Dataset Information

Predicting critical transitions in a model of systemic inflammation.

ABSTRACT: The human body can be viewed as a dynamical system, with physiological states such as health and disease broadly representing steady states. From this perspective, and given inter- and intra-individual heterogeneity, an important task is identifying the propensity to transition from one steady state to another, which in practice can occur abruptly. Detecting impending transitions between steady states is of significant importance in many fields, and thus a variety of methods have been developed for this purpose, but lack of data has limited applications in physiology. Here, we propose a model-based approach towards identifying critical transitions in systemic inflammation based on a minimal amount of assumptions about the availability of data and the structure of the system. We derived a warning signal metric to identify forthcoming abrupt transitions occurring in a mathematical model of systemic inflammation with a gradually increasing bacterial load. Intervention to remove the inflammatory stimulus was successful in restoring homeostasis if undertaken when the warning signal was elevated rather than waiting for the state variables of the system themselves to begin moving to a new steady state. The proposed combination of data and model-based analysis for predicting physiological transitions represents a step forward towards the quantitative study of complex biological systems.

SUBMITTER: Scheff JD

PROVIDER: S-EPMC3870185 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Predicting critical transitions in a model of systemic inflammation.

Scheff Jeremy D JD Calvano Steve E SE Androulakis Ioannis P IP

Journal of theoretical biology 20130821

The human body can be viewed as a dynamical system, with physiological states such as health and disease broadly representing steady states. From this perspective, and given inter- and intra-individual heterogeneity, an important task is identifying the propensity to transition from one steady state to another, which in practice can occur abruptly. Detecting impending transitions between steady states is of significant importance in many fields, and thus a variety of methods have been developed ...[more]

PMID: 23973206

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Project description:Severe acute pancreatitis (SAP) includes persistent systemic inflammation (SIRS) and multiorgan failure (MOF). The mechanism of transition from SIRS to MOF is unclear. We developed a fluid compartment model and used clinical data to test predictions. The model includes vascular, interstitial and "third-space" compartments with variable permeability of plasma proteins at the capillaries. Consented patients from University of Pittsburgh Medical Center Presbyterian Hospital were studied. Preadmission and daily hematocrit (HCT), blood urea nitrogen (BUN), creatine (Cr), albumin (Alb), and total protein (TP) were collected, and nonalbumin plasma protein (NAPP = TP minus the Alb) was calculated. Subjects served as their own controls for trajectory analysis. Of 57 SAP subjects, 18 developed MOF (5 died), and 39 were non-MOF (0 died). Compared with preadmission levels, admission HCT increased in MOF +5.00 [25%-75% interquartile range, IQR] versus non-MOF -0.10 [-1.55, 1.40] (P < 0.002) with HCT > +3 distinguishing MOF from non-MOF (odds ratio 17.7, P = 0.014). Preadmission Alb fell faster in MOF than non-MOF (P < 0.01). By day 2, TP and NAPP dropped in MOF but not non-MOF (P < 0.001). BUN and Cr levels increased in MOF (P = 0.001), but BUN-to-Cr ratios remained constant. Pancreatic necrosis was more common in MOF (56%) than non-MOF (23%). Changing capillary permeability to allow loss of NAPP in this model predicts loss of plasma oncotic pressure and reduced vascular volume, hypotension with prerenal azotemia and acute kidney dysfunction, pancreas necrosis, and pulmonary edema from capillary leak in the lung with acute respiratory distress syndrome. Sequential biomarker analysis in humans with or without MOF is consistent with this model. This study is registered on https://clinicaltrials.gov at NCT03075605.NEW & NOTEWORTHY Acute pancreatitis is a sudden inflammatory response to pancreatic injury that may spread to systemic inflammation, multiorgan failure, and death in some patients. With the use of the predictions of a new mechanistic model, we compared patients with severe acute pancreatitis with or without multiorgan failure. All biomarkers of capillary leak and clinical features of multiorgan failure were accurately predicted. This provides a new paradigm for understanding and developing new treatments for patients with severe acute pancreatitis.

Dataset Information

Predicting critical transitions in a model of systemic inflammation.

Publications

Predicting critical transitions in a model of systemic inflammation.

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