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.
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:
- 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.
- What are potential barriers that decrease the accuracy of dispatcher recognition (e.g. language barriers, caller characteristics, patient characteristics)?
- Dose the use of artificial intelligence improve recognition of cardiac arrest compared to emergency medical dispatcher recognition?
- What is the cost associated with implementing and monitoring dispatcher recognition programs?
- What is the most accurate dispatch algorithm, and the optimal criteria for rapidly recognizing cardiac arrest?
- 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
1. Berdowski, J., F. Beekhuis, A. H. Zwinderman, J. G. Tijssen and R. W. Koster (2009). "Importance of the first link: description and recognition of an out-of-hospital cardiac arrest in an emergency call." Circulation 119(15): 2096-2102.
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.
4. Bohm, K., M. Rosenqvist, J. Hollenberg, B. Biber, L. Engerstrom and L. Svensson (2007). "Dispatcher-assisted telephone-guided cardiopulmonary resuscitation: an underused lifesaving system." Eur J Emerg Med 14(5): 256-259.
5. Bohm, K., B. Stalhandske, M. Rosenqvist, J. Ulfvarson, J. Hollenberg and L. Svensson (2009). "Tuition of emergency medical dispatchers in the recognition of agonal respiration increases the use of telephone assisted CPR." Resuscitation 80(9): 1025-1028.
6. Cairns, K. J., A. J. Hamilton, A. H. Marshall, M. J. Moore, A. A. Adgey and F. Kee (2008). "The obstacles to maximising the impact of public access defibrillation: an assessment of the dispatch mechanism for out-of-hospital cardiac arrest." Heart 94(3): 349-353.
7. Castren, M., M. Kuisma, J. Serlachius and M. Skrifvars (2001). "Do health care professionals report sudden cardiac arrest better than laymen?" Resuscitation 51(3): 265-268.
8. Chien, C. Y., W. C. Chien, L. H. Tsai, S. L. Tsai, C. B. Chen, C. J. Seak, Y. S. Chou, M. Ma, Y. M. Weng, C. J. Ng, C. Y. Lin, I. S. Tzeng, C. C. Lin and C. H. Huang (2019). "Impact of the caller's emotional state and cooperation on out-of-hospital cardiac arrest recognition and dispatcher-assisted cardiopulmonary resuscitation." Emergency Medicine Journal 36(10): 595-600.
9. Clark, J. J., L. Culley, M. Eisenberg and D. K. Henwood (1994). "Accuracy of determining cardiac arrest by emergency medical dispatchers." Ann Emerg Med 23(5): 1022-1026.
10. Dami, F., V. Fuchs, L. Praz and J. P. Vader (2010). "Introducing systematic dispatcher-assisted cardiopulmonary resuscitation (telephone-CPR) in a non-Advanced Medical Priority Dispatch System (AMPDS): implementation process and costs." Resuscitation 81(7): 848-852.
11. Dami, F., E. Heymann, M. Pasquier, V. Fuchs, P. N. Carron and O. Hugli (2015). "Time to identify cardiac arrest and provide dispatch-assisted cardio-pulmonary resuscitation in a criteria-based dispatch system." Resuscitation 97: 27-33.
12. Deakin, C. D., S. England and D. Diffey (2017). "Ambulance telephone triage using 'NHS Pathways' to identify adult cardiac arrest." Heart 103(10): 738-744.
13. Deakin, C. D., S. England, D. Diffey and I. Maconochie (2017). "Can ambulance telephone triage using NHS Pathways accurately identify paediatric cardiac arrest?" Resuscitation 116: 109-112.
14. Derkenne, C., D. Jost, O. Thabouillot, F. Briche, S. Travers, B. Frattini, X. Lesaffre, R. Kedzierewicz, F. Roquet, F. de Charry, B. Prunet and F. Paris Fire Brigade Cardiac Arrest Task (2019). "Improving Emergency Call Detection of Out-of-Hospital Cardiac Arrests in the Greater Paris Area: Efficiency of a Global System with a New Method of Detection." Resuscitation 14: 14.
15. Flynn, J., F. Archer and A. Morgans (2006). "Sensitivity and specificity of the medical priority dispatch system in detecting cardiac arrest emergency calls in Melbourne." Prehosp Disaster Med 21(2): 72-76.
16. Fukushima, H., M. Imanishi, T. Iwami, H. Kitaoka, H. Asai, T. Seki, Y. Kawai, K. Norimoto, Y. Urisono, K. Nishio and K. Okuchi (2015). "Implementation of a dispatch-instruction protocol for cardiopulmonary resuscitation according to various abnormal breathing patterns: a population-based study." Scand J Trauma Resusc Emerg Med 23: 64.
17. Fukushima, H., M. Imanishi, T. Iwami, T. Seki, Y. Kawai, K. Norimoto, Y. Urisono, M. Hata, K. Nishio, K. Saeki, N. Kurumatani and K. Okuchi (2015). "Abnormal breathing of sudden cardiac arrest victims described by laypersons and its association with emergency medical service dispatcher-assisted cardiopulmonary resuscitation instruction." Emerg Med J 32(4): 314-317.
18. Fukushima, H., M. Panczyk, C. Hu, C. Dameff, V. Chikani, T. Vadeboncoeur, D. W. Spaite and B. J. Bobrow (2017). "Description of Abnormal Breathing Is Associated With Improved Outcomes and Delayed Telephone Cardiopulmonary Resuscitation Instructions." J Am Heart Assoc 6(9).
19. Fukushima, H., M. Panczyk, D. W. Spaite, V. Chikani, C. Dameff, C. Hu, T. S. Birkenes, H. Myklebust, J. Sutter, B. Langlais, Z. Wu and B. J. Bobrow (2016). "Barriers to telephone cardiopulmonary resuscitation in public and residential locations." Resuscitation 109: 116-120.
20.Garza, A. G., M. C. Gratton, J. J. Chen and B. Carlson (2003). "The accuracy of predicting cardiac arrest by emergency medical services dispatchers: the calling party effect." Acad Emerg Med 10(9): 955-960.
21. Green, J. D., S. Ewings, R. Wortham and B. Walsh (2019). "Accuracy of nature of call screening tool in identifying patients requiring treatment for out of hospital cardiac arrest." Emerg Med J 36(4): 203-207.
22. Hardeland, C., T. M. Olasveengen, R. Lawrence, D. Garrison, T. Lorem, G. Farstad and L. Wik (2014). "Comparison of Medical Priority Dispatch (MPD) and Criteria Based Dispatch (CBD) relating to cardiac arrest calls." Resuscitation 85(5): 612-616.
23. Hardeland, C., C. Skare, J. Kramer-Johansen, T. S. Birkenes, H. Myklebust, A. E. Hansen, K. Sunde and T. M. Olasveengen (2017). "Targeted simulation and education to improve cardiac arrest recognition and telephone assisted CPR in an emergency medical communication centre." Resuscitation 114: 21-26.
24. Hardeland, C., K. Sunde, H. Ramsdal, S. R. Hebbert, L. Soilammi, F. Westmark, F. Nordum, A. E. Hansen, J. E. Steen-Hansen and T. M. Olasveengen (2016). "Factors impacting upon timely and adequate allocation of prehospital medical assistance and resources to cardiac arrest patients." Resuscitation 109: 56-63.
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.
27. Huang, C. H., H. J. Fan, C. Y. Chien, C. J. Seak, C. W. Kuo, C. J. Ng, W. C. Li and Y. M. Weng (2017). "Validation of a Dispatch Protocol with Continuous Quality Control for Cardiac Arrest: A Before-and-After Study at a City Fire Department-Based Dispatch Center." J Emerg Med 53(5): 697-707.
28. Kuisma, M., J. Boyd, T. Vayrynen, J. Repo, M. Nousila-Wiik and P. Holmstrom (2005). "Emergency call processing and survival from out-of-hospital ventricular fibrillation." Resuscitation 67(1): 89-93.
29. Lee, S. Y., Y. S. Ro, S. D. Shin, K. J. Song, K. J. Hong, J. H. Park and S. Y. Kong (2018). "Recognition of out-of-hospital cardiac arrest during emergency calls and public awareness of cardiopulmonary resuscitation in communities: A multilevel analysis." Resuscitation 128: 106-111.
30. Lewis, M., B. A. Stubbs and M. S. Eisenberg (2013). "Dispatcher-assisted cardiopulmonary resuscitation: time to identify cardiac arrest and deliver chest compression instructions." Circulation 128(14): 1522-1530.
31. Linderoth, G., P. Hallas, F. K. Lippert, I. Wibrandt, S. Loumann, T. P. Moller and D. Ostergaard (2015). "Challenges in out-of-hospital cardiac arrest - A study combining closed-circuit television (CCTV) and medical emergency calls." Resuscitation 96: 317-322.
32. Ma, M. H., T. C. Lu, J. C. Ng, C. H. Lin, W. C. Chiang, P. C. Ko, F. Y. Shih, C. H. Huang, K. H. Hsiung, S. C. Chen and W. J. Chen (2007). "Evaluation of emergency medical dispatch in out-of-hospital cardiac arrest in Taipei." Resuscitation 73(2): 236-245.
33. Moller, T. P., C. Andrell, S. Viereck, L. Todorova, H. Friberg and F. K. Lippert (2016). "Recognition of out-of-hospital cardiac arrest by medical dispatchers in emergency medical dispatch centres in two countries." Resuscitation 109: 1-8.
34. Nuno, T., B. J. Bobrow, K. A. Rogge-Miller, M. Panczyk, T. Mullins, W. Tormala, A. Estrada, S. M. Keim and D. W. Spaite (2017). "Disparities in telephone CPR access and timing during out-of-hospital cardiac arrest." Resuscitation 115: 11-16.
35. Nurmi, J., V. Pettila, B. Biber, M. Kuisma, R. Komulainen and M. Castren (2006). "Effect of protocol compliance to cardiac arrest identification by emergency medical dispatchers." Resuscitation 70(3): 463-469.
36. Orpet, R., R. Riesenberg, J. Shin, C. Subido, E. Markul and T. Rea (2015). "Increasing bystander CPR: potential of a one question telecommunicator identification algorithm." Scand J Trauma Resusc Emerg Med 23: 39.
37. Plodr, M., A. Truhlar, J. Krencikova, M. Praunova, V. Svaba, J. Masek, D. Bejrova and J. Paral (2016). "Effect of introduction of a standardized protocol in dispatcher-assisted cardiopulmonary resuscitation." Resuscitation 106: 18-23.
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.
41. Stipulante, S., R. Tubes, M. El Fassi, A. F. Donneau, B. Van Troyen, G. Hartstein, V. D'Orio and A. Ghuysen (2014). "Implementation of the ALERT algorithm, a new dispatcher-assisted telephone cardiopulmonary resuscitation protocol, in non-Advanced Medical Priority Dispatch System (AMPDS) Emergency Medical Services centres." Resuscitation 85(2): 177-181.
42. Syvaoja, S., A. Salo, A. Uusaro, H. Jantti and M. Kuisma (2018). "Witnessed out-of-hospital cardiac arrest- effects of emergency dispatch recognition." Acta Anaesthesiol Scand 62(4): 558-567.
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.
45. Vaillancourt, C., M. Charette, A. Kasaboski, M. Hoad, V. Larocque, D. Crete, S. Logan, P. Lamoureux, J. McBride, S. Cheskes, G. A. Wells and I. G. Stiell (2015). "Cardiac arrest diagnostic accuracy of 9-1-1 dispatchers: a prospective multi-center study." Resuscitation 90: 116-120.
46. Vaillancourt, C., A. Verma, J. Trickett, D. Crete, T. Beaudoin, L. Nesbitt, G. A. Wells and I. G. Stiell (2007). "Evaluating the effectiveness of dispatch-assisted cardiopulmonary resuscitation instructions." Acad Emerg Med 14(10): 877-883.
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.