Out-of-hospital cardiac arrest termination of resuscitation (TOR) rules (EIT #642): Systematic Review

Commenting on this CoSTR is no longer possible

profile avatar

ILCOR staff

Draft for public comment
To read and leave comments, please scroll to the bottom of this page.

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

Smyth M, Perkins G, Coppola A, Gunson I, Ward A, Bhanji F, Bigham BL, Bray JE, Breckwoldt J, Cheng A, Duff JP, Glerup Lauridsen KG, Gilfoyle E, Hsieh MJ, Iwami T, Lockey AS, Ma M, Monsieurs KG, Okamoto D, Pellegrino JL, Yeung J, Finn J, Greif R. - on behalf of the International Liaison Committee on Resuscitation Education, Implementation and Teams Task Force.

Prehospital termination of resuscitation (TOR) rules Draft Consensus on Science with Treatment Recommendations. International Liaison Committee on Resuscitation (ILCOR) Education, Implementation and Teams Task Force, 2020, January 6. Available from: http://ilcor.org

Methodological Preamble (and Link to Published Systematic Review if applicable)

The continuous evidence evaluation process for the production of Consensus on Science with Treatment Recommendations (CoSTR) started with a systematic review of studies addressing prehospital termination of resuscitation. This systematic review was started by members of the ILCOR BLS Task Force (Smyth MA, Perkins GD). The search strategy was developed by an information scientist (Samantha Johnson). It sought studies addressing termination of resuscitation in the prehospital environment. Screening of results, critical appraisal of selected papers and extraction of data was completed by external collaborators (Coppola A, Gunson I, Ward A). Studies addressing termination of resuscitation among patients arriving at the Emergency Department by ambulance, and in-hospital termination of resuscitation were excluded.

Continued development of the systematic review was undertaken by the ILCOR Education, Implementation and Teams (EIT) Task Force with involvement of clinical content expert (Greif R). Evidence for adult and pediatric literature was sought and considered by the Basic Life Support, the Pediatric Life Support and the EIT Task Forces respectively. These data were taken into account when formulating the Treatment Recommendations.

PICOST

PICOST

Description

Population

Adults and children in cardiac arrest who do not achieve return of spontaneous circulation (ROSC) in the out-of-hospital environment.

Intervention

(Index test)

Termination of resuscitation (TOR) rules.

Comparison

(Reference standard)

In-hospital outcomes:

  • Died/survived
  • Favourable/unfavourable neurologic outcome

Outcomes

Ability of TOR to predict:

  • Death in hospital (critically important)
  • Unfavourable neurologic outcome (critically important)

Study Design

Cross sectional or cohort studies are eligible for inclusion. Unpublished studies (e.g., conference abstracts, trial protocols) are excluded.

Timeframe

All years and all languages were included as long as there was an English abstract. The search was completed on 10 July 2019.

PROSPERO Registration CRD42019131010

Quality of evidence was assessed using the QUADAS-2 framework for systematic reviews of diagnostic accuracy studies. This framework comprises 4 domains: patient selection, index test, reference standard, and flow and timing. Each domain is assessed in terms of risk of bias. Risk of bias for each outcome (prediction of death and prediction of poor neurologic outcome) was assessed independently. Risk of bias was ultimately found to be the same for both outcomes and is reported per outcome, across the range of studies. The domains of patient selection, index test, reference standard, are also assessed in terms of concerns regarding applicability.

Consensus on Science

The systematic review identified 31 studies {Bonnin 1993 1457; Cheong 2016 623; Chiang 2015 318; Chiang 2016 39; Cone 2005 276; Diskin 2014 910; Drennan 2014 1488; Fukuda 2014 1874; Glober 2019 8; Goto 2019 240; Grunau 2017 374; Haukoos 2004 145; Jordan 2017 75; Kajino 2013 54; Kashiura 2016 49; Kim 2015 104; Lee 2019 e134; Morrison 2007 266; Morrison 2009 324; Morrison 2014 486; Ong 2006 337; Ong 2007 244; Ruygrok 2008 239; Sasson 2008 1432; Shibahashi 2018 28; Skrifvars 2010 679; SOS-Kanto 2012 345; Verbeek 2002 671; Verhaert 2016 60; Yates 2018 21; Yoon 2019 73} addressing the use of termination of resuscitation rules. To facilitate improved insight into context and usefulness of the various termination of resuscitation rules, studies were grouped as follows across the two outcomes:

  • For the critically important outcome of prediction of death in hospital
  • Studies reporting the derivation and internal validation of a TOR rule to predict death after arrival at hospital
  • Studies reporting external validation of a TOR rule to predict death after arrival at hospital
  • Studies reporting clinical validation of a TOR rule to predict death after arrival at hospital

2) For the critically important outcome of prediction of poor neurologic outcome

  • Studies reporting the derivation and internal validation of a TOR rule to predict poor neurologic outcome
  • Studies reporting external validation of a TOR rule to predict poor neurologic outcome
  • Studies reporting clinical validation of a TOR rule to predict poor neurologic outcome
  • For the critically important outcome of prediction of death in hospital
  • Studies reporting the derivation and internal validation of a TOR rule to predict death in hospital

We identified VERY LOW certainty evidence (downgraded for risk of bias, inconsistency, indirectness, and imprecision ) from 9 non-randomised studies {Verbeek 2002 671; Morrison 2007 266; Glober 2019 8; Bonnin 1993 1457; Goto 2019 240; Yoon 2019 73; Lee 2019 e134; SOS-Kanto 2012 345; Chiang 2016 39} deriving and internally validating 12 distinct TOR rules to predict death after arrival at hospital. There was considerable heterogeneity in patient population, clinician population and emergency medical service (EMS) system design thus meta-analysis was not appropriate. Reported sensitivities and specificities of included papers are listed in table 1.

Table 1 Sensitivity and specificity of derivation and internal validation studies (death)

Author

Sensitivity [95% CI]

Specificity [95% CI]

Bonnin et al 1993{Bonnin 19931457}.

0.77 [0.74, 0.79]

0.93 [0.86, 0.98]

Chiang et al 2016{Chiang 2016 39}

0.17 [0.15, 0.20]

1.00 [0.91, 1.00]

Glober et al 2019{Glober 2019 8}

0.14 [0.13, 0.16]

1.00 [0.98, 1.00]

Goto et al 2019{Goto 2018, 240}

0.11 [0.11, 0.11]

1.00 [0.99, 1.00]

Lee et al 2019 (KOCARC1 TOR){Lee 2019 e134}

0.31 [0.29, 0.32]

0.97 [0.96, 0.99]

Lee et al 2019 (KOCARC2 TOR){Lee 2019 e134}

0.32 [0.31, 0.34]

0.98 [0.96, 0.99]

Morrison et al 2007{Morrison 2007 266}

0.51 [0.50, 0.53]

1.00 [0.98, 1.00]

SOS-Kanto 2013{SOS-Kanto 2012 345}

0.50 [0.49, 0.50]

0.95 [0.93, 0.96]

Verbeek et al 2002{ Verbeek 2002 671}

0.65 [0.62, 0.69]

1.00 [0.75, 1.00]

Yoon et al 2019 (KoCARC1 TOR){Yoon 2019, 73}

0.53 [0.51, 0.54]

0.92 [0.89, 0.94]

Yoon et al 2019(KoCARC2 TOR){Yoon 2019, 73}

0.53 [0.51, 0.54]

0.89 [0.86, 0.91]

Yoon et al 2019(KoCARC3 TOR){Yoon 2019, 73}

0.39 [0.38, 0.41]

0.95 [0.93, 0.97]

[95%CI] – 95% confidence interval,

  • Studies reporting external validation of a TOR rule to predict death in hospital

We identified VERY LOW certainty evidence (downgraded for risk of bias, inconsistency, indirectness, and imprecision ) from 22 non-randomised studies {Cheong 2016 623; Chiang 2015 318; Cone 2005 276; Diskin 2014 910; Drennan 2014 1488; Fukuda 2014 1874; Goto 2019 240; Grunau 2017 374; Jordan 2017 75; Kajino 2013 54; Kashiura 2016 49; Kim 2015 104; Lee 2019 e134; Morrison 2007 266; Morrison 2009 324; Ong 2006 337; Ong 2007 244; Sasson 2008 1432; Skrifvars 2010 679; SOS-Kanto 2012 345; Verhaert 2016 60; Yates 2018 21; Yoon 2019 73} externally validating 12 distinct TOR rules to predict death following arrival at hospital. There was considerable heterogeneity across TOR variables, patient populations, clinician populations and EMS systems thus meta-analysis was not appropriate. However, performance of three TOR rules (Basic Life Support TOR rule, Advanced Life Support TOR rule, Universal TOR rule) was reported in multiple papers (see below). Reported sensitivities and specificities of included papers are listed in table 2.

Table 2 Sensitivity and specificity of external validation studies (death)

Author

Sensitivity [95% CI]

Specificity [95% CI]

Cheong et al 2016 (BLS TOR){Cheong 2016 623}

0.66 [0.64, 0.68]

0.93 [0.85, 0.98]

Cheong et al 2016 (ALS TOR){Cheong 2016 623}

0.28 [0.26, 0.30]

0.99 [0.93, 1.00]

Chiang et al 2016 (BLS TOR){Chiang 2016 39}

0.64 [0.62, 0.66]

0.74 [0.67, 0.80]

Chiang et al 2016 (ALS TOR){Chiang 2016 39}

0.58 [0.56, 0.59]

0.76 [0.69, 0.81]

Cone et al 2005{Cone 2005 276}

0.58 [0.54, 0.63]

1.00 [0.74, 1.00]

Diskin et al 2014{Diskin 2014 910}

0.27 [0.21, 0.32]

1.00 [0.91, 1.00]

Drennan et al 2014{Drennan 2014 1488}

0.43 [0.42, 0.45]

0.89 [0.83, 0.94]

Fukada et al 2014 (BLS TOR){Fukuda 2014 1874}

0.70 [0.62, 0.78]

0.83 [0.36, 1.00]

Fukada et al 2014 (ALS TOR){Fukuda 2014 1874}

0.19 [0.08, 0.35]

1.00 [0.40, 1.00]

Goto et al 2019{Goto 2019, 240}

0.91 [0.91, 0.91]

0.62 [0.60, 0.63]

Grunau et al 2017{Grunau 2017 374}

0.72 [0.71, 0.73]

0.91 [0.89, 0.93]

Jordan et al 2017{Jordan 2017 75}

0.24 [0.16, 0.34]

1.00 [0.83, 1.00]

Kajinno et al 2013 (BLS TOR){Kajino 2013 54}

0.79 [0.79, 0.79]

0.88 [0.87, 0.88]

Kajinno et al 2013 (ALS TOR){Kajino 2013 54}

0.31 [0.30, 0.31]

0.92 [0.92, 0.93]

Kashiura et al 2016 (BLS TOR){Kashiura 2016 49}

0.82 [0.81, 0.83]

0.92 [0.88, 0.94]

Kashiura et al 2016 (ALS TOR){Kashiura 2016 49}

0.29 [0.28, 0.30]

0.91 [0.87, 0.95]

Kim et al 2015{Kim 2015 104}

0.74 [0.72, 0.75]

0.70 [0.65, 0.74]

Lee et al 2019 (BLS TOR){Lee 2019 e134}

0.72 [0.70, 0.73]

0.78 [0.74, 0.81]

Lee et al 2019{ (ALS TOR)Lee 2019 e134}

0.21 [0.20, 0.23]

0.97 [0.95, 0.98]

Lee et al 2019 (Goto TOR){Lee 2019 e134}

0.39 [0.37, 0.40]

0.95 [0.93, 0.97]

Lee et al 2019 (SOS-Kanto TOR){Lee 2019 e134}

0.27 [0.26, 0.28]

0.98 [0.97, 0.99]

Morrison et al 2007{Morrison 2007 266}

0.51 [0.50, 0.53]

1.00 [0.98, 1.00]

Morrison et al 2009 (BLS TOR){Morrison 2009 324}

0.33 [0.31, 0.35]

1.00 [0.97, 1.00]

Morrison et al 2009 (uTOR){Morrison 2009 324}

0.57 [0.55, 0.60]

1.00 [0.97, 1.00]

Ong et al 2006 (BLS TOR){Ong 2006 337}

0.53 [0.52, 0.54]

1.00 [0.99, 1.00]

Ong et al 2006 (Marsden TOR){Ong 2006 337}

0.19 [0.19, 0.20]

1.00 [0.99, 1.00]

Ong et al 2006 (Petrie TOR){Ong 2006 337}

0.10 [0.09, 0.10]

1.00 [0.99, 1.00]

Ong et al 2007{(BLS TOR)Ong 2007 244}

0.69 [0.67, 0.71]

0.81 [0.64, 0.93]

Ong et al 2007 (Marsden TOR){Ong 2007 244}

0.65 [0.63, 0.67]

0.91 [0.75, 0.98]

Ong et al 2007 (Petrie TOR){Ong 2007 244}

0.32 [0.30, 0.34]

0.94 [0.79, 0.99]

Sasson et al 2008 (BLS TOR){Sasson 2008 1432}

0.51 [0.49, 0.52]

0.99 [0.97, 1.00]

Sasson et al 2008 (ALS TOR){Sasson 2008 1432}

0.23 [0.22, 0.24]

1.00 [0.99, 1.00]

Skrifvars et al 2010 (uTOR){Skrifvars 2010 679}

0.27 [0.26, 0.27]

0.99 [0.97, 1.00]

Skrifvars et al 2010 (ERC TOR){Skrifvars 2010 679}

0.94 [0.94, 0.95]

0.95 [0.91, 0.97]

Skrifvars et al 2010 (Helsinki TOR){Skrifvars 2010 679}

0.55 [0.54, 0.56]

0.74 [0.68, 0.80]

SOS-Kanto 2013(BLS TOR){SOS-Kanto 2012 345}

0.78 [0.77, 0.79]

0.89 [0.86, 0.91]

SOS-Kanto 2013 (Goto TOR){SOS-Kanto 2012 345}

0.50 [0.49, 0.51]

0.95 [0.93, 0.96]

SOS-Kanto 2013 (SOS-Kanto 2){SOS-Kanto 2012 345}

0.44 [0.43, 0.45]

0.97 [0.96, 0.98]

SOS-Kanto 2013 (SOS-Kanto 3){SOS-Kanto 2012 345}

0.41 [0.40, 0.42]

0.99 [0.97, 0.99]

Verhaert et al 2016{Verhaert 2016 60}

0.07 [0.05, 0.10]

1.00 [0.96, 1.00]

Yates et al 2018{Yates 2018 21}

0.34 [0.27, 0.41]

0.17 [0.04, 0.41]

Yoon et al 2019{Yoon 2019, 73}

0.70 [0.69, 0.72]

0.81 [0.77, 0.84]

[95%CI] – 95% confidence interval

We identified VERY LOW certainty evidence (downgraded for risk of bias, inconsistency, indirectness, and imprecision) from 14 non-randomised studies {Cheong 2016 623; Chiang 2015 318; Fukada 2014 1874; Goto 2019 240; Kajino 2013 54; Kashiura 2016 49; Kim 2015 104; Lee 2019 e134; Morrison 2009 324; Ong 2006 337; Ong 2007 244; Sasson 2008 1432; SOS-Kanto 2012 345; Yoon 2019 73} reporting the accuracy of the BLS TOR rule to predict in-hospital death. There was considerable heterogeneity across patient populations, clinician populations and EMS systems, thus meta-analysis was not appropriate. We calculated estimates of effect, per 1000 patients, based upon the range of sensitivities, specificities and prevalences in the aforementioned studies (see table 2). Based upon the lowest prevalence of 88.32% {Lee 2019 e134} the estimate of false positives (TOR rule predicts death but patient will survive) per 1000 patients tested ranged from 0 to 36. Based upon the highest prevalence of 98.59% {Ong 2007 244} the estimate of false positives per 1000 patients tested ranged from 0 to 4.

We identified VERY LOW certainty evidence (downgraded for risk of bias, inconsistency, indirectness, and imprecision) from 10 non-randomised studies {Cheong 2016 623; Chiang 2015 318; Diskin 2014 910; Fukada 2014 1874; Kajino 2013 54; Kashiura 2016 49; Lee 2019 e134; Morrison 2009 324; Sasson 2008 1432; Verhaert 2016 60} reporting the accuracy of the ALS TOR rule to predict in-hospital death. There was considerable heterogeneity across patient populations, clinician populations and EMS systems thus meta-analysis was not appropriate. We calculated estimates of effect per 1000 patients based upon the range of sensitivities, specificities and prevalences in the aforementioned studies (see table 2). Based upon the lowest prevalence of 84.86% {Verhaert 2016 60} the estimate of false positives (TOR rule predicts death but patient will survive) per 1000 patients tested ranged from 0 to 36. Based upon the highest prevalence of 96.53% {Cheong 2016 623} the estimate of false positives (TOR rule predicts death but patient will survive) per 1000 patients tested ranged from 0 to 9.

We identified VERY LOW certainty evidence (downgraded for risk of bias, inconsistency, indirectness, and imprecision) from 6 non-randomised studies {Drennan 2014 1488; Grunau 2017 374; Jordan 2017 75; Morrison 2009 324; Skrifvars 2010 679; Yates 2018 21} reporting the accuracy of the universal TOR rule to predict in-hospital death. There was considerable heterogeneity across patient populations, clinician populations and EMS systems, thus meta-analysis was not appropriate. We calculated estimates of effect per 1000 patients based upon the range of sensitivities, specificities and prevalences in the aforementioned studies (see table 2). Based upon the lowest prevalence of 81.98% {Jordan 2017 75} the estimate of false positives (TOR rule predicts death but patient will survive) per 1000 patients tested ranged from 0 to 149. Based upon the highest prevalence of 98.93 % {Skrifvars 2010 679} the estimate of false positives (TOR rule predicts death but patient will survive) per 1000 patients tested ranged from 0 to 9.

  • Studies reporting clinical validation of a TOR rule to predict death in hospital

We identified MODERATE certainty evidence (downgraded for indirectness) from 1 non-randomised study {Morrison 2014 486} reporting a clinical validation of the universal TOR rule to predict in-hospital death. Sensitivity was 0.64 (95%CI 0.61 to 0.68), specificity was 1.00 (95%CI 0.92 to 1.00). Of 954 patients enrolled, the BLS TOR rule recommended transport in 367 cases. Of these 44 survived to discharge and 323 died in hospital. Of the remaining 586 (where the recommendation was to terminate resuscitation), 388 had resuscitation terminated in the field, while in 198 cases however crews transported to hospital. Among these 198 cases (transported to hospital where termination was recommended) no patient survived.

2) For the critically important outcome of prediction of poor neurologic outcome

  • Studies reporting the derivation and internal validation of a TOR rule to predict poor neurologic outcome

We identified VERY LOW certainty evidence (downgraded for risk of bias, inconsistency, indirectness, and imprecision ) from 5 non-randomised studies {Goto 2019 240; Yoon 2019 73; Lee 2019 e134; Haukoos 2004 145; Shibahashi 2018 28} deriving and internally validating 9 distinct TOR rules to predict poor neurologic outcome. There was considerable heterogeneity in patient population, clinician population and EMS system design thus meta-analysis was not appropriate. Reported sensitivities and specificities of included papers are listed in table 3.

Table 3 Sensitivity and specificity of derivation and internal validation studies (poor neurologic outcome)

Author

Sensitivity (95% CI)

Specificity (95% CI)

Goto et al 2019{Goto 2018, 240}

0.11 [0.10, 0.11]

1.00 [1.00, 1.00]

Haukoos et al 2004{ Haukoos 2004 145}

0.69 [0.65, 0.72]

0.52 [0.33, 0.71]

Lee et al 2019(KOCARC1 TOR){Lee 2019 e134}

0.30 [0.28, 0.31]

1.00 [0.99, 1.00]

Lee et al 2019 (KOCARC2 TOR){Lee 2019 e134}

0.31 [0.30, 0.33]

1.00 [0.99, 1.00]

Shibahashi et al 2018 (Shib1 TOR){Shibahashi 2018 28}

0.39 [0.38, 0.39]

0.95 [0.95, 0.96]

Shibahashi et al 2018 (Shib2 TOR){Shibahashi 2018 28}

0.59 [0.59, 0.59]

0.89 [0.88, 0.90]

Yoon et al 2019 (KoCARC1 TOR){Yoon 2019, 73}

0.52 [0.50, 0.53]

0.99 [0.97, 1.00]

Yoon et al 2019(KoCARC2 TOR){Yoon 2019, 73}

0.52 [0.50, 0.53]

0.98 [0.96, 0.99]

Yoon et al 2019(KoCARC3 TOR){Yoon 2019, 73}

0.38 [0.37, 0.40]

1.00 [0.98, 1.00]

[95%CI] – 95% confidence interval

  • Studies reporting external validation of a TOR rule to predict poor neurologic outcome

We identified VERY LOW certainty evidence (downgraded for risk of bias, inconsistency, indirectness, and imprecision) from 10 non-randomised studies {Cheong 2016 623; Kajino 2013 54; Kashiura 2016 49; Kim 2015 104; Lee 2019 e134; SOS-Kanto 2012 345; Ruygrok 2008 239; Skrifvars 2010 679; Yoon 2019 73} externally validating 10 distinct TOR rules to predict poor neurologic outcome. There was considerable heterogeneity across TOR rule variables, patient populations, clinician populations and EMS systems, thus meta-analysis was not appropriate. However, performance of two TOR rules (Basic Life Support TOR, Advanced Life Support TOR) was reported in multiple papers (see below). Reported sensitivities and specificities of included papers are listed in table 4.

Table 4 Sensitivity and specificity of external validation studies (poor neurologic outcome)

Author

Sensitivity (95% CI)

Specificity (95% CI)

Cheong et al 2016 (BLS TOR){Cheong 2016 623}

0.66 [0.64, 0.68]

1.00 [0.92, 1.00]

Cheong et al 2016 (ALS TOR){Cheong 2016 623}

0.27 [0.25, 0.29]

1.00 [0.92, 1.00]

Kajino et al 2013 (BLS TOR){Kajino 2013 54}

0.78 [0.78, 0.78]

0.97 [0.96, 0.97]

Kajino et al 2013 (ALS TOR){Kajino 2013 54}

0.30 [0.30, 0.30]

0.98 [0.97, 0.99]

Kashiura et al 2016 (BLS TOR){Kashiura 2016 49}

0.81 [0.80, 0.82]

0.97 [0.94, 0.99]

Kashiura et al 2016 (ALS TOR){Kashiura 2016 49}

0.28 [0.27, 0.29]

0.94 [0.87, 0.98]

Kim et al 2015{Kim 2015 104}

0.72 [0.71, 0.73]

0.90 [0.85, 0.94]

Lee et al 2019 (BLS TOR){Lee 2019 e134}

0.71 [0.70, 0.72]

0.93 [0.89, 0.95]

Lee et al 2019 (ALS TOR){Lee 2019 e134}

0.21 [0.20, 0.22]

0.99 [0.97, 1.00]

SOS-Kanto 2012 (BLS TOR){SOS-Kanto 2012 345}

0.77 [0.76, 0.78]

0.96 [0.94, 0.98]

SOS-Kanto 2012 (ALS TOR){SOS-Kanto 2012 345}

0.49 [0.48, 0.50]

0.98 [0.96, 0.99]

SOS-Kanto 2013 (SOS-Kanto 1){SOS-Kanto 2012 345}

0.49 [0.48, 0.50]

0.97 [0.95, 0.99]

SOS-Kanto 2013 (SOS-Kanto 2){SOS-Kanto 2012 345}

0.44 [0.43, 0.44]

0.99 [0.97, 1.00]

SOS-Kanto 2013 (SOS-Kanto 3){SOS-Kanto 2012 345}

0.40 [0.39, 0.41]

0.99 [0.98, 1.00]

Ruygrok et al 2008 (ALS TOR){Ruygrok 2008 239}

0.24 [0.21, 0.27]

1.00 [0.92, 1.00]

Ruygrok et al 2008 (uTOR){Ruygrok 2008 239}

0.34 [0.31, 0.38]

1.00 [0.92, 1.00]

Ruygrok et al 2008 (Neuro TOR){Ruygrok 2008 239}

0.06 [0.04, 0.08]

1.00 [0.92, 1.00]

Skrifvars et al 2010 (uTOR){Skrifvars 2010 679}

0.27 [0.26, 0.27]

1.00 [0.97, 1.00]

Skrifvars et al 2010 (ERC TOR){Skrifvars 2010 679}

0.94 [0.94, 0.95]

0.96 [0.93, 0.98]

Skrifvars et al 2010 (Helsinki TOR){Skrifvars 2010 679}

0.55 [0.54, 0.56]

0.79 [0.73, 0.85]

Yoon et al 2019{Yoon 2019, 73}

0.69 [0.68, 0.71]

0.94 [0.91, 0.96]

(95%CI) – 95% confidence interval, uTOR – universal termination of resuscitation rule

We identified VERY LOW certainty evidence (downgraded for risk of bias, inconsistency, indirectness, and imprecision) from 7 non-randomised studies {Cheong 2016 623; Kajino 2013 54; Kashiura 2016 49; Kim 2015 104; Lee 2019 e134; SOS-Kanto 2012 345; Yoon 2019 73} reporting the accuracy of the BLS TOR rule to predict poor neurologic outcome. There was considerable heterogeneity across patient populations, clinician populations and EMS systems, thus meta-analysis was not appropriate. We calculated estimates of effect per 1000 patients based upon the range of sensitivities, specificities and prevalences in the aforementioned studies (see table 4). Based upon the lowest prevalence of 92.14% the estimate of false positives (TOR predicts poor neurologic outcome but patient has favourable neurologic outcome) per 1000 patients tested ranged from 0 to 6. Based upon the highest prevalence of 97.99% the estimate of false positives per 1000 patients tested ranged from 0 to 1.

We identified VERY LOW certainty evidence (downgraded for risk of bias, inconsistency, indirectness, and imprecision) from 5 non-randomised studies {Cheong 2016 623; Kajino 2013 54; Kashiura 2016 49; Lee 2019 e134; Ruygrok 2008 239} reporting the accuracy of the ALS TOR rule to predict poor neurologic outcome. There was considerable heterogeneity across patient populations, clinician populations and EMS systems, thus meta-analysis was not appropriate. We calculated estimates of effect per 1000 patients based upon the range of sensitivities, specificities and prevalences in the aforementioned studies. Based upon the lowest prevalence of 92.14% the estimate of false positives (TOR rule predicts poor neurologic outcome but patient has favourable neurologic outcome) per 1000 patients tested ranged from 0 to 5. Based upon the highest prevalence of 98.62% the estimate of false positives per 1000 patients tested ranged from 0 to 1.

  • Studies reporting clinical validation of a TOR to predict poor neurologic outcome

We identified MODERATE certainty evidence (downgraded for indirectness) from 1 non-randomised study {Morrison 2014 486} reporting a clinical validation of the universal TOR rule to predict poor neurologic outcome. Sensitivity was 0.63 (95%CI 0.61 to 0.68), specificity was 1.00 (95%CI 0.92 to 1.00). Of 953 patients included, the BLS TOR rule recommended transport in 367 cases. Of these 17 survived with poor neurologic outcome (CPC 3 or 4) and 323 died in hospital.

Treatment Recommendations

We conditionally recommend the use of termination of resuscitation (TOR) rules to assist clinicans in deciding whether to discontinue resuscitation efforts at the scene or to transport to hospital with ongoing CPR (conditional recommendation / very-low certainty evidence).

Justification and Evidence to Decision Framework Highlights

The Task Force made a conditional recommendation for the use of termination of resuscitation (TOR) rules, recognising variation in patient values, resources available, and performance of TOR rules in different settings. In making a conditional recommendation the Task Force noted that the majority of studies describe either the derivation and internal validation of individual termination of resuscitation rules, or the external validation of previously published termination of resuscitation rules. We identified only one study addressing clinical validation (the use of a termination of resuscitation rule in clinical practice) of a TOR rule by emergency medical technicians (EMT’s) with defibrillators. Robust evidence to support the widespread implementation of termination of resuscitation (TOR) rules in clinical practice is therefore weak. Despite several studies reporting a specificity of 1.0, the Task Force acknowledges that implementation of a TOR rule may result in missed survivors. However, inclusion of a TOR within a termination guideline has the potential to reduce variation in practice associated with clinician judgement, and improve termination decisions more generally.

The task Force recognises that termination of resuscitation is common practice in many EMS systems. We support the principle of discontinuing resuscitation when treatment is futile as it preserves the dignity of the recently deceased, reduces risk for EMS providers and protects scarce healthcare resources. However, the Task Force also acknowledges that identification of futile cases is challenging and is often informed by both clinical guidelines and clinician insight.

The Task Force advocates for the adoption of termination of resuscitation guidelines that take into account the patients prior wishes and / or expectations, consideration of patient pre-existing co-morbidities and quality of life both before and after the cardiac arrest event. Such termination of resuscitation guidelines may be informed by the inclusion of an evidence based TOR rule, however the Task Force believes a TOR rule should not be the sole determinant of when to discontinue resuscitation.

In those EMS systems that do implement prehospital termination of resuscitation, the EMS system must ensure there is no conflict with legislation prohibiting non-physicians from discontinuing resuscitation and have appropriate governance arrangements to monitor practice. If an evidence based TOR rule is included to inform practice, the EMS system should consider the generalizability of the TOR rule in question to their health care system. In some health care systems it may be appropriate for EMS systems to communicate with organ donation teams prior to implementing change as prehospital termination of resuscitation may have significant impact on non-heartbeating organ donation

The Task Force acknowledge that prehospital termination of resuscitation may not be feasible in some instances. In some locations the legal infrastructure may require ambulance clinicians to provide resuscitation in all but a very limited number of circumstances (e.g. in the presence of rigor mortis). In other areas, it may not be culturally acceptable for non-physicians to make a clinical decision to stop resuscitation in the prehospital environment. Where this is the case, or where clinical governance arrangements are insufficient to monitor practice we suggest transport to hospital with ongoing CPR may be preferable.

Knowledge Gaps

There is a paucity of evidence addressing use of TOR rules in clinical practice. Studies are required to address:

  • Accuracy of TOR rules in clinical practice
  • Compliance with OOH-TOR rules
  • Implementation strategies of TOR rules for EMS based on evidence
  • Health economic implications of TOR rule implementation
  • Societal perceptions and acceptability of TOR rules
  • TOR rules specific for children
  • Impact of TOR rules on non-heart-beating organ donation

Attachments

Evidence-to-Decision Table: EIT-642 Out Of Hospital TOR TFSR

References

Bonnin MJ, Pepe PE, Kimball KT and Clark PS, Jr. Distinct criteria for termination of resuscitation in the out-of-hospital setting. Jama. 1993;270:1457-62.

Cheong RW, Li H, Doctor NE, Ng YY, Goh ES, Leong BS, Gan HN, Foo D, Tham LP, Charles R and Ong ME. Termination of Resuscitation Rules to Predict Neurological Outcomes in Out-of-Hospital Cardiac Arrest for an Intermediate Life Support Prehospital System. Prehosp Emerg Care. 2016;20:623-9.

Chiang WC, Huang YS, Hsu SH, Chang AM, Ko PC, Wang HC, Yang CW, Hsieh MJ, Huang EP, Chong KM, Sun JT, Chen SY and Ma MH. Performance of a simplified termination of resuscitation rule for adult traumatic cardiopulmonary arrest in the prehospital setting. Emerg Med J. 2017;34:39-45.

Chiang WC, Ko PC, Chang AM, Liu SS, Wang HC, Yang CW, Hsieh MJ, Chen SY, Lai MS and Ma MH. Predictive performance of universal termination of resuscitation rules in an Asian community: are they accurate enough? Emerg Med J. 2015;32:318-23.

Cone DC, Bailey ED and Spackman AB. The safety of a field termination-of-resuscitation protocol. Prehosp Emerg Care. 2005;9:276-81.

Diskin FJ, Camp-Rogers T, Peberdy MA, Ornato JP and Kurz MC. External validation of termination of resuscitation guidelines in the setting of intra-arrest cold saline, mechanical CPR, and comprehensive post resuscitation care. Resuscitation. 2014;85:910-4.

Drennan IR, Lin S, Sidalak DE and Morrison LJ. Survival rates in out-of-hospital cardiac arrest patients transported without prehospital return of spontaneous circulation: an observational cohort study. Resuscitation. 2014;85:1488-93.

Fukuda T, Matsubara T, Doi K, Fukuda-Ohashi N and Yahagi N. Predictors of favorable and poor prognosis in unwitnessed out-of-hospital cardiac arrest with a non-shockable initial rhythm. Int J Cardiol. 2014;176:910-5.

Glober NK, Tainter CR, Abramson TM, Staats K, Gilbert G and Kim D. A simple decision rule predicts futile resuscitation of out-of-hospital cardiac arrest. Resuscitation. 2019.

Goto Y, Funada A, Maeda T, Okada H and Goto Y. Field termination-of-resuscitation rule for refractory out-of-hospital cardiac arrests in Japan. J Cardiol. 2019;73:240-246.

Group SOS-Kanto . A New Rule for Terminating Resuscitation of Out-of-Hospital Cardiac Arrest Patients in Japan: A Prospective Study. J Emerg Med. 2017;53:345-352.

Grunau B, Taylor J, Scheuermeyer FX, Stenstrom R, Dick W, Kawano T, Barbic D, Drennan I and Christenson J. External Validation of the Universal Termination of Resuscitation Rule for Out-of-Hospital Cardiac Arrest in British Columbia. Ann Emerg Med. 2017;70:374-381.e1.

Haukoos JS, Lewis RJ and Niemann JT. Prediction rules for estimating neurologic outcome following out-of-hospital cardiac arrest. Resuscitation. 2004;63:145-55.

Jordan MR, O'Keefe MF, Weiss D, Cubberley CW, MacLean CD and Wolfson DL. Implementation of the universal BLS termination of resuscitation rule in a rural EMS system. Resuscitation. 2017;118:75-81.

Kajino K, Kitamura T, Iwami T, Daya M, Ong ME, Hiraide A, Shimazu T, Kishi M and Yamayoshi S. Current termination of resuscitation (TOR) guidelines predict neurologically favorable outcome in Japan. Resuscitation. 2013;84:54-9.

Kashiura M, Hamabe Y, Akashi A, Sakurai A, Tahara Y, Yonemoto N, Nagao K, Yaguchi A, Morimura N and Group S-KS. Applying the termination of resuscitation rules to out-of-hospital cardiac arrests of both cardiac and non-cardiac etiologies: a prospective cohort study. Crit Care. 2016;20:49.

Kim TH, Shin SD, Kim YJ, Kim CH and Kim JE. The scene time interval and basic life support termination of resuscitation rule in adult out-of-hospital cardiac arrest. J Korean Med Sci. 2015;30:104-9.

Lee DE, Lee MJ, Ahn JY, Ryoo HW, Park J, Kim WY, Shin SD, Hwang SO and Korean Cardiac Arrest Research C. New Termination-of-Resuscitation Models and Prognostication in Out-of-Hospital Cardiac Arrest Using Electrocardiogram Rhythms Documented in the Field and the Emergency Department. J Korean Med Sci. 2019;34:e134.

Morrison LJ, Verbeek PR, Vermeulen MJ, Kiss A, Allan KS, Nesbitt L and Stiell I. Derivation and evaluation of a termination of resuscitation clinical prediction rule for advanced life support providers. Resuscitation. 2007;74:266-75.

Morrison LJ, Verbeek PR, Zhan C, Kiss A and Allan KS. Validation of a universal prehospital termination of resuscitation clinical prediction rule for advanced and basic life support providers. Resuscitation. 2009;80:324-8.

Morrison LJ, Eby D, Veigas PV, Zhan C, Kiss A, Arcieri V, Hoogeveen P, Loreto C, Welsford M, Dodd T, Mooney E, Pilkington M, Prowd C, Reichl E, Scott J, Verdon JM, Waite T, Buick JE and Verbeek PR. Implementation trial of the basic life support termination of resuscitation rule: reducing the transport of futile out-of-hospital cardiac arrests. Resuscitation. 2014;85:486-91.

Ong MEH, Jaffey J, Stiell I and Nesbitt L. Comparison of termination-of-resuscitation guidelines for basic life support: Defibrillator providers in out-of-hospital cardiac arrest. Ann Emerg Med. 2006;47:337-343.

Ong ME, Tan EH, Ng FS, Yap S, Panchalingham A, Leong BS, Ong VY, Tiah L, Lim SH, Venkataraman A and group Cs. Comparison of termination-of-resuscitation guidelines for out-of-hospital cardiac arrest in Singapore EMS. Resuscitation. 2007;75:244-51.

Ruygrok ML, Byyny RL, Haukoos JS, Colorado Cardiac A, Resuscitation Collaborative Study G and the Denver Metro EMSMD. Validation of 3 termination of resuscitation criteria for good neurologic survival after out-of-hospital cardiac arrest. Ann Emerg Med. 2009;54:239-47.

Sasson C, Hegg AJ, Macy M, Park A, Kellermann A, McNally B and Group CS. Prehospital termination of resuscitation in cases of refractory out-of-hospital cardiac arrest. Jama. 2008;300:1432-8.

Shibahashi K, Sugiyama K and Hamabe Y. A potential termination of resuscitation rule for EMS to implement in the field for out-of-hospital cardiac arrest: An observational cohort study. Resuscitation. 2018;130:28-32.

Skrifvars MB, Vayrynen T, Kuisma M, Castren M, Parr MJ, Silfverstople J, Svensson L, Jonsson L and Herlitz J. Comparison of Helsinki and European Resuscitation Council "do not attempt to resuscitate" guidelines, and a termination of resuscitation clinical prediction rule for out-of-hospital cardiac arrest patients found in asystole or pulseless electrical activity. Resuscitation. 2010;81:679-84.

Verbeek PR, Vermeulen MJ, Ali FH, Messenger DW, Summers J and Morrison LJ. Derivation of a termination-of-resuscitation guideline for emergency medical technicians using automated external defibrillators. Acad Emerg Med. 2002;9:671-8.

Verhaert DV, Bonnes JL, Nas J, Keuper W, van Grunsven PM, Smeets JL, de Boer MJ and Brouwer MA. Termination of resuscitation in the prehospital setting: A comparison of decisions in clinical practice vs. recommendations of a termination rule. Resuscitation. 2016;100:60-5.

Yates EJ, Schmidbauer S, Smyth AM, Ward M, Dorrian S, Siriwardena AN, Friberg H and Perkins GD. Out-of-hospital cardiac arrest termination of resuscitation with ongoing CPR: An observational study. Resuscitation. 2018;130:21-27.

Yoon JC, Kim Y-J, Ahn S, Jin Y-H, Lee S-W, Song KJ, Shin SD, Hwang SO, Kim WY and Korean Cardiac Arrest Research Consortium Ko C. Factors for modifying the termination of resuscitation rule in out-of-hospital cardiac arrest. Am Heart J. 2019;213:73


Discussion

GUEST
Mark Frydenborg (266 posts)
Sad to say, 30 years after Utstein and just look at the bias, heterogeneity, and overall certainty of evidence presented here. Why is there not better overall coordination of the studies being done? I don’t fault ilcor. I know large multicenter trials are difficult to put together. But as someone who was practicing EM before and after Utstein I find the lack of progress a bit depressing.
Reply
GUEST
Robert Greif (266 posts)
Thanks for your insight, we agree fully with your wish for high stake multicenter trials.
GUEST
Keith Tyler (266 posts)
From an EMS perspective I can't disagree with the quality and lack of evidence but what I don't see is any modification for the risk of not terminating resuscitation on scene. This would result in a lights & sirens journey to hospital which places both the practitioners and public at a greater risk. I suggest too is a knowledge gap that needs addressing and adding to the wider picture of care
Reply
GUEST
Robert Greif (266 posts)
Thanks for your comment about possible impact of no TOR rules on hospital transport of futile patients. That was the reason why we added a paragraph about that in the Justification chapter of this CoSTR. EIT will consider your suggestion for the gap of knowledge.
GUEST
Patrick Druwé (266 posts)
Dear colleagues, I wonder why the study of Grunau et al. Resuscitation 2019 vol. 135 51 - 56 validating the Bokutoh criteria in North-America is not included in the analysis, this study was published within your time frame. My general comment is that there will always be unmeasured variability, 100% prediction is impossible and we need to accept the fact that uncertainty is inherent to medicine. As such we have to look at the number needed to treat (which is huge for some subpopulations, when for instance looking at the Bokutoh criteria (284 in their validation cohort) ) and allow and validate the clinical insight of the team to decide on an individual patient whether to proceed with resuscitation or whether to start at all. Especially since an estimated 13 - 20% of the cardiac arrests occur in dismal conditions (non-witnessed, non-shockable, old) the collateral damage we are causing needs to be stressed more, the extremely bad outcomes in non-shockable non-witnessed arrests in older people should not be treated by default but only in exceptional circumstances based on the clinical insight of the team or within a randomized controlled trial. I have a problem with the sentence: 'inclusion of a TOR within a termination guideline has the potential to reduce variation in practice associated with clinician judgment and improve termination decisions more generally." This variability can be justified and needs to be accepted due to the differences between patients.
Reply

Sort by

Time range

Categories

Domains