Fibromyalgia Assessment Screening Tool: Clues to Fibromyalgia on a Multidimensional Health Assessment Questionnaire for Routine Care

PROMIS-29 in rheumatoid arthritis patients who screen positive or negative for fibromyalgia on MDHAQ FAST4 (fibromyalgia assessment screening tool) or 2011 fibromyalgia criteria

BACKGROUND

PROMIS-29 T-scores query health-related quality of life (HRQL) in 7 domains, physical function, pain, fatigue, anxiety, depression, sleep quality, and social participation, to establish population norms. An MDHAQ (multidimensional health assessment questionnaire) scores these 7 domains and includes medical information such as a FAST4 (fibromyalgia assessment screening tool) index. We analyzed PROMIS-29 T-scores in rheumatoid arthritis (RA) patients vs population norms and for positive vs negative fibromyalgia (FM) screens and compared PROMIS-29 T-scores to MDHAQ scores to assess HRQL.

METHODS

A cross-sectional study was performed at one routine visit of 213 RA patients, who completed MDHAQ, PROMIS-29, and reference 2011 FM Criteria. PROMIS-29 T-scores were compared in RA vs population norms and in FM+ vs FM- RA patients, based on MDHAQ/FAST4 and reference criteria. Possible associations between PROMIS-29 T-scores and corresponding MDHAQ scores were analyzed using Spearman correlations and multiple regressions.

RESULTS

Median PROMIS-29 T-scores indicated clinically and statistically significantly poorer status in 26-29% FM+ vs FM- RA patients, with larger differences than in RA patients vs population norms for 6/7 domains. MDHAQ scores were correlated significantly with each of 7 corresponding PROMIS-29 domains (|rho|≥0.62, p<0.001). Linear regressions explained 55-73% of PROMIS-29 T-score variation by MDHAQ scores and 56%-70% of MDHAQ score variation by PROMIS-29 T-scores.

CONCLUSIONS

Scores for 7 PROMIS-29 domains and MDHAQ were highly correlated. The MDHAQ is effective to assess HRQL and offers incremental medical information, including FAST4 screening. The results indicate the importance of assessing comorbidities such as fibromyalgia screening in interpreting PROMIS-29 T-scores.

Physician estimate of inflammation vs global assessment in explaining variations in swollen joint counts in rheumatoid arthritis patients

Objective

To analyse patients with RA for inflammatory activity by physician estimate of global assessment (DOCGL) vs an estimate of inflammatory activity (DOCINF) to explain variation in the swollen joint count (SJC).

Methods

Patients with RA were studied at routine care visits. Patients completed a multidimensional health assessment questionnaire (MDHAQ) and the physician completed a 28-joint count for swollen (SJC), tender (TJC) and deformed (DJC) joints and a RheuMetric checklist with a 0-10 DOCGL visual numeric scale (VNS) and 0-10 VNS estimates of inflammation (DOCINF), damage (DOCDAM) and patient distress (DOCSTR). The disease activity score in 28 joints with ESR (DAS28-ESR), Clinical Disease Activity Index (CDAI) and Routine Assessment of Patient Index Data 3 (RAPID3) were calculated. Individual scores and RA indices were compared according to Spearman correlation coefficients and regression analyses.

Results

A total of 104 unselected patients were included, with a median age and disease duration of 54.5 and 5 years, respectively. The median DAS28-ESR was 2.9 (Q1-Q3: 2.0-3.7), indicating low activity. DOCINF was correlated significantly with DOCGL (ρ = 0.775). Both DOCGL and DOCINF were correlated significantly with most other measures; correlations with DOCGL were generally higher than with DOCINF other than for SJC. In regression analyses, DOCINF was more explanatory of variation in SJC than DOCGL and other DAS28-ESR components.

Conclusions

Variation in SJC is explained more by a 0-10 DOCINF VNS than the traditional DOCGL or any other measure in RA patients seen in routine care. DOCINF on a RheuMetric checklist can provide informative quantitative scores concerning inflammatory activity in RA patients monitored over long periods.

Screening for Anxiety in Patients With Inflammatory Arthritis Using the Multidimensional Health Assessment Questionnaire

OBJECTIVE

To analyze the Multidimensional Health Assessment Questionnaire (MDHAQ) in screening for anxiety in patients with rheumatoid arthritis (RA) and psoriatic arthritis (PsA), compared to the Hospital Anxiety and Depression Scale (HADS) as the reference standard.

METHODS

Patients with a physician diagnosis of RA or PsA were invited to complete the MDHAQ and HADS at their routine rheumatology clinic visit. Sensitivity, specificity, percent agreement, and [Formula: see text] statistics were used to evaluate agreement between 2 MDHAQ items for anxiety and HADS subscale for Anxiety (HADS-A) score of ≥ 8. The first item is a question asked on a 4-point scale (0-3.3), and the second is a yes or no (blank) question asked within a 60-item review of symptoms (ROS) checklist.

RESULTS

The study included 183 participants, of whom 126 (68.9%) had RA and 57 (31.1%) had PsA. The mean age was 57.3 years and 66.7% were female. Positive screening for anxiety according to a HADS-A score of ≥ 8 was seen in 39.3% of patients. Compared to those with a HADS-A score of ≥ 8, patients with an MDHAQ score of ≥ 2.2 or a positive on ROS had a sensitivity of 69.9%, specificity of 73.6% and substantial agreement (agreement 80.9%, [Formula: see text] 0.59).

CONCLUSION

The MDHAQ provides information similar to the HADS in screening for anxiety in patients with RA and PsA. The use of this single questionnaire, which can also be used to monitor clinical status and to screen for fibromyalgia and depression without requiring multiple questionnaires, may prove a valuable tool in routine clinical practice.

RheuMetric Quantitative 0 to 10 Physician Estimates of Inflammation, Damage, and Distress in Rheumatoid Arthritis: Validation Against Reference Measures

OBJECTIVE

To analyze a RheuMetric checklist, which includes four feasible physician 0 to 10 scores for DOCGL, inflammation (DOCINF), damage (DOCDAM), and distress (DOCSTR) for criterion and discriminant validity against standard reference measures.

METHODS

A prospective, cross-sectional assessment was performed at one routine care visit at Liverpool Hospital, Sydney, Australia. Rheumatologists recorded DOCGL, DOCINF, DOCDAM, DOCSTR, and 28 joint counts for swelling (SJC), tenderness (TJC), and limited motion/deformity (DJC). Patients completed a multidimensional health assessment questionnaire (MDHAQ), which includes routine assessment of patient index data (RAPID3), fibromyalgia assessment screening tool (FAST4), and MDHAQ depression screen (MDS2). Laboratory tests and radiographic scores were recorded. RheuMetric estimates of inflammation, damage, and distress were compared with reference and other measures using correlations and linear regressions.

RESULTS

In 173 patients with RA, variation in RheuMetric DOCINF was explained significantly by SJC and inversely by disease duration; variation in DOCDAM was explained significantly by DJC, radiographic scores, and physical function; and variation in DOCSTR was explained significantly by fibromyalgia and depression.

CONCLUSION

RheuMetric DOCINF, DOCDAM, and DOCSTR estimates were correlated significantly and specifically with reference measures of inflammation, damage, and distress, documenting criterion and discriminant validity.

Patient global assessment is elevated by up to 5 of 10 units in patients with inflammatory arthritis who screen positive for fibromyalgia (by FAST4) and/or depression (by MDS2) on a single MDHAQ

BACKGROUND

Patient global assessment (PATGL) is a component of rheumatoid arthritis (RA) and spondyloarthritis (SpA) activity indices, reflecting inflammation in selected clinical trial patients. In routine care, PATGL often may be elevated independently of inflammatory activity by fibromyalgia (FM) and/or depression, leading to complexities in interpretation. A feasible method to screen for FM and/or depression could help to clarify interpretation of high PATGL and index scores, including explanation of apparent limited responses to anti-inflammatory therapies.

PATIENTS AND METHODS

Patients with RA or SpA in routine care in Barcelona, Chicago, and Sydney complete a 2-page multidimensional health assessment questionnaire (MDHAQ) in 5-10 min. The MDHAQ includes PATGL and three indices, RAPID3 (routine assessment of patient index data) to assess clinical status, FAST4 (0-4 fibromyalgia assessment screening tool) and MDS2 (0-2 MDHAQ depression screen). PATGL was compared for each diagnosis at each site and pooled data in FAST4 positive (+) vs negative (-) and/or MDS2+ vs MDS2- patients using medians and median regressions.

RESULTS

Median PATGL was 5.0 in 393 RA and 175 SpA patients; 2.0-3.0 in 305 (58.9%) FAST4-,MDS2- patients, 5.5-6.0 in 71 (13.7%) FAST4-,MDS2+ patients, 7.0-7.5 in 50 (9.7%) FAST4+,MDS2- patients, and 7.0-8.0 in 92 (17.8%) FAST4+,MDS2+ patients. Positive FAST4 and/or MDS2 screens were seen in 41% of patients. Results were similar in RA and SpA at 3 settings on 3 continents.

CONCLUSION

Median 0-10 PATGL varied from 2-3/10 to 5.5-8/10, according to negative vs positive screening for FM and/or depression on a single MDHAQ for busy clinical settings.

Should Quantitative Measures and Management of Rheumatoid Arthritis Include More Than Control of Inflammatory Activity?

We agree strongly with Kremer et al that "metrics are essential for evaluating disease activity in patients with rheumatoid arthritis (RA)."¹ Nonetheless, data reported from the Corrona and the Brigham and Women's Rheumatoid Arthritis Sequential Study (BRASS) registries for Clinical Disease Activity Index (CDAI) and Routine Assessment of Patient Index Data 3 (RAPID3) are quite similar to those reported in the initial 2008 RAPID3 report.².

Chronic Pain Diagnosis Using Machine Learning, Questionnaires, and QST: A Sensitivity Experiment

In the last decade, machine learning has been widely used in different fields, especially because of its capacity to work with complex data. With the support of machine learning techniques, different studies have been using data-driven approaches to better understand some syndromes like mild cognitive impairment, Alzheimer’s disease, schizophrenia, and chronic pain. Chronic pain is a complex disease that can recurrently be misdiagnosed due to its comorbidities with other syndromes with which it shares symptoms. Within that context, several studies have been suggesting different machine learning algorithms to classify or predict chronic pain conditions. Those algorithms were fed with a diversity of data types, from self-report data based on questionnaires to the most advanced brain imaging techniques. In this study, we assessed the sensitivity of different algorithms and datasets classifying chronic pain syndromes. Together with this assessment, we highlighted important methodological steps that should be taken into account when an experiment using machine learning is conducted. The best results were obtained by ensemble-based algorithms and the dataset containing the greatest diversity of information, resulting in area under the receiver operating curve (AUC) values of around 0.85. In addition, the performance of the algorithms is strongly related to the hyper-parameters. Thus, a good strategy for hyper-parameter optimization should be used to extract the most from the algorithm. These findings support the notion that machine learning can be a powerful tool to better understand chronic pain conditions.

Multidimensional Health Assessment Questionnaire as an Effective Tool to Screen for Depression in Routine Rheumatology Care

OBJECTIVE

To analyze the use of the Multidimensional Health Assessment Questionnaire (MDHAQ) to screen for depression, as compared to 2 reference standards, the Patient Health Questionnaire 9 (PHQ-9) and the Hospital Anxiety and Depression Scale depression domain (HADS-D).

METHODS

Patients from Barcelona with a primary diagnosis of rheumatoid arthritis (RA) or spondyloarthritis (SpA) completed the MDHAQ, the PHQ-9 (depression ≥10), and the HADS-D (depression ≥8) measures. The MDHAQ includes 2 depression items, 1 in the patient-friendly HAQ, scored in a 4-point format from 0 to 3.3, and a yes/no item on a 60-symptom checklist. Percentage agreement and kappa statistics quantified the agreement between 6 screening criteria: yes on the 60-symptom checklist, a score of ≥1.1, a score of ≥2.2 on a 4-point scale, and either a response of yes on the 60-symptom checklist or scores of ≥2.2, PHQ-9 ≥10, and HADS-D ≥8.

RESULTS

Depression screening was positive according to 6 criteria in 19.6-32.4% of 102 patients with RA, and 27.9-44.8% of 68 with SpA (total = 170). All MDHAQ scores, including depression items, were higher in patients with SpA compared to patients with RA, and within each diagnostic group in patients who met PHQ-9 ≥10 and HADS-D ≥8 depression screening criteria. The highest percentage agreement between an MDHAQ screening criterion versus PHQ-9 ≥10 was 83.3% for either an answer of yes on the 60-symptom checklist or a score of ≥2.2 on a 4-point scale, which we have termed MDHAQ-Dep. The agreement of MDHAQ-Dep versus HADS-D ≥8 was 81.7%, similar to the agreement of PHQ-9 ≥10 versus HADS-D ≥8, which was 82.2%. Kappa measures of agreement were 0.63 for MDHAQ-Dep versus PHQ-9 ≥10, 0.60 for MDHAQ-Dep versus HADS-D ≥8, and 0.62 for PHQ-9 ≥10 versus HADS-D ≥8.

CONCLUSION

A positive MDHAQ-Dep response (either an answer of yes on a 60-symptom checklist or a score of ≥2.2 on a 4-point scale) yielded similar results to PHQ-9 ≥10 or HADS-D ≥8 to screen for depression in these RA and SpA patients.

Identifying co-morbid fibromyalgia in patients with systemic lupus erythematosus using the Multi-Dimensional Health Assessment Questionnaire

Objective

Fibromyalgia (FM) is prevalent but often under-recognized in patients with systemic lupus erythematosus (SLE). Patient-reported outcomes (PROs) from the Multi-Dimensional Health Assessment Questionnaire (MDHAQ) can identify co-morbid FM in patients with rheumatic diseases. The present study examined the utility of the MDHAQ in recognizing FM in patients with SLE during routine consultations.

Methods

Patients with SLE completed an MDHAQ. FM status was determined by the validated 2016 revision of the ACR 2010/2011 preliminary FM criteria. Individual PROs from the MDHAQ and composite Fibromyalgia Assessment Tool (FAST) indices of the discriminatory PROs were compared between patients with and without FM using Student’s unpaired t-test and receiver operating characteristic curve analysis to determine the area under the curve (AUC). The physician’s clinical impression of FM was recorded, and the SLE Disease Activity Index was used to assess disease activity.

Results

Of 88 patients with SLE, 23 (26%) satisfied the 2016 FM criteria. The FAST3 composite measure of two out of three of pain (≥6/10), joint count (≥16/48) and symptom checklist (≥16/60) correctly classified 89% of patients (AUC=0.90, kappa=0.71). Physician diagnosis demonstrated moderate agreement with the 2016 FM criteria (kappa=0.43) but missed 43% of patients with FM. In the presence of active disease, the FAST3 correctly classified 91% of patients.

Conclusions

Co-morbid FM is prevalent in SLE yet often underdiagnosed by physicians. The simple FAST3 index of the MDHAQ provides an easy-to-use self-reported tool to improve identification of FM in patients with SLE.

Reliability, Feasibility, and Patient Acceptance of an Electronic Version of a Multidimensional Health Assessment Questionnaire for Routine Rheumatology Care: Validation and Patient Preference Study

BACKGROUND

A multidimensional health assessment questionnaire (MDHAQ) that was developed primarily for routine rheumatology care has advanced clinical research concerning disease burden, disability, and mortality in rheumatic diseases. Routine Assessment of Patient Index Data 3 (RAPID3), an index within the MDHAQ, is the most widely used index to assess rheumatoid arthritis (RA) in clinical care in the United States, and it recognizes clinical status changes in all studied rheumatic diseases. MDHAQ physical function scores are far more significant in the prognosis of premature RA mortality than laboratory or imaging data. However, electronic medical records (EMRs) generally do not include patient questionnaires. An electronic MDHAQ (eMDHAQ), linked by fast healthcare interoperability resources (FIHR) to an EMR, can facilitate clinical and research advances.

OBJECTIVE

This study analyzed the reliability, feasibility, and patient acceptance of an eMDHAQ.

METHODS

Since 2006, all Rush University Medical Center rheumatology patients with all diagnoses have been asked to complete a paper MDHAQ at each routine care encounter. In April 2019, patients were invited to complete an eMDHAQ at the conclusion of the encounter. Analyses were conducted to determine the reliability of eMDHAQ versus paper MDHAQ scores, arithmetically and by intraclass correlation coefficient (ICC). The feasibility of the eMDHAQ was analyzed based on the time for patient completion. The patient preference for the electronic or paper version was analyzed through a patient paper questionnaire.

RESULTS

The 98 study patients were a typical routine rheumatology patient group. Seven paper versus eMDHAQ scores were within 2%, differences neither clinically nor statistically significant. ICCs of 0.86-0.98 also indicated good to excellent reliability. Mean eMDHAQ completion time was a feasible 8.2 minutes. The eMDHAQ was preferred by 72% of patients; preferences were similar according to age and educational level.

CONCLUSIONS

The results on a paper MDHAQ versus eMDHAQ were similar. Most patients preferred an eMDHAQ.