Rapid Universal Early Screening for Alzheimer's Disease and Related Dementia via Pattern Discovery in Diagnostic History

Alzheimer’s disease (AD) is a progressive, incurable and ultimately fatal neurodegenerative condition. In this study, we introduce the Zero-burden Co-morbid Risk (ZCoR) score to screen for the future risk of AD and related dementia (ADRD) 1 − 10 years before a clinical diagnosis. Requiring no new bloodwork or cognitive tests, ZCoR leverages uncharted comorbidity patterns, to potentially enable near-instantenous universal point-of-care screening of entire patient populations. In validation, ZCoR (n = 729, 018) achieves out-of-sample AUC > 90% for predicting a diagnosis immediately after screening, an AUC > 87% for a diagnosis made one year earlier than in current practice, and maintaining over > 80% AUC for predictions made a decade earlier, irrespective of sex. We achieve high predictability in patient slacking any of the currently suspected risk factors; demonstrating effectiveness in cohorts at higher risk of missed diagnoses. Additionally, ZCoR can target mild cognitive impairment (MCI) with performance at par with questionnaire-based assessments (AUC 88 − 90%), maintaining high effectiveness (AUC ≈ 80%) for predicting impairment up to 3 years into the future. Powered by stochastic learning algorithms that enhance standard machine learning, ZCoR enables discovery in electronic heath record databases, can reduce ADRD and MCI diagnostic delays, and the impact of socio-economic and demographic variables, with immediate impact on patient outcomes.