Advanced neural networks for classification of MRI in psoriatic arthritis, seronegative, and seropositive rheumatoid arthritis

OBJECTIVES

To evaluate whether neural networks can distinguish between seropositive RA, seronegative RA, and PsA based on inflammatory patterns from hand MRIs and to test how psoriasis patients with subclinical inflammation fit into such patterns.

METHODS

ResNet neural networks were utilized to compare seropositive RA vs PsA, seronegative RA vs PsA, and seropositive vs seronegative RA with respect to hand MRI data. Results from T1 coronal, T2 coronal, T1 coronal and axial fat-suppressed contrast-enhanced (CE), and T2 fat-suppressed axial sequences were used. The performance of such trained networks was analysed by the area under the receiver operating characteristics curve (AUROC) with and without presentation of demographic and clinical parameters. Additionally, the trained networks were applied to psoriasis patients without clinical arthritis.

RESULTS

MRI scans from 649 patients (135 seronegative RA, 190 seropositive RA, 177 PsA, 147 psoriasis) were fed into ResNet neural networks. The AUROC was 75% for seropositive RA vs PsA, 74% for seronegative RA vs PsA, and 67% for seropositive vs seronegative RA. All MRI sequences were relevant for classification, however, when deleting contrast agent-based sequences the loss of performance was only marginal. The addition of demographic and clinical data to the networks did not provide significant improvements for classification. Psoriasis patients were mostly assigned to PsA by the neural networks, suggesting that a PsA-like MRI pattern may be present early in the course of psoriatic disease.

CONCLUSION

Neural networks can be successfully trained to distinguish MRI inflammation related to seropositive RA, seronegative RA, and PsA.

OP0292 CLASSIFICATION OF PSORIATIC ARTHRITIS, SERONEGATIVE RHEUMATOID ARTHRITIS, AND SEROPOSITIVE RHEUMATOID ARTHRITIS USING DEEP LEARNING ON MAGNETIC RESONANCE IMAGING

Background

While MRI evaluation of joints has been primarily used to quantify inflammation at a cross-sectional and longitudinal level, less is known about the potential of MRI in distinguishing different patterns of inflammation in the various forms of arthritis.

Objectives

To evaluate (i) whether deep learning using neural networks can be trained to distinguish between seropositive rheumatoid arthritis (RA+), seronegative RA (RA-), and psoriatic arthritis (PsA) based on structural inflammatory patterns on hand magnetic resonance imaging and (ii) to assess if psoriasis patients with subclinical inflammation fit into such patterns.

Methods

ResNet 3D [1] neural networks were trained to distinguish (i) RA+ vs. PsA, (ii) RA- vs. PsA and (iii) RA+ vs. RA- with respect to hand MRI data. Diagnosis of patients was determined using the following guidelines: ACR/EULAR 2010 [2] for RA and CASPAR [3] for PsA. Results from T1 coronal, T2 coronal, T1 coronal and axial fat suppressed contrast-enhanced (CE) and T2 fat suppressed axial sequences were used. The performance of such trained networks was analyzed by the area-under-the-receiver-operating-characteristic curve (AUROC) with and without imputation of demographic and clinical parameters (Figure 1A). Additionally, the trained networks were applied to psoriasis patients without clinical signs of PsA.

Figure 1.

(A) Neural network combining MR sequences with optional additional clinical data. The prediction for a single case is formed by averaging the prediction of all sequences and the clinical data. (B) Plot of the AUROC for increasing percentages (0.6 – 60%) of training data for the differentiation between RA+ and PsA by the neural network. The light blue area around the dark blue mean indicates the uncertainty measured using a 5-fold cross-validation.

Results

MRI scans from 649 patients (135 RA-, 190 RA+, 177 PsA, 147 psoriasis) were included (Table 1). The AUROC for differentiation between disease entities was 75% (SD 3%) for RA+ vs. PsA, 74% (SD 8%) for RA- vs. PsA, and 67% (6%) for RA+ vs. RA-. All MRI sequences were relevant for classification, however, when deleting CE sequences, the loss of performance was only marginal. The addition of patient-specific data to the networks did not provide significant improvements. Increasing amounts of training data demonstrated improved performance of the networks (Figure 1B). Psoriasis patients were mostly assigned to PsA by the neural networks, suggesting that PsA-like MRI pattern may be present early in the course of psoriatic disease.

Table 1.

Overview of demographic and clinical information.

RA+RA-PsAPsoriasisTotal Number (N)649Number (N)190135177147Age (years), mean±SD56.9±12.660.5±10.356.3±12.049.6±13.8Sex (female/male)126/6493/4292/8571/76BMI (kg/m2), mean±SD26.6±10.527.6 ±9.329.1±11.326.7±6.9Disease duration (years), mean±SD2.6±4.91.3±2.30.8±2.34.2±5.1DAS28, mean±SD3.3±1.33.4±1.23.2±1.3-CRP (mg/L), mean±SD0.9±2.50.7±1.20.5±0.80.5±1.3HAQ, mean±SD0.8±0.60.9±0.80.6±0.60.3±0.4MedicationbDMARD88.46%83.87%81.32%35.01%csDMARD89.52%88.89%80.54%12.28%

Conclusion

Deep learning can be successfully applied to differentiate MRI inflammatory patterns related to RA+, RA-, and PsA. Early changes in psoriasis patients can be recognized by neural networks and are characterized by a pattern that allowed the networks to classify them as PsA.

References

[1]Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh 2018. Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet? In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 6546–6555).

[2]Aletaha D, Neogi T et al. 2010 Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum. 2010 Sep;62(9):2569-81.

[3]Helliwell PS, Taylor WJ. Classification and diagnostic criteria for psoriatic arthritis. Annals of the Rheumatic Diseases 2005;64:ii3-ii8.

Acknowledgements

The study was supported by the Deutsche Forschungsgemeinschaft (DFG-FOR2886 PANDORA and the CRC1181 Checkpoints for Resolution of Inflammation). Additional funding was received by the Bundesministerium für Bildung und Forschung (BMBF; project MASCARA), the ERC Synergy grant 4D Nanoscope, the IMI funded projects HIPPOCRATES and RTCure, the Emerging Fields Initiative MIRACLE of the Friedrich-Alexander-Universität Erlangen-Nürnberg and the Else Kröner-Memorial Scholarship (DS, no. 2019_EKMS.27). Furthermore, infrastructural and hardware support was provided by the d.hip Digital Health Innovation Platform.

Disclosure of Interests

None declared

POS1492-HPR EVALUATION OF A VIRTUAL REALITY-BASED APPLICATION TO EDUCATE HEALTHCARE PROFESSIONALS AND MEDICAL STUDENTS ABOUT INFLAMMATORY ARTHRITIS

Background:

Inflammatory arthritides (IA), such as rheumatoid arthritis or psoriatic arthritis, are disorders that can be difficult to comprehend for health professionals and students in terms of the heterogeneity of clinical symptoms and pathologies. New didactic approaches using innovative technologies such as Virtual Reality (VR) applications could be helpful to demonstrate disease manifestations as well as joint pathologies in a more comprehensive way. However, the potential of using a VR education concept in IA has not yet been evaluated.

Objectives:

We evaluated the feasibility of a VR application to educate healthcare professionals and medical students about IA.

Methods:

We developed a VR application using IA patients data as well as two- and three-dimensional visualized pathological joints from X-ray and computed tomography generated images (1). This VR application (called Rheumality) allows the user to interact with representative arthritic joint and bone pathologies of IA patients (Figure 1 A, B). In a consensus meeting an online questionnaire was designed to collect basic demographic data (age, sex), profession of the participants, and their feedback on the general impression, knowledge gain and potential areas of application of the VR application. The VR application was subsequently tested and evaluated by healthcare professionals (physicians, researchers, and other healthcare professionals) and medical students at predefined events (two annual rheumatology conferences and academic teaching seminars at two sites in Germany).

Results:

125 individuals participated in this study (56% female, 43% male, 1% non-binary). 59% of the participants were between 18-30 years of age, 18% between 31-40, 10% between 41-50, 8% between 51-60 and 5% were between 61-70. Of the participants, 50 were physicians, five researchers and four other health care professionals, the remaining were medical students (66). The participants rated the application as excellent (Figure 1 C, D), the mean rating of the VR application was 9.0/10 (SD 1.2) and many participants would recommend the use of the application, with a mean recommendation score of 3.2/4 (SD 1.1). A large majority stated that the presentation of pathological bone formation improves the understanding of the disease (120 out of 125 (96%)).

Conclusion:

The data show that IA-targeting innovative teaching approaches based on VR technology are feasible. The use of VR applications enables a disease-specific knowledge visualization and may add a new educational pillar to conventional educational approaches.

References:

[1]Kleyer A et al. Z Rheumatol 78, 112–115 (2019)

Figure 1.

Illustration of the VR application and evaluation results

Two- and three-dimensional visualized pathological joints from X-ray and computed tomography generated images in a patient with long-standing (inadequately treated) RA (A) and a patient with early RA (B). Overall rating (range 0-10) on the VR application divided into four different professional subgroups (C); recommendations of VR application in the four different professional subgroups (D). HC, health care professionals; Boxplot explanation: Crossbars represent medians, whiskers represent 5-95 percentiles (points below the whiskers are drawn as individual points), box always extends from the 25th to 75th percentiles (hinges of the plot).

Disclosure of Interests:

Philipp Klemm Consultant of: Lilly Deutschland GmbH, Arnd Kleyer Speakers bureau: Lilly Deutschland GmbH, Consultant of: Lilly Deutschland GmbH, Grant/research support from: Lilly Deutschland GmbH, Koray Tascilar: None declared, Louis Schuster: None declared, Timo Meinderink: None declared, Florian Steiger: None declared, Uwe Lange: None declared, Ulf Müller-Ladner: None declared, Johannes Knitza Speakers bureau: Lilly Deutschland GmbH, Philipp Sewerin Speakers bureau: Lilly Deutschland GmbH, Paid instructor for: Lilly Deutschland GmbH, Johanna Mucke Consultant of: Lilly Deutschland GmbH, Alexander Pfeil Speakers bureau: Lilly Deutschland GmbH, Paid instructor for: Lilly Deutschland GmbH, Consultant of: Lilly Deutschland GmbH, Georg Schett: None declared, Fabian Hartmann Consultant of: Lilly Deutschland GmbH, Axel Hueber Consultant of: Lilly Deutschland GmbH, Grant/research support from: Lilly Deutschland GmbH, David Simon Speakers bureau: Lilly Deutschland GmbH, Paid instructor for: Lilly Deutschland GmbH, Consultant of: Lilly Deutschland GmbH, Grant/research support from: Lilly Deutschland GmbH

OP0145 DIFFERENTIAL DIAGNOSIS OF RA AND PSA USING NEURAL NETWORKS ON THREE-DIMENSIONAL BONE SHAPE OF FINGER JOINTS

Background:

Early diagnosis and reliable differentiation between rheumatic diseases (RMDs) are crucial to start an adequate therapy and prevent irreversible damage. Since finger joints are commonly affected in rheumatoid arthritis (RA) and psoriatic arthritis (PsA), imaging of the peripheral skeleton is an essential step of diagnosis at a rheumatologist. High resolution peripheral quantitative computed tomography (HR-pQCT) allows an even more detailed and three-dimensional (3D) illustration of the peripheral bone than conventional radiographs. Segmented scans contain further information, such as the density, microstructure, and shape of the bones, which can be further analyzed by neural networks.

Objectives:

We hypothesize that, based on the shape of the second metacarpophalangeal (MCP) joint from HR-pQCT images, a neural network can be trained to differentiate between RA, PsA, and healthy controls and to reveal regions in the bone shape characteristic for the diseases.

Methods:

HR-pQCT images of MCP joints from patients with classified CCP positive RA, classified PsA, and healthy controls with low motion artifacts and appropriate scan region were selected as reported previously [3]. Scans were performed as part of the clinical routine and patients gave their informed consent to use pseudonymized data (Ethics approval 334_16B). Based on the assumption that pathognomonic changes develop over time, only images were used, where the period between classification and imaging exceeded one year.

Based on previous work [4], a pixel-wise mask of the second metacarpal bone was generated using a neural network based on the HR-pQCT scans of patients. Supervised auto-encoder [1] networks were used to predict the correct class given the bone mask only. For the neural network experiment, the patient scans were split on a patient-level into training (70%), validation (20%), and testing (10%). Guided backpropagation [2] was used as a method to investigate the regions influencing the class prediction most.

Results:

In total, images of 331 patients were included in the experiments. The evaluation of the model on the 33 test cases yielded a high accuracy for the healthy control with 94%, RA patients with 84%, and PsA patients with 89%. An area under the receiver operator curve of 91% could be achieved. The regions of the bone mask influencing the network´s decision most are highlighted exemplary in Figure 1.

Figure 1.

Visualization of the HR-pQCT slices with gradient maps. Higher values (red) represent regions that had a stronger contribution to the classification result. The HR-pQCT images are displayed for reference only. (a) Healthy patient, (b) RA diagnosed patient, and (c) PsA diagnosed patient. The first row shows the single slices with the highest values corresponding to the 3D bone masks in the second row.

Conclusion:

For the first time, a neural network-based approach successfully provides a differential diagnosis of RA and PsA based only on the shape of the second MCP in HR-pQCT images. The evaluation of the test set suggests that high curvatures of the bone surface in the joint region significantly influence the prediction of the network, suggesting an in-depth investigation of these regions for patients affected by RA and PsA. Based on these promising findings, we aim to extend the approach to seronegative RA as well as early RA and PsA.

References:

[1]Le, L. et al. (2018). Supervised autoencoders: Improving generalization performance with unsupervised regularizers. In Advances in Neural Information Processing Systems.

[2]Springenberg, J. T. et al. (2015). Striving for simplicity: The all convolutional net. 3rd International Conference on Learning Representations, ICLR 2015 - Workshop Track Proceedings.

[3]Simon, D. et al. (2017). Age- and Sex-Dependent Changes of Intra-articular Cortical and Trabecular Bone Structure and the Effects of Rheumatoid Arthritis. Journal of Bone and Mineral Research, 32(4), 722–730.

[4]Folle, L. et al. (2021). Fully Automatic Bone Mineral Density Measurements using Deep Learning. Manuscript submitted for publication.

Acknowledgements:

This work was supported by the emerging field initiative (project 4 Med 05 “MIRACLE”) of the University Erlangen-Nürnberg and MASCARA - Molecular Assessment of Signatures Characterizing the Remission of Arthritis grant 01EC1903A.

Disclosure of Interests:

Lukas Folle: None declared, Chang Liu: None declared, David Simon Speakers bureau: Lilly, Novartis, Consultant of: Lilly, Novartis, Gilead, BMS, Abbvie, Grant/research support from: Lilly, Novartis, Timo Meinderink: None declared, Anna-Maria Liphardt Consultant of: Mylan/Meda Pharma, Grant/research support from: Novartis, Gerhard Krönke Speakers bureau: Lilly, Novartis, Consultant of: Lilly, Novartis, Gilead, BMS, Abbvie, Grant/research support from: Lilly, Novartis, Georg Schett Speakers bureau: Lilly, Novartis, Consultant of: Lilly, Novartis, Gilead, BMS, Abbvie, Grant/research support from: Lilly, Novartis, Andreas Maier: None declared, Arnd Kleyer Speakers bureau: Lilly, Novartis, Consultant of: Lilly, Novartis, Gilead, BMS, Abbvie, Grant/research support from: Novartis, Lilly

OP0148 SPATIOTEMPORAL DYNAMICS OF BONE LOSS BEFORE AND AFTER THE ONSET OF RHEUMATOID ARTHRITIS

Background:

Rheumatoid Arthritis (RA) is preceded by a clinically silent pre-phase characterized by autoimmunity against anti-modified protein antibodies including anti-citrullinated protein antibodies (ACPA). At this pre-stage patients already experience significant loss of volumetric peripheral bone mineral density (vBMD) compared to healthy controls measured by high-resolution peripheral quantitative computed tomography (HR-pQCT) (1-2). However, the longitudinal course of vBMD changes during the preclinical phase, after diagnosis, and its association with time to disease onset have not been investigated.

Objectives:

To longitudinally characterize the changes of metacarpal and radial vBMD before and after the clinical onset of RA and its association with time to onset of arthritis.

Methods:

To explore the development of arthritis, we initiated a RA-at-risk cohort in 2011. (Ethics 334_16B). This prospective cohort includes adults positive for CCP-AB with or without musculoskeletal symptoms, excluding arthritis. Participants are regularly followed with clinical examination and HR-pQCT imaging of the MCP and radial bone to monitor early bone changes. HR-pQCT images with low motion grade artefacts were analyzed to obtain the total (D100), cortical (DComp) and trabecular (DTrab) vBMD (D100) in mg HA cm3.

We descriptively analyzed the vBMD time course in patients who developed RA by fitting regression curves separately for the pre-clinical and clinical periods and estimated time-conditional marginal mean VBMDs for the 5-year peri-RA period. We analyzed time to diagnosis of clinical RA defined by the 2010 ACR/EULAR classification criteria using Cox regression models. Hazard ratios indicate the relative risk of clinical disease onset associated with 1 standard deviation reduction in bone density.

Results:

130 subjects (mean [SD] age 47.0 [12.2], 89 female [68%]) between 2011 and 2020 were analyzed. Median (IQR) follow-up duration for the cohort was 18.6 (4.6-47.6) months. Participants underwent 233 HR-pQCT scans and 58 (45%) underwent 2 to 6 scans with a median interval of 16.2 (12.2-21.2) months. 49 (38%) patients who developed RA had a pre-diagnosis follow-up of 4.1 (2.5-13.4) months and post-diagnosis follow-up of 22.0 (8.8-38.9) months. The time course of scaled bone mineral densities depicted in Figure 1A suggest that bone density around the MCP joints deteriorate in the preclinical phase of RA, which is mostly prominent in the trabecular bone. Modelling (Figure 1B) suggests that trabecular bone loss around the MCP joints has a constant pace regardless of the clinical status. Whereas the radial bone densities are relatively stable in the preclinical phase and show a reduction after the clinical onset of RA. Age and sex adjusted hazard ratios (95%CI) for the risk of RA clinical onset were 1.52 (1.03 to 2.25) for radius D100 and 1.66 (1.07 to 2.55) for radius DComp (Table-1).

Table 1.

Relative risk of RA development in the total cohort; crude and age/sex adjusted hazard ratios for one standard-deviation reduction in vBMD.

CrudeAdjustedHR (95%CI)PHR (95%CI)PMCP.D-Comp1.16 (0.86 to 1.57)0.3361.20 (0.89 to 1.63)0.229MCP.D-Trab1.14 (0.83 to 1.57)0.4051.17 (0.85 to 1.62)0.341MCP.D1001.16 (0.83 to 1.61)0.3921.21 (0.86 to 1.71)0.265Rad.D-Comp1.42 (0.97 to 2.07)0.0711.66 (1.07 to 2.55)0.023Rad.D-Trab1.20 (0.87 to 1.66)0.2571.23 (0.88 to 1.71)0.223Rad.D1001.43 (0.99 to 2.06)0.0561.52 (1.03 to 2.25)0.033

Conclusion:

Metacarpal bone showed a constant decline that started already in the pre-phase of RA and continued after its clinical onset. In contrast, bone loss in the radius was not observed in the pre-phase but started at onset of RA. Low radial vBMD in the pre-clinical phase, however, was associated with a higher risk of RA onset. These findings suggest different spatiotemporal dynamics of bone loss before and after RA onset

References:

[1]Kleyer A. et. al. Ann Rheum Dis. 2014, 73:854-60

[2]Simon D. et. al. Ann Rheum Dis. 2020, doi:10.1002/art.41229

Disclosure of Interests:

None declared

A Virtual Reality-Based App to Educate Health Care Professionals and Medical Students About Inflammatory Arthritis: Feasibility Study

Background

Inflammatory arthritides (IA) such as rheumatoid arthritis or psoriatic arthritis are disorders that can be difficult to comprehend for health professionals and students in terms of the heterogeneity of clinical symptoms and pathologies. New didactic approaches using innovative technologies such as virtual reality (VR) apps could be helpful to demonstrate disease manifestations as well as joint pathologies in a more comprehensive manner. However, the potential of using a VR education concept in IA has not yet been evaluated.

Objective

We evaluated the feasibility of a VR app to educate health care professionals and medical students about IA.

Methods

We developed a VR app using data from IA patients as well as 2D and 3D-visualized pathological joints from X-ray and computed tomography–generated images. This VR app (Rheumality) allows the user to interact with representative arthritic joint and bone pathologies of patients with IA. In a consensus meeting, an online questionnaire was designed to collect basic demographic data (age, sex); profession of the participants; and their feedback on the general impression, knowledge gain, and potential areas of application of the VR app. The VR app was subsequently tested and evaluated by health care professionals (physicians, researchers, and other professionals) and medical students at predefined events (two annual rheumatology conferences and academic teaching seminars at two sites in Germany). To explore associations between categorical variables, the χ² or Fisher test was used as appropriate. Two-sided P values ≤.05 were regarded as significant.

Results

A total of 125 individuals participated in this study. Among them, 56% of the participants identified as female, 43% identified as male, and 1% identified as nonbinary; 59% of the participants were 18-30 years of age, 18% were 31-40 years old, 10% were 41-50 years old, 8% were 51-60 years old, and 5% were 61-70 years old. The participants (N=125) rated the VR app as excellent, with a mean rating of 9.0 (SD 1.2) out of 10, and many participants would recommend use of the app, with a mean recommendation score of 3.2 (SD 1.1) out of 4. A large majority (120/125, 96.0%) stated that the presentation of pathological bone formation improves understanding of the disease. We did not find any association between participant characteristics and evaluation of the VR experience or recommendation scores.

Conclusions

The data show that IA-targeting innovative teaching approaches based on VR technology are feasible.

Deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density

Arthritis patients develop hand bone loss, which leads to destruction and functional impairment of the affected joints. High resolution peripheral quantitative computed tomography (HR-pQCT) allows the quantification of volumetric bone mineral density (vBMD) and bone microstructure in vivo with an isotropic voxel size of 82 micrometres. However, image-processing to obtain bone characteristics is a time-consuming process as it requires semi-automatic segmentation of the bone. In this work, a fully automatic vBMD measurement pipeline for the metacarpal (MC) bone using deep learning methods is introduced. Based on a dataset of HR-pQCT volumes with MC measurements for 541 patients with arthritis, a segmentation network is trained. The best network achieves an intersection over union as high as 0.94 and a Dice similarity coefficient of 0.97 while taking only 33 s to process a whole patient yielding a speedup between 2.5 and 4.0 for the whole workflow. Strong correlation between the vBMD measurements of the expert and of the automatic pipeline are achieved for the average bone density with 0.999 (Pearson) and 0.996 (Spearman's rank) with [Formula: see text] for all correlations. A qualitative assessment of the network predictions and the manual annotations yields a 65.9% probability that the expert favors the network predictions. Further, the steps to integrate the pipeline into the clinical workflow are shown. In order to make these workflow improvements available to others, we openly share the code of this work.

Accuracy, patient-perceived usability, and acceptance of two symptom checkers (Ada and Rheport) in rheumatology: interim results from a randomized controlled crossover trial

BACKGROUND

Timely diagnosis and treatment are essential in the effective management of inflammatory rheumatic diseases (IRDs). Symptom checkers (SCs) promise to accelerate diagnosis, reduce misdiagnoses, and guide patients more effectively through the health care system. Although SCs are increasingly used, there exists little supporting evidence.

OBJECTIVE

To assess the diagnostic accuracy, patient-perceived usability, and acceptance of two SCs: (1) Ada and (2) Rheport.

METHODS

Patients newly presenting to a German secondary rheumatology outpatient clinic were randomly assigned in a 1:1 ratio to complete Ada or Rheport and consecutively the respective other SCs in a prospective non-blinded controlled randomized crossover trial. The primary outcome was the accuracy of the SCs regarding the diagnosis of an IRD compared to the physicians' diagnosis as the gold standard. The secondary outcomes were patient-perceived usability, acceptance, and time to complete the SC.

RESULTS

In this interim analysis, the first 164 patients who completed the study were analyzed. 32.9% (54/164) of the study subjects were diagnosed with an IRD. Rheport showed a sensitivity of 53.7% and a specificity of 51.8% for IRDs. Ada's top 1 (D1) and top 5 disease suggestions (D5) showed a sensitivity of 42.6% and 53.7% and a specificity of 63.6% and 54.5% concerning IRDs, respectively. The correct diagnosis of the IRD patients was within the Ada D1 and D5 suggestions in 16.7% (9/54) and 25.9% (14/54), respectively. The median System Usability Scale (SUS) score of Ada and Rheport was 75.0/100 and 77.5/100, respectively. The median completion time for both Ada and Rheport was 7.0 and 8.5 min, respectively. Sixty-four percent and 67.1% would recommend using Ada and Rheport to friends and other patients, respectively.

CONCLUSIONS

While SCs are well accepted among patients, their diagnostic accuracy is limited to date.

TRIAL REGISTRATION

DRKS.de, DRKS00017642 . Registered on 23 July 2019.

Advanced machine learning for predicting individual risk of flares in rheumatoid arthritis patients tapering biologic drugs

BACKGROUND

Biological disease-modifying anti-rheumatic drugs (bDMARDs) can be tapered in some rheumatoid arthritis (RA) patients in sustained remission. The purpose of this study was to assess the feasibility of building a model to estimate the individual flare probability in RA patients tapering bDMARDs using machine learning methods.

METHODS

Longitudinal clinical data of RA patients on bDMARDs from a randomized controlled trial of treatment withdrawal (RETRO) were used to build a predictive model to estimate the probability of a flare. Four basic machine learning models were trained, and their predictions were additionally combined to train an ensemble learning method, a stacking meta-classifier model to predict the individual flare probability within 14 weeks after each visit. Prediction performance was estimated using nested cross-validation as the area under the receiver operating curve (AUROC). Predictor importance was estimated using the permutation importance approach.

RESULTS

Data of 135 visits from 41 patients were included. A model selection approach based on nested cross-validation was implemented to find the most suitable modeling formalism for the flare prediction task as well as the optimal model hyper-parameters. Moreover, an approach based on stacking different classifiers was successfully applied to create a powerful and flexible prediction model with the final measured AUROC of 0.81 (95%CI 0.73-0.89). The percent dose change of bDMARDs, clinical disease activity (DAS-28 ESR), disease duration, and inflammatory markers were the most important predictors of a flare.

CONCLUSION

Machine learning methods were deemed feasible to predict flares after tapering bDMARDs in RA patients in sustained remission.

Machine Learning Electronic Health Record Identification of Patients with Rheumatoid Arthritis: Algorithm Pipeline Development and Validation Study

Background

Financial codes are often used to extract diagnoses from electronic health records. This approach is prone to false positives. Alternatively, queries are constructed, but these are highly center and language specific. A tantalizing alternative is the automatic identification of patients by employing machine learning on format-free text entries.

Objective

The aim of this study was to develop an easily implementable workflow that builds a machine learning algorithm capable of accurately identifying patients with rheumatoid arthritis from format-free text fields in electronic health records.

Methods

Two electronic health record data sets were employed: Leiden (n=3000) and Erlangen (n=4771). Using a portion of the Leiden data (n=2000), we compared 6 different machine learning methods and a naïve word-matching algorithm using 10-fold cross-validation. Performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC), and F1 score was used as the primary criterion for selecting the best method to build a classifying algorithm. We selected the optimal threshold of positive predictive value for case identification based on the output of the best method in the training data. This validation workflow was subsequently applied to a portion of the Erlangen data (n=4293). For testing, the best performing methods were applied to remaining data (Leiden n=1000; Erlangen n=478) for an unbiased evaluation.

Results

For the Leiden data set, the word-matching algorithm demonstrated mixed performance (AUROC 0.90; AUPRC 0.33; F1 score 0.55), and 4 methods significantly outperformed word-matching, with support vector machines performing best (AUROC 0.98; AUPRC 0.88; F1 score 0.83). Applying this support vector machine classifier to the test data resulted in a similarly high performance (F1 score 0.81; positive predictive value [PPV] 0.94), and with this method, we could identify 2873 patients with rheumatoid arthritis in less than 7 seconds out of the complete collection of 23,300 patients in the Leiden electronic health record system. For the Erlangen data set, gradient boosting performed best (AUROC 0.94; AUPRC 0.85; F1 score 0.82) in the training set, and applied to the test data, resulted once again in good results (F1 score 0.67; PPV 0.97).

Conclusions

We demonstrate that machine learning methods can extract the records of patients with rheumatoid arthritis from electronic health record data with high precision, allowing research on very large populations for limited costs. Our approach is language and center independent and could be applied to any type of diagnosis. We have developed our pipeline into a universally applicable and easy-to-implement workflow to equip centers with their own high-performing algorithm. This allows the creation of observational studies of unprecedented size covering different countries for low cost from already available data in electronic health record systems.

Bone Mass, Bone Microstructure and Biomechanics in Patients with Hand Osteoarthritis

The impact of primary hand osteoarthritis (HOA) on bone mass, microstructure, and biomechanics in the affected skeletal regions is largely unknown. HOA patients and healthy controls (HCs) underwent high-resolution peripheral quantitative computed tomography (HR-pQCT). We measured total, trabecular, and cortical volumetric bone mineral densities (vBMDs), microstructural attributes, and performed micro-finite element analysis for bone strength. Failure load and scaled multivariate outcome matrices from distal radius and second metacarpal (MCP2) head measurements were analyzed using multiple linear regression adjusting for age, sex, and functional status and reported as adjusted Z-score differences for total and direct effects. A total of 105 subjects were included (76 HC: 46 women, 30 men; 29 HOA: 23 women, six men). After adjustment, HOA was associated with significant changes in the multivariate outcome matrix from the MCP2 head (p < .001) (explained by an increase in cortical vBMD (Δz = 1.07, p = .02) and reduction in the trabecular vBMD (Δz = -0.07, p = .09). Distal radius analysis did not show an overall effect of HOA; however, there was a gender-study group interaction (p = .044) explained by reduced trabecular vBMD in males (Δz = -1.23, p = .02). HOA was associated with lower failure load (-514 N; 95%CI, -1018 to -9; p = 0.05) apparent in males after adjustment for functional status. HOA is associated with reduced trabecular and increased cortical vBMD in the MCP2 head and a reduction in radial trabecular vBMD and bone strength in males. Further investigations of gender-specific changes of bone architecture in HOA are warranted. © 2020 The Authors. Journal of Bone and Mineral Research published by American Society for Bone and Mineral Research.

AB1346-HPR REAL-WORLD EFFECTIVENESS AND PERCEIVED USEFULNESS OF SYMPTOM CHECKERS IN RHEUMATOLOGY: INTERIM REPORT FROM THE PROSPECTIVE MULTICENTER BETTER STUDY

Background:

Symptom checkers (SC) promise to reduce diagnostic delay, misdiagnosis and effectively guide patients through healthcare systems. They are increasingly used, however little evidence exists about their real-life effectiveness.

Objectives:

The aim of this study was to evaluate the diagnostic accuracy, usage time, usability and perceived usefulness of two promising SC, ADA (www.ada.com) and Rheport (www.rheport.de). Furthermore, symptom duration and previous symptom checking was recorded.

Methods:

Cross-sectional interim clinical data from the first of three recruiting centers from the prospective, real-world, multicenter bETTeR-study (DKRS DRKS00017642) was used. Patients newly presenting to a secondary rheumatology outpatient clinic between September and December 2019 completed the ADA and Rheport SC. The time and answers were recorded and compared to the patient’s actual diagnosis. ADA provides up to 5 disease suggestions, Rheport calculates a risk score for rheumatic musculoskeletal diseases (RMDs) (≥1=RMD). For both SC the sensitivity, specificity was calculated regarding RMDs. Furthermore, patients completed a survey evaluating the SC usability using the system usability scale (SUS), perceived usefulness, previous symptom checking and symptom duration.

Results:

Of the 129 consecutive patients approached, 97 agreed to participate. 38% (37/97) of the presenting patients presented with an RMD (Figure 1). Mean symptom duration was 146 weeks and a mean number of 10 physician contacts occurred previously, to evaluate current symptoms. 56% (54/96) had previously checked their symptoms on the internet using search engines, spending a mean of 6 hours. Rheport showed a sensitivity of 49% (18/37) and specificity of 58% (35/60) concerning RMDs. ADA’s top 1 and top 5 disease suggestions concerning RMD showed a sensitivity of 43% (16/37) and 54% (20/37) and a specificity of 58% (35/60) and 52% (31/60), respectively. ADA listed the correct diagnosis of the patients with RMDs first or within the first 5 disease suggestions in 19% (7/37) and 30% (11/37), respectively. The average perceived usefulness for checking symptoms using ADA, internet search engines and Rheport was 3.0, 3.5 and 3.1 on a visual analog scale from 1-5 (5=very useful). 61% (59/96) and 64% (61/96) would recommend using ADA and Rheport, respectively. The mean SUS score of ADA and Rheport was 72/100 and 73/100. The mean usage time for ADA and Rheport was 8 and 9 minutes, respectively.

Conclusion:

This is the first prospective, real-world, multicenter study evaluating the diagnostic accuracy and other features of two currently used SC in rheumatology. These interim results suggest that diagnostic accuracy is limited, however SC are well accepted among patients and in some cases, correct diagnosis can be provided out of the pocket within few minutes, saving valuable time.

Figure:

Acknowledgments:

This study was supported by an unrestricted research grant from Novartis.

Disclosure of Interests:

Johannes Knitza Grant/research support from: Research Grant: Novartis, Jacob Mohn: None declared, Christina Bergmann: None declared, Eleni Kampylafka Speakers bureau: Novartis, BMS, Janssen, Melanie Hagen: None declared, Daniela Bohr: None declared, Elizabeth Araujo Speakers bureau: Novartis, Lilly, Abbott, Matthias Englbrecht Grant/research support from: Roche Pharma, Chugai Pharma Europe, Consultant of: AbbVie, Roche Pharma, RheumaDatenRhePort GbR, Speakers bureau: AbbVie, Celgene, Chugai Pharma Europe, Lilly, Mundipharma, Novartis, Pfizer, Roche Pharma, UCB, David Simon Grant/research support from: Else Kröner-Memorial Scholarship, Novartis, Consultant of: Novartis, Lilly, Arnd Kleyer Consultant of: Lilly, Gilead, Novartis,Abbvie, Speakers bureau: Novartis, Lilly, Timo Meinderink: None declared, Wolfgang Vorbrüggen: None declared, Cay-Benedict von der Decken: None declared, Stefan Kleinert Shareholder of: Morphosys, Grant/research support from: Novartis, Consultant of: Novartis, Speakers bureau: Abbvie, Novartis, Celgene, Roche, Chugai, Janssen, Andreas Ramming Grant/research support from: Pfizer, Novartis, Consultant of: Boehringer Ingelheim, Novartis, Gilead, Pfizer, Speakers bureau: Boehringer Ingelheim, Roche, Janssen, Jörg Distler Grant/research support from: Boehringer Ingelheim, Consultant of: Boehringer Ingelheim, Paid instructor for: Boehringer Ingelheim, Speakers bureau: Boehringer Ingelheim, Peter Bartz-Bazzanella: None declared, Georg Schett Speakers bureau: AbbVie, BMS, Celgene, Janssen, Eli Lilly, Novartis, Roche and UCB, Axel Hueber Grant/research support from: Novartis, Lilly, Pfizer, Consultant of: Abbvie, BMS, Celgene, Gilead, GSK, Lilly, Novartis, Speakers bureau: GSK, Lilly, Novartis, Martin Welcker Grant/research support from: Abbvie, Novartis, UCB, Hexal, BMS, Lilly, Roche, Celgene, Sanofi, Consultant of: Abbvie, Actelion, Aescu, Amgen, Celgene, Hexal, Janssen, Medac, Novartis, Pfizer, Sanofi, UCB, Speakers bureau: Abbvie, Aescu, Amgen, Biogen, Berlin Chemie, Celgene, GSK, Hexal, Mylan, Novartis, Pfizer, UCB

Effect of disease-modifying anti-rheumatic drugs on bone structure and strength in psoriatic arthritis patients

OBJECTIVES

To address whether the use of methotrexate (MTX) and biological disease-modifying anti-rheumatic drugs (bDMARDs) impacts bone structure and biomechanical properties in patients with psoriatic arthritis (PsA).

METHODS

This is a cross-sectional study in PsA patients receiving no DMARDs, MTX, or bDMARDs. Volumetric bone mineral densities (vBMDs), microstructural parameters, and biomechanical properties (stiffness/failure load) were determined by high-resolution peripheral quantitative CT and micro-finite element analysis in the respective groups. Bone parameters were compared between PsA patients with no DMARDs and those receiving any DMARDs, MTX, or bDMARDs, respectively.

RESULTS

One hundred sixty-five PsA patients were analyzed, 79 received no DMARDs, 86 received DMARDs, of them 52 bDMARDs (TNF, IL-17- or IL-12/23 inhibitors) and 34 MTX. Groups were balanced for age, sex, comorbidities, functional index, and bone-active therapy, while disease duration was longest in the bDMARD group (7.8 ± 7.4 years), followed by the MTX group (4.6 ± 7.4) and the no-DMARD group (2.9 ± 5.2). No difference in bone parameters was found between the no-DMARD group and the MTX group. In contrast, the bDMARD group revealed significantly higher total (p = 0.001) and trabecular vBMD (p = 0.005) as well as failure load (p = 0.012) and stiffness (p = 0.012). In regression models, age and bDMARDs influenced total vBMD, while age, sex, and bDMARDs influenced failure load and stiffness.

CONCLUSION

Despite longer disease duration, bDMARD-treated PsA patients benefit from higher bone mass and better bone strength than PsA patients receiving MTX or no DMARDs. These data support the concept of better control of PsA-related bone disease by bDMARDs.