Dataset Information

Persistence of biologic treatments in psoriatic arthritis: a population-based study in Sweden.

ABSTRACT:

Objectives

TNF inhibitors (TNFis) and IL inhibitors are effective treatments for PsA. Treatment non-persistence (drug survival, discontinuation) is a measure of effectiveness, tolerability and patient satisfaction or preferences in real-world clinical practice. Persistence on these treatments is not well understood in European PsA populations. The aim of this study was to compare time to non-persistence for either ustekinumab (IL-12/23 inhibitor) or secukinumab (IL-17 inhibitor) to a reference group of adalimumab (TNFi) treatment exposures in PsA patients and identify risk factors for non-persistence.

Methods

A total of 4649 exposures of adalimumab, ustekinumab, and secukinumab in 3918 PsA patients were identified in Swedish longitudinal population-based registry data. Kaplan-Meier curves were constructed to measure treatment-specific real-world risk of non-persistence and adjusted Cox proportional hazards models were estimated to identify risk factors associated with non-persistence.

Results

Ustekinumab was associated with a lower risk of non-persistence relative to adalimumab in biologic-naïve [hazard ratio (HR) 0.48 (95% CI 0.33, 0.69)] and biologic-experienced patients [HR 0.65 (95% CI 0.56, 0.76)], while secukinumab was associated with a lower risk in biologic-naïve patients [HR 0.65 (95% CI 0.49, 0.86)] but a higher risk of non-persistence in biologic-experienced patients [HR 1.20 (95% CI 1.03, 1.40)]. Biologic non-persistence was also associated with female sex, axial involvement, recent disease onset, biologic treatment experience and no psoriasis.

Conclusion

Ustekinumab exhibits a favourable treatment persistency profile relative to adalimumab overall and across lines of treatment. The performance of secukinumab is dependent on biologic experience. Persistence and risk factors for non-persistence should be accounted for when determining an optimal treatment plan for patients.

SUBMITTER: Geale K

PROVIDER: S-EPMC7772250 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Persistence of biologic treatments in psoriatic arthritis: a population-based study in Sweden.

Geale Kirk K Lindberg Ingrid I Paulsson Emma C EC Wennerström E Christina M ECM Tjärnlund Anna A Noel Wim W Enkusson Dana D Theander Elke E

Rheumatology advances in practice 20201219 2

<h4>Objectives</h4>TNF inhibitors (TNFis) and IL inhibitors are effective treatments for PsA. Treatment non-persistence (drug survival, discontinuation) is a measure of effectiveness, tolerability and patient satisfaction or preferences in real-world clinical practice. Persistence on these treatments is not well understood in European PsA populations. The aim of this study was to compare time to non-persistence for either ustekinumab (IL-12/23 inhibitor) or secukinumab (IL-17 inhibitor) to a ref ...[more]

PMID: 33409449

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Project description:BackgroundAs the treatment arsenal for children with juvenile idiopathic arthritis (JIA) has expanded during the last decades, follow-up studies are needed on children diagnosed in the era of biological treatment to evaluate if this has improved the outcome. Our aim was to study the epidemiology and outcome of JIA in southern Sweden using a population-based cohort of children with a validated diagnosis of JIA collected over 9 years.MethodsPotential cases of JIA between 2002 and 2010 were collected after a database search, using the ICD codes M08-M09. The study area was Skåne, the southernmost county of Sweden (population 1.24 million; 17.6% aged <?16?years). The JIA diagnosis was validated and subcategorized through medical record review based on the criteria defined by the International League of Associations for Rheumatism (ILAR). Parameters on disease activity and pharmacologic treatment were recorded annually until the end of the study period (December 31, 2015).ResultsIn total, 251 cases of JIA were confirmed. The mean annual incidence rate for JIA was estimated to be 12.8/100,000 children <?16?years, with the highest age-specific annual incidence at the age of 2 years (36/100,000). Oligoarthritis was the largest subgroup (44.7%), and systemic JIA was the smallest subgroup (2.8%). Methotrexate was the most common disease-modifying anti-rheumatic drug prescribed (60.6%). Tumor necrosis factor alpha inhibitors were used as treatment for 23.9% of the children. Only 40.0% of the follow-up years, with a median follow-up time of 8 years, were free of arthritis or uveitis. Uveitis occurred in 10.8% of the children (8.0% chronic uveitis), and the need for joint corrective orthopedic surgery was 9.2%.ConclusionsThe incidence of JIA in this well-defined, population-based cohort is slightly lower than in previously published studies from Scandinavia. The need for orthopedic surgery and the presence of uveitis are diminished compared to studies with patients diagnosed more than 20?years ago. Children with JIA however still experience disease activity more than 50% of the time. In conclusion, we still have long-term challenges in the care for children with JIA, in spite of state-of-the-art treatment.

Project description:BackgroundPsoriasis (PsO) is a chronic, systemic, immune-mediated disease with multiorgan involvement. Psoriatic arthritis (PsA) is an inflammatory arthritis that is present in 6%-42% of patients with PsO. Approximately 15% of patients with PsO have undiagnosed PsA. Predicting patients with a risk of PsA is crucial for providing them with early examination and treatment that can prevent irreversible disease progression and function loss.ObjectiveThe aim of this study was to develop and validate a prediction model for PsA based on chronological large-scale and multidimensional electronic medical records using a machine learning algorithm.MethodsThis case-control study used Taiwan's National Health Insurance Research Database from January 1, 1999, to December 31, 2013. The original data set was split into training and holdout data sets in an 80:20 ratio. A convolutional neural network was used to develop a prediction model. This model used 2.5-year diagnostic and medical records (inpatient and outpatient) with temporal-sequential information to predict the risk of PsA for a given patient within the next 6 months. The model was developed and cross-validated using the training data and was tested using the holdout data. An occlusion sensitivity analysis was performed to identify the important features of the model.ResultsThe prediction model included a total of 443 patients with PsA with earlier diagnosis of PsO and 1772 patients with PsO without PsA for the control group. The 6-month PsA risk prediction model that uses sequential diagnostic and drug prescription information as a temporal phenomic map yielded an area under the receiver operating characteristic curve of 0.70 (95% CI 0.559-0.833), a mean sensitivity of 0.80 (SD 0.11), a mean specificity of 0.60 (SD 0.04), and a mean negative predictive value of 0.93 (SD 0.04).ConclusionsThe findings of this study suggest that the risk prediction model can identify patients with PsO at a high risk of PsA. This model may help health care professionals to prioritize treatment for target high-risk populations and prevent irreversible disease progression and functional loss.

Dataset Information

Persistence of biologic treatments in psoriatic arthritis: a population-based study in Sweden.

Objectives

Methods

Results

Conclusion

Publications

Persistence of biologic treatments in psoriatic arthritis: a population-based study in Sweden.

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