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Corresponding author: Mary Boyde, School of Nursing and Midwifery, University of Queensland, Second Floor, Building One, Princess Alexandra Hospital, Ipswich Rd, Woolloongabba, Queensland 4102, Australia (e-mail: m.boyde{at}uq.edu.au).
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Abstract |
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Objectives To investigate clinical variables that influence return of spontaneous circulation and survival to discharge after in-hospital cardiac arrest.
Methods An Utstein-style resuscitation template was implemented in a 750-bed hospital. Data on 158 events were collected from January 2004 through November 2004. Significant variables were analyzed by using a multiple logistic regression model.
Results Of the 158 events, 128 were confirmed cardiac arrests. Return of spontaneous circulation occurred in 69 cases (54%), and the patient survived to discharge in 41 (32%). An initial shockable rhythm was present in 42 cases (33%), with a return of spontaneous circulation in 32 (76%) and survival to discharge in 24 (57%). An initial nonshockable rhythm was present in the remaining 86 cases (67%), with a return of spontaneous circulation in 37 (43%) and survival to discharge in 17 (20%). Witnessed or monitored arrests (P=.006), time to arrival of the cardiac arrest team (P=.002), afternoon shift (P=.02), and initial shockable rhythm (P=.005) were independently associated with return of spontaneous circulation. Location of patient in a critical care area (P=.002), initial shockable rhythm (P<.001), and length of resuscitation (P=.02) were independently associated with survival to hospital discharge.
Conclusions The high rate of survival to discharge after cardiac arrest is attributed to extensive education and the incorporation of semiautomatic external defibrillators into basic life support management.
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The most widely recognized data collection tool for reporting in-hospital cardiac arrests is the Utstein template, which has been used internationally to measure and evaluate resuscitation attempts.1–4 Use of an in-hospital Utstein template therefore can optimize identification, measurement, and evaluation of clinical variables that lead to improved survival of patients.
We used an in-hospital Utstein template to collect data on cardiac arrest during an 11-month period (January through November 2004). We had 2 goals: to provide clinical data on the return of spontaneous circulation (ROSC) and survival to discharge and to identify the clinical variables that influence ROSC and survival to discharge for an Australian in-hospital adult population.
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Methods |
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All medical, nursing, and allied health staff undergo extensive training in basic life support with the incorporation of SAED training for medical and nursing staff. The medical registrars and the nurses from critical care areas (coronary care units, intensive care units, emergency department) participate in Advanced Life Support training that includes a yearly reassessment. Nurses with Advanced Life Support accreditation can perform manual defibrillation and external cardiac pacing, and they can administer intravenous epinephrine, lidocaine, and atropine sulfate according to the Australian Resuscitation Council guidelines and hospital policy. During this study, the nurse with Advanced Life Support accreditation ensured completion of the resuscitation record at each cardiac arrest.
| Identification of clinical variables affecting cardiac arrest outcomes is vital for improving patients’ survival.
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Data Collection
A resuscitation form adapted from the Utstein guidelines was implemented at the beginning of the study. Minor modifications to the form included the following:
| Fifty-four percent of patients achieved a return of spontaneous circulation and 32% survived to discharge.
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Data collection started on January 1, 2004, and ended on November 30, 2004. The following definition of a cardiac arrest was used: "the cessation of cardiac mechanical activity, confirmed by the absence of a detectable pulse, unresponsiveness and apnoea."5 Patients were excluded from the study if they came to the emergency department after an out-of-hospital cardiac arrest, had an in-hospital respiratory arrest, had do-not-resuscitate orders, or had a medical emergency but still had cardiac output. On receipt of the completed resuscitation form, the clinical nurse consultant for resuscitation or the project officer audited the patient’s chart to ensure that the data were valid.
Statistical Analysis
Data were entered into an ACCESS database from which data reports were generated. Statistical analysis was done with the SAS software package, version 8.2 for Windows (SAS Institute Inc, Cary, NC). The 2 outcomes, ROSC and survival to discharge, were dichotomized to ROSC versus other (futile, death, do not resuscitate) and discharged alive from hospital versus not. Univariate logistic regression was used to investigate possible associations between these 2 outcomes and the following variables: whether the arrest was witnessed or monitored, location of the patient in a critical care area, time elapsed before arrival of the cardiac arrest team, time elapsed before cardiopulmonary resuscitation, time of the cardiac arrest, age and sex of the patient, initial rhythm, length of resuscitation, and time elapsed before defibrillation. All univariately significant variables were entered into a multivariate logistic regression model, and non-significant variables were backwards eliminated one by one until just the independent predictors of outcome remained.
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Results |
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Event and Treatment Characteristics
Of the 128 cardiac arrests included in the study, 42 (33%) were initially documented as shockable and 86 (67%) were nonshockable (see Figure). Eighty-eight (69%) of the cardiac arrests were witnessed; the initial rhythms were asystole in 23 cases, pulseless electrical activity or electromechanical dissociation in 36 cases, ventricular fibrillation in 12 cases, and ventricular tachycardia in 17 cases. Of these 88 witnessed arrests, ROSC occurred in 56 (64%), and the patient was discharged from the hospital in 33 cases (38%; Table 2).
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Factors Associated With ROSC
Witnessed or monitored arrest, location of the patient in a critical care area, shorter time to arrival of the cardiac arrest team, afternoon shift (3–11 PM), and an initial shockable rhythm were related to survival to discharge for all cardiac arrests (Table 3). When these 5 significant variables were entered in a multiple logistic regression model and stepwise backwards elimination of nonsignificant variables was done, 4 variables remained that were independently associated with ROSC: witnessed or monitored arrest (odds ratio [OR] 0.26, 95% CI 0.1–0.67, P=.006), time to arrival of the cardiac arrest team (OR 1.48, 95% CI 1.15–1.90, P=.002), afternoon shift (OR 0.33, 95% CI 0.13–0.85, P=.02), and initial shockable rhythm (OR 0.26, 95% CI 0.10–0.67, P=.005).
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). When these 5 significant variables were entered in a multiple logistic regression model and stepwise backwards elimination of nonsignificant variables was done, only 3 variables remained that were independently associated with survival to discharge: location of the patient in a critical care area (OR 0.25, 95% CI 0.11–0.61, P=.002), initial shockable rhythm (OR 0.18, 95% CI 0.07–0.44, P < .001), and duration of resuscitation (OR 1.04, 95% CI 1.01–1.08, P=.02).
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An initial shockable rhythm (ie, ventricular fibrillation or ventricular tachycardia) was the only variable independently associated with ROSC for cardiac arrests in non-monitored areas (Table 5). Although marginal evidence suggested that both age and afternoon shift might be associated with ROSC at the univariate level, both of these variables become statistically nonsignificant when an initial shockable rhythm was entered into a multiple logistic regression model.
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| Initial rhythm of ventricular fibrillation/ventricular tachycardia was the only variable associated with return of spontaneous circulation and survival to discharge.
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Afternoon shift and an initial shockable rhythm were both protective factors for survival to hospital discharge for cardiac arrests that occurred in nonmonitored areas (Table 6). They also were independently associated with survival to hospital discharge; the following statistics were generated from a multiple logistic regression model: afternoon shift (OR 0.24, 95% CI 0.07–0.82, P=.02) and initial shockable rhythm (OR 0.17, 95% CI 0.05–0.59, P=.005).
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| Access to semiautomatic external defibrillators and training for nursing and medical staff improved in-hospital survival rates.
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Discussion |
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In our study, variables independently associated with ROSC were an initial shockable rhythm, witnessed or monitored arrest, time to arrival of the cardiac arrest team, and afternoon shift. Variables independently associated with survival to discharge were an initial shockable rhythm, location of the patient in a critical care area, and duration of resuscitation.
The only significant clinical variable associated with both ROSC and survival of the patient to discharge was an initial shockable rhythm (ventricular fibrillation or ventricular tachycardia). Researchers in several studies1,6,7 have reported that patients with an initial shockable rhythm have markedly better survival rates than other patients after cardiac arrest. The importance of early defibrillation for survival in the hospital is also well documented,8,9 and the ability of first responders to defibrillate leads to higher survival rates.2 The improved in-hospital survival rates in our study can be attributed to access to SAEDs and extensive training for nursing and medical staff; 76% of all patients with an initial shockable rhythm had ROSC, and 57% survived to discharge.
Our data indicated that a witnessed or monitored arrest was independently associated with ROSC. We made no differentiation between witnessed arrests and monitored areas. Patients may have been monitored only, had witnessed arrests only, or been monitored and had witnessed arrests. It is widely accepted that cardiac arrests that are witnessed or monitored have a better outcome.10,11
Factors that contributed to better outcomes in our study included early initiation of resuscitation, early defibrillation, and early use of Advanced Life Support procedures. Having the cardiac arrest occur in a critical care area was a significant clinical variable in our study and was independently associated with survival of the patient to discharge. Monitored units in our facility provide expert nursing staff and a central location. Herlitz et al10 reported a clear difference between monitored arrests and nonmonitored arrests depending on the population involved and the cause of the arrest; these differences also may contribute to the better outcomes for monitored cardiac arrests.
The results obtained for the cardiac arrests occurring in nonmonitored areas appear to be consistent with the results for the entire cohort. However, the analysis of the arrests that occurred in nonmonitored areas alone is a subgroup analysis; hence, it has lower numbers and thus lower power to detect significant differences.
Time to arrival of the cardiac arrest team was independently related to ROSC, but after other relevant variables were controlled for in the multivariate analysis, that variable was no longer associated with survival to discharge. The mean time elapsed before the team arrived was 1.33 minutes, with a range of 0 to 6 minutes. In 86% of all cardiac arrests, the team arrived in less than 3 minutes. Kinney et al12 reported that survival to discharge is improved if a cardiac arrest team arrives in less than 3 minutes. Although the time to arrival of the team is critical in a cardiac arrest, the specific expertise, skills, and organizational ability of the individual members of the team and their ability to form a cohesive team are of paramount importance in the diverse scenarios associated with cardiac arrests.
Cardiac arrests that occurred on the afternoon shift (3–11 PM) were independently associated with ROSC but not with survival to discharge. The reason a ROSC would be achieved more often during this specific time is not clear from our results. Perhaps the higher percentage of nursing staff than staff from other disciplines available in the nonmonitored areas between 3 and 11 PM is an explanation. Training for nursing staff focuses explicitly on following the prompts from the SAED. Published results vary with respect to the influence of the time of the arrest on survival. Some researchers have reported a decreased survival at night,11,13 others14 have reported decreased survival during the afternoon shift and night shift, and others7 have found no correlation between survival of a patient to discharge and the actual time of the arrest.
The only other variable independently associated with survival to discharge in our study was duration of resuscitation. The time for each resuscitation event was documented and a shorter resuscitation was associated with a higher rate of survival to discharge: a finding that makes intuitive sense. Schultz et al15 found a direct relationship between duration of resuscitation and subsequent mortality.
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Conclusion |
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Our results provide important data on survival after cardiac arrest in an Australian hospital. Our findings will provide confidence to hospitals that are deliberating over implementation of use of an SAED into basic life support. Our success can be attributed to nursing staff as the first responders performing defibrillation with SAEDs and having a resuscitation coordinator provide extensive education for nursing and medical staff. Collection and evaluation of resuscitation data by means of the Utstein tool will provide hospitals with a basis for changing resuscitation practices.
| Increased survival to discharge can be attributed to nursing staff performing defibrillation with SAEDs.
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This study was supported by a grant from the Clinical Services Evaluation Unit, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia.
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