Item request has been placed! ×
Item request cannot be made. ×
loading  Processing Request

Features Associated with Therapy Switch Among PPD CorEvitas Psoriasis Registry Patients.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
  • معلومة اضافية
    • نبذة مختصرة :
      Introduction: Switching biologics within or across classes can improve outcomes for patients with psoriasis who failed to meet their treatment goals on their original therapy. The objective of this study was to identify real-world baseline features which are associated with switching psoriasis therapies following sustained use of a biologic therapy. Methods: The study was a retrospective analysis of the prospective, multicenter, non-interventional PPD™ CorEvitas™ Psoriasis Registry cohort. Patient sociodemographics, comorbidities, treatment history, disease activity, and patient-reported outcome measures were assessed at baseline visits, along with changes in disease activity and treatment at follow-up visits. Patients were classified at each follow-up visit as either switchers from one biologic therapy to another or non-switchers. Three analytic strategies—logistic regression, random forest, and decision trees—were used to identify features associated with switching. Results: Patients contributed 14,729 follow-up visits, of which 995 episodes (6.8%) reflected a switch in biologic therapy. In logistic regression models, statistically significant associations with switching were seen for features including body surface area (BSA) involvement at baseline, change in BSA involvement from baseline to follow-up, and addition of at least one non-biologic systemic medication to treatment between baseline and follow-up. In random forest estimations, these three variables along with patient-reported fatigue and quality of life were determined to be most important. Finally, in the decision tree analysis, four subgroups of patients with moderate/severe BSA involvement at baseline in combination with other specific variables were identified as having a > 50% likelihood of switching. Conclusion: Identification and recognition of these features and combinations thereof can facilitate shared decision-making between clinicians and patients to improve both outcomes of and patient satisfaction with biologic therapy. Plain Language Summary: Switching biologic medications can help patients with psoriasis who are not achieving their treatment goals with their current therapy after sustained (at least 6 months) use of their current therapy. This study aimed to identify common characteristics of patients from the PPD™ CorEvitas™ Psoriasis Registry who switched their psoriasis treatments after using a biologic therapy for some time; that is, to identify factors, other than waning effectiveness, that influence therapy switch after some success of their given therapy. Researchers looked at patient demographics, other health conditions, treatment history, disease activity, and patient-reported outcomes at the start of the study and during follow-up visits. Patients were categorized as either switchers (those who changed biologic therapies) or non-switchers at each follow-up visit. Three statistical approaches—logistic regression, random forest analysis, and decision tree analysis—were used to find factors linked to switching treatments. Logistic regression and random forest analyses highlighted factors such as the extent of skin involvement (body surface area, BSA) at the start, changes in BSA over time, adding non-biologic systemic medications, patient-reported fatigue and quality of life as being significantly associated with switching. Decision tree analyses identified four distinct patient groups with moderate/severe BSA involvement at baseline and other specific factors as having a higher likelihood of switching. Recognizing these characteristics can help doctors and patients with shared decision-making to meet patient therapy needs. [ABSTRACT FROM AUTHOR]
    • نبذة مختصرة :
      Copyright of Dermatology & Therapy is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)