Understanding Frailty: Probabilistic Causality between Components and Their Relationship with Death through a Bayesian Network and Evidence Propagation

Abstract

Identifying relationships between components of an index helps to gain a better understanding of the condition they define. The Frailty Index (FI) measures the global health of individuals and can be used to predict outcomes as mortality. Previously, we modelled the relationship between the FI components (deficits) and death through an undirected graphical model and a social network analysis framework. Here, we model the FI components and death through an averaged Bayesian network obtained through a structural learning process and resampling, in order to understand how the FI components and death are causally related. We identified that components are not similarly related between them and that deficits are related according to their type. Two deficits were the most relevant in terms of their connections, and two others were directly associated with death. We obtained the strength of the relationships in order to identify the most plausible, identifying clusters of deficits. Finally, we propagated evidence and studied how FI components predict mortality, obtaining a correct assignation of almost 74% and a true positive rate (TPR) of 56%. Values were obtained after changing the model threshold (via Youden’s Index maximization) whose possible values are represented in a Receiving Operating Characteristic (ROC) curve (TPR vs. 1-True Negative Rate). The greater number of deficits included for the evidence, the best performances; nevertheless, the FI does not seem to be quite efficient to correctly differentiate between dead and living people.