Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury

• URL: http://arxiv.org/abs/2311.09137v1
• Date: Wed, 15 Nov 2023 17:29:24 GMT
• Title: Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury
• Authors: Izak Yasrebi-de Kom, Joanna Klopotowska, Dave Dongelmans, Nicolette De Keizer, Kitty Jager, Ameen Abu-Hanna, Giovanni Cin\`a
• Abstract summary: Current best practice 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 medical experts. Here, we pioneer a causal modeling approach using observational data to estimate a lower bound of the probability of causation (PC) We apply our method to the clinically relevant use-case of vancomycin-induced acute kidney injury in intensive care patients.
• Score: 0.282736966249181
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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