International journal of burns and trauma · 2015
Multivariate analysis of the volumetric capnograph for PaCO2 estimation.
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Belenkiy Slava M, Baker William L, Batchinsky Andriy I, Mittal Sumit, Watkins Taylor, Salinas Jose, Cancio Leopoldo C
Аннотация
PURPOSE: 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.
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.