Predicting fibromyalgia, a narrative review: are we better than fools and children?

Nurses' Perceptions of Patient Fibromyalgia Illness Experiences after Performing Group-Based Problem-Solving Therapy: A Qualitative Research Study

Fibromyalgia patients experience difficulties in their daily lives that are difficult to identify and recognize due to the stigma associated with the disease. Nurses can help identify them to establish biopsychosocial coping and treatment. The main aim of this study was to explore Spanish nurses' perceptions of the illness experiences of their fibromyalgia patients. Qualitative content analysis from the etic perspective was used. Eight nurses met in focus groups to report their perceptions of the illness experiences of FM patients after led group-based problem-solving therapy in fibromyalgia patients. Four themes emerged: (1) the presence of a "specific trigger" (stressful event) for FM symptoms; (2) fulfilling expected gender roles; (3) a lack of support from the family; (4) abuse. Nurses recognize the mind-body connection after the impact of stress on patients' bodies. The expected gender roles interfere with patients' recovery because they feel frustration and guilt about not being able to fulfil them. Managing emotions and improving communication in fibromyalgia is recommended. Clinicians might also consider issues such as abuse and the absence of social-family support for the comprehensive evaluation and effective management of fibromyalgia.

Chronic widespread pain in children and adolescents presenting in primary care: prevalence and associated risk factors

ABSTRACT

A significant proportion of children/adolescents report chronic widespread pain (CWP), but little is known about clinically relevant CWP or what factors lead to onset in this population. Objectives were to report the primary care consultation prevalence of CWP and investigate risk factors associated with onset. A validated algorithm for identifying CWP status from primary care electronic healthcare records was applied to a child or adolescent population (aged 8-18 years). The algorithm records patients who have recurrent pain consultations (axial skeleton and upper or lower limbs) or those with a nonspecific generalised pain disorder (eg, fibromyalgia). Prevalence was described, and a nested case-control study was established to identify risk factors associated with CWP onset using logistic regression producing odds ratios (ORs) and 95% confidence intervals (95% CIs). Two hundred seventy-one children or adolescents were identified with CWP, resulting in a 5-year consultation prevalence of 3.19%. Risk factors significantly associated with CWP onset were as follows: mental health (eg, anxiety/neurosis consultations), neurological (eg, headaches), genitourinary (eg, cystitis), gastrointestinal (eg, abdominal pain), and throat problems (eg, sore throats). Children or adolescents with 1 or 2 risk factors (OR 2.15, 95% CI 1.6-2.9) or 3 or more risk factors (OR 9.17, 95% CI 5.9-14.3) were at significantly increased odds of CWP onset compared with those with none. Findings show a significant proportion of the child or adolescent primary care population has CWP. Most risk factors involved pain-related conditions, suggesting potential pathways of pain development. Further work is now needed to better understand the development of CWP in children and adolescents.

Impact of daily yoga-based exercise on pain, catastrophizing, and sleep amongst individuals with fibromyalgia

Background

Fibromyalgia (FM) is a chronic widespread pain disorder characterized by negative affect, sleep disturbance, and fatigue. This uncontrolled pilot study investigated the efficacy of daily yoga-based exercise to improve FM symptoms and explored baseline phenotypic characteristics associated with the greatest benefit.

Methods

FM patients (n=46, with 36 completers) reported psychosocial functioning and a range of FM symptoms using validated instruments before and after participation in Satyananda yoga, which included weekly in-person pain-tailored group classes for 6 weeks and daily home yoga video practice.

Results

Changes in FM symptoms from pre- to post-yoga were variable amongst participants. Group means for pain decreased, as reported by average daily diary and Brief Pain Inventory, with greater home practice minutes associated with a greater decrease in pain. Average daily ratings of sleep and fatigue improved. Pain catastrophizing was decreased overall, with greater change correlated to a decrease in FM symptoms. We did not observe any group mean changes in actigraphy sleep efficiency, Patient-Reported Outcomes Measurement Information System-anxiety and the Revised Fibromyalgia Impact Questionnaire. Multilevel Modeling analysis revealed a significant interaction between anxiety and catastrophizing for end-study sleep efficiency, fatigue, and pain, such that patients with higher baseline catastrophizing and lower baseline anxiety reported less pain and fatigue, and higher sleep efficiency after the sixth week of yoga practice.

Conclusion

This pilot study suggests that yoga may reduce pain and catastrophizing, as well as improve sleep, but these changes were modest across study participants. Greater uptake of home yoga practice as well as a phenotype of higher baseline catastrophizing combined with lower baseline anxiety were associated with greater impact. Future randomized, controlled trials comparing different types of yoga or exercise will allow determination of the most effective treatments for FM and allow closer targeting to the patients who will benefit most from them.

Identification of a potential fibromyalgia diagnosis using random forest modeling applied to electronic medical records

Background

Diagnosis of fibromyalgia (FM), a chronic musculoskeletal condition characterized by widespread pain and a constellation of symptoms, remains challenging and is often delayed.

Methods

Random forest modeling of electronic medical records was used to identify variables that may facilitate earlier FM identification and diagnosis. Subjects aged ≥18 years with two or more listings of the International Classification of Diseases, Ninth Revision, (ICD-9) code for FM (ICD-9 729.1) ≥30 days apart during the 2012 calendar year were defined as cases among subjects associated with an integrated delivery network and who had one or more health care provider encounter in the Humedica database in calendar years 2011 and 2012. Controls were without the FM ICD-9 codes. Seventy-two demographic, clinical, and health care resource utilization variables were entered into a random forest model with downsampling to account for cohort imbalances (<1% subjects had FM). Importance of the top ten variables was ranked based on normalization to 100% for the variable with the largest loss in predicting performance by its omission from the model. Since random forest is a complex prediction method, a set of simple rules was derived to help understand what factors drive individual predictions.

Results

The ten variables identified by the model were: number of visits where laboratory/non-imaging diagnostic tests were ordered; number of outpatient visits excluding office visits; age; number of office visits; number of opioid prescriptions; number of medications prescribed; number of pain medications excluding opioids; number of medications administered/ordered; number of emergency room visits; and number of musculoskeletal conditions. A receiver operating characteristic curve confirmed the model’s predictive accuracy using an independent test set (area under the curve, 0.810). To enhance interpretability, nine rules were developed that could be used with good predictive probability of an FM diagnosis and to identify no-FM subjects.

Conclusion

Random forest modeling may help to quantify the predictive probability of an FM diagnosis. Rules can be developed to simplify interpretability. Further validation of these models may facilitate earlier diagnosis and enhance management.

Electronic medical record data to identify variables associated with a fibromyalgia diagnosis: importance of health care resource utilization

BACKGROUND

Diagnosis of fibromyalgia (FM) is often challenging. Identifying factors associated with an FM diagnosis may guide health care providers in implementing appropriate diagnostic and management strategies.

METHODS

This retrospective study used the de-identified Humedica electronic medical record (EMR) database to identify variables associated with an FM diagnosis. Cases (n=4,296) were subjects ≥18 years old with ≥2 International Classification of Diseases, Ninth Revision (ICD-9) codes for FM (729.1) ≥30 days apart during 2012, associated with an integrated delivery network, with ≥1 encounter with a health care provider in 2011 and 2012. Controls without FM (no-FM; n=583,665) did not have the ICD-9 codes for FM. Demographic, clinical, and health care resource utilization variables were extracted from structured EMR data. Univariate analysis identified variables showing significant differences between the cohorts based on odds ratios (ORs).

RESULTS

Consistent with FM epidemiology, FM subjects were predominantly female (78.7% vs 64.5%; P<0.0001) and slightly older (mean age 53.3 vs 52.7 years; P=0.0318). Relative to the no-FM cohort, the FM cohort was characterized by a higher prevalence of nearly all evaluated comorbidities; the ORs suggested a higher likelihood of an FM diagnosis (P<0.0001), especially for musculoskeletal and neuropathic pain conditions (OR 3.1 for each condition). Variables potentially associated with an FM diagnosis included higher levels of use of specific health care resources including emergency-room visits, outpatient visits, hospitalizations, and medications. Units used per subject for emergency-room visits, outpatient visits, hospitalizations, and medications were also significantly higher in the FM cohort (P<0.0001), confirming resource utilization as an important variable associated with an FM diagnosis.

CONCLUSION

Significant differences between the FM and no-FM cohorts were observed for nearly all the demographic, clinical, and health care resource variables, suggesting an association with FM diagnosis. These results also support use of EMR data for identifying variables associated with FM, which may help in the diagnosis and management of this condition.