A Probabilistic Function to Model the Relationship between Quality of Chest Compressions and the Physiological Response for Patients in Cardiac Arrest

Trygve Eftestøl1, Svein Erik Stokka2, Jan Terje Kvaløy1, Ali Bahrami Rad3, Unai Irusta4, Elisabete Aramendi4, Erik Alonso4, Trond Nordseth5, Eirik Skogvoll5, Lars Wik6, Jo Kramer-Johansen6
1University of Stavanger, 2University of Stavanger, Bouvet, 3Emory University, 4UPV/EHU, 5NTNU, 6Oslo University Hospital


Aims: Cardiopulmonary resuscitation quality (CPRQ) parameters can be derived from electric signals obtained during resuscitation, such as chest compression depth (CD) and thoracic impedance (TI). A patient's response to CPR can be estimated by analysing the cardiac rhythm transitions during cardiac arrest by assessing the electrocardiogram (ECG). We propose to model a probabilistic relationship between CPRQ parameters and the physiological response as judged by ECG-features, to guide therapy in a clinical context.

Methods: A total of 821 compression sequences were extracted from 394 out-of-hospital resuscitation episodes. Sequences were categorized as positive if the post-sequence cardiac rhythm had better prognosis than the pre-sequence rhythm, otherwise as negative. A total of 467 positive and 354 negative sequences were considered. For each sequence, six CPRQ parameters (mean and standard deviation) related to depth and rate were calculated from 3 second windows of CD and TI. Three alternative classification approaches were employed for the binary (positive/negative) decision making based on the QCPR parameters: quadratic discriminant analysis (QDA), logistic regression (LR), and artificial neural networks (ANN). The positive class discriminant function defined the probability of effective compressions (Pec).

Results: The discriminative accuracies for the three types of classifiers were 0.59 (QDA), 0.63 (ANN) and 0.63 (LR). Three compression depth (2-4,4-6,6-8 cm) and rate intervals (70-100,100-130,130-160 min-1) were analysed, corresponding to "less than recommended", "recommended", "more than recommended" CPRQ values, respectively. For the LR model the median (interquartile range) Pec were 0.3 (0.1-0.6), 0.7 (0.6-0.8), 0.2 (0.1-0.3) for the depth, and 0.5 (0.3-0.6), 0.6 (0.4-0.7), 0.2 (0.0-0.4) for the rate intervals, respectively.

Conclusion: We have proposed a novel method to relate the quality of chest compressions to the physiologic response to CPR. The highest probability estimates of effective chest compressions corresponded to the depth (5-6 cm) and rate (100-120 min-1) currently recommended in the CPR guidelines.