Toward a valid definition of gout flare: Results of consensus exercises using delphi methodology and cognitive mapping

100

Objective. To evaluate the efficacy and safety of the interleukin-1 inhibitor rilonacept (interleukin-1 Trap) for gout flare prevention during initiation of uric acid-lowering therapy (ULT). Methods. In total, 241 adult patients with gout, >2 gout flares within the past year, and a serum urate level >7.5 mg/dl were initiated on allopurinol 300 mg daily and randomly allocated in a 1:1:1 ratio to receive 16 once-weekly subcutaneous injections of placebo, rilonacept 80 mg, or rilonacept 160 mg, with a double (loading) dose on day 1. Allopurinol was titrated to achieve a serum urate level of <6.0 mg/dl. The study was powered for the primary efficacy end point, the number of gout flares per patient through week 16.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Randomization and Interventionsmentioning

confidence: 99%

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Rilonacept (interleukin‐1 trap) for prevention of gout flares during initiation of uric acid–lowering therapy: Results from a phase III randomized, double‐blind, placebo‐controlled, confirmatory efficacy study

Schumacher¹,

Evans²,

Saag³

et al. 2012

100

“…However, these clinical surrogates have not accurately identified gout flares, and potentially underestimate or overestimate the rates of gout flares. The lack of a standard definition of gout flare in observational studies demonstrates the intrinsic difficulty of flare identification (8). Therefore, better methods need to be investigated to identify gout flares.…”

Section: Introductionmentioning

confidence: 99%

Using Natural Language Processing and Machine Learning to Identify Gout Flares From Electronic Clinical Notes

Zheng

Rashid

et al. 2014

Objective. Gout flares are not well documented by diagnosis codes, making it difficult to conduct accurate database studies. We implemented a computer-based method to automatically identify gout flares using natural language processing (NLP) and machine learning (ML) from electronic clinical notes. Methods. Of 16,519 patients, 1,264 and 1,192 clinical notes from 2 separate sets of 100 patients were selected as the training and evaluation data sets, respectively, which were reviewed by rheumatologists. We created separate NLP searches to capture different aspects of gout flares. For each note, the NLP search outputs became the ML system inputs, which provided the final classification decisions. The note-level classifications were grouped into patient-level gout flares. Our NLP؉ML results were validated using a gold standard data set and compared with the claims-based method used by prior literatures. Results. For 16,519 patients with a diagnosis of gout and a prescription for a urate-lowering therapy, we identified 18,869 clinical notes as gout flare positive (sensitivity 82.1%, specificity 91.5%): 1,402 patients with >3 flares (sensitivity 93.5%, specificity 84.6%), 5,954 with 1 or 2 flares, and 9,163 with no flare (sensitivity 98.5%, specificity 96.4%). Our method identified more flare cases (18,869 versus 7,861) and patients with >3 flares (1,402 versus 516) when compared to the claims-based method. Conclusion. We developed a computer-based method (NLP and ML) to identify gout flares from the clinical notes. Our method was validated as an accurate tool for identifying gout flares with higher sensitivity and specificity compared to previous studies.

“…Gout‐related variables included symptoms duration, classification according to the 1977 preliminary American College of Rheumatology (ACR) criteria 12, disease‐related comorbidities, previous and current treatment for gout, swollen and tender joint counts on 66 of 68 joints, measurement of tophi (count and tape measure of all the clinically evaluable tophi) 13, 0–10 on a visual analog scale (VAS) for pain, patient's global assessment of disease activity (PtGA), general health and physician's assessment of response to treatment (PhGA), flare occurrence (patient‐reported acute and significant worsening of joint pain) 14, and sUA levels. All patients completed the Italian versions of the Health Assessment Questionnaire (HAQ), the Short‐Form 36 (SF‐36) health survey, and the Gout Impact Scale (GIS) 15, 16, 17.…”

Section: Methodsmentioning

confidence: 99%

Development and First Validation of a Disease Activity Score for Gout

Scirè

Carrara

Viroli

et al. 2016

ObjectiveTo develop a new composite disease activity score for gout and provide its first validation.MethodsDisease activity has been defined as the ongoing presence of urate deposits that lead to acute arthritis and joint damage. Every measure for each Outcome Measures in Rheumatology core domain was considered. A 3‐step approach (factor analysis, linear discriminant analysis, and linear regression) was applied to derive the Gout Activity Score (GAS). Decision to change treatment or 6‐month flare count were used as the surrogate criteria of high disease activity. Baseline and 12‐month followup data of 446 patients included in the Kick‐Off of the Italian Network for Gout cohort were used. Construct‐ and criterion‐related validity were tested. External validation on an independent sample is reported.ResultsFactor analysis identified 5 factors: patient‐reported outcomes, joint examination, flares, tophi, and serum uric acid (sUA). Discriminant function analysis resulted in a correct classification of 79%. Linear regression analysis identified a first candidate GAS including 12‐month flare count, sUA, visual analog scale (VAS) of pain, VAS global activity assessment, swollen and tender joint counts, and a cumulative measure of tophi. Alternative scores were also developed. The developed GAS demonstrated a good correlation with functional disability (criterion validity) and discrimination between patient‐ and physician‐reported measures of active disease (construct validity). The results were reproduced in the external sample.ConclusionThis study developed and validated a composite measure of disease activity in gout. Further testing is required to confirm its generalizability, responsiveness, and usefulness in assisting with clinical decisions.