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A logistic regression model to predict the occurrence of ventricular fibrillation in out-of-hospital cardiac arrest
  1. AJ Hamilton1,
  2. KJ Cairns2,
  3. AAJ Adgey1,
  4. F Kee2
  1. 1Royal Victoria Hospital, Belfast, UK,
  2. 2Centre for Clinical and Population Science, Queen’s University of Belfast, Belfast, UK


Background The emergency medical services (EMS) in the UK have a target to respond to 75% of life-threatening emergencies within 8 minutes. It is unclear how closely this relates to survival from out-of-hospital cardiac arrest (OHCA) in the modern setting. The presence of ventricular fibrillation following OHCA is generally assumed to follow a linear decline with time such that survival declines by 10% per minute delay from collapse to defibrillation. The Northern Ireland Public Access Defibrillation (NIPAD) project established robust data collection techniques using multiple source surveillance to establish the baseline response to OHCA before and after the addition of lay first responder defibrillation. The objective of this study was to develop a model to predict the probability a patient is in ventricular fibrillation (VF) on EMS arrival as a function of the call-to-response interval (CRI), for cases of witnessed OHCA.

Methods Logistic regression analysis was performed on a series of OHCA from the NIPAD trial region containing a population of 285 347 individuals with 29 585 emergency “999” ambulance calls across a 115-week period (January 2004–April 2006). The main outcome measure was whether the initial cardiac rhythm was VF for witnessed arrests. Patient age, gender, initiation of cardiopulmonary resuscitation (CPR) by bystanders, CRI, location of arrest and interaction terms were considered.

Results 609 OHCA were attended by the Northern Ireland ambulance service, of which 200 OHCA cases were witnessed. Witnessed OHCA accounted for only 32.8% of all OHCA. In those cases of unwitnessed OHCA 24/409 (5.9%) were in VF compared with 71/200 (35.5%) of witnessed OHCA in VF. Logistic regression analysis suggested that reduced CRI (p = 0.002), patient age under 55 years (p = 0.016), location of arrest not at home (p = 0.026) and initiation of CPR by bystanders (p = 0.041) were significantly associated with the rhythm of VF on EMS arrival. When age, CRI, location of arrest and level of bystander CPR were included in a more complex model the c statistic for the area under the receiver operator characteristic curve was 0.738. Gender, geographical region and interaction terms were not significant (p>0.25). A predictive model was produced to quantify the effect of the CRI in minutes. The probability of VF given a witnessed OHCA equals 1/(1 + exp (−0.601 + 0.149 × CRI)) where exp ≈ 2.718. Using this equation at one minute post-witnessed OHCA the probability of being in VF is 61%, at 5 minutes the probability is 46.4% and at 8 minutes it is 35.6%.

Conclusion The simplified model suggests that there is an upper limit for the incidence of VF in OHCA of 64.5% at the moment of collapse. Conversely, without an increase in the level of bystander CPR the percentage probability of being in VF following a witnessed OHCA in Northern Ireland is 35.6% at the ambulance service 8-minute response target.

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