Volumetric and End-Tidal Capnography for the Detection of Cardiac Output Changes in Mechanically Ventilated Patients Early after Open Heart Surgery

Background

Exhaled carbon dioxide (CO₂) reflects cardiac output (CO) provided stable ventilation and metabolism. Detecting CO changes may help distinguish hypovolemia or cardiac dysfunction from other causes of haemodynamic instability. We investigated whether CO₂ measured as end-tidal concentration (EtCO₂) and eliminated volume per breath (VtCO₂) reflect sudden changes in cardiac output (CO).

Methods

We measured changes in CO, VtCO₂, and EtCO₂ during right ventricular pacing and passive leg raise in 33 ventilated patients after open heart surgery. CO was measured with oesophageal Doppler.

Results

During right ventricular pacing, CO was reduced by 21% (CI 18–24; p < 0.001), VtCO₂ by 11% (CI 7.9–13; p < 0.001), and EtCO₂ by 4.9% (CI 3.6–6.1; p < 0.001). During passive leg raise, CO increased by 21% (CI 17–24; p < 0.001), VtCO₂ by 10% (CI 7.8–12; p < 0.001), and EtCO₂ by 4.2% (CI 3.2–5.1; p < 0.001). Changes in VtCO₂ were significantly larger than changes in EtCO₂ (ventricular pacing: 11% vs. 4.9% (p < 0.001); passive leg raise: 10% vs. 4.2% (p < 0.001)). Relative changes in CO correlated with changes in VtCO₂ (ρ=0.53; p=0.002) and EtCO₂ (ρ=0.47; p=0.006) only during reductions in CO. When dichotomising CO changes at 15%, only EtCO₂ detected a CO change as judged by area under the receiver operating characteristic curve.

Conclusion

VtCO₂ and EtCO₂ reflected reductions in cardiac output, although correlations were modest. The changes in VtCO₂ were larger than the changes in EtCO₂, but only EtCO₂ detected CO reduction as judged by receiver operating characteristic curves. The predictive ability of EtCO₂ in this setting was fair. This trial is registered with NCT02070861.

Noninvasive cardiac output monitoring in a porcine model using the inspired sinewave technique: a proof-of-concept study

Background

Cardiac output (Q˙) monitoring can support the management of high-risk surgical patients, but the pulmonary artery catheterisation required by the current ‘gold standard’—bolus thermodilution (Q˙T)—has the potential to cause life-threatening complications. We present a novel noninvasive and fully automated method that uses the inspired sinewave technique to continuously monitor cardiac output (Q˙IST).

Methods

Over successive breaths the inspired nitrous oxide (N₂O) concentration was forced to oscillate sinusoidally with a fixed mean (4%), amplitude (3%), and period (60 s). Q˙IST was determined in a single-compartment tidal ventilation lung model that used the resulting amplitude/phase of the expired N₂O sinewave. The agreement and trending ability of Q˙IST were compared with Q˙T during pharmacologically induced haemodynamic changes, before and after repeated lung lavages, in eight anaesthetised pigs.

Results

Before lung lavage, changes in Q˙IST and Q˙T from baseline had a mean bias of –0.52 L min⁻¹ (95% confidence interval [CI], –0.41 to –0.63). The concordance between Q˙IST and Q˙T was 92.5% as assessed by four-quadrant analysis, and polar plot analysis revealed a mean angular bias of 5.98° (95% CI, –24.4°–36.3°). After lung lavage, concordance was slightly reduced (89.4%), and the mean angular bias widened to 21.8° (–4.2°, 47.6°). Impaired trending ability correlated with shunt fraction (r=0.79, P<0.05).

Conclusions

The inspired sinewave technique provides continuous and noninvasive monitoring of cardiac output, with a ‘marginal–good’ trending ability compared with cardiac output based on thermodilution. However, the trending ability can be reduced with increasing shunt fraction, such as in acute lung injury.

Optimizing target control of the vessel rich group with volatile anesthetics

The ability to monitor the inspired and expired concentrations of volatile anesthetic gases in real time makes these drugs implicitly targetable. However, the end-tidal concentration only represents the concentration within the brain and the vessel rich group (VRG) at steady state, and very poorly approximates the VRG concentration during common dynamic situations such as initial uptake and emergence. How should the vaporization of anesthetic gases be controlled in order to optimally target VRG concentration in clinical practice? Using a generally accepted pharmacokinetic model of uptake and redistribution, a transfer function from the vaporizer setting to the VRG is established and transformed to the time domain. Targeted actuation of the vaporizer in a time-optimal manner is produced by a variable structure, sliding mode controller. Direct mathematical application of the controller produces rapid cycling at the limits of the vaporizer, further prolonged by low fresh gas flows. This phenomenon, known as "chattering", is unsuitable for operating real equipment. Using a simple and clinically intuitive modification to the targeting algorithm, a variable low-pass boundary layer is applied to the actuation, smoothing discontinuities in the control law and practically eliminating chatter without prolonging the time taken to reach the VRG target concentration by any clinically significant degree. A model is derived for optimum VRG-targeted control of anesthetic vaporizers. An alternate and further application is described, in which deliberate perturbation of the vaporization permits non-invasive estimation of parameters such as cardiac output that are otherwise difficult to measure intra-operatively.

Sequential gas delivery provides precise control of alveolar gas exchange

Of the factors determining blood gases, only alveolar ventilation (VA) is amenable to manipulation. However, current physiology text books neither describe how breath-by-breath VA can be measured, nor how it can be precisely controlled in spontaneously breathing subjects. And such control must be effected independent of minute ventilation (VE) and the pattern of breathing. Control of VA requires the deliberate partition of inhaled gas between the alveoli and the anatomical deadspace. This distribution is accomplished by sequential gas delivery (SGD): each breath consists of a chosen volume of 'fresh' gas followed by previously exhaled gas. Control of VA through SGD is a simple, inexpensive, yet powerful tool with many applications. Here we describe how to implement SGD, how it precisely controls VA, and consequently how it controls arterial blood gases.