A Step Towards a Better Understanding of Pain Phenotypes: Latent Class Analysis in Chronic Pain Patients Receiving Multimodal Inpatient Treatment
Received 13 July 2019
Accepted for publication 13 March 2020
Published 14 May 2020 Volume 2020:13 Pages 1023—1038
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Michael Schatman
Alexander Obbarius,1 Felix Fischer,1 Gregor Liegl,1 Nina Obbarius,1 Jan van Bebber,1 Tobias Hofmann,1 Matthias Rose1,2
1Department of Psychosomatic Medicine, Center for Internal Medicine and Dermatology, Charité – Universitätsmedizin Berlin, Berlin, Germany; 2Quantitative Health Sciences, Outcomes Measurement Science, University of Massachusetts Medical School, Worcester, MA, USA
Correspondence: Alexander Obbarius
Department of Psychosomatic Medicine, Center for Internal Medicine and Dermatology, Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
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.
Keywords: chronic pain, phenotyping, patient-reported outcomes, latent class analysis, multimodal treatment
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