Literature DB >> 26907503

Longitudinal Treatment Patterns and Associated Outcomes in Patients With Newly Diagnosed Systemic Lupus Erythematosus.

Hong Kan1, Saurabh Nagar2, Jeetvan Patel2, Daniel J Wallace3, Charles Molta4, David J Chang4.   

Abstract

PURPOSE: The treatment of systemic lupus erythematosus (SLE) is complex, with a wide range of drugs commonly prescribed. The aims of this study were to identify longitudinal treatment patterns in patients with incident SLE and to estimate the associations of treatment patterns with clinical and economic outcomes.
METHODS: This retrospective, observational cohort study used a US managed care claims database to identify patients with newly diagnosed SLE and 4-year treatment follow-up. Patients were aged ≥ 18 years, with continuous medical and pharmacy benefits for 12 months before and 48 months after the index date (first medical claim with a diagnosis of SLE). Longitudinal treatment patterns were grouped using a k-means cluster analysis. Therapies were included in the cluster analysis if the mean number of prescriptions in each year was ≥ 0.05. Clinical and economic outcomes were compared across clusters using multivariate regression analyses.
FINDINGS: Data from 1611 patients with incident SLE were analyzed (91.4% women; mean [SD] age, 44.5 [9.5] years; 56.2% managed primarily by a specialist). Hydroxychloroquine and corticosteroids were the most commonly prescribed therapies; methotrexate, azathioprine, and mycophenolate mofetil also met the criteria for inclusion in the cluster analysis. Ten treatment clusters were identified; the most common was minimally treated patients (42.8%). Hydroxychloroquine monotherapy, corticosteroid monotherapy, and corticosteroid/hydroxychloroquine combination therapy were received by 34.0%, 11.2%, and 7.8% of patients, respectively. Methotrexate or azathioprine with a corticosteroid/hydroxychloroquine were received by 4.2% of patients. Changes in therapy, except discontinuations, were rare. Compared with the minimally treated cluster, those that received corticosteroid monotherapy (mean dose, >12.0 mg/d) had poorer clinical and economic outcomes; the hydroxychloroquine-monotherapy cluster had similar or better outcomes; and patients who received a corticosteroid/hydroxychloroquine with or without methotrexate or azathioprine demonstrated outcomes that were poorer but that appeared better than those with corticosteroid monotherapy. SLE-related visits with a nonspecialist were common (~45%) and remained unchanged over time despite better clinical and economic outcomes associated with specialist visits. IMPLICATIONS: This study utilized cluster analysis, an unsupervised machine-learning method, to systematically discern treatment patterns over 4 years and to estimate outcomes associated with the identified treatment patterns. The results suggest that minimal treatment is the most common approach in patients with newly diagnosed SLE. Clinical and economic outcomes are poorest with corticosteroid monotherapy but may improve with the addition of hydroxychloroquine and/or an immunosuppressive agent. A large proportion of SLE care is provided by nonspecialists despite the potential benefits of involving a specialist.
Copyright © 2016 Elsevier HS Journals, Inc. All rights reserved.

Entities:  

Keywords:  clinical outcomes; cluster analysis; economic outcomes; systemic lupus erythematosus; treatment patterns

Mesh:

Substances:

Year:  2016        PMID: 26907503     DOI: 10.1016/j.clinthera.2016.01.016

Source DB:  PubMed          Journal:  Clin Ther        ISSN: 0149-2918            Impact factor:   3.393


  8 in total

Review 1.  A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.

Authors:  I S Stafford; M Kellermann; E Mossotto; R M Beattie; B D MacArthur; S Ennis
Journal:  NPJ Digit Med       Date:  2020-03-09

Review 2.  Machine Learning in Rheumatic Diseases.

Authors:  Mengdi Jiang; Yueting Li; Chendan Jiang; Lidan Zhao; Xuan Zhang; Peter E Lipsky
Journal:  Clin Rev Allergy Immunol       Date:  2021-02       Impact factor: 8.667

3.  Clinical and Serologic Features in Patients With Incomplete Lupus Classification Versus Systemic Lupus Erythematosus Patients and Controls.

Authors:  Teresa Aberle; Rebecka L Bourn; Melissa E Munroe; Hua Chen; Virginia C Roberts; Joel M Guthridge; Krista Bean; Julie M Robertson; Kathy L Sivils; Astrid Rasmussen; Meghan Liles; Joan T Merrill; John B Harley; Nancy J Olsen; David R Karp; Judith A James
Journal:  Arthritis Care Res (Hoboken)       Date:  2017-11-14       Impact factor: 4.794

4.  Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 1-Overview of Knowledge Discovery Techniques in Artificial Intelligence.

Authors:  Maurizio Sessa; Abdul Rauf Khan; David Liang; Morten Andersen; Murat Kulahci
Journal:  Front Pharmacol       Date:  2020-07-16       Impact factor: 5.810

5.  Disease and economic burden increase with systemic lupus erythematosus severity 1 year before and after diagnosis: a real-world cohort study, United States, 2004-2015.

Authors:  Miao Jiang; Aimee M Near; Barnabas Desta; Xia Wang; Edward R Hammond
Journal:  Lupus Sci Med       Date:  2021-09

6.  Characteristics and Symptom Severity of Patients Reporting Systemic Lupus Erythematosus in the PatientsLikeMe Online Health Community: A Retrospective Observational Study.

Authors:  Elisabeth Nyman; Timothy Vaughan; Barnabas Desta; Xia Wang; Volkan Barut; Cathy Emmas
Journal:  Rheumatol Ther       Date:  2020-02-01

7.  Update on prevalence of diagnosed systemic lupus erythematosus (SLE) by major health insurance types in the US in 2016.

Authors:  Yiting Wang; Laura L Hester; Jennifer Lofland; Shawn Rose; Chetan S Karyekar; David M Kern; Margaret Blacketer; Kourtney Davis; Kimberly Shields-Tuttle
Journal:  BMC Res Notes       Date:  2022-01-09

Review 8.  A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.

Authors:  I S Stafford; M Kellermann; E Mossotto; R M Beattie; B D MacArthur; S Ennis
Journal:  NPJ Digit Med       Date:  2020-03-09
  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.