Targeting Persistent Pathological States in ME/CFS and Long COVID: A Systems-Theoretic, Phenotype-Guided Intervention Protocol

This paper presents a phenotype-guided, systems-theoretic intervention protocol for ME/CFS and Long COVID, based on the Persistent Systemic Threat-Signaling State (PSTS) model, which provides the theoretical foundation for this protocol. The framework conceptualizes post-acute infection syndromes (PAIS) as persistent pathological regulatory states within a nonlinear, multisystem network characterized by sustained endogenous threat-signaling and reduced effective recovery capacity. Rather than targeting symptoms in isolation, the protocol focuses on controlled modulation of system dynamics through state-dependent, phenotype-guided interventions. Participants are stratified based on dominant regulatory instability (autonomic, metabolic, or immune/neuroinflammatory) using continuous physiological monitoring, with heart rate variability (HRV) serving as the primary indicator of system state and recovery capacity, supported by glycemic dynamics and additional contextual markers. Interventions are introduced sequentially in a cumulative and state-dependent manner. Each intervention is added and maintained upon demonstrated stabilization, preserving previously achieved system control and enabling structured, stepwise modulation of regulatory dynamics. Progression, continuation, or regression is governed by predefined decision rules based on longitudinal physiological trends, with HRV functioning as the leading signal within a hierarchical monitoring framework. Although operationalized as a sequence of intervention phases, the protocol should not be interpreted as a linear treatment pathway. It represents a closed-loop control strategy applied to a nonlinear physiological system, in which decisions are continuously informed by system behavior and may result in stabilization, continuation, or step-back at any stage. The study is designed as a series of parallel n-of-1 trials, prioritizing within-subject causal interpretability over population-average treatment effects. This approach enables the capture of delayed, nonlinear, and state-dependent responses that are not adequately addressed by conventional trial designs. The protocol explicitly distinguishes between stabilization and resolution. Sequential intervention aims to reduce regulatory instability and restore controllability, but does not by itself induce exit from a pathological state. This version extends the framework by introducing a resolution-phase model, outlining candidate biological mechanisms that may enable state transition once sufficient stabilization has been achieved. This work provides a structured and mechanistically interpretable framework for intervention in complex post-infectious conditions and establishes a direct link between clinical practice and systems-level analysis of recovery dynamics and state transitions.