Ultrasound in Psoriatic Arthritis Treatment

BACKGROUND Psoriatic arthritis (PsA) is a systemic inflammatory disease with articular and extra-articular features. To date remission is considered to be the ultimate goal of therapy in PsA, however, due to the characteristics of the disease with involvement of different domains, remission may be difficult to achieve and maintain and so a minimal disease activity (MDA) could be an acceptable goal. In view of the therapeutic target of remission or MDA, the identification of adverse prognostic factors and the best treatment strategy are two of the most important items in research agenda of PsA. Establishing the prognosis of a patient with PsA is hence important to define the treatment strategy. Currently, observational and prospective cohort studies have identified prognostic factors correlating with the achievement of therapeutic response. Nevertheless, despite the importance of identifying prognostic factors in a disease with a functional disability comparable to rheumatoid arthritis (RA), the studies are still limited. Undoubtedly, the concept of treat-to-target (T2T) in PsA needs further studies, especially with longer follow up and larger samples. In T2T strategies, it is necessary to quantify the disease activity and this is possible by composite indices. The heterogeneity of PsA including axial and peripheral involvement and specific features (i.e. dactylitis, enthesitis) as well as extra-articular features makes the use of a single composite index (e.g. Disease Activity for Psoriatic Arthritis-DAPSA, Composite Psoriatic Disease Activity Index-CPDAI) a challenge not resolved yet. An interesting possibility is to integrate musculoskeletal ultrasonography (US) with clinical examination to stratify patients and to decide treatments in a T2T strategy. In the last years the role of imaging is grown up and EULAR recommendations on the use of imaging techniques in chronic arthritis recognize the high sensitivity of US to detect disease activity better than clinical examination alone although the utility of US in clinical practice is not supported by sufficient evidence yet AIM The aim of this study is to identify clinical and US predictors of achieving MDA in PsA patients with active peripheral arthritis starting a new course of therapy. Identifying prognostic factors of achieving remission or low disease activity will allow a better selection of patients with poorer outcome and a following improvement of the therapeutic strategies. Furthermore the possibility that US could be an added prognostic value makes this study a clear example of integration between the clinic and US. This study will respond to the need of tailoring treatment that would allow clinicians to practice a more effective and personalized medicine, optimizing the outcomes of patients with PsA as well as the treatments management. STUDY OBJECTIVES PRIMARY OBJECTIVE In clinically diagnosed PsA with clinically active joint disease starting a new course of therapy, to evaluate the additional value of US-score (US-score PsA-SIR) over clinical examination in detecting patients achieving MDA at 6 months. SECONDARY OBJECTIVES In clinically diagnosed PsA with clinically active joint disease starting a new course of therapy: * to evaluate the additional value of US over clinical examination in detecting patients: * achieving MDA at 12 months (including sustained) * achieving DAPSA remission at 6 and 12-months (including sustained) * achieving American College of Rheumatology (ACR) remission at 6 and 12 months (including sustained) * with X-ray structural progression using the modified Sharp-van der Heijde score (mSvHs) at 12 and 24 months * with US structural progression (US-damage score) at 12 and 24 months * with functional worsening measured using Health Assessment Questionnaire (delta HAQ\>0.23) at 12 and 24 months * with impairment of Health Related Quality of Life (HRQoL) measured with Psoriatic Arthritis Impact of Disease (PsAID) questionnaire at 12 and 24 months; * to evaluate the relationship between time-integrated US-detected inflammation and US-detected damage at 12 and 24 months; * to evaluate the comparative effectiveness of different treatment strategies on MDA, DAPSA remission, HAQ\>0.23, X-ray progression (mSvHs), US-inflammation score, US-damage score; * to evaluate residual US activity in patients in MDA remission; * to explore whether clinically-detected disease activity due to joint tenderness without swelling is related to joint or extra-articular US-detected inflammation evaluated by US; * to explore clinical features and US-lesions related to X-ray detected bony apposition. STUDY DESIGN The study follows a multi-centre observational prospective cohort study design. SAFETY MONITORING An adverse event (AE) is any untoward medical occurrence (i.e., any unfavourable and unintended sign (including abnormal laboratory findings), symptom or disease) in a subject after providing written informed consent for participation in the study. Therefore, an AE may or may not be temporally or causally associated with the use of a specific medicinal product. DEFINITION OF SAE An SAE is defined as any adverse event (appearance of (or worsening of any pre-existing) undesirable sign(s), symptom(s) or medical conditions(s) which meets any one of the following criteria: * is fatal or life-threatening * results in persistent or significant disability/incapacity * constitutes a congenital anomaly/birth defect * requires inpatient hospitalization or prolongation of existing hospitalization. STATISTICAL ANALYSIS PLAN VARIABLES OUTCOME MEASURES PRIMARY: o MDA at 6 months SECONDARY: * MDA at 12 months, sustained MDA at 6 AND 12 months * DAPSA \<3.3 at 6 months, 12 months, 6 AND 12 months * deltam SvHs 0-12 and 0-24 months * delta HAQ 0-12 and 0-24 months * delta PSAID 0-12 and 0-24 months * US-score PsA-SIR damage subscore at 0-6-12 months US PREDICTORS * US-score PsA-SIR * US-score PsA-SIR inflammation subscore * US-score PsA-SIR damage subscore CLINICAL PREDICTORS Based on the relevant literature clinical variables to be considered in the model include: * Demographic and environmental factors: * Age * Gender * Smoking * BMI * Clinical factors: * subset of PsA * time from symptoms onset to diagnosis * disease duration * disease activity (DAPSA and BASDAI) * HAQ score * tender joints / pain * comorbidities (FM, MetS) * Serological factors: * Acute phase reactants * Therapy factors: * Treatment (NSAIDs, steroids, DMARDs) DESCRIPTIVE ANALYSES Descriptive data will be provided for all outcomes according to data type; number of patients (N), mean, standard deviation (for interval data), median 25% and 75% quartiles (for ordinal data). Frequency (absolute and relative) distributions will be provided for categorical data. Two-sided p-values will be presented throughout. PRIMARY ENDPOINT ANALYSIS Prediction of 6 month MDA will use multivariate adjusted logistic models. A baseline model will include all the clinical variables. US predictors will be added as covariates to the clinical variables, assuming an additive model. The derived ß coefficients were used to calculate prognostic indices, thereby creating weighted prediction models. Model performance will be evaluated by C-indices (area under the ROC curve, AUC), net reclassification indices (NRI), integrated discrimination improvement (IDI), and plotted ROC curves. NRI can be used to compare the clinical impact of different models (it is a comparison of the proportion of subjects with disease who have appropriately increased risk scores with the new model, and the proportion of subjects without disease who have appropriately decreased risk scores with the new model). IDI represents desired improvements in average sensitivity corrected for undesirable increases in 1-specificity, it therefore compared whether the new models improved sensitivity without affecting specificity.( ) Data management and analysis will be performed using RedCap, R, Stata. SAMPLE SIZE The sample size calculation was done with the objective of minimizing the number of false positives (i.e the number of false non-responsive to treatment) in order to minimize the risk of over-treating patients who actually have a good response to therapy. Therefore, sample size was calculated to minimize this risk by 40% (null hypothesis H0) to 20% (alternative hypothesis H1), maintaining stable at 70% (both for the H0 for both the H1), the percentage of true positives (true unresponsive to therapy). The simulations were carried out using the procedure rocsize Stata (by M. Pepe) 30, which allows to determine the power to detect an improvement in the ROC curve. The procedure requires the specification of the percentage of false positive and true positive both for the null hypothesis is for the alternative hypothesis and the percentage of positive / diseased. Using the command rocsize 0.7 0.2, na (150) ndb (100) tpnull (0.7) fpnull (0.4) of Stata, 250 patients are sufficient to evaluate the performance of a model (and its ROC curve) with 90% power and 5% alpha. Specifically, we have assumed a 70% and a 20% of subjects true positives and false positives, respectively, according to the alternative hypothesis, a 70% and a 40% of subjects true positives and false positives, respectively, according to the null hypothesis, and a percentage of diseased of 60% (150 of 250 subjects). Under the assumption of maximum attrition of 20%, the sample size will be increased to 300 patients. The same sample size is sufficient to precisely estimate a logistic model of achievement of a MDA (probability of 0.4 at 6 months) with 10 predictors (rule of thumbs).( ) Based on the pre-study activities, 35-40 centres will be involved, 15 tertiary and 20-25 secondary rheumatology centres. Assuming 4 eligible patients/months for tertiary and 1/month in secondary centres, a 40% of enrolment rate, about 30 patients/months are expected. ETHICS This study will be conducted in accordance with all applicable laws and regulations including, but not limited to, the International Conference on Harmonisation (ICH) Guideline for Good Clinical Practice (GCP), EU guidelines and the ethical principles that have their origins in the Declaration of Helsinki. The institutional review board (IRB)/independent ethics committee (IEC) must review and approve the protocol and informed consent form before any subjects are enrolled. Before any protocol-required procedures are performed, the subject must sign and date the IRB/IEC-approved informed consent form. Clinical data (including AEs and concomitant medications) will be entered into a validated data capture system provided by the Italian Society for Rheumatology. The data system will include password protection and internal quality checks, such as automatic range checks, to identify data that appear inconsistent, incomplete, or inaccurate.