Acute kidney injury in sepsis evolves over hours to days, yet most ICU models emphasize onset and provide limited insight into cardio-renal interactions. We developed AKI-twinX, an organ-structured, explainable digital twin that jointly forecasts acute kidney injury onset, acute kidney injury trajectory, and near-term mortality risk. The model learns renal and cardiovascular latent states with sparse feature gating and captures cross-organ coupling with attention. We trained AKI-twinX on MIMIC-IV sepsis using 5-fold cross-validation and evaluated it on an Indiana University Health cohort. Discrimination was consistent across systems (AUC: mortality 0.86-0.88, acute kidney injury onset 0.78-0.82, acute kidney injury trajectory 0.73-0.78). In vasopressor-treated windows, 12-hour systolic blood pressure forecasts tracked observed values (mean absolute error 8.5 mmHg). Counterfactual vasopressor withdrawal shifted predicted blood pressure downward and increased predicted risk, supporting sensitivity to clinically meaningful interventions. AKI-twinX enables trajectory-aware forecasting with bedside auditability in sepsis.

The authors have declared no competing interest.

. J.S. and A.E.G. were financially supported by the National Library of Medicine of the National Institutes of Health under award number R01LM013771. J.S. and J.C. were financially supported by the Indiana University Precision Health Initiative.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Indiana University Human Subject & Institutional Review Boards ethical review granted an IRB waiver for the project.

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The IUH dataset is available via email to Lingzhong Meng (menglziu.edu) and is pending institutional approval. The MIMIC-IV datasets are available from their respective sources.