A new paradigm for human resuscitation research using intelligent devices
ABSTRACT To develop new methods for studying correlations between the performance and outcome of resuscitation efforts in real-world clinical settings using data recorded by automatic devices, such as automatic external defibrillators (AEDs), and to explore effects of shock timing and chest compression depth in the field.
In 695 records of AED use in the pre-hospital setting, continuous compression data were recorded using AEDs capable of measuring sternal motion during compressions, together with timing of delivered shocks and the electrocardiogram. In patients who received at least one shock, putative return of spontaneous circulation (P-ROSC) was defined as a regular, narrow complex electrical rhythm > 40 beats/min with no evidence of chest compressions at the end of the recorded data stream. Transient return of spontaneous circulation (t-ROSC) was defined as the presence of a post-shock organized rhythm > 40 beats/min within 60s, and sustained > or = 30 s. 2x2 contingency tables were constructed to examine the association between these outcomes and dichotomized time of shock delivery or chest compression depth, using the Mood median test for statistical significance.
The probability of P-ROSC for first shocks delivered < 50 s (the median time) after the start of resuscitation was 23%, versus 11% for first shocks > 50 s (p=0.028, one tailed). Similarly, the probability of t-ROSC for shorter times to shock was 29%, compared to the 15% for delayed first shocks (p=0.016). For shocks occurring > 3 min after initiation of rescue attempts, the probability of t-ROSC with pre-shock average compression depth > 5 cm was more than double that with compression depth < 5 cm (17.7% vs. 8.3%, p=0.028). For shocks > 5 min, the effect of deeper compressions increased (23.4% versus 8.2%, p=0.008).
Much can be learned from analysis of performance data automatically recorded by modern resuscitation devices. Use of the Mood median test of association proved to be sensitive, valid, distribution independent, noise-resistant and also resistant to biases introduced by the inclusion of hopeless cases. Efforts to shorten the time to delivery of the first shock and to encourage deeper chest compressions after the first shock are likely to improve resuscitation success. Such refinements can be effective even after an unknown period of preceding downtime.
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ABSTRACT: Chest compression (CC) is a significant emergency medical procedure for maintaining circulation during cardiac arrest. Although CC produces the necessary blood flow for patients with heart arrest, improperly deep CC will contribute significantly to the risk of chest injury. In this paper, an optimal CC closed-loop controller for a mechanical chest compressor (OCC-MCC) was developed to provide an effective trade-off between the benefit of improved blood perfusion and the risk of ribs fracture. The trade-off performance of the OCC-MCC during real automatic mechanical CCs was evaluated by comparing the OCC-MCC and the traditional mechanical CC method (TMCM) with a human circulation hardware model based on hardware simulations. A benefit factor (BF), risk factor (RF) and benefit versus risk index (BRI) were introduced in this paper for the comprehensive evaluation of risk and benefit. The OCC-MCC was developed using the LabVIEW control platform and the mechanical chest compressor (MCC) controller. PID control is also employed by MCC for effective compression depth regulation. In addition, the physiological parameters model for MCC was built based on a digital signal processor for hardware simulations. A comparison between the OCC-MCC and TMCM was then performed based on the simulation test platform which is composed of the MCC, LabVIEW control platform, physiological parameters model for MCC and the manikin. Compared with the TMCM, the OCC-MCC obtained a better trade-off and a higher BRI in seven out of a total of nine cases. With a higher mean value of cardiac output (1.35 L/min) and partial pressure of end-tidal CO2 (15.7 mmHg), the OCC-MCC obtained a larger blood flow and higher BF than TMCM (5.19 vs. 3.41) in six out of a total of nine cases. Although it is relatively difficult to maintain a stable CC depth when the chest is stiff, the OCC-MCC is still superior to the TMCM for performing safe and effective CC during CPR. The OCC-MCC is superior to the TMCM in performing safe and effective CC during CPR and can be incorporated into the current version of mechanical CC devices for high quality CPR, in both in-hospital and out-of-hospital CPR settings.Medical & Biological Engineering & Computing 03/2015; DOI:10.1007/s11517-015-1258-y · 1.50 Impact Factor
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ABSTRACT: Introduction Quality cardiopulmonary resuscitation (CPR) and timely defibrillation are associated with increasing survival to hospital discharge from out-of-hospital cardiac arrest (OHCA). The objective of this study was to demonstrate that performance coaching during an OHCA would improve compression depth and time to defibrillation (TTD). Methods This study was conducted in a single emergency medical services (EMS) agency and utilized data collected from 815 patients treated between 1/1/2012-12/31/2013. The intervention used multiple Plan-Do-Study-Act (PDSA) cycles to train fire captains to translate performance data into active direction. Testing began in simulation with small-scale expansions prior to system-wide implementation. Performance metrics included average (reported as a percentage) and actual compression depth (reported in millimeters), and TTD (an average in seconds). Analysis was conducted using Xbar and S control charts with standard assessment of special cause for performance data. A statistical shift was seen in means and standard deviations for both depth metrics. Results Average depth of compressions improved from 69.8% (SD=28.0%) to 80.4 (SD=21.8%). Depth of compressions delivered increased from 43.6mm (SD=8.2mm) to 47.2mm (SD=8.1mm). Analysis of the S charts indicates a statistical shift in process variation for TTD. Conclusion Early results indicate that utilization of a CPR coach during OHCA improves compression depth and TTD. Further data are needed to assess sustainability.Resuscitation 09/2014; 85(12). DOI:10.1016/j.resuscitation.2014.09.016 · 3.96 Impact Factor
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ABSTRACT: Aim: The aim of the study was to determine the quality of basic life support (BLS) in out-of-hospital cardiac arrests (OHCAs) receiving bystander cardiopulmonary resuscitation (CPR) and public automated external defibrillator (AED) application. Methods: From January 2006 to December 2012, data were prospectively collected from OHCA) and impending cardiac arrests treated with and without public AED before emergency medical technician (EMT) arrival. Basic life support actions and outcomes were compared between cases with and without public AED application. Interruptions of CPR were compared between 2 groups of AED users: health care provider (HCP) and non-HCP. Results: Public AEDs were applied in 10 and 273 cases of impending cardiac arrest and non–EMT-witnessed OHCAs, respectively (4.3% of 6407 non–EMT-witnessed OHCAs). Defibrillation was delivered to 33 (13.3%) cases. Public AED application significantly improved the rate of 1-year neurologically favorable survival in bystander CPR–performed cases with shockable initial rhythm but not in those with nonshockable rhythm. Emergency calls were significantly delayed compared with other OHCAs without public AED application (median: 3 and2minutes, respectively; P b .0001). Analysis of AED records obtained from 136 (54.6%) of the 249 cases with AED application revealed significantly lower rate of compressions delivered per minute and significantly greater proportion of CPR pause in the non-HCP group. Time interval between power on and the first electrocardiographic analysis widely varied in both groups and was significantly prolonged in the non-HCP group (P =.0137). Conclusions: Improper BLS responses were common in OHCAs treated with public AEDs. Periodic training for proper BLS is necessary for both HCPs and non-HCPs.American Journal of Emergency Medicine 10/2014; DOI:10.1016/j.ajem.2014.10.018 · 1.15 Impact Factor