Background: The Addenbrooke’s Cognitive Examination-III (ACE-III) is a brief cognitive screening tool which is widely used for the detection and monitoring of dementia. Recent findings suggest that the three variants of primary progressive aphasia (PPA) can be distinguished based on their distinct profiles on the five ACE-III subdomain scores. Here, we investigated the utility of the ACE-III to differentiate the PPA variants based on their item-by-item performance profiles on this test. From these results, we created an interactive PPA ACE-III diagnostic calculator which predicts the PPA variant based on a patient’s unique ACE-III item-by-item profile.
Method: Twenty-eight logopenic (lv-PPA), 25 non-fluent (nfv-PPA), and 37 semantic (sv-PPA) variant PPA patients and 104 healthy controls completed the ACE-III at first clinical presentation. Multinomial regression analyses were conducted to establish ACE-III performance profiles among PPA variants; and R Shiny from RStudio was used to create the interactive ACE-III PPA diagnostic calculator. To verify its accuracy, probability values of the regression model were derived based on a 5-fold cross validation of cases. The calculator’s accuracy was then verified in an independent sample of 17 lv-PPA, 19 nfv-PPA, and 13 sv-PPA patients and 68 Alzheimer’s disease patients who had completed the ACE-III or ACE-Revised (ACE-R) and had in vivo amyloid-PET imaging/and or brain autopsy pathological confirmation.
Results: Cross-validation of cases in the calculator model revealed variables rates of accuracy in classifying PPA variants: sv-PPA = 80.1%, nfv-PPA = 71.2%, lv-PPA = 60.9%; healthy controls were distinguished from PPA with an accuracy rate of 99.8%. Verification of in vivo amyloid and/or autopsy confirmed patients showed that the calculator correctly classified 10/13 (77%) sv-PPA, 3/19 (16%) nfv-PPA, and 4/17 (24%) lv-PPA patients. Importantly, for patients who were not classified, diagnostic probability values mostly pointed toward the correct diagnosis. Further, misclassified diagnoses were rare (1/49; 2%). While 22 out of the 68 AD patients (32%) were misclassified with PPA, the majority of these patients were misclassified with lv-PPA (19/22; i.e., falling within the same neuropathological entity), with only 1 misclassified as nfv-PPA and 2 as sv-PPA.
Conclusion: The ACE-III-PPA diagnostic calculator is a promising adjunct to the ACE-III. It demonstrates sound precision in differentiating the PPA variants based on an item-by-item ACE-III profile and is freely available for use in clinical settings. This calculator represents a new frontier in using data-driven approaches to diagnose the PPA variants.
