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 < 0.05). Conclusions There 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.

Unrecognized Out of Hospital Cardiac Arrest Symptoms during Thailand’s Emergency Medical Services

“…The most common symptom of unrecognized cardiac arrest was unconsciousness, which was the same as the study of Crabb et al [12]. Unconsciousness was also the most reported symptom of cardiac arrest in our study as in the study of Watkins et al and a recent American Heart Association adult basic and advanced life support guideline [1], [3]. By adding a recognition of breathing patterns such as absent or agonal or abnormal breathing, we may achieve a higher recognition rate of cardiac arrest.…”

“…By adding a recognition of breathing patterns such as absent or agonal or abnormal breathing, we may achieve a higher recognition rate of cardiac arrest. In the study of Watkins et al determining breathing pattern can lead to an increase of 7 points of recognized cardiac arrest if the call handler was strict on the protocol [3]. The main obstacle of recognizing OHCA was the difficulty of determining the patient's respiratory status for bystanders [12].…”

“…EMS call handlers to have three roles in OHCA: (1) Recognition; (2) facilitation of rapid EMS attendance; and (3) provision of bystander resuscitation instructions including identification of public access defibrillators [3]. In Thailand, the EMS's task is guided by criteria-based dispatch algorithms.…”

“…These factors can give rise to delayed response, poor rates of bystander resuscitation instructions, and led to reduced survival rates. A recent study has shown that factors such as a reduced or fluctuating level of consciousness, abnormal pulse/ heart rate, and patients being female were associated with a lack of recognition of OHCA by call handlers [3].…”

The Feasibility of Using Machine Learning to Classify Calls to South African Emergency Dispatch Centres According to Prehospital Diagnosis, by Utilising Caller Descriptions of the Incident

“…It is therefore crucial, in an African context, that the call-takers accurately identify high-acuity cases so that resources are allocated appropriately to those who require emergency medical assistance. The misallocation of emergency medical resources due to poor call-handling sensitivity and over-prioritisation is also a significant challenge in higher income countries, such as Germany [ 4 ] and The United Kingdom [ 5 ]. The impact of this work extends far beyond under-resourced countries as the improvement of the accuracy of call triage remains a global challenge.…”