State-Dependent Parameter Relevance in Intensive Care: Syndrome-Specific Centroids Improve Orbit-Based Mortality Prediction from AUC 0.59 to 0.83 in 59,362 Predictions

Background: The Therapeutic Distance framework (Paper 1) achieved AUC 0.61 for orbit-based mortality prediction in 11,627 sepsis patients. We hypothesised that incorporating state-dependent parameter relevance would substantially improve prediction. Methods: We extended the framework to 84,176 ICU patients from MIMIC-IV v3.1 across 16 clinical syndromes. Validation included full-population leave-one-out (n=59,362), head-to-head comparison against SAPS-II and logistic regression on 34,467 matched patients with bootstrap confidence intervals, temporal validation, outcome permutation, sensitivity analysis, and calibration assessment. Results: Full-population leave-one-out achieved AUC 0.832 (n=59,362). On 34,467 matched patients, Therapeutic Distance (AUC 0.841) significantly outperformed both SAPS-II (0.786; delta=+0.055, 95% CI +0.048 to +0.061, p<0.001) and logistic regression (0.788). Temporal validation showed stable performance (delta=-0.006). Outcome permutation confirmed genuine signal (AUC 0.859 to 0.498 with shuffled mortality). Sensitivity analysis demonstrated near-zero variation (delta 0.0006-0.003). The framework performed well for 8 of 16 syndromes (AUC >0.70) and failed for DKA and post-cardiac surgery (AUC <0.40). Conclusions: Therapeutic Distance provides therapy-specific risk stratification that exceeds both established severity scores and standard machine learning while remaining robust to hyperparameter choices, temporal drift, and outcome permutation.