Dispatch Diagnosis of Cardiac Arrest (BLS): Systematic Review

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This Review is a draft version prepared by ILCOR, with the purpose to allow the public to comment and is labeled “Draft for Public Comment". The comments will be considered by ILCOR. The next version will be labelled “draft" to comply with copyright rules of journals. The final Review will be published on this website once a summary article has been published in a scientific Journal and labeled as “final”.

COSTR Citation

Drennan IR, Geri G, Couper K, Brooks S, Kudenchuk PJ, Pellegrino J, Schexnayder S, Hatanaka T, Castren M, Chung C, Considine J, Mancini MB, Nishiyama C, Perkins GD, Ristagno G, Semeraro F, Smyth M, Vaillancourt C, Olasveengen TM, Morley PT, on behalf of the International Liaison Committee on Resuscitation Basic Life Support, Pediatric Life Support, and Education Implementation and Teams Task Forces. Criteria to diagnose cardiac arrest in dispatch centres Consensus on Science with Treatment Recommendations [Internet] Brussels, Belgium: International Liaison Committee on Resuscitation (ILCOR) Basic Life Support Task Force, 2019 December 30. Available from: http://ilcor.org.

Methodological Preamble and Link to Published Systematic Review

The continuous evidence evaluation process for the production of Consensus on Science with Treatment Recommendations (CoSTR) started with a systematic review of diagnostic ability of dispatch centres for cardiac arrest (Drennan, 2019, CRD 42019140265– PROSPERO citation) conducted by two ILCOR evidence reviewers (Drennan and Geri) with involvement of clinical content experts. Evidence for literature was sought and considered by the Basic Life Support (BLS) Task Force, the Education, Implementation and Teams (EIT) Task Force, and the Pediatric Life Support (PLS) Task Force groups.

Given the significant potential for confounding data was grouped for studies that utilized similar dispatch algorithms in the identification of cardiac arrest and for similar dispatcher education / qualifications, however no pooled analysis was done due concerns with the quality of studies and significant heterogeneity across studies.

Systematic review

Not yet available.

PICOST

The PICOST (Population, Intervention, Comparator, Outcome, Study Designs and Timeframe)

Population: Adults and pediatrics with in-hospital (IHCA) or out-of-hospital (OHCA) cardiac arrest.

Intervention: characteristics of the call process (these might include the specific words by the caller, language or idioms spoken by the caller and understood by the call taker, perceptions of the call receiver, emotional state of the caller, other caller characteristics, type of personnel receiving the call, background noises etc.).

Comparators: absence of identified characteristics of the call process.

Outcomes: Any diagnostic test outcome.

Study Designs: Randomised controlled trials (RCTs) and non-randomised studies (non-randomised controlled trials, interrupted time series, controlled before-and-after studies, cohort studies) were eligible for inclusion. Unpublished studies (e.g., conference abstracts, trial protocols) were excluded.

Timeframe: All years and all languages were included provided there was an English abstract. Literature search updated Nov 28, 2019.

Consensus on Science

A variety of algorithms and criteria (both commercial and locally developed) are used by dispatch centers to identify potential life-threatening events such as cardiac arrest and triage emergency responders to the scene appropriately. The overall accuracy of these current approaches as reported by dispatch centers was initially assessed.

OHCA

For the critical outcome (O) of sensitivity (proportion of patients who are correctly identified as being in cardiac arrest) of emergency medical dispatchers in recognition of OHCA, we identified very-low-certainty evidence (downgraded for risk of risk of bias, imprecision, and inconsistency) from 46 observational studies{Clark 1994 1022; Castren 2001 265; Garza 2003 955; Hauff 2003 731; Kuisma 2005 89; Flynn 2006 72; Nurmi 2006 463; Bohm 2007 256; Ma 2007 236; Vaillancourt 2007 877; Cairns 2008 349; Berdowski 2009 2096; Bohm 2009 1025; Roppolo 2009 769; Dami 2010 848; Lewis 2013 1522; Hardeland 2014 612; Stipulante 2014 177; Tanaka 2014 1751; Travers 2014 1720; Besnier 2015 590; Dami 2015 27; Fukushima 2015 64; Fukushima 2015 314; Linderoth 2015 317; Orpet 2015 29; Vaillancourt 2015 116; Fukushima 2016 116; Hardeland 2016 56; Ho 2016 149; Moller 2016 1; Plodr 2016 1;, Deakin 2017 738; Fukushima 2017 ;, Hardeland 2017 21; Huang 2017 697; Nuno 2017 11; Viereck 2017 141; Lee 2018 106; Shah 2018 222; Syvaoja 2018 558; Blomberg 2019 322; Chien 2019 595; Derkenne 2019 14; Green 2019 203; Saberian 2019} reporting unadjusted analyses involving 84,534 adult OHCA subjects (P). The unadjusted data showed sensitivities ranging from 0.46 (95% CI 0.45 - 0.46) to 0.98 (95% CI 0.96 - 0.98). For pediatric cardiac arrest we found a single observational study,{Deakin 2017 109} low certainty of evidence with 122 patients. The sensitivity was 0.71 (95% CI 0.63 - 0.79).

For the critical outcome of false negative rate (incorrectly diagnosing the absence of cardiac arrest, when the patient is in cardiac arrest), we identified very-low-certainty evidence (downgraded for risk of bias, imprecision, and inconsistency) from 46 observational studies{Clark 1994 1022; Castren 2001 265; Garza 2003 955; Hauff 2003 731; Kuisma 2005 89; Flynn 2006 72; Nurmi 2006 463; Bohm 2007 256; Ma 2007 236; Vaillancourt 2007 877; Cairns 2008 349; Berdowski 2009 2096; Bohm 2009 1025; Roppolo 2009 769; Dami 2010 848; Lewis 2013 1522; Hardeland 2014 612; Stipulante 2014 177; Tanaka 2014 1751; Travers 2014 1720; Besnier 2015 590; Dami 2015 27; Fukushima 2015 64; Fukushima 2015 314; Linderoth 2015 317; Orpet 2015 29; Vaillancourt 2015 116; Fukushima 2016 116; Hardeland 2016 56; Ho 2016 149; Moller 2016 1; Plodr 2016 1;, Deakin 2017 738; Fukushima 2017 ;, Hardeland 2017 21; Huang 2017 697; Nuno 2017 11; Viereck 2017 141; Lee 2018 106; Shah 2018 222; Syvaoja 2018 558; Blomberg 2019 322; Chien 2019 595; Derkenne 2019 14; Green 2019 203; Saberian 2019} reporting unadjusted analyses involving 84,534 OHCA subjects. The unadjusted data showed a range of false negative rates of 0.03 (95% CI 0.01 - 0.06) to 0.54 (95% CI 0.54 - 0.55). For pediatric cardiac arrests we found a single observational study,{Deakin 2017 109} of low-certainty of evidence from 122 patients. The false negative rate was 0.29 (95% CI 0.21 - 0.37).

For the important outcome of specificity (proportion of patients who are correctly identified as not being in cardiac arrest) of emergency medical dispatchers in recognition of OHCA, we identified low-certainty evidence (downgraded for risk of bias and inconsistency) from 12 observational studies{Clark 1994 1022; Flynn 2006 72; Nurmi 2006 463; Berdowski 2009 2096; Tanaka 2014 1751; Fukushima 2015 314; Orpet 2015 29; Vaillancourt 2015 116; Plodr 2016 18; Deakin 2017 738; Green 2019 203; Saberian 2019} reporting unadjusted analyses involving 789,004 OHCA subjects. The unadjusted data showed a specificity ranging from 0.32 (95% CI 0.29 - 0.36) to 1.00 (95% CI 1.00 - 1.00). We further identified a single observational study examining pediatric cardiac arrest (n=53,089 patients),{Deakin 2017 109} of low-certainty of evidence that found a specificity of 0.96 (95% CI 0.96 - 0.97).

For the important outcome of false positive rate (incorrectly diagnosing a cardiac arrest, when a patient is not in cardiac arrest), we identified low-certainty evidence (downgraded for risk of bias and inconsistency) from 12 observational studies{lark 1994 1022; Flynn 2006 72; Nurmi 2006 463; Berdowski 2009 2096; Tanaka 2014 1751; Fukushima 2015 314; Orpet 2015 29; Vaillancourt 2015 116; Plodr 2016 18; Deakin 2017 738; Green 2019 203; Saberian 2019} reporting unadjusted analyses involving 789,004 adult OHCA subjects and 1 observational study{Deakin 2017 109}, low-certainty of evidence reporting unadjusted analyses involving 53,089 pediatric OHCA subjects. The unadjusted data showed a range in false positive rates of cardiac arrest recognition for adult cardiac arrest from a low of 0.002 (95% CI 0.001 - 0.002) to a high of 0.68 (95% CI 0.64 - 0.71) and for pediatric cardiac arrest 0.04 (95% CI 0.04 - 0.04).

For the important outcome of negative predictive value (probability that a subject recognized as not being in cardiac arrest was not actually in cardiac arrest), we identified low certainty evidence (downgraded for risk of bias and inconsistency) from 12 observational studies reporting unadjusted analyses involving 789,004 adult OHCAs {Clark 1994 1022; Flynn 2006 72; Nurmi 2006 463; Berdowski 2009 2096; Tanaka 2014 1751; Fukushima 2015 314; Orpet 2015 29; Vaillancourt 2015 116; Plodr 2016 18; Deakin 2017 738; Green 2019 203; Saberian 2019} and 1 study{Deakin 2017 109} involving pediatric OHCA (n=53,089). The unadjusted data showed a range of negative predictive values from 0.29 (95% CI 0.26 - 0.32) to 1.00 (95% CI 1.00 - 1.00) and for pediatric cardiac arrest a negative predictive value of 1.00 (95% CI 1.00 - 1.00).

For the important outcome of positive predictive value (probability that a subject recognized being in cardiac arrest was actually in cardiac arrest), we identified low certainty evidence (downgraded for risk of bias and inconsistency) from 12 observational studies reporting unadjusted analyses involving 789,004 adult OHCAs {Clark 1994 1022; Flynn 2006 72; Nurmi 2006 463; Berdowski 2009 2096; Tanaka 2014 1751; Fukushima 2015 314; Orpet 2015 29; Vaillancourt 2015 116; Plodr 2016 18; Deakin 2017 738; Green 2019 203; Saberian 2019} and 1 study{Deakin 2017 109} involving pediatric OHCA (n=53,089). The unadjusted data showed a range of negative predictive values from 0.09 (95% CI 0.08 - 0.10) to 0.95 (95% CI 0.90 - 0.98) and for pediatric cardiac arrest a positive predictive value of 0.04 (95% CI 0.03 - 0.05).

For the important outcome of positive and negative likelihood ratios, we identified low-certainty evidence (downgraded for risk of bias and inconsistency) from 12 observational studies reporting unadjusted analyses involving 789,004 adult OHCAs{Clark 1994 1022; Flynn 2006 72; Nurmi 2006 463; Berdowski 2009 2096; Tanaka 2014 1751; Fukushima 2015 314; Orpet 2015 29; Vaillancourt 2015 116; Plodr 2016 18; Deakin 2017 738; Green 2019 203; Saberian 2019} and 1 study{Deakin 2017 109} involving pediatric OHCAs (n=53,089). The unadjusted data showed a range of positive likelihood ratios from 0.97 (95% CI 0.92 - 1.04) to 591.8 (95% CI 474.2 - 738.5) and negative likelihood ratios from 0.04 (95% CI 0.03 - 0.07) to 1.06 (95% CI 0.93 - 1.20) for adult patients and a positive likelihood ratio of 19.3 (95% CI 17.1 - 21.7) and negative likelihood ratio of 0.30 (95% CI 0.23 - 0.39) for pediatric OHCA.

IHCA

We identified no specific data on dispatcher recognition of cardiac arrest for in-hospital cardiac arrest.

Subgroups

We analysed sub-groups of studies that utilized similar dispatching algorithms or criteria for diagnosis of cardiac arrest and studies that had similar backgrounds/training for emergency dispatchers. There were no identifiable differences noted in these subgroup analyses. Heterogeneity in studies and lack of adjusted analyses precluded meta-analysis for any subgroup.

Dispatch Algorithms versus Criteria-Based Dispatch

We were unable to pool data based on different dispatching algorithms/criteria due to heterogeneity between studies and concerns regarding risk of bias. We were unable to identify any differences in diagnostic accuracy between different criteria or algorithms utilized based on the included studies.

Emergency Medical Dispatcher Background and training

We were unable to pool data based on different dispatcher background or previous training due to heterogeneity between studies and concerns regarding risk of bias. We were unable to identify any differences in diagnostic accuracy based on the qualifications of emergency medical dispatch personnel.

Treatment Recommendations

We recommend dispatch centres implement a standardized algorithm and/or standardized criteria to immediately determine if a patient is in cardiac arrest at the time of emergency call. (Strong Recommendation, very-low certainty of evidence).

  • We recommend that dispatch centres monitor and track diagnostic capability
  • We recommend that dispatch centres look for ways to optimize sensitivity (minimize false negatives)
  • We recommend high quality research that examines gaps in this area

Justification and Evidence to Decision Highlights

In making this recommendation we prioritized the desirable benefits, increase in potential life-saving treatment, that would result from the immediate accurate identification of cardiac arrest by dispatchers. These benefits include the provision of dispatcher-assisted bystander CPR and dispatching of appropriate emergency medical service resources, compared to the undesirable consequences of lack of early recognition of the event, such as delays to patient care including early provision of CPR. We realize that efforts to minimize the frequency of “missed” (false negative) cardiac arrest events may increase the frequency of false positive cases and concern of “over calls”. Importantly, whether ultimately found in cardiac arrest or not, the potential acuity of such patients still demands the need for immediate EMS assistance at the scene. In tiered response systems, should first-arriving EMS responders find a less emergent situation on arrival, the need for a secondary (ALS) response could be cancelled. In either event, the consequences of failing to recognize a bon a fide cardiac arrest are sufficiently substantial such that a degree of tolerance for a proportion of false positive events is justified.

We were unable to make any recommendations on specific algorithms or criteria for identification of cardiac arrest as the variability between studies did not allow for direct comparisons or pooling of data. Further, due to the unexplained variability across studies utilizing similar dispatch criteria there was considerable variation in diagnostic accuracy across studies which did not allow for pooling data to find overall diagnostic accuracy measures for each of the algorithms. One factor that significantly influences the diagnostic accuracy is the prevalence of cardiac arrest in the reported population. In multiple studies the denominator of calls was different, some studies reporting cardiac arrests as a proportion of all emergency calls and others reporting cardiac arrests as a proportion of calls who patients were described as being unconscious. Reporting cardiac arrest as a proportion of all emergency calls produces diagnostic statistics that are falsely elevated as the majority of emergency calls it is obvious at the time of call that the patient is not in cardiac arrest.

Last, while studies were identified that examined barriers to cardiac arrest identification these studies were not done in a meaningful way to allow for calculation of the effect of these characteristics on diagnostic accuracy. The impact of these call characteristics diagnosis is not known. More importantly, perhaps, is to examine these characteristics not in terms of diagnostic accuracy but in terms of their association with dispatcher recognition.

Knowledge Gaps

Current knowledge gaps include but are not limited to:

  1. Are there other potentially important criteria or tools that would improve dispatcher recognition of cardiac arrest in addition to standard dispatch algorithms? These might include use of a remote video link or pulse detection technologies via a caller’s cellular telephone.
  1. What are potential barriers that decrease the accuracy of dispatcher recognition (e.g. language barriers, caller characteristics, patient characteristics)?
  1. Dose the use of artificial intelligence improve recognition of cardiac arrest compared to emergency medical dispatcher recognition?
  1. What is the cost associated with implementing and monitoring dispatcher recognition programs?
  1. What is the most accurate dispatch algorithm, and the optimal criteria for rapidly recognizing cardiac arrest?

6. What is the relationship between dispatch algorithms and time to recognition and time to initiation of dispatcher-assisted CPR?

Attachments

Evidence-to-Decision Table: Dispatch Diagnosis Et D-Framework

References

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2. Besnier, E., C. Damm, B. Jardel, B. Veber, V. Compere and B. Dureuil (2015). "Dispatcher-assisted cardiopulmonary resuscitation protocol improves diagnosis and resuscitation recommendations for out-of-hospital cardiac arrest." Emerg Med Australas 27(6): 590-596.

3. Blomberg, S. N., F. Folke, A. K. Ersboll, H. C. Christensen, C. Torp-Pedersen, M. R. Sayre, C. R. Counts and F. K. Lippert (2019). "Machine learning as a supportive tool to recognize cardiac arrest in emergency calls." Resuscitation 138: 322-329.

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25. Hauff, S. R., T. D. Rea, L. L. Culley, F. Kerry, L. Becker and M. S. Eisenberg (2003). "Factors impeding dispatcher-assisted telephone cardiopulmonary resuscitation." Ann Emerg Med 42(6): 731-737.

26. Ho, A. F., Z. J. Sim, N. Shahidah, Y. Hao, Y. Y. Ng, B. S. Leong, S. Zarinah, W. K. Teo, G. S. Goh, H. Jaafar and M. E. Ong (2016). "Barriers to dispatcher-assisted cardiopulmonary resuscitation in Singapore." Resuscitation 105: 149-155.

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38. Roppolo, L. P., A. Westfall, P. E. Pepe, L. L. Nobel, J. Cowan, J. J. Kay and A. H. Idris (2009). "Dispatcher assessments for agonal breathing improve detection of cardiac arrest." Resuscitation 80(7): 769-772.

39. Saberian, P., M. Sadeghi, P. Hasani-Sharamin, M. Modabber and A. Baratloo (2019). "Diagnosis of out-of-hospital cardiac arrest by emergency medical dispatchers: A diagnostic accuracy study." Australasian Journal of Paramedicine 16(no pagination).

40. Shah, M., C. Bartram, K. Irwin, K. Vellano, B. McNally, T. Gallagher and R. Swor (2018). "Evaluating Dispatch-Assisted CPR Using the CARES Registry." Prehosp Emerg Care 22(2): 222-228.

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43. Tanaka, Y., T. Nishi, K. Takase, Y. Yoshita, Y. Wato, J. Taniguchi, Y. Hamada and H. Inaba (2014). "Survey of a protocol to increase appropriate implementation of dispatcher-assisted cardiopulmonary resuscitation for out-of-hospital cardiac arrest." Circulation 129(17): 1751-1760.

44. Travers, S., D. Jost, Y. Gillard, V. Lanoe, M. Bignand, L. Domanski, J. P. Tourtier and G. Paris Fire Brigade Cardiac Arrest Work (2014). "Out-of-hospital cardiac arrest phone detection: those who most need chest compressions are the most difficult to recognize." Resuscitation 85(12): 1720-1725.

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47. Viereck, S., T. P. Moller, A. K. Ersboll, J. S. Baekgaard, A. Claesson, J. Hollenberg, F. Folke and F. K. Lippert (2017). "Recognising out-of-hospital cardiac arrest during emergency calls increases bystander cardiopulmonary resuscitation and survival." Resuscitation 115: 141-147.


CPR

Discussion

GUEST
Robert O’Connor (301 posts)
Great document! My only comment is that It is crucial to properly train dispatchers to recognize cardiac arrest as rapidly as possible. My recommendation would be to add this as a treatment recommendation. Without proper training, the other treatment recommendations lose their impact.
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