The Constrained Disorder Principle Accounts for the Variability That Characterizes Breathing: A Method for Treating Chronic Respiratory Diseases and Improving Mechanical Ventilation

Abstract

Variability characterizes breathing, cellular respiration, and the underlying quantum effects. Variability serves as a mechanism for coping with changing environments; however, this hypothesis does not explain why many of the variable phenomena of respiration manifest randomness. According to the constrained disorder principle (CDP), living organisms are defined by their inherent disorder bounded by variable boundaries. The present paper describes the mechanisms of breathing and cellular respiration, focusing on their inherent variability. It defines how the CDP accounts for the variability and randomness in breathing and respiration. It also provides a scheme for the potential role of respiration variability in the energy balance in biological systems. The paper describes the option of using CDP-based artificial intelligence platforms to augment the respiratory process's efficiency, correct malfunctions, and treat disorders associated with the respiratory system.

Keywords: EEVL: end-expiratory lung volume; TTOT: VT respiratory time; artificial intelligence; constrained disorder; digital health; energy; mitochondria; variability.

Figure 1

Overcoming tolerance to bronchodilators by implementing variability-based therapeutic regimens: The figure summarizes the design and function of a CDP-based second-generation AI system for medical treatment, specifically in the context of lung conditions. The system is based on three levels of operation: (A) The AI provides randomized medication regimens within a pre-defined range, aimed to overcome treatment tolerance. (B) The AI collects data on the output and uses clinically meaningful endpoints as inputs to personalize the output, which alters randomization to reach a better outcome. (C) The AI receives inputs from the patient’s quantified variability signatures and uses large datasets from big-data resources to further personalize the treatment for the individual patient. CDP, constrained disorder principle. AI, artificial intelligence. Created with Biorender.com.

Figure 2

Inserting variability into mechanical ventilation to improve the efficacy of ventilation and ease the weaning process: Breathing variability serves as a predictor for successful extubation and improves the efficacy of mechanical ventilation. (A) Variability in breathing patterns, as measured by indices such as the CV of tidal volume/inspiratory time and CV of Inspiratory time/respiratory period, is an important predictor of extubation success or failure. (B) Variable ventilation was shown to be beneficial in animal models and in human patients, increasing arterial oxygenation, lowering shunt fraction and increasing pulmonary compliance. CV, coefficient of variability. Created with Biorender.com.