The Bayesian approach for real‐world implementation of plasma p‐tau217 in tertiary care memory clinics in Thailand

Abstract

AbstractINTRODUCTIONPlasma phosphorylated tau (p‐tau)217 is a promising biomarker for Alzheimer's disease (AD) diagnosis, but its clinical implementation remains challenging. We propose a strategy based on Bayes’ theorem and test it in real‐life memory clinics.METHODSMemory clinic patients were evaluated by neurocognitive specialists for prespecified diagnosis and subsequently underwent blood collection for p‐tau217, cerebrospinal fluid, or amyloid positron emission tomography. Using cross‐validation, the Bayesian approach (pretest probability × individualized likelihood ratio) was compared to other models for AD diagnosis.RESULTSThe Bayesian strategy demonstrated an area under the receiver operating characteristic curve (AUC) of 0.98 (95% confidence interval [CI]: 0.96–1.0), significantly outperforming multivariable logistic regression (p‐tau217, age, apolipoprotein E; AUC 0.95, p = 0.024) and p‐tau217 alone (AUC = 0.94, p = 0.007). When applying the two‐threshold approach, the Bayesian strategy yielded an accuracy of 0.94 (95% CI: 0.88–1.0) without requiring confirmatory tests in 62.9% of the iterations.DISCUSSIONThe Bayesian strategy offers an effective and flexible approach to address the limitations of plasma p‐tau217 in clinical practice.Highlights Incorporating pretest probability into the interpretation of plasma phosphorylated tau (p‐tau)217 improves the diagnostic performance significantly. The strategy could obviate the need for confirmatory testing in most of the patients. Plasma p‐tau217 proves useful as a biomarker for Alzheimer's disease in low‐ and middle‐income country such as Thailand.