The utility of the digital clock drawing test as an accurate early screening tool for those with preclinical Alzheimer’s disease

Abstract

AbstractBackgroundWith new drug treatments emerging, the need for early Alzheimer’s disease (AD) detection methods that can be done in a primary care setting has become urgent. Significant advances have been made in validating blood‐based AD biomarkers, but the absence of a validated, non‐invasive clinical indicator remains a barrier for determining those who might be optimal candidates for treatment. The digital clock drawing test (dCDT) has been previously shown to be related to AD PET biomarkers. However, it is unknown whether and the extent to which dCDT would predict AD as compared to blood‐based AD biomarkers. The current study used an interpretable machine learning approach to determine whether the dCDT, independently and in combination with blood‐based AD biomarkers (e.g., amyloid‐β 42 (Aβ42); total tau (T‐tau)), was predictive of incident AD.MethodThis study included participants from the Framingham Heart Study Generation 2 cohort (Gen 2; n = 527) who had dCDT measures, plasma Aβ42, and T‐tau. The fast interpretable greedy‐tree sums method was used to identify clinical decision rules for AD prediction. Synthetic minority oversampling technique was employed to handle the class imbalance. The predictive ability of plasma Aβ42, T‐tau and dCDT measures was evaluated by both area under the curve (AUC) and standardized partial AUC. The permutation feature importance method was used to show the importance of each measure.ResultInterpretable clinical decision rules were generated as multivariate biomarker signatures for AD prediction (Figure 1). The dCDT (AUC 0.859) was better at incident AD prediction than AD blood‐based biomarkers (AUC 0.839). The combination of dCDT, Aβ42, and T‐tau best predicted incident AD (AUC 0.884) (Figure 2). The combination of dCDT and blood‐based biomarkers also has higher partial AUC than individual ones (Table 1). The total score and information processing scores were the most important contributing dCDT measures.ConclusionThese results highlight the efficiency of detecting early AD through the integration of digital measures as a clinical indicator combined with AD specific biomarkers. The interpretable decision rules have the potential to provide automated results that can be used in practical clinical settings, although external validation is warranted.