Effect of detection time interval for out-of-hospital cardiac arrest on outcomes in dispatcher-assisted cardiopulmonary resuscitation: A nationwide observational study

Ko, Seo Young; Shin, Sang Do; Ro, Young Sun; Song, Kyoung Jun; Hong, Ki Jeong; Park, Jeong Ho; Lee, Seung−Chul

doi:10.1016/j.resuscitation.2018.06.002

Cited by 20 publications

(10 citation statements)

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“…A study found that every 30 s delay in the recognition of OHCA was associated with a 3% reduction in good neurological recovery. 4 We found devastating 5 However, DA-CPR can only save lives if provided within 10-15 min while there still remains a chance of survival. The time from collapse to CPR in this study far exceeded this time window, rendering the quality of CPR irrelevant.…”

Section: Original Researchmentioning

confidence: 90%

When dispatcher assistance is not saving lives: assessment of process compliance, barriers and outcomes in out-of-hospital cardiac arrest in a metropolitan city in China

et al. 2020

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BackgroundSeveral Chinese cities have implemented dispatcher-assisted cardiopulmonary resuscitation (DA-CPR), although out-of-hospital cardiac arrest (OHCA) survival rates remain low. We aimed to assess the process compliance, barriers and outcomes of OHCA in one of the earliest implemented (DA-CPR) programmes in China.MethodsWe retrospectively reviewed OHCA emergency dispatch records of Suzhou emergency medical service from 2014 to 2015 and included adult OHCA victims (>18 years) with a bystander-witnessed atraumatic OHCA that was subsequently confirmed by on-site emergency physician. The circumstances and DA-CPR process related to the OHCA event were analysed. Dispatch audio records were reviewed to identify potential barriers to implementation during the DA-CPR process.ResultsOf the 151 OHCA victims, none survived. The median time from patient collapse to call for emergency services and that from call to provision of cardiopulmonary resuscitation instructions was 30 (IQR 20–60) min and 115 (IQR 90–153) s, respectively. Only 110 (80.3%) bystanders/rescuers followed the dispatcher instructions; of these, 51 (46.3%) undertook persistent chest compressions. Major barriers to following the DA-CPR instructions were present in 104 (68.9%) cases, including caller disconnection of the call, distraught mood or refusal to carry out either compressions or ventilations.ConclusionsThe OHCA survival rate and the DA-CPR process were far from optimal. The zero survival rate is disproportionally low compared with survival statistics in high-income countries. The prolonged delay in calling the emergency services negated and rendered futile any DA-CPR efforts. Thus, efforts targeted at developing public awareness of OHCA, calling for help and competency in DA-CPR should be increased.

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Section: Original Researchmentioning

confidence: 90%

When dispatcher assistance is not saving lives: assessment of process compliance, barriers and outcomes in out-of-hospital cardiac arrest in a metropolitan city in China

et al. 2020

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“…Delivery of CPR before the arrival of emergency medical services improves survival, making medical dispatchers' recognition of the condition during emergency calls pivotal. [3][4][5] However, because patients in cardiac arrest constitute a small minority of the overall call volume, recognition of cardiac arrest is difficult and approximately one quarter of all OHCAs are not recognized during the initial emergency conversation with the medical dispatcher. 3,6,7 This missed cardiac arrest recognition and subsequent provision of life-saving CPR for OHCAs remains a major challenge.…”

Section: Introductionmentioning

confidence: 99%

Machine learning as a supportive tool to recognize cardiac arrest in emergency calls

et al. 2019

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Background: Emergency medical dispatchers fail to identify approximately 25% of cases of out of hospital cardiac arrest, thus lose the opportunity to provide the caller instructions in cardiopulmonary resuscitation. We examined whether a machine learning framework could recognize out-of-hospital cardiac arrest from audio files of calls to the emergency medical dispatch center.Methods: For all incidents responded to by Emergency Medical Dispatch Center Copenhagen in 2014, the associated call was retrieved. A machine learning framework was trained to recognize cardiac arrest from the recorded calls. Sensitivity, specificity, and positive predictive value for recognizing out-of-hospital cardiac arrest were calculated. The performance of the machine learning framework was compared to the actual recognition and time-torecognition of cardiac arrest by medical dispatchers.Results: We examined 108,607 emergency calls, of which 918 (0.8%) were out-of-hospital cardiac arrest calls eligible for analysis. Compared with medical dispatchers, the machine learning framework had a significantly higher sensitivity (72.5% vs. 84.1%, p < 0.001) with lower specificity (98.8% vs. 97.3%, p < 0.001). The machine learning framework had a lower positive predictive value than dispatchers (20.9% vs. 33.0%, p < 0.001). Time-torecognition was significantly shorter for the machine learning framework compared to the dispatchers (median 44 seconds vs. 54 s, p < 0.001).Conclusions: A machine learning framework performed better than emergency medical dispatchers for identifying out-of-hospital cardiac arrest in emergency phone calls. Machine learning may play an important role as a decision support tool for emergency medical dispatchers.

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“…Time delays to start of CPR instructions are reported to wary between 140 to 328 seconds, 19 and may decrease the efficiency of dispatcher-assisted CPR. 20 We could not measure time delays from call to start of chest compressions in the present study since our data relies on EMS reports only and does not include evaluation of emergency call processes.…”

Section: Discussionmentioning

confidence: 99%

Survival after dispatcher-assisted cardiopulmonary resuscitation in out-of-hospital cardiac arrest

et al. 2020

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Aim: Strategies to increase provision of bystander CPR include mass education of laypersons. Additionally, programs directed at emergency dispatchers to provide CPR instructions during emergency calls to untrained bystanders have emerged. The aim of this study was to evaluate the association between dispatcher-assisted CPR (DA-CPR) and 30-day survival compared with no CPR or spontaneously initiated CPR by lay bystanders prior to emergency medical services in out of hospital cardiac arrest (OHCA).Methods: Nationwide observational cohort study including all consecutive lay bystander witnessed OHCAs reported to the Swedish Register for Cardiopulmonary Resuscitation in 2010À2017. Exposure was categorized as: no CPR (NO-CPR), DA-CPR and spontaneously initiated CPR (SP-CPR) prior to EMS arrival. Propensity-score matched cohorts were used for comparison between groups. Main Outcome was 30-day survival.Results: A total of 15 471 patients were included and distributed as follows: NO-CPR 6440 (41.6%), DA-CPR 4793 (31.0%) and SP-CPR 4238 (27.4%).Survival rates to 30 days were 7.1%, 13.0% and 18.3%, respectively. In propensity-score matched analysis (DA-CPR as reference), NO-CPR was associated with lower survival (conditional OR 0.61, 95% CI 0.52À0.72) and SP-CPR was associated with higher survival (conditional OR 1.21 (95% CI 1.05À1.39).Conclusions: DA-CPR was associated with a higher survival compared with NO-CPR. However, DA-CPR was associated with a lower survival compared with SP-CPR. These results reinforce the vital role of DA-CPR, although continuous efforts to disseminate CPR training must be considered a top priority if survival after out of hospital cardiac arrest is to continue to increase.

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Effect of detection time interval for out-of-hospital cardiac arrest on outcomes in dispatcher-assisted cardiopulmonary resuscitation: A nationwide observational study

Cited by 20 publications

References 26 publications

When dispatcher assistance is not saving lives: assessment of process compliance, barriers and outcomes in out-of-hospital cardiac arrest in a metropolitan city in China

When dispatcher assistance is not saving lives: assessment of process compliance, barriers and outcomes in out-of-hospital cardiac arrest in a metropolitan city in China

Machine learning as a supportive tool to recognize cardiac arrest in emergency calls

Survival after dispatcher-assisted cardiopulmonary resuscitation in out-of-hospital cardiac arrest

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