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Optimizing the Diagnostic Journey in Interstitial Lung Disease: The OPTIMIZE-ILD-1 Trial
The OPTIMIZE-ILD-1 trial is a prospective, randomized, open-label clinical trial designed to evaluate the impact of a coordinated diagnostic pathway on patients with suspected interstitial lung disease (ILD). In routine clinical practice, diagnostic workflows for ILD are frequently fragmented, involving multiple independent appointments that can lead to significant delays and increased burden for patients and caregivers. This study compares the standard diagnostic pathway against an optimized circuit where core diagnostic procedures-such as high-resolution CT, pulmonary function tests, and laboratory panels-are pre-bundled and scheduled within a coordinated and compressed timeframe. All eligible patients referred for suspected ILD are included consecutively to ensure a pragmatic, real-world representation of the referral population. The primary objective is to measure the time to diagnostic communication, defined as the duration from randomization to the date the patient is formally informed of the final diagnosis following a multidisciplinary team (MDT) consensus. Secondary objectives include assessing the time to MDT diagnosis, the time to treatment initiation (when clinically indicated), socioeconomic cost-burden, and the environmental carbon footprint of the diagnostic journey. Furthermore, the study evaluates health-related quality of life, psychological distress, and clinical frailty, while exploring factors such as language proficiency as determinants of diagnostic equity. Caregiver-related outcomes, including burden and experience measures, are contingent upon the presence of a primary caregiver and the provision of their independent informed consent. The design of this protocol was informed by a patient focus group and is officially endorsed by the 'AIRE' Associació Catalana de Malalts i Trasplantats Pulmonars, ensuring a patient-centered approach that prioritizes the diagnostic journey's efficiency and human impact.
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