Improving Predictive Models With Causal Methods – Study Protocol
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Background
In 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.
Objectives
The objectives are to:
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Establish and validate a causal graph of elective surgery in a hospital setting
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Test whether causal inference can be used in algorithm development to improve generalisation of predictive models to different patient cohorts
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Implement and test the concept of ‘preventable risk’, risk stratification that combines risk prediction with causal effect; to assist in decision making
Method
In order to achieve the objectives, we will:
Apply causal discovery methods to the INSPIRE dataset (Lim et al. [2]), to create a causal graph
Validate the graph by combining clinical input with data analysis, to identify relevant confounding and collider variables
Methodically control for confounders and colliders, while training and evaluating predictive models for length-of-stay, mortality, readmission or complications
Measure the generalisation of predictive models across patient populations, when controlling for identified confounders and colliders
Implement Conditional Average Treatment Estimate (CATE) and combine it with risk prediction to calculate preventable risk
