title: Using what you get: dynamic physiologic signatures of critical illness.
abstract: The development and resolution of cardiopulmonary instability take time to become clinically apparent, and the treatments provided take time to have an impact. The characterization of dynamic changes in hemodynamic and metabolic variables is implicit in physiologic signatures. When primary variables are collected with high enough frequency to derive new variables, this data hierarchy can be used to develop physiologic signatures. The creation of physiologic signatures requires no new information; additional knowledge is extracted from data that already exist. It is possible to create physiologic signatures for each stage in the process of clinical decompensation and recovery to improve outcomes.
authors.affiliation: Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Andre L
authors.initials: AL
authors.lastname: Holder
authors.affiliation: Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA. Electronic address: clermontg@ccm.upmc.edu.
authors.forename: Gilles
authors.initials: G
authors.lastname: Clermont
mesh_terms: D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000628:therapy; D003422:Critical Care / Q000379:methods*; D016638:Critical Illness / Q000628:therapy; D003627:Data Interpretation, Statistical; D006439:Hemodynamics / Q000502:physiology*; D006801:Humans; D008171:Lung Diseases / Q000175:diagnosis* / Q000628:therapy; D008991:Monitoring, Physiologic / Q000379:methods*
keywords: Cardiopulmonary instability; Data hierarchy; Fused parameter; Machine learning; Physiologic signature