Baseline characteristics associated with the first year treatment interval of intravitreal faricimab in neovascular age-related macular degeneration (nAMD).
{"title":"Baseline characteristics associated with the first year treatment interval of intravitreal faricimab in neovascular age-related macular degeneration (nAMD).","authors":"Parth Shah, Neala Rafijah, Yannan Tang, Sobha Sivaprasad, Thibaud Mathis, Philippe Margaron, Aachal Kotecha","doi":"10.1136/bmjophth-2024-001855","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>To identify baseline characteristics that best correlate to treatment interval for naive neovascular age-related macular degeneration patients treated with faricimab in the first year (Y1) of the TENAYA and LUCERNE phase 3 trials, and to further understand how these characteristics may impact treatment intervals.</p><p><strong>Methods: </strong>This post-hoc analysis of Y1 data from the TENAYA and LUCERNE trials evaluated ocular baseline characteristics associated with Y1 treatment intervals. Patients were categorised into three subgroups based on their Y1 treatment interval: Q16W, Q12W or Q8W. Baseline characteristics (central subfield thickness (CST), best-corrected visual acuity, presence of subretinal fluid in centre 1 mm, presence of retinal fluid in centre 1 mm, macular neovascularisation (MNV) location and MNV type) were inputted into an R package 'rpart' to create a classification tree model. A data-driven tree model based on CST was fitted, producing CST subgroups of low, middle and high ranges. Within each CST subgroup, the model identified the most impactful variables and associated thresholds.</p><p><strong>Results: </strong>After fitting the data to produce data-driven CST ranges, the model chose MNV location, followed by MNV lesion type as the most impactful baseline characteristics with these factors having a p value <0.05 in a multivariate analysis.</p><p><strong>Conclusions: </strong>Among the selected ocular baseline characteristics from TENAYA and LUCERNE trial, CST, MNV type and MNV location were seen as the most relevant variables to enable extension of treatment intervals during Y1. While this analysis provides insights for treatment intervals during the first year, further analysis incorporating Y2 data from the TENAYA and LUCERNE studies will be needed to assess factors influencing treatment intervals over a longer period.</p>","PeriodicalId":9286,"journal":{"name":"BMJ Open Ophthalmology","volume":"9 1","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11448128/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMJ Open Ophthalmology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1136/bmjophth-2024-001855","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Aims: To identify baseline characteristics that best correlate to treatment interval for naive neovascular age-related macular degeneration patients treated with faricimab in the first year (Y1) of the TENAYA and LUCERNE phase 3 trials, and to further understand how these characteristics may impact treatment intervals.
Methods: This post-hoc analysis of Y1 data from the TENAYA and LUCERNE trials evaluated ocular baseline characteristics associated with Y1 treatment intervals. Patients were categorised into three subgroups based on their Y1 treatment interval: Q16W, Q12W or Q8W. Baseline characteristics (central subfield thickness (CST), best-corrected visual acuity, presence of subretinal fluid in centre 1 mm, presence of retinal fluid in centre 1 mm, macular neovascularisation (MNV) location and MNV type) were inputted into an R package 'rpart' to create a classification tree model. A data-driven tree model based on CST was fitted, producing CST subgroups of low, middle and high ranges. Within each CST subgroup, the model identified the most impactful variables and associated thresholds.
Results: After fitting the data to produce data-driven CST ranges, the model chose MNV location, followed by MNV lesion type as the most impactful baseline characteristics with these factors having a p value <0.05 in a multivariate analysis.
Conclusions: Among the selected ocular baseline characteristics from TENAYA and LUCERNE trial, CST, MNV type and MNV location were seen as the most relevant variables to enable extension of treatment intervals during Y1. While this analysis provides insights for treatment intervals during the first year, further analysis incorporating Y2 data from the TENAYA and LUCERNE studies will be needed to assess factors influencing treatment intervals over a longer period.