Development and validation of the Florey Dementia Risk Score web-based tool to screen for Alzheimer's disease in primary care

Background It is estimated that similar to 60% of people with Alzheimer's disease (AD) are undetected or undiagnosed, with higher rates of underdiagnosis in low-to middle-income areas with limited medical resources. To promote health equity, we have developed a web-based tool that utilizes easy-to-collect clinical data to enhance AD detection rate in primary care settings. Methods This study was leveraged on the data collected from participants of the Australian Imaging, Biomarker & Lifestyle (AIBL) study and the Religious Orders Study and Memory and Aging Project (ROSMAP). The study included three phases: (1) constructing and evaluating a model on retrospective cohort data (1407 AIBL participants), (2) performing simulated trials to assess model accuracy (30 AIBL participants) and missing data tolerability (30 AIBL participants), and (3) external evaluation using a non-Australian dataset (500 ROSMAP participants). The auto-score machine learning algorithm was employed to develop the Florey Dementia Risk Score (FDRS). All the simulated trials and evaluation were performed using a web-based FDRS tool. Findings FDRS achieved an area under the curve (AUC) of approximately 0.82 [95% CI, 0.75-0.88], - 0.88], with a sensitivity of 0.74 [0.60-0.86] - 0.86] and a specificity fi city of 0.73 [0.70-0.79]. - 0.79]. The accuracy of the simulated pilot trial for 30 AIBL participants with complete record was 87% (26/30 correct), while it only slightly decreased (80.0-83.3%, - 83.3%, depending on imputation methods) for another 30 AIBL participants with one or two missing data. FDRS achieved an AUC of 0.82 [0.77-0.86] - 0.86] of 500 ROSMAP participants. Interpretation The FDRS tool offers a potential low-cost solution to AD screening in primary care. The present study warrants future trials of FDRS for optimization and to confirm fi rm its generalizability across a more diverse population, especially people in low-income countries. 2024;76: Published https://doi.org/10. 1016/j.eclinm.2024. 102834