Causal prediction models for medication safety monitoring: The diagnosis
  of vancomycin-induced acute kidney injury
Izak Yasrebi-de Kom Joanna Klopotowska Dave Dongelmans Nicolette De Keizer Kitty Jager Ameen Abu-Hanna Giovanni Cinà
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.