Coping Equilibrium and Stability Margins: A Deterministic Viability-Control Architecture for Complex Adaptive Systems From Optimization to Viability-Preserving Control

Abstract Modern perioperative monitoring measures individual physiological variables with remarkable precision. Blood pressure, heart rate, oxygen saturation, ventilatory parameters, and depth of anesthesia can be continuously monitored; however, clinical alarms are typically activated only after a variable has crossed a predefined threshold. The present study introduces a different approach. The Ruin Horizon Framework (RHF) does not primarily ask whether an individual physiological variable has reached a critical value. Instead, it examines how the internal dynamics and compensatory capacity of the multivariable system change over time. The framework is based on the hypothesis that the viability of a complex adaptive system can be maintained within a dynamically changing operating domain. At the boundary of this domain lies the Ruin Horizon: a critical stability region in which the system's regenerative, compensatory, and adaptive capacities progressively narrow as the boundary is approached. The control objective of RHF is therefore not to achieve a nominal physiological optimum, but to maintain a dynamic state in which the system preserves the greatest possible effective distance from its own Ruin Horizon. We define this state as Coping Equilibrium. The concept was examined using open-access perioperative data from VitalDB. In a detailed proof-of-concept analysis of a surgical time series containing 8,700 consecutive time points, four hypotensive events were identified. One event could not be evaluated because the required preceding observation window was unavailable. In two further events, the system-level RHF instability signal preceded crossing of the conventional MAP < 65 mmHg threshold by 1 minute 56 seconds and 4 minutes 25 seconds, respectively. In the fourth event, a non-specific signal was observed. The same basic analytical approach was subsequently explored in five additional perioperative time series, in which a similar fundamental early-warning mechanism was observed. These findings do not constitute clinical validation; therefore, only the first surgical time series is presented in detail in the present study. The findings raise a simple question: Could some forms of clinical instability be recognized from changes in the system's internal coordination before any conventionally monitored variable crosses its critical threshold?