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◆ International journal of chronic obstructive pulmonary disease2026-01-01

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

LiFeng Fang, Fei Wang, Junkang Chen, Yulong Zheng

一句话结论 · In one sentence

In this single-center retrospective cohort, acute-phase clinical variables and TCM syndrome elements were associated with 12-month functional outcome in GBS. The neural-network model had the highest numerical AUC and lowest Brier score, but it was not statistically superior to random forest; therefore, no superiority over random forest is claimed. External validation, comparison with established prognostic scores when required variables are available, and prospective implementation studies are required before clinical use.

原始摘要(英文原文)· Original abstract
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.
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Machine Learning Prediction and Causal Forest Analysis of Severe Acute Kidney Injury in ICU Patients with COPD: A MIMIC-IV Study. — 科研速览 Science Skim