Improving Predictive Models With Causal Methods -- Study Protocol

BackgroundIn a previous study, Kowadlo et al. [1] developed algorithms (POP - Patient OPtimizer) to predict outcomes for surgical patients at Austin Health. The POP algorithms predict postoperative complications, kidney failure, and hospital length-of-stay. The findings highlight the potential of risk prediction to improve health outcomes and justify further work to improve performance and generalisability. ObjectivesThe objectives are to: O_LIEstablish and validate a causal graph of elective surgery in a hospital setting C_LIO_LITest whether causal inference can be used in algorithm development to improve generalisation of predictive models to different patient cohorts C_LIO_LIImplement and test the concept of preventable risk, risk stratification that combines risk prediction with causal effect; to assist in decision making C_LI MethodIn order to achieve the objectives, we will: O_LIApply causal discovery methods to the INSPIRE dataset (Lim et al. [2]), to create a causal graph C_LIO_LIValidate the graph by combining clinical input with data analysis, to identify relevant confounding and collider variables C_LIO_LIMethodically control for confounders and colliders, while training and evaluating predictive models for length-of-stay, mortality, readmission or complications C_LIO_LIMeasure the generalisation of predictive models across patient populations, when controlling for identified confounders and colliders C_LIO_LIImplement Conditional Average Treatment Estimate (CATE) and combine it with risk prediction to calculate preventable risk C_LI