Objectives: The objective of this study was to evaluate video-assisted dispatcher cardiopulmonary resuscitation (V-DACPR) versus audio-assisted dispatcher CPR impacts on compression quality in simulated out-of-hospital cardiac arrest (OHCA) scenarios.
Methods: Network meta-analysis of randomized controlled trials (RCTs) compared V-DACPR versus audio-assisted dispatcher CPR (A-DACPR) and control. The primary outcome was the compression rate; the secondary outcomes included compression depth, time to first compression, and interruption time. Network meta-analysis of RCTs compared dispatcher-guided CPR with video feedback versus telephone-only instructions in simulated OHCA scenarios using high-fidelity manikins. Three intervention arms were compared: video-assisted CPR, audio-assisted CPR, and unguided CPR (control). Standardized mean differences (SMD) and surface under the cumulative ranking curve (SUCRA) were calculated using Bayesian network meta-analysis methodology.
Results: Fifteen trials (n = 1,556) were analyzed. V-DACPR showed superior compression rates versus A-DACPR (impact size: -21.37, 95% CI: -36.10, -7.41) and control (-43.04, 95% CI: -63.05, -22.52). V-DACPR demonstrated better time to first compression versus control (-42.23, 95% CI: -83.31, -1.42) and favorable trends in compression depth (-5.06, 95% CI: -12.40 to 2.12) and interruption time, though several comparisons between V-DACPR and A-DACPR did not reach statistical significance. Heterogeneity was low to moderate (I2 = 12-63%). Confidence in network meta-analysis (CINeMA) assessment supported moderate to high-quality evidence.
Conclusions: V-DACPR demonstrated significant advantages in compression rate in simulated scenarios, with favorable trends in other quality metrics compared to A-DACPR. These findings support the potential for video assistance technology in dispatcher-guided CPR, particularly for optimizing compression rates. However, these results were observed in simulation studies and require validation in real-world clinical settings to determine their impact on patient outcomes.