Chest Compression Fraction Alone Does Not Adequately Measure Cardiopulmonary Resuscitation Quality in Out-of-Hospital Cardiac Arrest.

BACKGROUND: High-quality cardiopulmonary resuscitation in out-of-hospital cardiac arrest is important for increased survival and improved neurological outcome. Chest compression fraction measures the proportion of time chest compressions are given during a cardiac arrest resuscitation. Chest compression fraction has not been compared with the quality of chest compressions delivered at the recommended rate and depth of 100-120/min and 2.0-2.4 inches, respectively. OBJECTIVES: We evaluate whether chest compression fraction correlates with compressions at a target rate of 100-120/min and depth of 2.0-2.4 inches in chest diameter. METHODS: A prospective, observational study design was used to compare chest compression fraction to compressions in target in out-of-hospital cardiac arrest patients in a prehospital urban setting. We include all adult, non-traumatic out-of-hospital cardiac arrest patients with a resuscitation attempt during January 1, 2019 through September 30, 2019, for a total of 9 months. Spearman's rank correlation was used to determine correlation between compression fraction and compressions in target. RESULTS: A total of 120 out-of-hospital cardiac arrest cases were included in the study. We found a high chest compression fraction median of 83% (interquartile range 72-90%), but a low compression in target median of 13% (interquartile range 5-29%). There was no significant correlation between chest compression fraction and compressions in target when analyzed linearly (Spearman's Rho = 0.165, p = 0.072). No difference was found when dichotomizing chest compression fraction into high and low variables in comparison with compressions in target (14% vs. 10%, p = 0.119). CONCLUSION: Chest compression fraction is not associated with compressions in target for rate and depth for out-of-hospital cardiac arrest cardiopulmonary resuscitation.

Are EMS call volume predictions based on demand pattern analysis accurate?

UNLABELLED: Most EMS systems determine the number of crews they will deploy in their communities and when those crews will be scheduled based on anticipated call volumes. Many systems use historical data to calculate their anticipated call volumes, a method of prediction known as demand pattern analysis. OBJECTIVE: To evaluate the accuracy of call volume predictions calculated using demand pattern analysis. METHODS: Seven EMS systems provided 73 consecutive weeks of hourly call volume data. The first 20 weeks of data were used to calculate three common demand pattern analysis constructs for call volume prediction: average peak demand (AP), smoothed average peak demand (SAP), and 90th percentile rank (90%R). The 21st week served as a buffer. Actual call volumes in the last 52 weeks were then compared to the predicted call volumes by using descriptive statistics. RESULTS: There were 61,152 hourly observations in the test period. All three constructs accurately predicted peaks and troughs in call volume but not exact call volume. Predictions were accurate (+/-1 call) 13% of the time using AP, 10% using SAP, and 19% using 90%R. Call volumes were overestimated 83% of the time using AP, 86% using SAP, and 74% using 90%R. When call volumes were overestimated, predictions exceeded actual call volume by a median (Interquartile range) of 4 (2-6) calls for AP, 4 (2-6) for SAP, and 3 (2-5) for 90%R. Call volumes were underestimated 4% of time using AP, 4% using SAP, and 7% using 90%R predictions. When call volumes were underestimated, call volumes exceeded predictions by a median (Interquartile range; maximum under estimation) of 1 (1-2; 18) call for AP, 1 (1-2; 18) for SAP, and 2 (1-3; 20) for 90%R. Results did not vary between systems. CONCLUSION: Generally, demand pattern analysis estimated or overestimated call volume, making it a reasonable predictor for ambulance staffing patterns. However, it did underestimate call volume between 4% and 7% of the time. Communities need to determine if these rates of over-and underestimation are acceptable given their resources and local priorities.