Dataset Information

Predictors of ROSC in witnessed aeromedical cardiac arrests.

ABSTRACT:

Introduction

Aeromedical agencies are used routinely to transport critically ill patients to specialty centers. The characteristics of patients suffering a cardiac arrest during transport by aeromedical flight personnel are not well-documented. We completed a retrospective analysis of aeromedical patient care records in order to describe the pre-arrest characteristics and the return of spontaneous circulation (ROSC) in this subset of patients.

Materials and methods

A retrospective chart reviews of patients suffering from cardiac arrest while being treated by a single aeromedical transport service between 1998 and 2000. Crew configurations were paramedic/nurse or paramedic/physician. Data were directly abstracted by the authors and descriptive data of patient demographics, vital signs and medical history were obtained. Data were separated into medical and traumatic arrests and analyzed by chi2- and t-test. Logistic regression analyses were performed to determine predictors of ROSC.

Results

During the 24-month interval, 12,140 patient transports occurred. Of these, 134 cardiac arrests occurred (1.1%) and are reviewed. Of these, 57 were medical arrests and 76 were traumatic arrests. Nine medical arrests and 37 traumatic arrests were excluded as the patient was in cardiac arrest before crew arrival. In the medical arrest cohort, the presence of a peripheral i.v. before crew arrival and initial rhythm of arrest were associated with ROSC at destination (p=0.05). In the traumatic arrest cohort, patients with lower diastolic blood pressures had a trend toward lower rates of ROSC (p=0.06).

Conclusion

Cardiac arrest during aeromedical transport is infrequent. Patients with poor i.v. access are less likely to experience ROSC should they experience a cardiac arrest. Patients with traumatic injury and diastolic hypotension may be less likely to survive cardiac arrest during transport.

SUBMITTER: Rittenberger JC

PROVIDER: S-EPMC2254173 | biostudies-literature | 2008 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predictors of ROSC in witnessed aeromedical cardiac arrests.

Rittenberger Jon C JC Hostler David P DP Tobin Thomas T Gaines Jeffrey J Callaway Clifton W CW

Resuscitation 20070803 1

<h4>Introduction</h4>Aeromedical agencies are used routinely to transport critically ill patients to specialty centers. The characteristics of patients suffering a cardiac arrest during transport by aeromedical flight personnel are not well-documented. We completed a retrospective analysis of aeromedical patient care records in order to describe the pre-arrest characteristics and the return of spontaneous circulation (ROSC) in this subset of patients.<h4>Materials and methods</h4>A retrospective ...[more]

PMID: 17688998

Similar Datasets

Project description:Importance:Out-of-hospital cardiac arrests (OHCAs) are a major public health concern and a leading cause of death worldwide. Exposure to ambient air pollution is associated with increases in morbidity and mortality and has been recognized as a leading contributor to global disease burden. Objective:To examine the association between short-term exposure to particulate matter with a diameter of 2.5 ?m or smaller (PM2.5) and the incidence of OHCAs of cardiac origin and with the development of initial cardiac arrest rhythm. Design, Setting, and Participants:This case-control study used data from cases registered between January 1, 2005, and December 31, 2016, in the All-Japan Utstein Registry, a prospective, nationwide, population-based database for OHCAs across all 47 Japanese prefectures. These OHCA cases included patients who had bystander-witnessed OHCAs and for whom emergency medical services responders initiated resuscitation before hospital transfer. A case-crossover design was employed for the study analyses. A prefecture-specific, conditional logistic regression model to estimate odds ratios was applied, and a random-effects meta-analysis was used to obtain prefecture-specific pooled estimates. All analyses were performed from May 7, 2019, to January 23, 2020. Main Outcomes and Measures:The main outcome was the association of short-term PM2.5 exposure with the incidence of bystander-witnessed OHCAs of cardiac origin. The differences in the distribution of initial cardiac arrest rhythm in OHCAs among those with exposure to PM2.5 were also examined. Results:In total, 103?189 OHCAs witnessed by bystanders were included in the final analysis. Among the patients who experienced such OHCAs, the mean (SD) age was 75 (15.5) years, and 62?795 (60.9%) were men. Point estimates of the percentage increase for a 10-?g/m3 increase in PM2.5 at lag0-1 (difference in mean PM2.5 concentrations measured on the case day and 1 day before) demonstrated a statistically significantly higher incidence of OHCA across most of the 47 prefectures, without significant heterogeneity (I2?=?20.1%; P?=?.12). A stratified analysis found an association between PM2.5 exposure and OHCAs (% increase, 1.6; 95% CI, 0.1%-3.1%). An initial shockable rhythm, such as ventricular fibrillation or pulseless ventricular tachycardia (% increase, 0.6; 95% CI, -2.0% to 3.2%), was not associated with PM2.5 exposure. However, an initial nonshockable rhythm, such as pulseless electrical activity and asystole, was associated with PM2.5 exposure (% increase, 1.4; 95% CI, 0.1%-2.7%). Conclusions and Relevance:Findings from this study suggest that increased PM2.5 concentration is associated with bystander-witnessed OHCA of cardiac origin that commonly presents with nonshockable rhythm. The results support measures to reduce PM2.5 exposure to prevent OHCAs of cardiac origin.

Project description:ObjectivesAirway management during in-hospital cardiac arrest represents a fundamental component of resuscitative efforts, yet little is known about temporal trends in intubation during in-hospital cardiac arrest. Our objective was to investigate changes in in-hospital cardiac arrest airway management over time and in response to national guideline updates.DesignObservational cohort study of a prospectively collected database.SettingMulticenter study of hospitals participating in the "Get With The Guidelines-Resuscitation" registry from January 1, 2001, to December 31, 2018.SubjectsAdult patients who experienced an in-hospital cardiac arrest and did not have an invasive airway in place prior to the arrest.InterventionsThe primary outcome was the rate of intra-arrest intubation from 2001 to 2018. We constructed multivariable regression models with generalized estimating equations to determine the annual adjusted odds of intubation. We also assessed the timing of intubation relative to the onset of pulselessness and other arrest measures. We used an interrupted time-series analysis to assess the association between the 2010 Advanced Cardiac Life Support guideline update and intubation rates.Measurements and main resultsOne thousand sixty-six eight hundred patients from 797 hospitals were included. From 2001 to 2018, the percentage of patients intubated during an arrest decreased from 69% to 55% for all rhythms, 73% to 60% for nonshockable rhythms, and 58% to 36% for shockable rhythms (p < 0.001 for trend for all 3 groups). The median time from onset of pulselessness to intubation increased from 5 minutes in 2001 (interquartile range, 2-8 min) to 6 minutes in 2018 (interquartile range, 4-10 min) (p < 0.001 for trend). Following the 2010 guideline update, there was a downward step change and a steeper decrease over time in the rate of intubation as compared to the preintervention period (p < 0.001).ConclusionsEndotracheal intubation rates during in-hospital cardiac arrest have decreased significantly over time, with a more substantial decline following the updated 2010 guideline that prioritized chest compressions over airway management.

Dataset Information

Predictors of ROSC in witnessed aeromedical cardiac arrests.

Introduction

Materials and methods

Results

Conclusion

Publications

Predictors of ROSC in witnessed aeromedical cardiac arrests.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets