Cardiopulmonary resuscitation (CPR) involves delivering chest compressions and positive pressure ventilation to cardiac arrest victims to maintain circulatory blood flow and oxygen delivery [
Another approach to optimizing CPR involves defining and establishing reliable, appropriate CPR performance measures and then modifying CPR parameters to enhance these measures. Brain ischemia is a primary contributor to postarrest morbidity; thus, oxygen delivery and carbon dioxide elimination (endtidal carbon dioxide [ETCO_{2}]) have been established as important factors for measuring CPR performance [
In this study, we aimed to define a reliable CPR performance measure, optimize the quality of CPR based on the defined performance measure, and design a dynamically optimized scheme that varies CPR parameters to optimize CPR performance.
The Institutional Review Board at the Children’s Hospital of Philadelphia approved this study.
The combination of systemic oxygen delivery and carbon dioxide elimination at the lungs was selected as an objective function for maximization. Delivery of systemic carbon dioxide to the lung for elimination is associated with partial pressure of endtidal carbon dioxide PETCO_{2} and consequently has been correlated with CPR outcome [
During CPR, the blood gas (oxygen and carbon dioxide) delivery to any organ in the body can be estimated using the following simplified equation [
To calculate ∆CO_{2} and ∆
In the steadystate condition, the oxygen and carbon dioxide balance in the lungs can be expressed using the following equations:
Since changes in concentrations are small, they are in the linear range of dissociation curves, or in mathematical terms:
Finally, combining the above equations will result in mathematical expressions for systemic oxygen delivery and carbon dioxide delivery:
In the next step, CPR parameters were divided into three categories: rescuer dependent, patient dependent, and constant parameters. Rescuerdependent parameters are parameters that depend on rescuer performance, such as
Parameter values and their respective ranges.
Parameter  Range  Unit 

CPR parameters  

[0.132,0.134]  Min 

[600,1000]  mL 

[0.008,0.012]  Min 

[0.13,0.19]  mL·O_{2}/mL gas 

[0.0002,0.0006]  mL·O_{2}/mL gas 


Patient specific parameter  

[400,1000]  mL/min 


Constant parameters  

150  mL 

0.8  mL/mL 

1.5  mL/mL 
The range for each parameter shows the lower and upper limit of variable changes during the optimization. Except for the constant parameters, parameter values change during the CPR delivery.
Next, two different optimization schemes were developed: a global optimization scheme and a sequential optimization scheme. Both of the methods used simulated annealing as the optimization technique. Simulated annealing is a computational intelligence technique that aims to solve combinatorial optimization problems to minimize the defined cost function which is characterized by a large number of solutions [
Unlike most of the optimization techniques which could be considered analogous to rapid cooling methods, simulated annealing is analogous to a process of slowly cooling a physical system by providing ample time in order to obtain states with globally minimum energy [
First, we considered global optimization. In this method, for each patient group, i.e., a fixed
This method involved applying a sequential optimization scheme to actively vary the CPR parameters in order to maximize the defined objective function and thereby improve CPR performance. In this scheme, instead of finding the global maximum of the objective function for each patient group, at each CPR sequence, the rescuerdependent parameters were varied based on a predefined protocol to find the global maximum of blood gas delivery. We defined a CPR sequence as one cycle of compressions and positive pressure ventilation. During each CPR sequence, a compression to ventilation ratio that maximized the total delivery was found, and then the CPR was performed. Total delivery was then maximized again by varying other rescuerdependent parameters. These new parameters were used as the starting point of the next CPR sequence, and this procedure was continued until reaching the maximum total delivery for each group.
The steps of the sequential optimization algorithm can hence be summarized as follows:
Start with nominal values.
Optimize the total delivery based on varying
Rescuer performs the CPR cycle.
Optimize the remaining rescuer parameters to maximize the oxygen delivery with a fixed
Go to Step 2 and repeat until objective function shown in Equation (
To simulate a wide range of patient groups, we varied
Results obtained from two different optimization prospective are presented in Figure
Results obtained from the two proposed schemes for optimizing total blood gas delivery during cardiopulmonary resuscitation. For systemic oxygen delivery,
Figure
Plot of compression to ventilation ratio for three different
Figure
The results obtained for average ventilation time
Plot of average ventilation time for three different
The results obtained for compression speed 1/
Plot of compression speed over time for three different
In this study, we present an analysis of dynamic modeling of CPR parameters during CPR to improve CPR performance. While previous research efforts to optimize CPR have focused on finding the best compression to ventilation ratio and keeping the ratio constant during the CPR procedure, we propose a sequential optimization scheme to vary the rescuerdependent parameters as needed during CPR sequences to optimize CPR performance.
Our results suggest that as CPR progresses, the compression to ventilation ratio should decrease over time to optimize performance. One possible explanation for this observation in our model is that when CPR typically commences, blood oxygen content is sufficient for satisfactory systemic delivery and the carbon dioxide level has only started to rise above normal levels. As CPR progresses, oxygen decreases in the blood and carbon dioxide starts to accumulate, so ventilation becomes increasingly important. Hence, our model suggests that the optimal compression to ventilation ratio should begin higher than is currently practiced (greater than 30 compressions per ventilation) and then decrease over time.
In this study, we followed to a large extent the methods as described in a conference paper [
There were several limitations to this study. First, we used one published systemic oxygen delivery model to develop analytical expressions for both systemic oxygen delivery and carbon dioxide delivery to the lungs [
In summary, our study illustrates the potential benefit of considering dynamic changes in rescuerdependent parameters during CPR in order to improve performance. Validation in an animal model and an assessment of generalizability to real data sets are important future steps.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that they have no conflicts of interest.
This research was supported by a grant from the National Institutes of Health (no. 1 R01 NS 72338 01A1).