Objectives: To determine whether high-resolution (HighRes) and multimodal integration of physiologic signals improve prediction of return of spontaneous circulation (ROSC) during pediatric cardiopulmonary resuscitation (CPR) compared with low-resolution (LowRes) and single-modality approaches.
Design: Retrospective analysis of experimental data using machine learning models for outcome prediction.
Setting: Laboratory setting with pediatric swine models of cardiac arrest.
Subjects: A total of 187 pediatric swine undergoing standardized cardiac arrest and CPR protocols.
Interventions: Animals were monitored using multiple physiologic signals during CPR, including aortic blood pressure (ABP), right atrial pressure (RAP), capnography, and electrocardiography. No therapeutic interventions were evaluated.
Measurements and main results: Four data approaches were evaluated: 1) Waveform-HighRes (100 Hz waveforms); 2) Compression-HighRes (compression-by-compression physiologic series); 3) Waveform-LowRes (15-s averaged waveforms); and 4) Compression-LowRes (15-s averaged compression-by-compression series). Models were developed to predict ROSC using segments 2-4, 2-6, 2-8, and 2-10 minutes of CPR, using both single and combined signal modalities. Area under the receiver operating characteristic curve (AUROC) was used to evaluate models' performance. In early CPR (2-4 min), Compression-HighRes outperformed both LowRes approaches for ABP (AUROC, 0.74 [0.65-0.82] vs. 0.65 [0.55-0.74] and 0.54 [0.44-0.64]) and RAP (0.70 [0.62-0.79] vs. 0.61 [0.51-0.70] and 0.57 [0.48-0.66]; p < 0.05). In multimodal models, LowRes data performed comparably to HighRes models (AUROC, 0.76-0.79). Across time points, ABP-based model performance improved, reaching AUROC 0.90 (0.84-0.95) for the full CPR period (2-10 min)-comparable to the multimodal model (0.89 [0.83-0.95]).
Conclusions: HighRes monitoring improved early ROSC prediction for individual signals, especially ABP and RAP. However, combining multiple modalities compensates for lower resolution, enabling comparable predictive performance. These findings support data-driven strategies for selecting physiologic targets and technical requirements in physiology-directed CPR.
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