Objective.To assess whether the association between psoriatic nail dystrophy and radiographic damage in the hands of patients with psoriatic arthritis (PsA) is specific to the distal interphalangeal (DIP) joints.Methods.A convenience sample of patients was collated from the Bath longitudinal PsA cohort. All patients had PsA according to the ClASsification for Psoriatic ARthritis criteria (CASPAR) criteria, scored radiographs of their hands, and documented nail scores as measured by the Psoriatic Nail Severity Score. Chi-square tests were performed to examine for association between features of nail dystrophy and radiographic damage in the DIP joints, and proximal interphalangeal or metacarpophalangeal (non-DIP) joints of the corresponding digits.Results.There were 134 patients included, with a median age of 53 years (interquartile range; IQR 44–61) and disease duration of 7 years (IQR 3–17). The presence of any form of psoriatic nail dystrophy was associated with erosion at the DIP joints of the corresponding digit (OR 1.9, 95% CI 1.23–2.83; p < 0.004) and this association was primarily driven by the presence of nail onycholysis (OR 1.72; 95% CI 1.12–2.62; p = 0.02). Nail subungual hyperkeratosis was more strongly associated with joint space narrowing, erosions, and osteoproliferation at the corresponding DIP joint compared to non-DIP joints (p < 0.001). Nail pitting was not associated with erosions or osteoproliferation.Conclusion.The presence of psoriatic nail dystrophy, particularly onycholysis, is associated with erosive disease at the DIP joints. Subungual hyperkeratosis is more strongly associated with erosive damage at the DIP than non-DIP joints. These findings support the anatomical and pathological link between nail and DIP joint disease.
Objective To test shortened versions of the psoriatic arthritis (PsA) composite measures for use in routine clinical practice. Methods Clinical and patient-reported outcome measures (PROMs) were assessed in patients with PsA at 3 consecutive follow-up visits in a UK multicenter observational study. Shortened versions of the Composite Psoriatic Arthritis Disease Activity Index (CPDAI) and Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) Composite Exercise (GRACE) measures were developed using PROMs and tested against the Disease Activity Score in 28 joints (DAS28), composite Disease Activity in Psoriatic Arthritis, and Routine Assessment of Patient Index Data (RAPID3). Discrimination between disease states and responsiveness were tested with the t-score, standardized response mean (SRM), and effect size (ES). Data were presented to members at the GRAPPA 2020 annual meeting and members voted on the recommended composite routine practice. Results The SRM for the GRACE, 3 visual analog scale (VAS), and 4VAS were 0.67, 0.77, and 0.63, respectively, and for CPDAI and shortened CPDAI (sCPDAI) were 0.54 and 0.55, respectively. Shortened versions of the GRACE increased the t-score from 7.8 to 8.7 (3VAS) and 9.0 (4VAS), but reduced the t-score in the CPDAI/sCPDAI from 6.8 and 6.1. The 3VAS and 4VAS had superior performance characteristics to the sCPDAI, DAS28, Disease Activity in Psoriatic Arthritis, and RAPID3 in all tests. Of the members, 60% agreed that the VAS scales contained enough information to assess disease and response to treatment, 53% recommended the 4VAS for use in routine care, and 26% the 3VAS, while leaving 21% undecided. Conclusion Shortening the GRACE to VAS scores alone enhances the ability to detect status and responsiveness and has the best performance characteristics of the tested composite measures. GRAPPA members recommend further testing of the 3VAS and 4VAS in observational and trial datasets.
Objective To test the addition of pain and fatigue to the Composite Psoriatic Arthritis Disease Activity (CPDAI) and the Group for Research and Assessment of Psoriasis and PsA (GRAPPA) Composite Exercise (GRACE) composite measures of psoriatic arthritis (PsA). Methods Clinical and patient-reported outcome measures were assessed in patients with PsA at 3 consecutive follow-up visits over 6 months in a UK multicenter observational study. A pain visual analog scale and Functional Assessment of Chronic Illness Therapy Fatigue scale were added as modifications to the CPDAI and GRACE composite measures. Original and modified versions were tested against the PsA Disease Activity Score (PASDAS) and the Disease Activity Index for PsA (DAPSA). Discrimination between disease states and responsiveness were tested with t-scores, standardized response means (SRMs), and effect sizes. Data were presented to members at the 2020 annual meeting who then voted on the GRAPPArecommended composite and treatment targets for clinical trials. Results One hundred forty-one patients were recruited with a mean PsA disease duration of 6.1 years (range 0–41 yrs). The SRMs for the GRACE and modified GRACE (mGRACE) were 0.67 and 0.64, respectively, and 0.54 and 0.46, respectively, for the CPDAI and modified CPDAI (mCPDAI). The t-scores for the GRACE and mGRACE were unchanged at 7.8 for both, and 6.8 and 7.0 for the CPDAI and mCPDAI, respectively. The PASDAS demonstrated the best responsiveness (SRM 0.84) and discrimination (t-scores 8.3). Most members (82%) agreed the composites should not be modified and 77% voted for the PASDAS as the GRAPPA-recommended composite for clinical trials, with 90% minimal disease activity (MDA) as the target. Conclusion Modifying the CPDAI and GRACE with the addition of pain and fatigue does not enhance responsiveness nor the measures’ ability to detect disease status in terms of requiring treatment escalation. GRAPPA members voted for the PASDAS as the composite measure in clinical trials and MDA as the target.
Computed tomography (CT) imaging of the thorax is widely used for the detection and monitoring of pulmonary embolism (PE). However, CT images can contain artifacts due to the acquisition or the processes involved in image reconstruction. Radiologists often have to distinguish between such artifacts and actual PEs. Our main contribution comes in the form of a scalable hypothesis testing method for CT, to enable quantifying uncertainty of possible PEs. In particular, we introduce a Bayesian Framework to quantify the uncertainty of an observed compact structure that can be identified as a PE. We assess the ability of the method to operate under high noise environments and with insufficient data.
Background Understanding the impact of psoriatic arthritis (PsA) on an individual and how the disease evolves over time is important when determining a therapeutic strategy. However, little is known about the natural course of arthritis, psoriasis, and enthesitis, their relative impact on quality of life and how disease burden in real world cohorts compares to clinical trial populations. We set out to describe the burden of disease and impact on quality of life in an observational UK secondary care cohort. Methods Cross sectional cohorts were selected from the Bath PsA cohort, group one with early disease (within 24 months of diagnosis), groups two and three with established disease (commencing therapy with first or second line biologic Disease Modifying Anti-Rheumatic Drug- bDMARD therapy respectively; assessments both at initiation and 3 months after initiation). Results Analyses were undertaken where sufficient data was available on 154 patients eligible for entry into the early PsA cohort (group 1), 240 patients eligible for the biologic initiator cohort (group 2) and 103 for the second line biologics initiator cohort. Cohort characteristics are reported in Table 1. The burden of joint disease was high in all cohorts and approximately comparable to clinical trial populations in group 2 commencing bDMARD (mean tender joints count 22 (SD 14), swollen 7 (SD 5) cDAPSA mean 36 (SD 15)). Mean Body Mass Index (BMI) rose from overweight in early disease to obese in established disease (table 1). There was little enthesitis in any group (LEI mean <1). There was a significant burden of joint disease (cDAPSA), fatigue (FACT) and work disability among those 3 months post commencing bDMARD despite good clinical responses (table 1). The level of skin (PASI) and nail disease (Bath Nail score) was low but remains strongly associated with worse quality of life and the association was stronger in established disease. Conclusion We report high levels of residual joint disease, fatigue, obesity and work disability in both early and established PsA cohorts. Despite low absolute levels of residual skin and nail disease both become more strongly associated with worse quality of life in more established disease. Disclosures W. Tillett: Honoraria; Eli Lilly, Janssen, Novartis, Pfizer, UCB. Grants/research support; Abbvie, Eli Lilly, Celgene. A. Rambojun: Grants/research support; EPSRC. A. Bradley: Shareholder/stock ownership; Eli Lilly and Company Limited. Other; Employee of Eli Lilly and Company Limited. J. Mount: Shareholder/stock ownership; Eli Lilly and Company Limited. Other; Employee of Eli Lilly and Company Limited. C. Cavill: Grants/research support; Eli Lilly and Company Limited. E. Korendowych: Honoraria; Abbvie, Eli Lilly, Janssen, Novartis. Grants/research support; Eli Lilly and Company Limited. N. McHugh: Consultancies; Abbvie. Grants/research support; Eli Lilly and Company Limited.
Shape models have been used extensively to regularise segmentation of objects of interest in images, e.g. bones in medical x-ray radiographs, given supervised training examples. However, approaches usually adopt simple linear models that do not capture uncertainty and require extensive annotation effort to label a large number of set template landmarks for training. Conversely, supervised deep learning methods have been used on appearance directly (no explicit shape modelling) but these fail to capture detailed features that are clinically important.We present a supervised approach that combines both a non-linear generative shape model and a discriminative appearance-based convolutional neural network whilst quantifying uncertainty and relaxes the need for detailed, template based alignment for the training data. Our Bayesian framework couples the uncertainty from both the generator and the discriminator; our main contribution is the marginalisation of an intractable integral through the use of radial basis function approximations. We illustrate this model on the problem of segmenting bones from Psoriatic Arthritis hand radiographs and demonstrate that we can accurately measure the clinically important joint space gap between neighbouring bones.
Background:Biologic interventions using highly specific immuno-modulatory biologic disease-modifying anti-rheumatic drugs (bDMARDs) represent a rapidly developing therapeutic approach to the treatment of Psoriatic Arthritis (PsA). However, despite high rates of response, adverse events, primary and secondary inefficacy are common, and multiple sequential lines of bDMARDs are often required. Data on drug persistence, as a surrogate for response, from national registries indicates switching has become accepted routine practice. One third of patients will fail or discontinue their first biologic with a significant proportion switching on to a 3rd biologic or higher.1-4 Due to a lack of evidence on the response to sequential therapies, individual patients may not have further lines routinely funded after three bDMARDs in the UK. While limiting lines of therapy remains a UK concern, many countries with rationed healthcare systems follow the UK model of drug usage.Objectives:To describe the response to sequential lines of bDMARD therapy prescribed in routine care in a UK single centre cohort.Methods:A retrospective sample of patients with PsA who fulfilled CASPAR criteria and had received at least one bDMARD were taken from the Bath longitudinal cohort for inclusion in the study. Clinical and laboratory variables that constitute physician and patient-reported outcome measures were collected at baseline and after a median (range) follow-up of 3 months (2-5) into their respective therapy line in accordance with the National Institute for Health and Care Excellence (NICE) rules. The mean change with a 95% confidence interval (CI) was used to report the difference between the baseline and follow-up measures. All patients provided consent to use their data collected during routine care, and ethical approval by the local committee was granted.Results:The patients mean age was 57.7 (SD 12.2) with a median (range) disease duration of 14.4 years (9.7 – 23.2). Data was available for 194 patients commencing 1st line bDMARD, 106 (2nd line), 93 (3rd line), 33 (4th line), 12 (5th line), and 9 (6th line and higher) from a total of 759 patients in the cohort. Mean tender and swollen joint count at baseline 1st bDMARD was 7 (SD 4.7) and 22 (SD 14.0), pain visual analogue scale 50 (SD 27.6) and PASI 1.3 (SD 2.2). Reasons for changing biological therapies include lack or loss of efficacy, intolerance, side effects, and comorbidities. Mean levels of joint disease at drug initiation did not diminish with subsequent lines of therapy. Clinical and patient reported outcomes by line of therapy are reported in Figure 1. Clinical responses were greatest to first line bDMARD, however clinically relevant DAPSA improvements were seen up to 5th line. Absolute levels of psoriasis in the cohort were low, however improvement in PASI was achieved across all lines of therapy. Patient and Physician Global Assessments (1-5 on Likert scale) and the Pain Visual analogue score (VAS on 1-10 Likert scale) showed a similar trend with greatest improvement to first line treatment across all lines of therapy.Conclusion:In this study we report the clinical response to sequential lines of bDMARD therapy for active PsA in routine clinical practice. Clinical response was greatest to the first line bDMARD but overall improvement in DAPSA, PASI or pain response did not appear to diminish up to 5th line. Further study in larger cohorts is required to confirm this finding and build on our understanding of clinical response to sequential lines of bDMARD therapy.References:[1]Hyrish et al 2006 Rheum 45, 1558-65[2]Kawabe A. 2020 Arth Res Ther 22, 136[3]Park DJ. 2017 Clin Rheum 36, 1013-22[4]Karlsson. 2007 JA Rheum 47,507-13Figure 1.Clinical and patient reported outcomes by line of therapyDisclosure of Interests:Abuelmagd Abdalla: None declared, Adwaye Rambojun: None declared, Laura C Coates Speakers bureau: AbbVie, Amgen, Biogen, Celgene, Gilead, Eli Lilly, Janssen, Medac, Novartis, Pfizer, and UCB., Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Eli Lilly, Gilead, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Amgen, Celgene, Eli Lilly, Pfizer, and Novartis, Eleanor Korendowych Consultant of: Abbvie, Celgene, Janssen, Lilly and Novartis., Neil McHugh: None declared, William Tillett Speakers bureau: AbbVie, Amgen, Celgene, Lilly, Janssen, Novartis, Pfizer Inc., and UCB, Consultant of: AbbVie, Amgen, Celgene, Lilly, Janssen, Novartis, MSD, Pfizer Inc., and UCB., Grant/research support from: AbbVie, Celgene, Eli Lilly, Janssen, Novartis, Pfizer Inc., and UCB.
BackgroundMachine learning (ML) algorithms could facilitate the standardisation of joint damage assessment in Psoriatic Arthritis (PsA) and improve its accessibility in clinical and research settings. ML algorithms trained on manually annotated hand and wrist radiographs have promising performance characteristics[1]. A large volume of annotated radiographs is needed, and annotation is time consuming and subject to reliability issues given X-Rays (XRs) are 2D representations.ObjectivesTo develop a reliable method for the annotation of hand and wrist bones on XRs in order to facilitate the development of supervised ML algorithms for joint damage detection.Methods10 bilateral hand and wrist XRs were selected at random from the Bath PsA XR database. 5 XRs were independently annotated by 3 annotators; (AA & WT (rheumatologist) and YHR (radiologist)) using the ASPAX software[2]. Annotations were visually inspected for areas of discordance and consensus annotation guidelines were developed. Annotation was repeated using the annotation guidelines on second set of 5 XRs. With annotator 1 (WT) representing ground truth, the mean error (ME; in pixels) of the annotation (deviation from ground truth) and the mean fractional error (MFE; corrects for the perimeter measurements of the bone), was estimated in pre- and post-training annotations. The ME and MFE within a single annotator (AA) were estimated in 5 radiographs after a 2-month interval.ResultsVisual inspection determined that the areas of discordance in annotation were the 1stinterphalangeal joint, the metacarpal bases, the hamate and capitate bones, and the trapezium and trapezoid bones (Figure 1). The MFE between the annotators and ground truth improved for all bones following the development of annotation guidelines, with the largest improvement evident in the annotation of the metacarpal bones (Table 1). The intra-reader and inter-reader MFEs were comparable (Table 1).ConclusionStandardised instructions may facilitate reliable hand and wrist bone annotation and enable the acquisition of large volumes of annotated training data for supervised ML algorithms.References[1]Adwaye Rambojun, William Tillett, Tony Shardlow, Neill D. F. Campbell; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 2043-2052[2]Machine Learning and Rheumatic Diseases (Website)https://people.bath.ac.uk/amr62/Projects/malard/malard.htmlTable 1.Reliability ExerciseExercise 1: Inter-rater errorExercise 2: Inter-rater errorExercise 2: Intra-rater errorMean Error (±sd)PixelsMean Fractional Error (±sd)Mean Error (±sd)PixelsMean Fractional Error (±sd)Mean Error (±sd)PixelsMean Fractional Error (±sd)Distal phalanges201.02 (119.282)3.97 x 10-4(3.747 x 10-3)550.54(85.201)1.66 x10-5(1.293 x10-5)1.39 x10-2(8.740 x10-3)2.40 x10-5(1.335 x10-5)Middle phalanges218.83 (119.891)7.43 x 10-4(3.232 x 10-3)544.37(76.012)4.33 x 10-4(1.178 x 10-3)7.85 x10-3(2.368 x10-3)1.02 x10-5(4.055 x10-5)Proximal phalanges231.48 (116.095)8.52 x10-6(8.113 x10-6)585.33(104.098)7.13 x10-6(3.119 x10-6)7.80 x10-3(3.554 x10-3)6.88 x10-6(3.972 x10-6)Metacarpals184.94(115.208)1.74 x 10-4(1.345 x 10-3)571.73(62.721)8.65 x10-6(4.308 x10-6)9.82 x10-3(6.102 x10-3)6.07 x10-6(3.307 x10-6)Radius232.90(122.620)1.75 x 10-4(2.086 x 10-4)552.12(79.655)3.25 x10-5(1.775 x10-5)1.83215 x10-2(9.617 x10-3)1.25 x10-5(6.534 x10-6)Ulna236.60(121.709)3.31 x 10-4(3.778 x 10-4)557.25(80.019)2.19 x10-5(1.793 x10-5)1.68 x10-2(1.842 x10-2)1.41 x10-5(1.656 x10-5)Carpal bonesa197.42 (141.548)2.75 x 10-4(3.009 x 10-4)Carpal bonesb542.31(77.896)8.89 x10-5(1.025 x 10-4)2.86 x10-2(3.285 x10-2)5.28 x10-5(7.673 x10-5)a. Trapezium, Trapezoid, Scaphoid, Lunate, Pisiform, Triquetrum, Hamate Capitateb. Trapezium, Hamate/Capitate, Lunate, ScaphoidFigure 1.Pre- and Post-training annotation examplesAcknowledgements:NIL.Disclosure of InterestsAdwaye Rambojun: None declared, Anna Antony Speakers bureau: Eli Lilly, AbbVie, Ynyr Hughes-Roberts: None declared, William Tillett Speakers bureau: Abbvie, Amgen, Eli Lilly, GSK, Janssen, Novartis, Pfizer, UCB, Consultant of: Abbvie, Amgen, Eli Lilly, GSK, Janssen, Novartis, Ono Pharma, Pfizer, UCB, Grant/research support from: Janssen, UCB, Pfizer, Eli-Lilly.
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