Dataset Information

Predictors of recognition of out of hospital cardiac arrest by emergency medical services call handlers in England: a mixed methods diagnostic accuracy study.

ABSTRACT:

Background

The aim of this study was to identify key indicator symptoms and patient factors associated with correct out of hospital cardiac arrest (OHCA) dispatch allocation. In previous studies, from 3% to 62% of OHCAs are not recognised by Emergency Medical Service call handlers, resulting in delayed arrival at scene.

Methods

Retrospective, mixed methods study including all suspected or confirmed OHCA patients transferred to one acute hospital from its associated regional Emergency Medical Service in England from 1/7/2013 to 30/6/2014. Emergency Medical Service and hospital data, including voice recordings of EMS calls, were analysed to identify predictors of recognition of OHCA by call handlers. Logistic regression was used to explore the role of the most frequently occurring (key) indicator symptoms and characteristics in predicting a correct dispatch for patients with OHCA.

Results

A total of 39,136 dispatches were made which resulted in transfer to the hospital within the study period, including 184 patients with OHCA. The use of the term 'Unconscious' plus one or more of symptoms 'Not breathing/Ineffective breathing/Noisy breathing' occurred in 79.8% of all OHCAs, but only 72.8% of OHCAs were correctly dispatched as such. 'Not breathing' was associated with recognition of OHCA by call handlers (Odds Ratio (OR) 3.76). The presence of key indicator symptoms 'Breathing' (OR 0.29), 'Reduced or fluctuating level of consciousness' (OR 0.24), abnormal pulse/heart rate (OR 0.26) and the characteristic 'Female patient' (OR 0.40) were associated with lack of recognition of OHCA by call handlers (p-values ConclusionsThere is a small proportion of calls in which cardiac arrest indicators are described but the call is not dispatched as such. Stricter adherence to dispatch protocols may improve call handlers' OHCA recognition. The existing dispatch protocol would not be improved by the addition of further terms as this would be at the expense of dispatch specificity.

SUBMITTER: Watkins CL

PROVIDER: S-EPMC7789721 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predictors of recognition of out of hospital cardiac arrest by emergency medical services call handlers in England: a mixed methods diagnostic accuracy study.

Watkins Caroline L CL Jones Stephanie P SP Hurley Margaret A MA Benedetto Valerio V Price Christopher I CI Sutton Christopher J CJ Quinn Tom T Bangee Munirah M Chesworth Brigit B Miller Colette C Doran Dawn D Siriwardena Aloysius Niroshan AN Gibson Josephine M E JME

Scandinavian journal of trauma, resuscitation and emergency medicine 20210106 1

<h4>Background</h4>The aim of this study was to identify key indicator symptoms and patient factors associated with correct out of hospital cardiac arrest (OHCA) dispatch allocation. In previous studies, from 3% to 62% of OHCAs are not recognised by Emergency Medical Service call handlers, resulting in delayed arrival at scene.<h4>Methods</h4>Retrospective, mixed methods study including all suspected or confirmed OHCA patients transferred to one acute hospital from its associated regional Emerge ...[more]

PMID: 33407699

Similar Datasets

Project description:Importance:Emergency medical services (EMS) deliver essential initial care for patients with out-of-hospital cardiac arrest (OHCA), but the extent to which patient outcomes vary between different EMS agencies is not fully understood. Objective:To quantify variation in patient outcomes after OHCA across EMS agencies. Design, Setting, and Participants:This observational cohort study was conducted in the Resuscitation Outcomes Consortium (ROC) Epistry, a prospective multicenter OHCA registry at 10 sites in North America. Any adult with OHCA treated by an EMS from April 2011 through June 2015 was included. Data analysis occurred from May 2017 to March 2018. Exposure:Treating EMS agency. Main Outcomes and Measures:The primary outcome was survival to hospital discharge. Secondary outcomes were return of spontaneous circulation at emergency department arrival and favorable functional outcome at hospital discharge (defined as a modified Rankin scale score ≤3). Multivariable hierarchical logistic regression models were used to adjust confounders and clustering of patients within EMS agencies, and calculated median odds ratios (MORs) were used to quantify the extent of residual variation in outcomes between EMS agencies. Results:We identified 43 656 patients with OHCA treated by 112 EMS agencies. At EMS agency level, we observed large variations in survival to hospital discharge (range, 0%-28.9%; unadjusted MOR, 1.43 [95% CI, 1.34-1.54]), return of spontaneous circulation on emergency department arrival (range, 9.0%-57.1%; unadjusted MOR, 1.53 [95% CI, 1.43-1.65]), and favorable functional outcome (range, 0%-20.4%; unadjusted MOR, 1.54 [95% CI, 1.40-1.73]). This variation persisted despite adjustment for patient-level and EMS agency-level factors known to be associated with outcomes (adjusted MOR for survival 1.56 [95% CI 1.44-1.73]; adjusted MOR for return of spontaneous circulation at emergency department arrival, 1.50 [95% CI, 1.41-1.62]; adjusted MOR for functionally favorable survival, 1.53 [95% CI, 1.37-1.78]). After restricting analysis to those who survived more than 60 minutes after hospital arrival and including hospital treatment characteristics, the variation persisted (adjusted MOR for survival, 1.49 [95% CI, 1.36-1.69]; adjusted MOR for functionally favorable survival, 1.34 [95% CI, 1.20-1.59]). Conclusions and Relevance:We found substantial variations in patient outcomes after OHCA between a large group of EMS agencies in North America that were not explained by documented patient-level and EMS agency-level variables.

Project description:ImportanceEmergency medical dispatchers fail to identify approximately 25% of cases of out-of-hospital cardiac arrest (OHCA), resulting in lost opportunities to save lives by initiating cardiopulmonary resuscitation.ObjectiveTo examine how a machine learning model trained to identify OHCA and alert dispatchers during emergency calls affected OHCA recognition and response.Design, setting, and participantsThis double-masked, 2-group, randomized clinical trial analyzed all calls to emergency number 112 (equivalent to 911) in Denmark. Calls were processed by a machine learning model using speech recognition software. The machine learning model assessed ongoing calls, and calls in which the model identified OHCA were randomized. The trial was performed at Copenhagen Emergency Medical Services, Denmark, between September 1, 2018, and December 31, 2019.InterventionDispatchers in the intervention group were alerted when the machine learning model identified out-of-hospital cardiac arrest, and those in the control group followed normal protocols without alert.Main outcomes and measuresThe primary end point was the rate of dispatcher recognition of subsequently confirmed OHCA.ResultsA total of 169?049 emergency calls were examined, of which the machine learning model identified 5242 as suspected OHCA. Calls were randomized to control (2661 [50.8%]) or intervention (2581 [49.2%]) groups. Of these, 336 (12.6%) and 318 (12.3%), respectively, had confirmed OHCA. The mean (SD) age among of these 654 patients was 70 (16.1) years, and 419 of 627 patients (67.8%) with known gender were men. Dispatchers in the intervention group recognized 296 confirmed OHCA cases (93.1%) with machine learning assistance compared with 304 confirmed OHCA cases (90.5%) using standard protocols without machine learning assistance (P?=?.15). Machine learning alerts alone had a significantly higher sensitivity than dispatchers without alerts for confirmed OHCA (85.0% vs 77.5%; P?<?.001) but lower specificity (97.4% vs 99.6%; P?<?.001) and positive predictive value (17.8% vs 55.8%; P?<?.001).Conclusions and relevanceThis randomized clinical trial did not find any significant improvement in dispatchers' ability to recognize cardiac arrest when supported by machine learning even though artificial intelligence did surpass human recognition.Trial registrationClinicalTrials.gov Identifier: NCT04219306.

Project description:BackgroundStudies have established that sex and age influence outcomes following out-of-hospital cardiac arrest (OHCA). However, a knowledge gap exists regarding their interaction. This study aimed to investigate the interaction of age and sex and how they cooperatively influence OHCA outcomes.MethodsThis retrospective cohort study included adult, nontraumatic OHCA patients admitted to a university hospital and its affiliated hospitals in Taiwan from January 2017 to December 2021. Data including sex, age, body mass index, cardiac rhythm, and resuscitation information in the emergency department (ED) were collected from medical records. The study outcomes encompassed survival to intensive care unit (ICU) admission, survival to hospital discharge, and a favorable neurological outcome. Multivariable logistic regression was performed to estimate the influence of sex on study outcomes.ResultsWe analyzed a total of 2,826 eligible subjects categorized into three groups: young (18-44 years, 149 males and 57 females), middle-aged (45-64 years, 524 males and 188 females), and old (≥65 years, 1,049 males and 859 females). Analysis of the effects of sex according to age stratification showed that old males had higher odds for survival to ICU admission (OR: 1.49, 95% CI: 1.21-1.83) and favorable neurological outcomes (OR: 2.74, 95% CI: 1.58-4.76) than did old females. Analysis of the effects of age according to sex stratification revealed that old males had lower odds for survival to hospital discharge (OR: 0.33, 95% CI: 0.21-0.51) and favorable neurological outcomes (OR: 0.26, 95% CI: 0.16-0.43) than did young males. Old females also showed the same trend as males, with lower odds for survival to hospital discharge (OR: 0.37, 95% CI: 0.17-0.78) and favorable neurological outcomes (OR: 0.11, 95% CI: 0.05-0.25) than did young females.ConclusionsThe interaction between sex and age in patients with OHCA results in diverse outcomes. Within the same sex, age demonstrated varying effects on distinct outcomes.

Project description:BackgroundAlthough studies have reported variation in out-of-hospital cardiac arrest (OHCA) survival by geographic location, little is known about variation in OHCA survival at the level of emergency medical service (EMS) agencies-which may have modifiable practices, unlike counties and regions. We quantified the variation in OHCA survival across EMS agencies and explored whether variation in 2 specific EMS resuscitation practices were associated with survival to hospital admission.MethodsWithin the Cardiac Arrest Registry to Enhance Survival, a prospective registry representing ≈51% of the US population, we identified 258 342 OHCAs from 764 EMS agencies with >10 OHCA cases annually during 2015 to 2019. Using hierarchical logistic regression, risk-standardized rates of survival to hospital admission were computed for each EMS agency. We quantified inter-agency variation in survival with median odds ratios and assessed the association of 2 resuscitation practices (EMS response time and the proportion of OHCAs with termination of resuscitation without meeting futility criteria) with EMS agency survival rates to hospital admission.ResultsAcross 764 EMS agencies comprising 258 342 OHCAs, the median risk-standardized rate of survival to hospital admission was 27.3% (interquartile range, 24.5%-30.1%; range: 16.0%-45.6%). The adjusted median odds ratio was 1.35 (95% CI, 1.32-1.39), denoting that the odds of survival of 2 patients with identical covariates varied by 35% at 2 randomly selected EMS agencies. EMS agencies in the lowest quartile of risk-standardized survival had longer EMS response times when compared with the highest quartile (12.0±3.4 versus 9.0±2.6 minutes; P<0.001), and a higher proportion of OHCAs with termination of resuscitation without meeting futility criteria (27.9±16.1% versus 18.9±11.4%; P<0.001).ConclusionsSurvival after OHCA varies widely across EMS agencies. EMS response times and termination of resuscitation practices were associated with agency-level rates of survival to hospital admission, suggesting potentially modifiable practices which can improve OHCA survival.

Dataset Information

Predictors of recognition of out of hospital cardiac arrest by emergency medical services call handlers in England: a mixed methods diagnostic accuracy study.

Background

Methods

Results

Publications

Predictors of recognition of out of hospital cardiac arrest by emergency medical services call handlers in England: a mixed methods diagnostic accuracy study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets