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• W2897319627 abstract "Personalized risk predictions are highly relevant in light of future disease modifying drugs. Previously, we developed prognostic models based on patient characteristics (demographic model), CSF biomarkers (CSF model) and MRI biomarkers (Hippocampal volume (HCV) model). Widespread implementation of these models is hampered by the fact that values of CSF concentrations and MRI measurements vary considerably across different methods. Here, we update these models to ensure generalizability across different cohorts, CSF platforms and MRI methods. 2610 patients with mild cognitive impairment (MCI) (mean age=70±8, 44%F) were included from four large mono- and multicenter cohorts; European Medical Information Framework (EMIF-AD, n=883; we included data from 3 multicentre studies and 5 single center studies in Europe), Alzheimer's Disease Neuroimaging Initiative (ADNI, n=829), Amsterdam Dementia Cohort (ADC, n=665) and Swedish BioFINDER study (n=233). All biomarkers were centered within each cohort to remove measuring scale. Clinical end-point was progression to Alzheimer's disease dementia (AD). To assess prognostic performance in each cohort, we performed a Cox regression on the prognostic index (PI) and report Harrell's C-statistics for each cohort. If discrimination was less than originally reported, we refitted the regression coefficients to improve the model or additional variables were included. During 3±2 years follow up, 849 (33%) MCI patients showed clinical progression to AD. For the demographic and the HCV model, model performance is equal across different cohorts (Table 1). However, the CSF model performed worse than in the original publication in all cohorts. We updated the model by adding age and an interaction between age and Abeta and this improved the CSF model (LR chi2(5)=263.25, p<0.001, Harrell's C=0.77)(Table 1). For all models the observed and predicted probabilities were largely comparable (Table 2). We showed that personalized risk models are able to discriminate between stable and progressive MCI patients and that the personalized predictions are well calibrated across different cohorts and biomarker methods. As a next step, the models will be fine-tuned to allow use of unadjusted CSF and MRI values to further facilitate translation to clinical practice. References: 1. Royston P, Altman DG. External validation of a Cox prognostic model: principles and methods. BmcMed Res Methodol. 2013;13." @default.
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• W2897319627 date "2018-07-01" @default.
• W2897319627 modified "2023-10-02" @default.
• W2897319627 title "O2‐15‐04: ROBUST INDIVIDUALIZED PREDICTION MODELS WHICH ARE APPLICABLE ACROSS DIFFERENT COHORTS" @default.
• W2897319627 doi "https://doi.org/10.1016/j.jalz.2018.06.2727" @default.
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