Developing and validating a prediction tool for cerebral amyloid angiopathy neuropathological severity.

ABSTRACT:

Introduction

Cerebral amyloid angiopathy (CAA) is a cerebrovascular condition, the severity of which can only be determined post mortem. Here, we developed machine learning models, the Florey CAA Score (FCAAS), to predict CAA severity (none/mild/moderate/severe).

Methods

Building on an auto-score-ordinal algorithm, the FCAAS models were developed and validated using data collected by three cohort studies of aging and dementia. The developed FCAAS models were digitized as a web-based tool. A pilot trial was conducted using this web-based tool.

Results

The FCAAS-4 achieved a mean area under the receiver operating characteristic curve (AUC-ROC) of 0.74 (95% confidence interval: 0.71-0.77) and a Harrell generalized c-index of 0.72 (0.70-0.75). Pilot trial results obtained

SUBMITTER: Chu C 

PROVIDER: S-EPMC11881621 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature