Investigating the consequences of mechanical ventilation in clinical intensive care settings through an evolutionary game-theoretic framework

TL;DR

The paper addresses MV management in ICUs by formulating a joint patient-ventilator-care system (J6) and applying an evolutionary game theory (EGT) framework to relate breath phenotypes to short-term MV consequences (costs). It discretizes breath data into context-dependent phenotypes and infers a skew-symmetric payoff matrix $P$ via an inverse game-inversion regression, enabling context-aware cost attribution and serving as a foundation for reinforcement learning-based MV optimization. Synthetic verification demonstrates identifiable payoffs under well-specified phenotypes, while real-world applications to ARDS and non-ARDS cohorts reveal time- and context-dependent variation in MV costs and highlight heterogeneity across subgroups. The approach provides a principled, data-driven pathway to MV personalization and RL-guided decision support, while acknowledging limitations from discretization, phenotype granularity, and non-stationarity, and outlining directions for extending to replicator dynamics and non-linear payoff formulations.

Abstract

Identifying the effects of mechanical ventilation strategies and protocols in critical care requires analyzing data from heterogeneous patient-ventilator systems within the context of the clinical decision-making environment. This research develops a framework to help understand the consequences of mechanical ventilation (MV) and adjunct care decisions on patient outcome from observations of critical care patients receiving MV. Developing an understanding of and improving critical care respiratory management requires the analysis of existing secondary-use clinical data to generate hypotheses about advantageous variations and adaptations of current care. This work introduces a perspective of the joint patient-ventilator-care systems (so-called J6) to develop a scalable method for analyzing data and trajectories of these complex systems. To that end, breath behaviors are analyzed using evolutionary game theory (EGT), which generates the necessary quantitative precursors for deeper analysis through probabilistic and stochastic machinery such as reinforcement learning. This result is one step along the pathway toward MV optimization and personalization. The EGT-based process is analytically validated on synthetic data to reveal potential caveats before proceeding to real-world ICU data applications that expose complexities of the data-generating process J6. The discussion includes potential developments toward a state transition model for the simulating effects of MV decision using empirical and game-theoretic elements.

Figures (15)

• Figure 1: Linking MV management to patient-health consequences that include VILI requires considering care decision impacts on the patient-ventilator cyborg. The system of interest is the joint cyborgs-in-care system (J6) depicted here, in which each joint patient+ventilator+care processes exists within a common care decision environment. PVS cyborg behaviors are independent, but aspects of their evolution (ventilator settings, position, sedation, etc.) are determined by common care practices and protocols. Evaluation of these protocols' impact on observed PVS behaviors and patient outcome is the wider focus of this research. abbreviations: PVS=patient-ventilator system, MV=mechanical ventilation, wf=waveform
• Figure 1: The baseline estimation results, Experiment 1. (top): Entry wise values of the estimated payoff parameter vector $\mathbf{\widehat{p}}$ and its error are shown in relation to increasing sample size (colors). Strategies are sampled from $K=10$ breath types sorted in increasing cost and decreasing frequency, while cost data for breath types are polluted with 3% additive random noise when sampled. Rare-vs-rare type comparisons (those with higher parameter index) require a higher number of samples to accurately resolve phenotype-cost associations. (bottom): The bottom left plot shows decrease with respect to sample size for Frobenius norm errors between the estimated payoff matrix $\mathbf{\widehat{P}}$ and true payoff matrix. The values in upper triangle of the true payoff matrix ('True'), the lowest-error estimated payoff matrix ('Best'), and the difference between them ('Error') are shown shown as the heatmaps in the remaining plots of the bottom row. Payoffs heatmap values correspond to row-minus-column phenotype cost differences.
• Figure 1: These are the phenotypes from Application 2 as defined by similarity structure At left, UMAP based similarity identifies three globally distinct groups of joint waveform+ventilator settings data. At right, application of Density-based spatial clustering of applications with noise (DBSCAN) to those coarse groups identifies a total of 26 locally similar groups adopted as phenotypes
• Figure 1: Stochastic phenotype transition model underlying the RL formulation, with arrows indicating transitions between phenotypes.. Dashed lines outline three distinct contexts that are mutually accessible by changes in care (e.g., vent mode, position, sedation, or other drug administration such as vasopressor use) or patient phenotype (e.g., gross health status). Bidirectional changes (blue) between contexts are not universal (e.g., time-changing contexts are directed) as illustrated here. Within contexts, the limited ventilator settings (PEEP, VT, et al.) can be altered by care processes (red). However, the context and vent settings do determine the system, as waveform components vary under those conditions (black). Integrated transitions over short times define strategies, the states defined for RL below.
• Figure 2: Effects of under-specified phenotypes, Experiment 2.(top): The upper row shows the heatmap of the matrix used distribute $K=10$ cost-distinct breath types into 7 assumed phenotypes. True breath types are sorted in increasing cost and decreasing frequency, but only the first 7 are correctly specified. The last 3 types (8,9,10) are the rarest also most costly/injurious/detrimental breath types but are mid-identified disproportionately across the first 7 types. (bottom): Panels parallel the lower panels of the previous figure, showing, left-to-right, Frobenius norm errors vs. sample size, and heatmaps of expected payoff matrix given reassignment probabilities, the approximation based on the largest sample size, and the errors. Errors reduce but quickly stagnate; the expected payoff matrix cannot be accurately estimated by increasing the sampling.