From run-to-failure to condition-based care: a multi-domain framework for preventive psychiatry.

Abstract

Mental disorders are the leading cause of years lived with disability worldwide, yet the dominant clinical model remains reactive, intervening only after diagnostic thresholds are met. Drawing on the engineering distinction between run-to-failure and condition-based maintenance, this review argues that psychiatry faces a quantifiable intervention threshold gap. Synthesising recent meta-analytic evidence, we show that psychological interventions at subclinical symptom levels reduce major depression incidence by 43% at post-treatment and 33% at 12-month follow-up (individual-participant-data meta-analysis; 30 trials; N = 7,201), that shorter duration of untreated illness is associated with 70% greater likelihood of treatment response, and that early intervention for first-episode psychosis reduces hospitalisation by 26%. We review convergent evidence across five modifiable domains - sleep and glymphatic clearance, nutritional psychiatry, allostatic load regulation, autonomic function, and psychoneuroimmunological monitoring - selected for objective measurability, meta-analytic interventional support, identified mechanistic pathways, and population-level scalability. These domains form a mechanistically interconnected network in which deterioration in one can cascade across others. Clinical staging models, adapted from oncology, provide the graduated diagnostic architecture for condition-based care. We present a speculative multi-domain monitoring protocol with parameters, frequencies, and action thresholds calibrated to clinical stage. Among the five domains, inflammatory monitoring via C-reactive protein emerges as the most implementation-ready, with established clinical thresholds, demonstrated treatment-selection utility, and symptom-specific associations with neurovegetative features consistent with an immuno-metabolic depression subtype. Prospective multi-domain monitoring trials are identified as the most urgent research priority.