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:

Establish and validate a causal graph of elective surgery in a hospital setting

Test whether causal inference can be used in algorithm development to improve generalisation of predictive models to different patient cohorts

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

The authors have declared no competing interest.

This study did not receive any funding

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Yes

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Yes

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