Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury
This work addresses medication safety monitoring for hospitalized patients, offering a data-driven alternative to manual expert reviews, but it is incremental as it builds on existing causal inference methods.
The authors tackled the problem of diagnosing adverse drug events like vancomycin-induced acute kidney injury by developing a causal modeling approach using observational data to estimate a lower bound of the probability of causation, comparing it to expert qualitative estimates.
The current best practice approach for the retrospective diagnosis of adverse drug events (ADEs) in hospitalized patients relies on a full patient chart review and a formal causality assessment by multiple medical experts. This evaluation serves to qualitatively estimate the probability of causation (PC); the probability that a drug was a necessary cause of an adverse event. This practice is manual, resource intensive and prone to human biases, and may thus benefit from data-driven decision support. Here, we pioneer a causal modeling approach using observational data to estimate a lower bound of the PC (PC$_{low}$). This method includes two key causal inference components: (1) the target trial emulation framework and (2) estimation of individualized treatment effects using machine learning. We apply our method to the clinically relevant use-case of vancomycin-induced acute kidney injury in intensive care patients, and compare our causal model-based PC$_{low}$ estimates to qualitative estimates of the PC provided by a medical expert. Important limitations and potential improvements are discussed, and we conclude that future improved causal models could provide essential data-driven support for medication safety monitoring in hospitalized patients.