Which factors influence spontaneous state transitions during resuscitation?

Resuscitation. 2009 Aug;80(8):863-9. doi: 10.1016/j.resuscitation.2009.04.042. Epub 2009 Jun 13.

Abstract

Background: The clinical state (i.e. ventricular fibrillation/tachycardia: VF/VT, asystole: ASY, pulseless electrical activity: PEA, or return of spontaneous circulation, ROSC) during cardiopulmonary resuscitation determines patient management. We investigate how spontaneous transitions (i.e. not forced by DC shock) between these states are influenced by factors like age, gender, bystander CPR, CPR quality, proportion of time spent in a state, or the number of state transitions.

Methods: Detailed recordings from CPR attempts in 304 out-of-hospital cardiac arrests in Akershus (Norway), Stockholm (Sweden), and London (UK) were obtained from modified Heartstart 4000 defibrillators. Spontaneous state transitions were studied using a non-parametric intensity regression method that can handle dynamic factors like the state history properly.

Results: The initial state tended to preserve itself, as did cumulative time in any state. Recent DC shock, bystander CPR, location, response time, gender, compression depth, and ventilation rate were important for some transitions. More ventilation during PEA might possibly avert development to ASY and favour ROSC; otherwise observed variations in CPR quality had little impact.

Conclusion: Using a novel intensity regression approach we studied the influence of various factors on spontaneous (i.e. non-shock) state transitions during CPR. State development was largely determined by the initial state, the proportion of time spent in a state, and the transition frequency; all probably reflecting the underlying aetiology.

Publication types

  • Comparative Study
  • Multicenter Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Age Factors
  • Cardiopulmonary Resuscitation / methods*
  • Heart Arrest / epidemiology
  • Heart Arrest / etiology
  • Heart Arrest / therapy*
  • Hemodynamics / physiology*
  • Humans
  • Incidence
  • Markov Chains
  • Models, Statistical
  • Risk Factors
  • Treatment Outcome