• Travis J Moss, Douglas E Lake, J Forrest Calland, Kyle B Enfield, John B Delos, Karen D Fairchild, and J Randall Moorman.
• 1Division of Cardiovascular Medicine, Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA.2Department of Statistics, University of Virginia, Charlottesville, VA.3Division of Acute Care and Trauma Surgery, Department of Surgery, University of Virginia School of Medicine, Charlottesville, VA.4Division of Pulmonary and Critical Care, Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA.5Department of Physics, College of William and Mary, Williamsburg, VA.6Division of Neonatology, Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA.7Department of Biomedical Engineering, University of Virginia, Charlottesville, VA.8Department of Molecular Physiology, University of Virginia, Charlottesville, VA.
• Crit. Care Med. 2016 Sep 1; 44 (9): 1639-48.

ObjectivesPatients in ICUs are susceptible to subacute potentially catastrophic illnesses such as respiratory failure, sepsis, and hemorrhage that present as severe derangements of vital signs. More subtle physiologic signatures may be present before clinical deterioration, when treatment might be more effective. We performed multivariate statistical analyses of bedside physiologic monitoring data to identify such early subclinical signatures of incipient life-threatening illness.DesignWe report a study of model development and validation of a retrospective observational cohort using resampling (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis type 1b internal validation) and a study of model validation using separate data (type 2b internal/external validation).SettingUniversity of Virginia Health System (Charlottesville), a tertiary-care, academic medical center.PatientsCritically ill patients consecutively admitted between January 2009 and June 2015 to either the neonatal, surgical/trauma/burn, or medical ICUs with available physiologic monitoring data.InterventionsNone.Measurements And Main ResultsWe analyzed 146 patient-years of vital sign and electrocardiography waveform time series from the bedside monitors of 9,232 ICU admissions. Calculations from 30-minute windows of the physiologic monitoring data were made every 15 minutes. Clinicians identified 1,206 episodes of respiratory failure leading to urgent unplanned intubation, sepsis, or hemorrhage leading to multi-unit transfusions from systematic individual chart reviews. Multivariate models to predict events up to 24 hours prior had internally validated C-statistics of 0.61-0.88. In adults, physiologic signatures of respiratory failure and hemorrhage were distinct from each other but externally consistent across ICUs. Sepsis, on the other hand, demonstrated less distinct and inconsistent signatures. Physiologic signatures of all neonatal illnesses were similar.ConclusionsSubacute potentially catastrophic illnesses in three diverse ICU populations have physiologic signatures that are detectable in the hours preceding clinical detection and intervention. Detection of such signatures can draw attention to patients at highest risk, potentially enabling earlier intervention and better outcomes.

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