Real-world evidence for secukinumab in UK patients with psoriatic arthritis or radiographic axial spondyloarthritis: interim 2-year analysis from SERENA

Objectives

The aim was to evaluate retention rates for secukinumab in patients with active PsA or radiographic axial spondyloarthritis (r-axSpA) treated in routine UK clinical practice.

Methods

SERENA (CAIN457A3403) is an ongoing, non-interventional, international study of patients with moderate-to-severe chronic plaque psoriasis, active PsA or active r-axSpA, who had received secukinumab for ≥16 weeks before enrolment. The primary objective of this interim analysis was to assess treatment retention rates in patients with PsA or r-axSpA who were enrolled and followed for ≥2 years at centres in the UK. The safety analysis set includes all patients who received at least one dose of secukinumab. The target population set includes all patients who fulfilled the patient selection criteria.

Results

The safety set comprised 189 patients (PsA, n = 81; r-axSpA, n = 108), and the target population set comprised 183 patients (PsA, n = 78; r-axSpA, n = 105). In the safety set, 107 patients (45 of 81 with PsA and 62 of 108 with r-axSpA) had previously received a biologic agent. Retention rates were similar between patients with PsA and r-axSpA after 1 year (PsA 91.0%, 95% CI: 84.0, 98.0; r-axSpA 89.2%, 95% CI: 82.7, 95.7) and 2 years (PsA 77.6%, 95% CI: 67.6, 87.7; r-axSpA 76.2%, 95% CI: 67.4, 85.0) of observation. Overall, 17.5% of patients (33 of 189) experienced at least one treatment-related adverse event, and 12.7% of patients (24 of 189) discontinued secukinumab because of adverse events.

Conclusion

This analysis of real-world data from the UK demonstrates high retention rates for secukinumab over 2 years in patients with PsA or r-axSpA, with a favourable safety profile.

P261 Early and accurate diagnosis of patients with axial spondyloarthritis using machine learning: a predictive analysis from electronic health records in the United Kingdom

Background/Aims

On average, there is a delay of 6.7 years between symptom onset and diagnosis of axial spondyloarthritis (axSpA). Since traditional approaches to improving early axSpA identification have had limited success, predictive automated analyses using patient records may help alleviate the burden on healthcare providers. We report results from a machine learning (ML) algorithm developed with UK electronic health records (EHRs) Clinical Practice Research Datalink (CPRD) data to estimate the probability or likelihood of a patient being diagnosed with axSpA based on prior clinical indicators and patient history.

Methods

Primary care UK EHR data - CPRD GOLD was used to identify patients with axSpA and healthy controls (HC). Patients aged ≥18 years with first diagnosis date of axSpA within the identification period (01-Jan-2005 to 31-Dec-2018) and fulfilling CPRD research acceptability criteria were included. Data pertaining to clinical presentation, consultation, referral, test, and therapy history were extracted for individual patients prior to diagnosis of axSpA. A total of 5,090 patients with axSpA satisfied the acceptability criteria. HC were randomly sampled to create a subset of one unique HC matched to each patient with axSpA, resulting in 5,089 HC. ML usable features derived from the total population (patients with axSpA and HC) numbered 820. After using a further exclusion criterion for the patients with axSpA and HC who had ≥1 of 820 usable features, the final dataset included 7,813 patients (3,902 with axSpA and 3,911 HC). This combined dataset was randomly split (67:33) into a train (n = 5237) and a test (n = 2576) dataset. A random forest (RF) model was trained on the train dataset. Cross-validation was performed for hyper-parameter tuning of the RF classifier. Once the model was trained, accuracy, precision, and F-1 scores were obtained with the test dataset.

Results

The RF-based algorithm resulted in a high level of accuracy (88.12%), with precision of 0.95 for patients with axSpA and 0.83 for HC. The RF algorithm identified 89 best clinical predictors (out of 820 used as inputs) that differentiated between patient and HC such as: total number of tests, total number of referrals, first age of consultation, first symptom age, and number of low back pain symptoms. The model sensitivity was 0.75 and positive predictive value was 80.88%. The model specificity was 0.96 and negative predictive value was 82.56%.

Conclusion

The ML algorithm demonstrated a high level of accuracy and precision in the identification of possible cases of axSpA, which may be useful in reducing the delay in diagnosis. Previous studies have successfully demonstrated automated cohort identification of axSpA in large datasets, with only a few using ML-based approaches for diagnosis from patient medical history. While our model supports previous work in axSpA, it needs further validation in routine clinical practice (exploration ongoing).

Disclosure

R. Sengupta: Honoraria; AbbVie, Biogen, Celgene, Lilly, MSD, Novartis, Roche, UCB. Grants/research support; AbbVie, Celgene, Novartis, UCB. S. Narasimham: Shareholder/stock ownership; Novartis. Other; Employee of Novartis. B.S. Mato: Shareholder/stock ownership; Novartis. Other; Employee of Novartis. M. Meglic: Other; Employee of Novartis. C. Perella: Other; Employee of Novartis. P. Pamies: Other; Employee of Novartis. P. Emery: Consultancies; AbbVie, Astra-Zeneca, BMS, Boehringer Ingelheim, Celltrion, Gilead, Janssen, MSD, Lilly, Novartis, Pfizer, Roche, Samsung, UCB. Grants/research support; AbbVie, BMS, Lilly, Novartis, Pfizer, Roche, Samsung.

MANAGEMENT OF FINGOLIMOD FIRST DOSE OBSERVATION (FDO) IN THE UK

Background

Fingolimod (Gilenya) is a sphingosine 1-phosphate receptor (S1PR) modulator approved for use in relapsing-remitting multiple sclerosis. Fingolimod can lead to reduced pacemaker cell excitability, slowing heart rate (HR) and, possibly, atrioventricular block. Cardiac monitoring is required for a minimum of 6 hours on commencing the first dose of fingolimod.

Regent's Park Heart Clinics (RPHC) is a cardiology services provider engaged by Novartis Pharmaceuticals UK to provide NHS clinicians with a fingolimod FDO service.

Objective

To describe the results of a UK FDO service for fingolimod.

Methods

RPHC provides a cardiac physiologist/nurse to visit sites between 0800–1700 hrs with electrocardiography (ECG) equipment. 12-lead ECG is performed at baseline and six hours after initiation with continuous cardiac monitoring throughout. Blood pressure and HR measurement is performed hourly. Where patients have evidence of clinically important cardiac effects, monitoring is extended up to eight hours or overnight as required.

Results

From 4/July/2012 to 26/January/2015, RPHC provided FDO for 1013 patients. 975 (96%) were discharged at six hours; 29 (3%) were discharged at eight hours. 9 (1%) required an overnight stay. 1 patient discontinued treatment.

Conclusion

96% of 1013 fingolimod patients were discharged at 6-hours post-first dose. No patients required pharmacological intervention.