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Improving the primary care physicians' decision making for fibromyalgia in clinical practice: development and validation of the Fibromyalgia Detection (FibroDetect®) screening tool.

Baron R, Perrot S, Guillemin I, Alegre C, Dias-Barbosa C, Choy E, Gilet H, Cruccu G, Desmeules J, Margaux J, Richards S, Serra E, Spaeth M, Arnould B - Health Qual Life Outcomes (2014)

Bottom Line: Acceptability and applicability were assessed and the tool modified accordingly, then assessed in clinical practice.The FibroDetect included 14 questions assessing patients' pain and fatigue, personal history and attitudes, symptoms and impact on lives.Six questions were retained in the final scoring, demonstrating satisfactory discriminative power between ACR + and ACR- patients with area under the Receiver Operating Characteristic curve of 0.74.

View Article: PubMed Central - PubMed

ABSTRACT

Background: Fibromyalgia diagnosis is a challenging and long process, especially among primary care physicians (PCPs), because of symptom heterogeneity, co-morbidities and clinical overlap with other disorders. The purpose was to develop and validate a screening tool in French (FR), German (DE) and English (UK) to help PCPs identify patients with fibromyalgia.

Methods: The FibroDetect questionnaire was simultaneously developed in FR, DE and UK based on information obtained from a literature review, focus groups conducted with clinicians, and face-to-face interviews with fibromyalgia patients (FR, DE and UK, n = 23). The resulting tool was comprehension-tested in patients with diagnosed or suspected fibromyalgia (n = 3 and n = 2 in each country, respectively). Acceptability and applicability were assessed and the tool modified accordingly, then assessed in clinical practice. A scoring method was created using an iterative process based on statistical and clinical considerations with American College of Rheumatology + (ACR+) patients and ACR- patients (n = 276), and validated with fibromyalgia and non-fibromyalgia patients (n = 312).

Results: The FibroDetect included 14 questions assessing patients' pain and fatigue, personal history and attitudes, symptoms and impact on lives. Six questions were retained in the final scoring, demonstrating satisfactory discriminative power between ACR + and ACR- patients with area under the Receiver Operating Characteristic curve of 0.74. The predictive accuracy of the tool increased to 0.86 for fibromyalgia and non-fibromyalgia patient detection, with a sensitivity of 90% and a specificity of 67% for a cut-off of 6 on the score.

Conclusions: The FibroDetect is a self-administered tool that can be used as a screening classification surrogate to the ACR criteria in primary care settings to help PCPs detect potential fibromyalgia patients among a population complaining of chronic widespread pain.

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Related in: MedlinePlus

ROC curve of the final FibroDetect discriminant model; A: Investigational group (ACR + and ACR-); B: Control groups (FM + and FM-). Sensitivity: probability for ACR+ or FM+ patients to be correctly classified as ACR+ / FM +; 1 – Specificity: probability for ACR– or non-fibromyalgia patients to be incorrectly classified; Diagonal: AUC = 0.5, i.e., predictions are not better than random guessing AUC under the ROC curve: Area Under the Receiver Operating Characteristic curve; ACR, American College of Rheumatology; FM, fibromyalgia.
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Fig3: ROC curve of the final FibroDetect discriminant model; A: Investigational group (ACR + and ACR-); B: Control groups (FM + and FM-). Sensitivity: probability for ACR+ or FM+ patients to be correctly classified as ACR+ / FM +; 1 – Specificity: probability for ACR– or non-fibromyalgia patients to be incorrectly classified; Diagonal: AUC = 0.5, i.e., predictions are not better than random guessing AUC under the ROC curve: Area Under the Receiver Operating Characteristic curve; ACR, American College of Rheumatology; FM, fibromyalgia.

Mentions: Item coding was simplified based on item distribution, content and clinical considerations. The best performing and simplest model found to separate ACR + from ACR– patients included questions 1 to 6 (about physical evaluation) and question 13 (about patient recognizing self in the questions asked in the questionnaire) (Table 3). Further PLS-DA were performed on models combining clinical variables collected at the doctor visit with FibroDetect questions or using weighted scores; this did not substantially increase the ability of the model to discriminate between ACR + and ACR − patients. The FibroDetect score corresponding to the discriminant model described above was calculated as the sum of the 9 item scores, thus ranging from 0 to 9. The FibroDetect score was calculated only if all 9 items were completed; otherwise the score was set to missing. The AUC corresponding to the model was of 0.74, with a sensitivity of 77% and a specificity of 61% for a score threshold of 6 (Figure 3).Table 3


Improving the primary care physicians' decision making for fibromyalgia in clinical practice: development and validation of the Fibromyalgia Detection (FibroDetect®) screening tool.

Baron R, Perrot S, Guillemin I, Alegre C, Dias-Barbosa C, Choy E, Gilet H, Cruccu G, Desmeules J, Margaux J, Richards S, Serra E, Spaeth M, Arnould B - Health Qual Life Outcomes (2014)

ROC curve of the final FibroDetect discriminant model; A: Investigational group (ACR + and ACR-); B: Control groups (FM + and FM-). Sensitivity: probability for ACR+ or FM+ patients to be correctly classified as ACR+ / FM +; 1 – Specificity: probability for ACR– or non-fibromyalgia patients to be incorrectly classified; Diagonal: AUC = 0.5, i.e., predictions are not better than random guessing AUC under the ROC curve: Area Under the Receiver Operating Characteristic curve; ACR, American College of Rheumatology; FM, fibromyalgia.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4221679&req=5

Fig3: ROC curve of the final FibroDetect discriminant model; A: Investigational group (ACR + and ACR-); B: Control groups (FM + and FM-). Sensitivity: probability for ACR+ or FM+ patients to be correctly classified as ACR+ / FM +; 1 – Specificity: probability for ACR– or non-fibromyalgia patients to be incorrectly classified; Diagonal: AUC = 0.5, i.e., predictions are not better than random guessing AUC under the ROC curve: Area Under the Receiver Operating Characteristic curve; ACR, American College of Rheumatology; FM, fibromyalgia.
Mentions: Item coding was simplified based on item distribution, content and clinical considerations. The best performing and simplest model found to separate ACR + from ACR– patients included questions 1 to 6 (about physical evaluation) and question 13 (about patient recognizing self in the questions asked in the questionnaire) (Table 3). Further PLS-DA were performed on models combining clinical variables collected at the doctor visit with FibroDetect questions or using weighted scores; this did not substantially increase the ability of the model to discriminate between ACR + and ACR − patients. The FibroDetect score corresponding to the discriminant model described above was calculated as the sum of the 9 item scores, thus ranging from 0 to 9. The FibroDetect score was calculated only if all 9 items were completed; otherwise the score was set to missing. The AUC corresponding to the model was of 0.74, with a sensitivity of 77% and a specificity of 61% for a score threshold of 6 (Figure 3).Table 3

Bottom Line: Acceptability and applicability were assessed and the tool modified accordingly, then assessed in clinical practice.The FibroDetect included 14 questions assessing patients' pain and fatigue, personal history and attitudes, symptoms and impact on lives.Six questions were retained in the final scoring, demonstrating satisfactory discriminative power between ACR + and ACR- patients with area under the Receiver Operating Characteristic curve of 0.74.

View Article: PubMed Central - PubMed

ABSTRACT

Background: Fibromyalgia diagnosis is a challenging and long process, especially among primary care physicians (PCPs), because of symptom heterogeneity, co-morbidities and clinical overlap with other disorders. The purpose was to develop and validate a screening tool in French (FR), German (DE) and English (UK) to help PCPs identify patients with fibromyalgia.

Methods: The FibroDetect questionnaire was simultaneously developed in FR, DE and UK based on information obtained from a literature review, focus groups conducted with clinicians, and face-to-face interviews with fibromyalgia patients (FR, DE and UK, n = 23). The resulting tool was comprehension-tested in patients with diagnosed or suspected fibromyalgia (n = 3 and n = 2 in each country, respectively). Acceptability and applicability were assessed and the tool modified accordingly, then assessed in clinical practice. A scoring method was created using an iterative process based on statistical and clinical considerations with American College of Rheumatology + (ACR+) patients and ACR- patients (n = 276), and validated with fibromyalgia and non-fibromyalgia patients (n = 312).

Results: The FibroDetect included 14 questions assessing patients' pain and fatigue, personal history and attitudes, symptoms and impact on lives. Six questions were retained in the final scoring, demonstrating satisfactory discriminative power between ACR + and ACR- patients with area under the Receiver Operating Characteristic curve of 0.74. The predictive accuracy of the tool increased to 0.86 for fibromyalgia and non-fibromyalgia patient detection, with a sensitivity of 90% and a specificity of 67% for a cut-off of 6 on the score.

Conclusions: The FibroDetect is a self-administered tool that can be used as a screening classification surrogate to the ACR criteria in primary care settings to help PCPs detect potential fibromyalgia patients among a population complaining of chronic widespread pain.

Show MeSH
Related in: MedlinePlus