Dataset Information

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

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.

SUBMITTER: Obbarius A

PROVIDER: S-EPMC7234963 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Obbarius Alexander A Fischer Felix F Liegl Gregor G Obbarius Nina N van Bebber Jan J Hofmann Tobias T Rose Matthias M

Journal of pain research 20200514

<h4>Purpose</h4>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.<h4>Patients and methods</h4>A latent class analysis (LCA ...[more]

PMID: 32523372

Similar Datasets

Project description:BackgroundFrailty is an important concept in the care of older adults although controversy remains regarding its defining features and clinical utility. Both the Fried phenotype and the Rockwood deficit accumulation approaches cast frailty as a "burden" without exploring the relative salience of its cardinal markers and their relevance to the patient. New multifactorial perspectives require a reliable assessment of frailty that can validly predict postoperative health outcomes.MethodsIn a retrospective study of 2828 unselected surgical patients, we used item response theory to examine the ability of 32 heterogeneous markers capturing limitations in physical, functional, emotional, and social activity domains to indicate severity of frailty as a latent continuum. Eighteen markers efficiently indicated frailty severity and were then subject to latent class analysis to derive discrete phenotypes. Next, we validated the obtained frailty phenotypes against patient-reported 30-day postoperative outcomes using multivariable logistic regression. Models were adjusted for demographics, comorbidity, type and duration of surgery, and cigarette and alcohol consumption.ResultsThe 18 markers provided psychometric evidence of a single reliable continuum of frailty severity. Latent class analyses produced 3 distinct subtypes, based on patients' endorsement probabilities of the frailty indicators: not frail (49.7%), moderately frail (33.5%), and severely frail (16.7%). Unlike the moderate class, severely frail endorsed emotional health problems in addition to physical burdens and functional limitations. Models adjusting for age, sex, type of anesthesia, and intraoperative factors indicated that severely frail (odds ratio, 1.89; 95% confidence interval, 1.42-2.50) and moderately frail patients (odds ratio, 1.31; 95% confidence interval, 1.03-1.67) both had higher odds of experiencing postoperative complications compared to not frail patients. In a 3-way comparison, a higher proportion of severely frail patients (10.7%) reported poorer quality of life after surgery compared to moderately frail (9.2%) and not frail (8.3%) patients (P < .001). There was no significant difference among these groups in proportions reporting hospital readmission (5.6%, 5.1%, and 3.8%, respectively; P = .067).ConclusionsSelf-report frailty items can accurately discern 3 distinct phenotypes differing in composition and their relations with surgical outcomes. Systematically assessing a wider set of domains including limitations in functional, emotional, and social activities can inform clinicians on what precipitates loss of physiological reserve and profoundly influences patients' lives. This information can help guide the current discussion on frailty and add meaningful clinical tools to the surgical practice.

Project description:BackgroundGiven the relatively high rates of suicidal ideation and attempt among people with chronic pain, there is a need to understand the underlying factors to target suicide prevention efforts. To date, no study has examined the association between pain phenotypes and suicide related behaviors among those with mild traumatic brain injuries.ObjectiveTo determine if pain phenotypes were independently associated with suicidal ideation / attempt or if comorbidities within the pain phenotypes account for the association between pain phenotypes and suicide related behaviors.MethodsThis is a longitudinal retrospective cohort study of suicide ideation/attempts among pain phenotypes previously derived using general mixture latent variable models of the joint distribution of repeated measures of pain scores and pain medications/treatment. We used national VA inpatient, outpatient, and pharmacy data files for Post-9/11 Veterans with mild traumatic injury who entered VA care between fiscal years (FY) 2007 and 2009. We considered a counterfactual causal modeling framework to assess the extent that the pain phenotypes during years 1-5 of VA care were predictive of suicide ideation/attempt during years 6-8 of VA care conditioned on covariates being balanced between pain phenotypes.ResultsWithout adjustment, pain phenotypes were significant predictors of suicide related behaviors. When we used propensity scores to balance the comorbidities present in the pain phenotypes, the pain phenotypes were no longer significantly associated with suicide related behaviors.ConclusionThese findings suggest that suicide ideation/attempt is associated with pain trajectories primarily through latent multimorbidity. Therefore, it is critical to identify and manage comorbidities (e.g., depression, post-traumatic stress disorder) to prevent tragic outcomes associated with suicide related behaviors throughout the course of chronic pain and mild traumatic brain injury management.

Project description:BackgroundSince COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.Research questionDoes the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes?Study design and methodsWe included adult patients (≥ 18 years) positive for laboratory-confirmed SARS-CoV-2 infection from a prospective COVID-19 registry database in the Cleveland Clinic Health System in Ohio and Florida. The patients were split into training and testing sets. Using latent class analysis (LCA), we first identified phenotypic clusters of patients with COVID-19 based on demographics, comorbidities, and presenting symptoms. We then identified subphenotypes of hospitalized patients with additional blood biomarker data measured on hospital admission. The associations of phenotypes/subphenotypes and clinical outcomes were investigated. Multivariable prediction models were established to predict assignment to the LCA-defined phenotypes and subphenotypes and then evaluated on an independent testing set.ResultsWe analyzed data for 20,572 patients. Seven phenotypes were identified on the basis of different profiles of presenting COVID-19 symptoms and existing comorbidities, including the following groups: young, no symptoms; young, symptoms; middle-aged, no symptoms; middle-aged, symptoms; middle-aged, comorbidities; old, no symptoms; and old, symptoms. The rates of inpatient hospitalization for the phenotypes were significantly different (P < .001). Five subphenotypes were identified for the subgroup of hospitalized patients, including the following subgroups: young, elevated WBC and platelet counts; middle-aged, lymphopenic with elevated C-reactive protein; middle-aged, hyperinflammatory; old, leukopenic with comorbidities; and old, hyperinflammatory with kidney dysfunction. The hospital mortality and the times from hospitalization to ICU transfer or death were significantly different (P < .001). The models for predicting the LCA-defined phenotypes and subphenotypes showed high discrimination (concordance index, 0.92 and 0.91).InterpretationHypothesis-free LCA-defined phenotypes and subphenotypes of patients with COVID-19 can be identified. These may help clinical investigators conduct stratified analyses in clinical trials and assist basic science researchers in characterizing the pathobiology of the spectrum of COVID-19 presentations.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets