title: Multivariate analysis of the volumetric capnograph for PaCO2 estimation.
abstract: OBJECTIVE
  End-tidal CO2 (eTCO2) can be used to estimate the arterial CO2 (PaCO2) under steady-state conditions, but that relationship deteriorates during hemodynamic or respiratory instability. We developed a multivariate method to improve our ability to estimate the PaCO2, by using additional information contained in the volumetric capnograph (Vcap) waveform. We tested this approach using data from a porcine model of chest trauma/hemorrhage.
  
  
  METHODS
  This experiment consisted of 3 stages: pre-injury, injury/resuscitation, and post-injury. In stage I, anesthetized pigs (n=26) underwent ventilator maneuvers (tidal volume and respiratory rate) to induce hypo-or hyper-ventilation. In stage II, pigs underwent either (A) unilateral pulmonary contusion, hemorrhage, and resuscitation (n=13); or (B) bilateral pulmonary contusion (n=13) followed by 30 min of monitoring. In stage III, the ventilator maneuvers were repeated. The following Vcap features were measured: eTCO2, phase 2 slope (p2m), phase 3 slope (p3m), and inter-breath interval. The data were fit to 2 models: (1) multivariate linear regression and (2) a machine-learning model (M5P).
  
  
  RESULTS
  1750 10-breath sets were analyzed. Univariate models employing eTCO2 alone were adequate during stages I and III. During stage II, mean error for the linear model was -8.44 mmHg (R(2)=0.14, P<0.001) and for M5P it was -5.98 mmHg (R(2)=0.13, P<0.01). By adding Vcap features, all models exhibited improvement. In stage II, the mean error of the linear model improved to -4.64 mmHg (R(2)=0.11, P<0.01), and that of the M5P model improved to -1.62 mmHg (R(2)=0.25, P<0.01).
  
  
  CONCLUSIONS
  By incorporating Vcap waveform features, multivariate methods modestly improved PaCO2 estimation, especially during periods of hemodynamic and respiratory instability. Further work would be needed to produce a clinically useful CO2 monitoring system under these challenging conditions.
authors.affiliation: U.S. Army Institute of Surgical Research USA.
authors.forename: Slava M
authors.initials: SM
authors.lastname: Belenkiy
authors.affiliation: U.S. Army Institute of Surgical Research USA.
authors.forename: William L
authors.initials: WL
authors.lastname: Baker
authors.affiliation: U.S. Army Institute of Surgical Research USA ; University of Texas Medical Branch, Galveston TX, USA.
authors.forename: Andriy I
authors.initials: AI
authors.lastname: Batchinsky
authors.affiliation: U.S. Army Institute of Surgical Research USA.
authors.forename: Sumit
authors.initials: S
authors.lastname: Mittal
authors.affiliation: U.S. Army Institute of Surgical Research USA.
authors.forename: Taylor
authors.initials: T
authors.lastname: Watkins
authors.affiliation: U.S. Army Institute of Surgical Research USA.
authors.forename: Jose
authors.initials: J
authors.lastname: Salinas
authors.affiliation: U.S. Army Institute of Surgical Research USA ; University of Texas Health Science Center at San Antonio TX, USA.
authors.forename: Leopoldo C
authors.initials: LC
authors.lastname: Cancio
keywords: End-tidal carbon dioxide; arterial carbon dioxide; capnography; hemorrhage; machine learning; pulmonary contusion