scholarly journals FRI0018 USING SELF-REPORTED OUTCOMES TO DETECT NEW-ONSET FLARE IN A REAL-WORLD STUDY OF PARTICIPANTS WITH RHEUMATOID ARTHRITIS - INTERIM RESULTS FROM THE DIGITAL TRACKING OF ARTHRITIS LONGITUDINALLY (DIGITAL) STUDY

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 580-581 ◽  
Author(s):  
V. S. Haynes ◽  
J. Curtis ◽  
F. Xie ◽  
I. Lipkovich ◽  
H. Zhao ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Test Data ◽  
Training Data ◽  
Training Dataset ◽  
Main Study ◽  
Passive Data ◽  
Digital Tracking ◽  
Flare Instrument ◽  
Daily Pain ◽  
New Onset

Background:Patients with rheumatoid arthritis (RA) experience fluctuating symptoms, increased pain, decreased function and variable quality of life; such changes often occur between visits to clinicians. Digital Tracking of Arthritis Longitudinally (DIGITAL) study2is evaluating the use of electronically captured patient-reported outcomes (ePRO) and passive data collection from a Fitbit device to identify disease worsening in a real-world study of participants (pts) with RA.Objectives:Evaluate agreement between self-reported new-onset flare and ePROs in an interim analysis from DIGITAL using a classification model.Methods:Members of the ArthritisPower registry with RA were invited to participate in DIGITAL. Pts who successfully completed a two-week Lead-in period entered the Main Study in which they wore a smartwatch and provided daily (pain and fatigue numeric rating scales (NRS)) and weekly ePROs, including the OMERACT RA Flare Questionnaire (FLARE) and PROMIS measures. This interim analysis is of ePRO data from pts who completed at least 30 days of the Main Study. A “Yes” response to the FLARE item, “Are you having a flare now?” identified flare. For modeling association between new-onset flare and ePRO, the dataset was split into training (the first 30 days of the Main Study) and test data (Day 31 and following). Within each dataset, repeated binary outcomes (Flare/No Flare) per pt were defined each week. To focus on new-onset flare, within each dataset, outcomes for patient weeks for which flare was present in the previous week were excluded.Candidate variables for the model included baseline and current FLARE score (0-50 scale) and each of its 5 items, daily pain, daily fatigue, and several PROMIS weekly instruments and their lagged values (last week or last 6 days for daily). ‘Baseline’ was calculated in non-flare weeks. Training data was used for logistic regression model selection combining clinical expertise with backward elimination. Performance of the final model was evaluated using test data.Results:The training data was composed of outcomes from 128 pts who reported 388 weekly flare assessments as no flare or onset flare over 2800 days during the first month of the Main Study. Of pts in the training dataset, 92.2% were female, 87.5% white, with mean age (SD) 52.7 (11.0) and years since RA diagnosis 10.4 (10.3); 62.5% were on a biologic. Among those in the training dataset, 58 flare outcomes occurred in 50 (39.1%) unique pts.The test data comprised outcomes from 123 pts who reported 442 weekly flare assessments as no flare or onset flare over 3366 days in which 64 flare outcomes occurred, and primarily included continued observations from pts who contributed to the training dataset.The best-performing model to classify flare in training data included the current and baseline FLARE instrument activity question (i.e. “Considering how active your rheumatoid arthritis has been, how much difficulty have you had when taking part in activities such as work, family life, social events that are typical for you during the last week”), current daily pain, and baseline daily pain average and standard deviation. In test data, this model had an area under the receiver operator curve of 0.81 (Figure). At a cut point requiring specificity to be ≥0.80, sensitivity to detect flare was 0.62 and overall accuracy was 0.78.Conclusion:New-onset flare is common among RA patients, and the FLARE instrument and daily pain scores appear effective to classify it. Evaluation of passive data as a proxy for self-reported new-onset flare is ongoing.References:[1]Bartlett SJ, et al. JRheumatol, 2017;44:1536-43.[2]Nowell WB, et al. JMIR Res Protoc, 2019;8:e14665.Disclosure of Interests:Virginia S. Haynes Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Fenglong Xie: None declared, Ilya Lipkovich Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Hong Zhao: None declared, Carol L. Kannowski Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Jiat-Ling Poon Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Kelly Gavigan: None declared, David Curtis: None declared, Sandra K. Nolot Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, W. Benjamin Nowell: None declared

THU0564 PARTICIPANT ENGAGEMENT IN AN ARTHRITISPOWER REAL-WORLD STUDY TO CAPTURE SMARTWATCH AND PATIENT-REPORTED OUTCOME DATA AMONG RHEUMATOID ARTHRITIS PATIENTS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 523-524
Author(s):  
W. B. Nowell ◽  
J. Curtis ◽  
F. Xie ◽  
H. Zhao ◽  
D. Curtis ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Real World ◽  
Rating Scales ◽  
Patient Reported Outcome ◽  
Time Of Day ◽  
Main Study ◽  
Time Zone ◽  
Activity Data ◽  
Patient Reported ◽  
Passive Data

Background:Clear characterization of how different types of patient-generated data reflect patient experience is needed to guide integration of electronic patient-reported outcome (ePRO) measures and biometrics in generating real-word evidence (RWE) related to rheumatoid arthritis (RA).Objectives:To characterize the level of participant (pt) engagement/adherence and data completeness in an ongoing study of 250 RA pts enrolled in the Digital Tracking of Arthritis Longitudinally (DIGITAL) study1of the ArthritisPower real-world registry.Methods:ArthritisPower pts with RA were invited to join a digital RWE study with 14-day lead-in and 12-week main study period. In the lead-in, pts were required to electronically complete: a) two daily single-item Pain and Fatigue numeric rating scales and b) longer weekly sets of ePROs. Successful completers of the lead-in were mailed a smartwatch (Fitbit Versa) and study materials. The smartwatch collected activity, heart rate, and sleep duration/quality biosensor data; a study-specific customization of the ArthritisPower mobile application collected ePROs. The main study period included automated and manual reminders/prompts about completing ePROs, wearing the smartwatch and regularly syncing it. Study coordinators monitored pt data and contacted pts via email, text and/or phone to resolve adherence issues during the conduct of the study based on pre-determined rules triggering pt contact. Rules were based chiefly on consecutive spans of missing data. Pts were considered adherent in giving complete data for each week if providing (1) daily ePROs for ≥5 of 7 days/week, (2) weekly ePROs and (3) ≥80% of synced activity data for ≥5 of 7 days/week. Composite adherence for the first month of the main study period required meeting >70% weekly adherence parameters during the first 30 days, ie completing daily ePROs for ≥5 of 7 days/week, weekly ePROs ≥3 of 4 weeks and ≥80% of synced activity data for ≥5 of 7 days/week.Results:As of December 2019, 170 ArthritisPower members enrolled and completed at least 30 days of the main study period; 92.9% female with mean (SD) age 52.5 (10.7) and 10.5 (10.4) years since diagnosis. The overall conversion rate from initial interest to successful completion of the lead-in period was 49.0%. Pts who advanced to the main study were significantly more likely than those who did not to be currently employed (52.9% vs. 41.8%, p=0.038) and be on biologic DMARD monotherapy (64.7% vs. 47.5%, p=0.001). Overall, daily ePRO data had the lowest adherence with 70.0% of pts providing >70% of the requested data consistently across the first 30 days of the main study period (Figure 1). Composite adherence was met by 66.5% of pts. The most common time of day to provide ePRO data was morning, in the hours around scheduled app and email notifications at 10 a.m. in pt’s local time zone. Activity data had the highest adherence and persistence, with 92.9% of pts providing 80% or more of activity data for each 24-hour period in the first 30 days (Figures 1 & 2). Observed weekly adherence did not decline over time. Of 5100 possible person days in the study at day 30, we observed 643 days (91.0% of actual to maximum possible total patient days) where activity data was provided for at least 80% of the 24-hour period.Conclusion:RWE studies involving passive data collection in RA require pt-centric implementation and design to minimize pt burden, promote longitudinal engagement and maximize adherence. Passive data capture via activity trackers such as smartwatches, along with regular contact such as automated reminders, may facilitate greater pt adherence in providing longitudinal data for clinical trials.References:[1]Nowell WB, et al. JMIR Res Protoc. 2019;8(9):e14665.Disclosure of Interests:W. Benjamin Nowell: None declared, Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Fenglong Xie: None declared, Hong Zhao: None declared, David Curtis: None declared, Kelly Gavigan: None declared, Shilpa Venkatachalam: None declared, Laura Stradford: None declared, Jessica Boles: None declared, Justin Owensby: None declared, Cassie Clinton: None declared, Ilya Lipkovich Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Amy Calvin Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Virginia S. Haynes Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company

scholarly journals Predicting Limit-Setting Behavior of Gamblers Using Machine Learning Algorithms: A Real-World Study of Norwegian Gamblers Using Account Data

2019 ◽  
Author(s):  
Michael Auer ◽  
Mark D. Griffiths
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Test Data ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Responsible Gambling ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Training Dataset ◽  
Limit Setting

AbstractPlayer protection and harm minimization have become increasingly important in the gambling industry along with the promotion of responsible gambling (RG). Among the most widespread RG tools that gaming operators provide are limit-setting tools that help players limit the amount of time and/or money they spend gambling. Research suggests that limit-setting significantly reduces the amount of money that players spend. If limit-setting is to be encouraged as a way of facilitating responsible gambling, it is important to know what variables are important in getting individuals to set and change limits in the first place. In the present study, 33 variables assessing the player behavior among Norsk Tipping clientele (N = 70,789) from January to March 2017 were computed. The 33 variables which reflect the players’ behavior were then used to predict the likelihood of gamblers changing their monetary limit between April and June 2017. The 70,789 players were randomly split into a training dataset of 56,532 and an evaluation set of 14,157 players (corresponding to an 80/20 split). The results demonstrated that it is possible to predict future limit-setting based on player behavior. The random forest algorithm appeared to predict limit-changing behavior much better than the other algorithms. However, on the independent test data, the random forest algorithm’s accuracy dropped significantly. The best performance on the test data along with a small decrease in accuracy in comparison to the training data was delivered by the gradient boost machine learning algorithm. The most important variables predicting future limit-setting using the gradient boost machine algorithm were players receiving feedback that they had reached 80% of their personal monthly global loss limit, personal monthly loss limit, the amount bet, theoretical loss, and whether the players had increased their limits in the past. With the help of predictive analytics, players with a high likelihood of changing their limits can be proactively approached.

scholarly journals Improved Training of CAE-Based Defect Detectors Using Structural Noise

2021 ◽  
Vol 11 (24) ◽  
pp. 12062
Author(s):  
Reina Murakami ◽  
Valentin Grave ◽  
Osamu Fukuda ◽  
Hiroshi Okumura ◽  
Nobuhiko Yamaguchi
Keyword(s):  
Test Data ◽  
Gaussian Noise ◽  
Visual Inspection ◽  
Noisy Data ◽  
Training Data ◽  
Noise Factor ◽  
Training Dataset ◽  
Structural Noise

Appearances of products are important to companies as they reflect the quality of their manufacture to customers. Nowadays, visual inspection is conducted by human inspectors. This research attempts to automate this process using Convolutional AutoEncoders (CAE). Our models were trained using images of non-defective parts. Previous research on autoencoders has reported that the accuracy of image regeneration can be improved by adding noise to the training dataset, but no extensive analyse of the noise factor has been done. Therefore, our method compares the effects of two different noise patterns on the models efficiency: Gaussian noise and noise made of a known structure. The test datasets were comprised of “defective” parts. Over the experiments, it has mostly been observed that the precision of the CAE sharpened when using noisy data during the training phases. The best results were obtained with structural noise, made of defined shapes randomly corrupting training data. Furthermore, the models were able to process test data that had slightly different positions and rotations compared to the ones found in the training dataset. However, shortcomings appeared when “regular” spots (in the training data) and “defective” spots (in the test data) partially, or totally, overlapped.

P–165 Using Artificial Intelligence to Classify Embryo Shape: An International Perspective

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
R Hariharan ◽  
P He ◽  
C Hickman ◽  
J Chambost ◽  
C Jacques ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Test Data ◽  
Focal Plane ◽  
Learning Algorithm ◽  
Cohort Analysis ◽  
Training Data ◽  
Training Dataset ◽  
Blind Test ◽  
Cell Embryo

Abstract Study question Is a pre-trained machine learning algorithm able to accurately detect cellular arrangement in 4-cell embryos from a different continent? Summary answer Artificial Intelligence (AI) analysis of 4-cell embryo classification is transferable across clinics globally with 79% accuracy. What is known already Previous studies observing four-cell human embryo configurations have demonstrated that non-tetrahedral embryos (embryos in which cells make contact with fewer than 3 other cells) are associated with compromised blastulation and implantation potential. Previous research by this study group has indicated the efficacy of AI models in classification of tetrahedral and non-tetrahedral embryos with 87% accuracy, with a database comprising 2 clinics both from the same country (Brazil). This study aims to evaluate the transferability and robustness of this model on blind test data from a different country (France). Study design, size, duration The study was a retrospective cohort analysis in which 909 4-cell embryo images (“tetrahedral”, n = 749; “non-tetrahedral”, n = 160) were collected from 3 clinics (2 Brazilian, 1 French). All embryos were captured at the central focal plane using Embryoscope™ time-lapse incubators. The training data consisted solely of embryo images captured in Brazil (586 tetrahedral; 87 non-tetrahedral) and the test data consisted exclusively of embryo images captured in France (163 tetrahedral; 72 non-tetrahedral). Participants/materials, setting, methods The embryo images were labelled as either “tetrahedral” or “non-tetrahedral” at their respective clinics. Annotations were then validated by three operators. A ResNet–50 neural network model pretrained on ImageNet was fine-tuned on the training dataset to predict the correct annotation for each image. We used the cross entropy loss function and the RMSprop optimiser (lr = 1e–5). Simple data augmentations (flips and rotations) were used during the training process to help counteract class imbalances. Main results and the role of chance Our model was capable of classifying embryos in the blind French test set with 79% accuracy when trained with the Brazilian data. The model had sensitivity of 91% and 51% for tetrahedral and non-tetrahedral embryos respectively; precision was 81% and 73%; F1 score was 86% and 60%; and AUC was 0.61 and 0.64. This represents a 10% decrease in accuracy compared to when the model both trained and tested on different data from the same clinics. Limitations, reasons for caution Although strict inclusion and exclusion criteria were used, inter-operator variability may affect the pre-processing stage of the algorithm. Moreover, as only one focal plane was used, ambiguous cases were interpoloated and further annotated. Analysing embryos at multiple focal planes may prove crucial in improving the accuracy of the model. Wider implications of the findings: Though the use of machine learning models in the analysis of embryo imagery has grown in recent years, there has been concern over their robustness and transferability. While previous results have demonstrated the utility of locally-trained models, our results highlight the potential for models to be implemented across different clinics. Trial registration number Not applicable

scholarly journals Pengembangan Sistem Penilaian Kematangan Tandan Buah Segar Kelapa Sawit menggunakan Citra 680 dan 750 NM

2020 ◽  
Vol 7 (2) ◽  
pp. 379
Author(s):  
Agung Wahyu Setiawan ◽  
Alfie R. Ananda
Keyword(s):  
Support Vector Machine ◽  
Test Data ◽  
Palm Oil ◽  
Image Contrast ◽  
Human Vision ◽  
Training Data ◽  
Training Dataset ◽  
Support Vector ◽  
Light Sources ◽  
Maturity Level

<p class="Abstrak">Salah satu permasalahan utama dalam industri kelapa sawit adalah proses sortasi Tandan Buah Segar (TBS) di pabrik kelapa sawit. Parameter yang digunakan dalam sortasi TBS adalah jumlah brondolan kelapa sawit. Pada saat ini, sortasi dilakukan oleh <em>grader</em> yang bersifat subyektif dan sering kali tidak konsisten. Hal ini terjadi karena keterbatasan penglihatan dan kemampuan manusia untuk mengolah informasi jumlah brondolan setiap TBS dalam waktu yang terbatas. Oleh karena itu, pada penelitian ini dikembangkan sistem penilaian kematangan TBS kelapa sawit berbasis spektroskopi dan nilai kontras citras. Sumber cahaya yang digunakan pada penelitian ini adalah lampu berjenis <em>Light-emitting Diode</em> (LED) dengan panjang gelombang 680 dan 750 nm. Akuisisi citra TBS dilakukan dengan menggunakan kamera DSLR yang telah dimodifikasi. sehingga diperoleh dua citra TBS pada panjang gelombang 680 dan 750 nm. Kemudian, dilakukan perhitungan nilai kontras kedua citra tersebut. Dalam penelitian ini, terdapat 24 TBS yang digunakan sebagai data latih, dengan komposisi 10 TBS matang dan 14 TBS mentah. Data uji yang digunakan berjumlah 77 TBS yang terdiri dari 38 matang dan 39 mentah. Pada penelitian ini, <em>Support Vector Machine</em> (SVM) digunakan sebagai metode klasifikasi. Akurasi data latih yang diperoleh adalah 66,67%. Sedangkan akurasi data uji dari sistem yang dikembangkan dalam penelitian ini adalah 57,14%. Hasil yang diperoleh ini masih perlu diperbaiki untuk meningkatkan akurasi sistem dengan cara menambah jumlah data, baik data latih maupun uji, serta menggunakan pembelajaran mesin.</p><p class="Abstrak"> </p><p class="Abstrak"><strong><em>Abstract</em></strong></p><p class="Abstrak"><em>One of the main problems in the palm oil industry is the grading of Fresh Fruit Bunches (FFB) in the palm oil mills. The parameter used for the process is the number of fruitlets detached from the bunch. Nowadays, the FFB grading is conducted by graders which is subjective and often inconsistent due to the limitation of human vision and ability to process information on the number of fruitlets detached per FFB in a very limited time. Therefore, this study developed a grading system to assess and estimate the FFB maturity based on spectroscopy and image contrast value. From the literature review, visible light and NIR spectrum in 680 and 780 nm can be used as light sources to detect the maturity level of FFB. DSLR camera is used to acquire the FFB image. Using this scheme, two FFB images in 680 and 750 nm are obtained. The next process is to calculate the image contrast. In this research, there are 24 FFB that are used as training data that consists of 10 ripe and 14 unripe. A total of 77 FFB are used as test data that consists of 38 ripe and 39 unripe. Support Vector Machine (SVM) is used in this research to classify the maturity level of FFB. The accuracy of the training dataset is 66.67%. Meanwhile, the accuracy of the test data is 57.14%. Future works will focus on enhancing accuracy of the system through increasing the number of training and testing data using machine learning.</em></p>

DeepSSPred: A Deep Learning Based Sulfenylation site predictor via a novel n-segmented optimize federated feature encoder

2020 ◽  
Vol 27 ◽  
Author(s):  
Zaheer Ullah Khan ◽  
Dechang Pi
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Research Work ◽  
Training Data ◽  
Training Dataset ◽  
Validation Dataset ◽  
Cytokine Signaling ◽  
Minority Class ◽  
Independent Dataset ◽  
Feature Encoding

Background: S-sulfenylation (S-sulphenylation, or sulfenic acid) proteins, are special kinds of post-translation modification, which plays an important role in various physiological and pathological processes such as cytokine signaling, transcriptional regulation, and apoptosis. Despite these aforementioned significances, and by complementing existing wet methods, several computational models have been developed for sulfenylation cysteine sites prediction. However, the performance of these models was not satisfactory due to inefficient feature schemes, severe imbalance issues, and lack of an intelligent learning engine. Objective: In this study, our motivation is to establish a strong and novel computational predictor for discrimination of sulfenylation and non-sulfenylation sites. Methods: In this study, we report an innovative bioinformatics feature encoding tool, named DeepSSPred, in which, resulting encoded features is obtained via n-segmented hybrid feature, and then the resampling technique called synthetic minority oversampling was employed to cope with the severe imbalance issue between SC-sites (minority class) and non-SC sites (majority class). State of the art 2DConvolutional Neural Network was employed over rigorous 10-fold jackknife cross-validation technique for model validation and authentication. Results: Following the proposed framework, with a strong discrete presentation of feature space, machine learning engine, and unbiased presentation of the underline training data yielded into an excellent model that outperforms with all existing established studies. The proposed approach is 6% higher in terms of MCC from the first best. On an independent dataset, the existing first best study failed to provide sufficient details. The model obtained an increase of 7.5% in accuracy, 1.22% in Sn, 12.91% in Sp and 13.12% in MCC on the training data and12.13% of ACC, 27.25% in Sn, 2.25% in Sp, and 30.37% in MCC on an independent dataset in comparison with 2nd best method. These empirical analyses show the superlative performance of the proposed model over both training and Independent dataset in comparison with existing literature studies. Conclusion : In this research, we have developed a novel sequence-based automated predictor for SC-sites, called DeepSSPred. The empirical simulations outcomes with a training dataset and independent validation dataset have revealed the efficacy of the proposed theoretical model. The good performance of DeepSSPred is due to several reasons, such as novel discriminative feature encoding schemes, SMOTE technique, and careful construction of the prediction model through the tuned 2D-CNN classifier. We believe that our research work will provide a potential insight into a further prediction of S-sulfenylation characteristics and functionalities. Thus, we hope that our developed predictor will significantly helpful for large scale discrimination of unknown SC-sites in particular and designing new pharmaceutical drugs in general.

scholarly journals POS0517 A LONGITUDINAL STUDY OF SARCOPENIA, LOCOMOTIVE SYNDROME, AND FRAILTY IN PATIENTS WITH RHEUMATOID ARTHRITIS: FROM THE CHIKARA STUDY

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 492.2-492
Author(s):  
K. Mandai ◽  
M. Tada ◽  
Y. Yamada ◽  
T. Koike ◽  
T. Okano ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Disease Activity ◽  
Multivariate Logistic Regression Analysis ◽  
Locomotive Syndrome ◽  
Chi Squared ◽  
Higher Doses ◽  
Rheumatoid Arthritis Data ◽  
Onset Rate ◽  
New Onset

Background:Rheumatoid arthritis (RA) patients have a high frequency of sarcopenia, and they commonly have reduced physical function. We previously reported that the prevalence of sarcopenia was 28%, that of frailty was 18.9%, and that of pre-frailty was 38.9% in RA patients1,2, and 13.2% of RA patients developed sarcopenia within a year 3.Objectives:To investigate the risk factors for new onset of sarcopenia, locomotive syndrome, and frailty in patients with RA and the course of each disease.Methods:Two-year follow-up data from the rural group of the prospective, observational CHIKARA study were used. Sarcopenia was diagnosed using the criteria of the Asian Working Group for Sarcopenia 2014, locomotive syndrome was diagnosed using locomotive 5, and frailty was diagnosed using the basic checklist. New onset of the disease over the 2-year follow-up period was studied, excluding cases that had the disease at baseline. Improvement was defined as cases with disease at baseline that no longer met the diagnostic criteria after 2 years. Differences in the characteristics of each disease were tested using the Chi-squared test and the paired t-test.Results:The 81 patients with RA (82.7% female) had mean age 66.9±11.5 years, mean DAS28-ESR 2.9±1.2, methotrexate use in 81.5% (with a dose of 9.9±2.7 mg/week), and glucocorticoid (GC) use in 22.2% (with a dose of 3.1±1.7 mg/week). The baseline prevalence was 44.4% for sarcopenia, 35.8% for locomotive syndrome, and 25.9% for frailty, and the new onset rate was 4.4% for sarcopenia, 15.4% for locomotive syndrome, and 13.3% for frailty. Of the patients with each disease at baseline, 36.1% had sarcopenia, 20.7% had locomotive syndrome, and 33.3% had frailty, and of those with each disease at 2 years, 36.1% had sarcopenia, 20.7% had locomotive syndrome, and 33.3% had frailty. The new onset sarcopenia and locomotive syndrome groups had significantly higher rates of GC use (p=0.036, p=0.007, paired t-test) and significantly higher doses (p=0.01, p=0.001, paired t-test) than the groups without new onset sarcopenia and locomotive syndrome. High baseline disease activity was an independent predictor of new onset of locomotive syndrome on multivariate logistic regression analysis (OR=3.21, p=0.015).Conclusion:The new onset rates at 2 years were 4.4% for sarcopenia, 15.4% for locomotive syndrome, and 13.3% for frailty. In the new onset sarcopenia and locomotive syndrome groups, both GC use and dosage were significantly higher.References:[1]Tada M, et al. Matrix metalloprotease 3 is associated with sarcopenia in rheumatoid arthritis - results from the CHIKARA study. Int J Rheum Dis. 2018 Nov;21(11):1962-1969.[2]Tada M, et al. Correlation between frailty and disease activity in patients with rheumatoid arthritis: Data from the CHIKARA study. Geriatr Gerontol Int. 2019 Dec;19(12):1220-1225.[3]Yamada Y, et al. Glucocorticoid use is an independent risk factor for developing sarcopenia in patients with rheumatoid arthritis: from the CHIKARA study. Clin Rheumatol. 2020 Jun;39(6):1757-1764.Disclosure of Interests:None declared

scholarly journals SAT0032 INCIDENCE OF RHEUMATOID ARTHRITIS IN PATIENTS WITH NEW ONSET OF MUSCULOSKELETAL SYMPTOMS AND ANTI-CPP POSITIVITY COMPARED TO ANTI-CPP NEGATIVE PATIENTS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 946.1-946
Author(s):  
S. Dauth ◽  
M. Köhm ◽  
T. Oberwahrenbrock ◽  
U. Henkemeier ◽  
T. Rossmanith ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Point Of Care ◽  
Early Arthritis ◽  
Clinical Parameters ◽  
Research Support ◽  
Point Of Care Test ◽  
Test Elisa ◽  
Patient Reported ◽  
New Onset

Background:Rheumatoid Arthritis (RA) is a chronic inflammatory joint disease. Strategies for its early detection and diagnosis are of high importance as prompt treatment improves clinical and structural outcome. Autoantibodies against cyclic citrullinated proteins (anti-CCP) have been associated with RA-development. Non-specific musculoskeletal (nsMSK) symptoms are often described prior to RA development. Majority of patients with nsMSK symptoms present to their general practice (GP) first. Studies of early arthritis cohorts have shown that many early arthritis patients cannot be accurately diagnosed at their first visit and are often referred as undifferentiated arthritis patients.Objectives:To evaluate the incidence of anti-CCP positivity in patients with new onset of nsMSK symptoms and the incidence of RA in these patients over a 3-year follow-up period compared to anti-CPP negative patients.Methods:In this prospective study (PANORA), 978 patients with new onset of nsMSK symptoms were included in 77 GP sites in Germany. Patients with a positive anti-CCP rapid-test (CCPoint®) were referred to Rheumatology Department (RD) for rheumatological assessment, RA-evaluation and an anti-CCP validation test (ELISA). ELISA anti-CCP positive patients without RA were monitored every 6 months for a total follow-up of 36 months or until RA-diagnosis. Patients with a negative anti-CPP result (CCPoint® or ELISA) are followed up with a questionnaire after 1 and 3 y.Results:From 978 included patients, 105 (10.7%) were CCPoint® positive. 96 were tested with ELISA and 27 (28.1%) were confirmed anti-CCP positive. 9 (33.3%) were diagnosed with RA at the first RD visit (study visit 2); 4 further patients were diagnosed with RA during the follow-up (FU) period so far. Overall, 48.1% of ELISA-positive (ELISA+) patients were diagnosed with RA up to now; 11 ELISA+ patients are still in the FU period of the study. Of the 868 CCPoint® negative patients, currently, 282 have filled out a 1-year FU questionnaire; 3.5% of those reported a RA diagnosis (Table 1). As expected, clinical parameters at V2 (e.g. CRP, swollen and tender joint count) were worse in the ELISA+/RA+ group compared to the ELISA-/RA- group, but no obvious differences were detected between ELISA+ patients who were diagnosed with RA during the FU period (after V2) and ELISA-/RA- patientsTable 1.Number and percentage of patients with a RA diagnosisAnti-CCP statusVisit 2Follow-up*TotalPoint-of-Care Test --3.5% (10 of 282)#3.5% (10 of 282)#Point-of-Care Test + / ELISA -2.9% (2 of 69)0% (0 of 34)#2.9% (2 of 69)Point-of-Care Test + / ELISA +33.3% (9 of 27)14.8% (4 of 27)48.1% (13 of 27)$* 1 year-questionnaire for Point-of-Care Test and ELISA negative patients or every 6 months follow-up for ELISA positive patients;#Patient-reported;$11 patients are still in the follow-up phase of the studyConclusion:Currently, 48.1% of anti-CCP+ (ELISA) patients have received a RA diagnosis, whereas 3.5% of the anti-CCP- (CCPoint®) received a RA diagnosis (patient reported), which underlines, that anti-CCP can be used as a marker to identify high-risk patients in GP setting. While clinical parameters are correlated with the diagnosis of RA, they are not suited for predicting future RA development alone. Anti-CCP, possibly in combination with additional parameters imaging, might increase the likelihood to early diagnose or predict RA development.Figure 1.Study overview: Patient distribution depending on anti-CCP results and RA diagnosis.Disclosure of Interests:Stephanie Dauth Grant/research support from: BMS, Michaela Köhm Grant/research support from: Pfizer, Janssen, BMS, LEO, Consultant of: BMS, Pfizer, Speakers bureau: Pfizer, BMS, Janssen, Novartis, Timm Oberwahrenbrock Grant/research support from: BMS, Ulf Henkemeier: None declared, Tanja Rossmanith Grant/research support from: Janssen, BMS, LEO, Pfizer, Karola Mergenthal Grant/research support from: BMS, Juliana J. Petersen Grant/research support from: BMS, Harald Burkhardt Grant/research support from: Pfizer, Roche, Abbvie, Consultant of: Sanofi, Pfizer, Roche, Abbvie, Boehringer Ingelheim, UCB, Eli Lilly, Chugai, Bristol Myer Scripps, Janssen, and Novartis, Speakers bureau: Sanofi, Pfizer, Roche, Abbvie, Boehringer Ingelheim, UCB, Eli Lilly, Chugai, Bristol Myer Scripps, Janssen, and Novartis, Frank Behrens Grant/research support from: Pfizer, Janssen, Chugai, Celgene, Lilly and Roche, Consultant of: Pfizer, AbbVie, Sanofi, Lilly, Novartis, Genzyme, Boehringer, Janssen, MSD, Celgene, Roche and Chugai

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