they are not necessarily generalizable to other settings, for example, from pain clinic to primary care or across countries.…”