Leveraging structured EMR data for efficient patient prescreening: a practical approach to reducing screen-failure rates in Light Touch Trial

Le résumé fourni par la source

Screening for clinical trials represents a significant challenge in ophthalmic research due to inefficiencies and costs related to manual review of medical records and ocular imaging [ 1 , 2 , 3 , 4 ]. Screen failures rates are also high. This constraint is particularly acute in competitive therapeutic areas like neovascular age-related macular degeneration (nAMD), where the identification of eligible patients from a large population of patients undergoing treatment requires meticulous extraction of a highly specific patient subgroup [ 5 , 6 , 7 ]. Although manual prescreening is a common practice, the implementation of systematic, automated prescreening using electronic medical record (EMR) data is an emerging and underutilized methodology in ophthalmology trials [ 8 ]. Penberthy et al. estimated screening costs to be up to $336.48 per enrolled patient, but this figure considered only personnel expenses for research nurses and clinical associates [ 9 ]. For a modern nAMD trial, however, where screening includes advanced imaging and multiple complex assessments, the costs are substantially higher. For instance, within the Light Touch study—a multicentre randomized controlled phase III non-inferiority clinical trial comparing the effectiveness of a lighter versus standard initial dosing regimen of faricimab in pretreated nAMD patients—a screening visit comprising visual acuity checks with refraction, spectral-domain optical coherence tomography (SD-OCT), OCT-angiography (OCTA), and a suite of patient-reported outcomes carries an estimated cost of £2,500 ($3,125 USD) per screened patient. In this commentary, we detail the integration and outcomes of a pragmatic, two-step prescreening strategy for the Light Touch Trial [ 10 ]. Our experience offers a replicable model for substantially improving trial efficiency.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.