18F GTP1 Tau PET image‐derived features improve the prediction of CDR‐SB progression in a multi‐modal model in prodromal to moderate AD

Abstract

AbstractBackgroundPrevious work from our group has shown that a model using a limited number of common clinical, demographic and MRI features can robustly predict CDR‐SB progression (Hibar et al. CTAD 2021) and that baseline Tau‐PET levels are associated with subsequent cognitive decline (Teng et al., 2021). However, the gain from combining these predictors has not previously been assessed. The aim of this work was to assess the additive prognostic value of including baseline [18F]GTP1 tau‐PET with the other common predictors.MethodBaseline [18F]GTP1 images and MRI features were available from two crenezumab (NCT02670083 and NCT03114657; n = 51) and two semorinemab (NCT03289143; n = 360 and NCT038287747 placebo arm; n = 113) randomized clinical trials in prodromal to moderate AD populations. The benchmark linear model included age, sex, APOE4 status, education, diagnosis, baseline ADAS‐Cog 11, ADCS‐ADL, CDR‐SB and MMSE and hippocampus, ventricles and whole brain volume as predictors. In addition, the extended model included the whole cortical grey and temporal meta‐ROI [18F]GTP1 SUVR. A gradient boosted decision tree ensemble (XGBoost) model aimed to uncover potential nonlinearities and interaction among the features. Model performance was evaluated by r2 using bootstrapped confidence intervals and calibration.ResultOur analyses included data from 524 participants (prodromal AD: 151, mild AD: 318, moderate AD: 55). The benchmark model yielded r2 = 0.175 [CI 0.122 – 0.241] with CDR‐SB in the overall sample. The [18F]GTP1 only model had r2 = 0.122 [0.071 – 0.185]. The extended linear model yielded r2 = 0.226 [0.163 – 0.299] and remained well calibrated. Point estimates of r2 were lower in the prodromal and mild versus the moderate subgroups. The performance of the XGBoost model on the holdout set (r2 = 0.230 [0.123 – 0.382]) was marginally better than the linear model, but results were variable among the diagnosis subgroups and further validation needs to be performed on a larger independent dataset.ConclusionThe extended model including [18F]GTP1 SUVR features showed increased performance relative to the benchmark model. Therefore, [18F]GTP1 SUVR features may provide additional, meaningful information about patient prognosis and may be used as covariate adjustment factors to enhance treatment efficacy estimates in clinical trials. Analysis using [18F]GTP1 SUVR in other brain regions is being evaluated.