Dataset Information

Implication of the Nociplastic Features for Clinical Diagnosis of Fibromyalgia: Development of the Preliminary Nociplastic-Based Fibromyalgia Features (NFF) Tool.

ABSTRACT:

Objective

Nociplastic concept incorporates a broad continuum of pain phenotypes shared with clinical peculiarity. This study aimed to develop and validate a diagnostic tool, the preliminary Nociplastic-based Fibromyalgia Features (NFF), to detect fibromyalgia (FM) in patients with chronic pain.

Methods

Items requiring yes or no responses and relating to the most relevant clinical nociplastic pain (NP) features of FM were compiled by a group of expert rheumatologists. The provisional list was tested in a prospective study on 185 consecutive patients with chronic pain (126 patients with FM and 59 patients with non-FM non-inflammatory chronic pain) diagnosed based on expert decision. Identification of the most discriminant combinations of items for FM and the calculation of their sensitivity and specificity were based on both univariate and multivariate (stepwise logistic regression) analyses. All participants were investigated through the final NFF, the 2011 American College of Rheumatology (ACR) criteria, and the 2016 ACR criteria. NFF performance was assessed with receiver operating characteristic curve analysis.

Results

Based on multivariate analyses, we retained only seven items in the final version of the NFF. A cut-off score of 4 (corresponding to the number of positive items) gave the highest rate of correct identification of patients (85%), with a sensitivity of 82% and a specificity of 91%. The NFF showed the highest concordance rate with expert diagnosis (85%) and the lowest value (77%) with the ACR 2016 criteria.

Conclusion

The preliminary NFF with respect to the various aspects of NP showed good performance for detection of the FM in the clinical setting. This tool may provide a more pragmatic approach to the timely diagnosis of FM.

SUBMITTER: Ghavidel-Parsa B

PROVIDER: S-EPMC8916565 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Implication of the Nociplastic Features for Clinical Diagnosis of Fibromyalgia: Development of the Preliminary Nociplastic-Based Fibromyalgia Features (NFF) Tool.

Ghavidel-Parsa Banafsheh B Bidari Ali A Atrkarroushan Zahra Z Khosousi Mohammad-Javad MJ

ACR open rheumatology 20211222 3

<h4>Objective</h4>Nociplastic concept incorporates a broad continuum of pain phenotypes shared with clinical peculiarity. This study aimed to develop and validate a diagnostic tool, the preliminary Nociplastic-based Fibromyalgia Features (NFF), to detect fibromyalgia (FM) in patients with chronic pain.<h4>Methods</h4>Items requiring yes or no responses and relating to the most relevant clinical nociplastic pain (NP) features of FM were compiled by a group of expert rheumatologists. The provision ...[more]

PMID: 34936234

Similar Datasets

Project description:BackgroundIn contrast to all other areas of medicine, psychiatry is still nearly entirely reliant on subjective assessments such as patient self-report and clinical observation. The lack of objective information on which to base clinical decisions can contribute to reduced quality of care. Behavioral health clinicians need objective and reliable patient data to support effective targeted interventions.ObjectiveWe aimed to investigate whether reliable inferences-psychiatric signs, symptoms, and diagnoses-can be extracted from audiovisual patterns in recorded evaluation interviews of participants with schizophrenia spectrum disorders and bipolar disorder.MethodsWe obtained audiovisual data from 89 participants (mean age 25.3 years; male: 48/89, 53.9%; female: 41/89, 46.1%): individuals with schizophrenia spectrum disorders (n=41), individuals with bipolar disorder (n=21), and healthy volunteers (n=27). We developed machine learning models based on acoustic and facial movement features extracted from participant interviews to predict diagnoses and detect clinician-coded neuropsychiatric symptoms, and we assessed model performance using area under the receiver operating characteristic curve (AUROC) in 5-fold cross-validation.ResultsThe model successfully differentiated between schizophrenia spectrum disorders and bipolar disorder (AUROC 0.73) when aggregating face and voice features. Facial action units including cheek-raising muscle (AUROC 0.64) and chin-raising muscle (AUROC 0.74) provided the strongest signal for men. Vocal features, such as energy in the frequency band 1 to 4 kHz (AUROC 0.80) and spectral harmonicity (AUROC 0.78), provided the strongest signal for women. Lip corner-pulling muscle signal discriminated between diagnoses for both men (AUROC 0.61) and women (AUROC 0.62). Several psychiatric signs and symptoms were successfully inferred: blunted affect (AUROC 0.81), avolition (AUROC 0.72), lack of vocal inflection (AUROC 0.71), asociality (AUROC 0.63), and worthlessness (AUROC 0.61).ConclusionsThis study represents advancement in efforts to capitalize on digital data to improve diagnostic assessment and supports the development of a new generation of innovative clinical tools by employing acoustic and facial data analysis.

Project description:Standard bioassays allow hazard assessment at the population level, but much remains to be learned about the molecular level response of organisms to stressors. The main aim of this study was the development of a DNA microarray for Enchytraeus albidus, a common soil worm species. Further, this microarray was tested using worms exposed to Cu, phenmedipham, and different soil types. Hybridization onto the developed microarray revealed several genes with homology to known sequences. Genes of interest were confirmed through real-time polymerase chain reaction. It was possible to discriminate between natural and chemical stressors and chemical concentrations. Gene responses were detected under conditions known to have effects in the reproduction of individuals. It was confirmed that the integration of different endpoints improves the assessment process and enhances the understanding of the modes of action of stressors. The chemical stressM-bM-^@M-^Sinduced genes were related to factors such as immune response, stress response, metabolic processes, and/or signal transduction. The present study represents the first step of a gene-level study in the ecologically relevant and standard test species E. albidus. It demonstrates the usefulness of cDNA normalization in the production of cDNA libraries of ecotoxicological standard organisms that are not genome models like E. albidus. Fluorescently labelled cDNA, from enchytraeids exposed during 2 days to control LUFA 2.2 (Cy3) soil and to the different exposure conditions (Cy5), was synthesized for microarray analysis and hybridizations were performed. After scanning, spots were identified and ratios quantified using the Genepix software 5.0 (Axon Instruments). Statistical analysis of the microarrays was performed using limmaGUI package (1.18.0) (Smyth, 2005) in the R (2.8.0) software environment (http://www.R-project.org/). After being submitted to local background subtraction, microarrays were normalized using global loess method. To statistically evaluate the differential gene expression between the different conditions, a gene-per-gene linear model (limma M-bM-^@M-^S linear model for microarray analysis) and empirical Bayes methods were applied. The results were then corrected for multiple testing using the Benjamini-HochbergM-bM-^@M-^Ys method (adjusted p<0.05 was considered significant) (Benjamini and Hochberg, 1995).

Project description:Fibromyalgia (FM) is a complex disease that is characterized by chronic musculoskeletal pain and has great economic impact. FM prevalence is about 2% to 4% worldwide, affecting mainly middle-aged women, and its complex pathophysiology complicates diagnosis, treatment and the findings of solid biomarkers. Previous studies have suggested an association between the disease and oxidative stress, mitochondrial metabolism, intestinal microbiota and inflammation, providing sufficient data to support the multifactorial origin of FM. Hence, the objective of this randomized, prospective, low-interventional, double-blinded and placebo-controlled clinical trial is the development of a specific panel of FM biomarkers and the evaluation of their response to a six-month nutritional intervention based on the Mediterranean diet supplemented with extra virgin olive oil (EVOO). For this purpose, the experimental design implies the recruitment of a large cohort of female Spanish patients. Middle-aged women who meet the diagnostic criteria for FM according to the American College of Rheumatology (ACR) will be eligible, along with age-matched healthy women. Both groups will be randomly divided into placebo (olive oil, OO) and treatment groups (extra virgin olive oil, EVOO), and will provide samples at the beginning (T0), after 3 months of nutritional intervention (T1), at the end of the nutritional intervention in 6 months (T2), and 6 months after the end of nutritional intervention (TF), being enrolled for 1 year. Data will be collected through health questionnaires, and whole blood and stool samples will be taken and analyzed. Blood will be used for western-blotting and proteomic analysis of mitochondrial homeostasis and plasma proteome, while stool will undergo metagenomic analysis, respectively. This study represents the first low-interventional investigation with more than 200 participants focused on exploring the association of oxidative stress, mitochondrial metabolism, intestinal microbiota and related pathways with a nutritional intervention in the context of FM. As a result, the outcomes of this study will significantly contribute to the development of a comprehensive and robust panel of diagnostic biomarkers, and will shed some light on their modulation with non-pharmacological therapies such as nutrition. Clinical trial registration: https://clinicaltrials.gov/study/NCT05921409, identifier: NCT05921409.

Project description:BackgroundThe aim of this study was to develop a simple and effective prediction model for calculating the probability of breast cancer by selecting clinical and sonographic features associated with breast cancer.MethodsA total of 402 lesions from 304 adult females from the ultrasound department of of PLA General Hospital from March 1st, 2020 to April 1st, 2021, were prospectively collected as the development group. The validation group included 121 lesions from 98 patients in our physical examination center from April 1st, 2021 to March 1st, 2022. Least absolute shrinkage and selection operator (LASSO) was applied to select clinical and ultrasonic variables, and R language was applied to build a web version of the interactive dynamic column line graph. The prediction model was validated by the validation group and the Breast Imaging Reporting and Data System (BI-RADS) categories. Calibration, differentiation and effectiveness were evaluated by R2, receiver operating characteristic (ROC) and decision curve analysis (DCA), respectively.ResultsOne hundred and seventy-nine malignant lesions and 223 benign lesions were included in the development group after exclusion and follow-up, whereas 62 malignant lesions and 59 benign lesions were enrolled in the validation group. Age, bloody nipple discharge, irregular shape, irregular border, heterogeneous echo, microcalcification, attenuation effects, decreased echo in surrounding tissues, lesions in ducts, abnormal lymph node morphology, nourishing vessel and nourishing vessel's resistance index (RI) greater than 0.70 were selected as independent risk factors. There was no significant difference in the area under the curve (AUC) of the development group between the prediction model and the BI-RADS category (0.959 vs. 0.953, P>0.05), and so as the validation group (0.952 vs. 0.932, P>0.05). For the prediction model, R2 of the development and validation group was 0.78 and 0.72. The DCA showed that the net benefits (NB) of the development group were higher than that of the validation group (0-100% vs. 0-90%).ConclusionsA prediction model was developed with the clinical and ultrasonic features for the precise and intuitive probability of breast cancer. This could provide a reliable reference for further examination.

Dataset Information

Implication of the Nociplastic Features for Clinical Diagnosis of Fibromyalgia: Development of the Preliminary Nociplastic-Based Fibromyalgia Features (NFF) Tool.

Objective

Methods

Results

Conclusion

Publications

Implication of the Nociplastic Features for Clinical Diagnosis of Fibromyalgia: Development of the Preliminary Nociplastic-Based Fibromyalgia Features (NFF) Tool.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets