Machine Learning Prediction and Causal Forest Analysis of Severe Acute Kidney Injury in ICU Patients with COPD: A MIMIC-IV Study.

BACKGROUND

Severe acute kidney injury (AKI) is frequent in critically ill patients with chronic obstructive pulmonary disease (COPD), but predictive importance does not establish causality.

OBJECTIVE

To model severe AKI (KDIGO stage 3), evaluate temporal robustness at a 24-hour ICU landmark, and explore adjusted exposure-effect estimates.

METHODS

This retrospective MIMIC-IV v3.1 cohort included 4,705 adults with COPD. The original modeling dataset was split into training (n = 3,294) and testing (n = 1,411) sets. Consensus features from LASSO, Boruta, and random-forest importance informed 15 classifiers; the highest observed test-set AUC was interpreted with SHAP. A 24-hour landmark analysis excluded earlier stage 3 AKI and used pre-landmark predictors for incident stage 3 AKI thereafter. CausalForestDML estimated exploratory adjusted effects of the most recent valid creatinine within 365 days before ICU admission.

RESULTS

Severe AKI occurred in 1,085 patients (23.06%). The original six-feature CatBoost model included total ICU length of stay and had an AUC of 0.848 (95% CI, 0.825-0.872); it is interpreted as a retrospective trajectory model. The landmark cohort included 4,171 patients and 841 events. The temporal five-feature AUC was 0.679 (95% CI, 0.641-0.717); without pre-ICU creatinine it was 0.686 (95% CI, 0.648-0.724; paired difference, -0.0067; P = 0.293). The adjusted creatinine risk difference was 0.0547 per 1 mg/dL (95% CI, -0.0021 to 0.1115) and 0.0705 (95% CI, 0.0022-0.1387) after 1st-99th percentile trimming.

CONCLUSION

This study establishes a COPD-specific benchmark showing that a parsimonious model can characterize severe-AKI trajectories and that temporal separation materially changes performance and interpretation. Integrating model comparison, SHAP, adjusted-effect estimation, and landmark validation provides a rigorous framework for distinguishing prognostic importance from causal relevance and defines priorities for external validation.