摘要：SummaryCritical transition theory suggests that complex systems should experience increased temporal variability just before abrupt state changes. We tested this hypothesis in 763 patients on long-term hemodialysis, using 11 biomarkers collected every two weeks and all-cause mortality as a proxy for critical transitions. We find that variability—measured by coefficients of variation (CVs)—increases before death for all 11 clinical biomarkers, and is strikingly synchronized across all biomarkers: the first axis of a principal component analysis on all CVs explains 49% of the variance. This axis then generates powerful predictions of mortality (HR95 = 9.7, p < 0.0001, where HR95 is a scale-invariant metric of hazard ratio; AUC up to 0.82) and starts to increase markedly ∼3 months prior to death. Our results provide an early warning sign of physiological collapse and, more broadly, a quantification of joint system dynamics that opens questions of how system modularity may break down before critical transitions.Graphical abstractDisplay OmittedHighlights•Critical transition theory can be applied to long-term hemodialysis•Variability of diverse clinical biomarkers is broadly synchronized•Biomarker dynamics can be used as an early warning sign of mortality•Our integrative index of variability increases markedly ∼3 months prior to deathHealth sciences; Medicine; Health informatics; Biological sciences; Bioinformatics; Computational bioinformatics
