BACKGROUND: The Trial-Ready Cohort for Preclinical/Prodromal Alzheimer's Disease (TRC-PAD) Informatics Platform (TRC-PAD IP) was developed to facilitate the efficient selection, recruitment, and assessment of study participants in support of the TRC-PAD program. OBJECTIVES: Describe the innovative architecture, workflows, and components of the TRC-PAD IP. DESIGN: The TRC-PAD IP was conceived as a secure, scalable, multi-tiered information management platform designed to facilitate high-throughput, cost-effective selection, recruitment, and assessment of TRC-PAD study participants and to develop a learning algorithm to select amyloid-bearing participants to participate in trials of early-stage Alzheimer's disease. SETTING: TRC-PAD participants were evaluated using both web-based and in-person assessments to predict their risk of amyloid biomarker abnormalities and eligibility for preclinical and prodromal clinical trials. Participant data were integrated across multiple stages to inform the prediction of amyloid biomarker elevation. PARTICIPANTS: TRC-PAD participants were age 50 and above, with an interest in participating in Alzheimer's research. MEASUREMENTS: TRC-PAD participants' cognitive performance and subjective memory concerns were remotely assessed on a longitudinal basis to predict participant risk of biomarker abnormalities. Those participants determined to be at the highest risk were invited to an in-clinic screening visit for a full battery of clinical and cognitive assessments and amyloid biomarker confirmation using positron emission tomography (PET) or lumbar puncture (LP). RESULTS: The TRC-PAD IP supported growth in recruitment, screening, and enrollment of TRC-PAD participants by leveraging a secure, scalable, cost-effective cloud-based information technology architecture. CONCLUSIONS: The TRC-PAD program and its underlying information management infrastructure, TRC-PAD IP, have demonstrated feasibility concerning the program aims. The flexible and modular design of the TRC-PAD IP will accommodate the introduction of emerging diagnostic technologies.
BACKGROUND: The Trial-Ready Cohort for Preclinical/Prodromal Alzheimer's Disease (TRC-PAD) Informatics Platform (TRC-PAD IP) was developed to facilitate the efficient selection, recruitment, and assessment of study participants in support of the TRC-PAD program. OBJECTIVES: Describe the innovative architecture, workflows, and components of the TRC-PAD IP. DESIGN: The TRC-PAD IP was conceived as a secure, scalable, multi-tiered information management platform designed to facilitate high-throughput, cost-effective selection, recruitment, and assessment of TRC-PAD study participants and to develop a learning algorithm to select amyloid-bearing participants to participate in trials of early-stage Alzheimer's disease. SETTING: TRC-PAD participants were evaluated using both web-based and in-person assessments to predict their risk of amyloid biomarker abnormalities and eligibility for preclinical and prodromal clinical trials. Participant data were integrated across multiple stages to inform the prediction of amyloid biomarker elevation. PARTICIPANTS: TRC-PAD participants were age 50 and above, with an interest in participating in Alzheimer's research. MEASUREMENTS: TRC-PAD participants' cognitive performance and subjective memory concerns were remotely assessed on a longitudinal basis to predict participant risk of biomarker abnormalities. Those participants determined to be at the highest risk were invited to an in-clinic screening visit for a full battery of clinical and cognitive assessments and amyloid biomarker confirmation using positron emission tomography (PET) or lumbar puncture (LP). RESULTS: The TRC-PAD IP supported growth in recruitment, screening, and enrollment of TRC-PAD participants by leveraging a secure, scalable, cost-effective cloud-based information technology architecture. CONCLUSIONS: The TRC-PAD program and its underlying information management infrastructure, TRC-PAD IP, have demonstrated feasibility concerning the program aims. The flexible and modular design of the TRC-PAD IP will accommodate the introduction of emerging diagnostic technologies.
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Authors: S Walter; T B Clanton; O G Langford; M S Rafii; E J Shaffer; J D Grill; G A Jimenez-Maggiora; R A Sperling; J L Cummings; P S Aisen Journal: J Prev Alzheimers Dis Date: 2020
Authors: S Walter; O G Langford; T B Clanton; G A Jimenez-Maggiora; R Raman; M S Rafii; E J Shaffer; R A Sperling; J L Cummings; P S Aisen Journal: J Prev Alzheimers Dis Date: 2020
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