Multi-Organ Intervention State Space (MOISS): A Collision Geometry Framework for Quantifying Therapeutic Windows Across 10 Organ Systems in 301,470 ICU Patients

Background: Severity scoring systems such as SOFA, NEWS2, and qSOFA effectively identify deteriorating ICU patients by aggregating physiological parameters into composite indices that trigger clinical alerts. However, these systems evaluate patient state at discrete time points and do not model the temporal dynamics of organ deterioration or the pharmacokinetic constraints that govern whether a given intervention can achieve therapeutic effect before an organ trajectory crosses an irreversible threshold. This limitation is consequential because interventions across critical care span pharmacokinetic onset times from seconds (vasopressors) to hours (metabolic corrections, blood products, enzymatic cofactors), yet no existing framework quantifies timing adequacy as a function of these intervention-specific pharmacokinetic properties. Methods: We developed the Multi-Organ Intervention State Space (MOISS), a collision geometry framework that classifies intervention timing adequacy by computing the temporal relationship between the predicted time for a biomarker trajectory to reach a critical threshold and the time required for the administered intervention to achieve peak therapeutic effect. Biomarker trajectories were estimated using the Kunche Adaptive Estimator (KAE), a reliability-adaptive Kalman filter that provides continuous position and velocity estimates from intermittent laboratory measurements. MOISS assigns each intervention event to one of six ordinal categories: PROPHYLACTIC, ON_TIME, PARTIAL, MARGINAL, FUTILE, or TOO_LATE. We applied this framework to 301,470 ICU patients across three databases (eICU-CRD, MIMIC-IV, MIMIC-III), evaluating 65 distinct intervention-organ pairs spanning 10 organ systems: Cardiovascular, Metabolic, Respiratory, Renal, Hematologic, Hepatic, Gastrointestinal, Infection, Endocrine, and Neurological. Results: Timing-mortality associations were identified across all 10 organ systems, with 87 intervention-database combinations achieving statistical significance (p<0.05). The highest timing sensitivity was observed in metabolic corrections: thiamine supplementation for metabolic acidosis (OR 5.76; 95% CI 4.86-6.83 in MIMIC-IV), sodium bicarbonate (OR 4.99; 95% CI 4.27-5.82 in MIMIC-IV). Respiratory interventions showed comparable magnitude: mechanical ventilation initiation (OR 5.03; 95% CI 4.42-5.73 in MIMIC-IV). Hematologic interventions demonstrated strong timing dependency: platelet transfusion (OR 4.25; 95% CI 3.68-4.90), fresh frozen plasma (OR 3.41; 95% CI 2.94-3.95). Cardiovascular agents ranged from OR 1.40 for norepinephrine (consistent with its rapid 1-2 minute onset providing a forgiving therapeutic window) to OR 2.23 for milrinone. Infection-directed therapies, hepatic support, renal replacement, endocrine correction, gastrointestinal interventions, and neurological agents all contained timing-sensitive members. Cross-database consistency was demonstrated for 29 of 52 testable interventions (55.8%), with 6 interventions achieving significance across all three databases. Conclusions: Intervention timing sensitivity is pervasive across the entire spectrum of critical care therapeutics, spanning all 10 organ systems and all pharmacokinetic classes evaluated. MOISS provides a systematic framework for quantifying this timing adequacy that complements existing severity scoring by adding the pharmacokinetic timing dimension: where SOFA, NEWS2, and qSOFA identify that a patient is deteriorating, MOISS computes whether the specific planned intervention can still achieve its intended effect given the current organ trajectory and pharmacokinetic constraints. The universality of timing sensitivity across organ systems argues for multi-organ trajectory monitoring as the foundation for next-generation clinical decision support.