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 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.

Keywords: AutoScore ordinal algorithm; amyloid‐related imaging abnormalities; cerebral amyloid angiopathy; machine learning; risk prediction.

FIGURE 1

Feature ranking. The x axis displays the importance determined by the random forest feature selection, and the y axis lists the names of all the features included in the study. A wider bar indicates greater importance. APOE, apolipoprotein E; MMSE, Mini‐Mental State Examination.

FIGURE 2

Parsimony plot for the FCAAS‐4. The plot displays the average mean AUC‐ROC values as an increasing number of features are used by the FCAAS‐4 model. The number annotated on each bar represents the cumulative number of features used. Taller bars indicate better performance. APOE, apolipoprotein E; AUC‐ROC, area under the receiver operating characteristic curve; FCAAS‐4, Florey Cerebral Amyloid Angiopathy Score, 4‐category model; MMSE, Mini‐Mental State Examination.

FIGURE 3

Score intervals and risk probabilities for the FCAAS‐4 model. The x axis represents the FCAAS score intervals, and the y axis indicates the probability of CAA severity (color‐coded). As the FCAAS score increases, the probability of more severe CAA also increases. CAA, cerebral amyloid angiopathy; FCAAS‐4, Florey Cerebral Amyloid Angiopathy Score, 4‐category model.