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

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 a mean AUC-ROC of 0.82 (0.71-0.85) and Harrell generalized c-index 0.79 (0.73-0.82).

DISCUSSION: The FCAAS models demonstrate a promising performance in predicting CAA severity. This framework holds the potential for predicting development of amyloid-related imaging abnormalities (ARIAs), given the CAA-ARIAs link.

HIGHLIGHTS: The severity of cerebral amyloid angiopathy (CAA) can only be determined post mortem. A web tool, the Florey CAA Score (FCAAS), was developed to predict CAA severity. The FCAAS holds the potential to be used for CAA risk stratification in clinics. CAA is linked to increased risk of amyloid-related imaging abnormalities (ARIAs). The framework used by FCAAS can possibly be adapted to predict ARIAs risk.