The Causal Artificial Intelligence Clinician for early haemodynamic management of septic shock in ICU

Introduction: Standardizing fluid and vasopressor resuscitation in sep- tic shock is challenging due to patient heterogeneity. We trained a causal model to identify optimal dosing during the first six hours of intensive care unit (ICU) admission. Methods: Graphical causal inference models were applied to estimate het- erogeneous treatment effects. Grounding models in expert clinical knowl- edge minimizes bias from spurious correlations to generate robust, contextu- ally meaningful recommendations. Our model was trained on 1,702 MIMIC database admissions and externally validated on 1,434 eICU admissions. Pri- mary outcomes were in-hospital survival and 24-hour clinical improvement (SOFA score reduction of two points or more). Findings: The cohort comprised 3,136 participants (median age 65 years [IQR 53-75]; 42.7% female). Deviation from vasopressor recommendations was associated with increased in-hospital mortality (median OR 5.61, 95% CI 5.44-5.78) and failed clinical improvement (median OR 6.33, 95% CI 6.17-6.50). Fluid deviations yielded corresponding median ORs of 1.02 (95% CI 1.02-1.02) and 1.14 (95% CI 1.14-1.14). In external validation, the model achieved a median survival AUROC of 0.73 (95% CI 0.69-0.77) and clini- cal improvement AUROC of 0.69 (95% CI 0.66-0.72), matching predictive baselines. Treatment effects were heterogeneous: optimal fluids increased survival by up to 4% in low-severity subgroups, while vasopressor responses varied from 0.5% to 17% across acute severity levels. Sensitivity analyses across 36 scenarios confirmed primary associations in 33 cases (91.7%). Interpretation: Recommendations from expert-grounded causal models correlate with improved septic shock outcomes in external validation, cap- turing significant heterogeneity in patient response.