Therapeutic Distance: An Orbit-Based Framework for ICU Decision Support - Initial Validation in 11,627 Sepsis Patients from MIMIC-IV

Background: Patient matching in intensive care databases yields sample sizes too small for individualised outcome analysis. Current AI systems provide population-level guideline summaries but omit stratification variables that may invert therapy signals at the individual level. Methods: We developed the Therapeutic Distance framework, which computes the z-standardised distance between a patient's clinical parameters and the centroid of MIMIC-IV patients who received each therapy: d(P,T) = sum of wi(T) x |(Li - mui(T)) / sigmai|. We hypothesise that patients at the same distance to a therapy (same orbit) have comparable outcomes. Six validation experiments were performed on 11,627 sepsis patients (SAPS-II 30-80) from MIMIC-IV v3.1. Results: Echo-stratified vasopressin recipients showed mortality of 30.1% (n=146, 95% CI 22.6-37.7%) versus 53.9% without echo (n=2,426, 95% CI 51.9-55.9%). Confidence intervals did not overlap (bootstrap, 1,000 resamples). However, echo-stratified patients had lower general severity (SAPS-II 49.2 vs 53.9) but higher cardiac biomarkers (troponin 1.0 vs 0.51 ng/mL), indicating that the observed difference is compatible with both severity confounding and a possible cardiac-specific vasopressin effect. Leave-one-out prediction with uniform weights achieved AUC 0.61 as a structural baseline. Conclusions: Therapeutic Distance replaces patient matching with orbit matching, substantially increasing usable sample sizes. The echo-vasopressin finding is hypothesis-generating and mechanistically plausible but not causally proven. The framework is intended as a clinical decision support signal under uncertainty, not as a causal inference method.