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

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ILCOR staff

Final draft

This Review is a draft version prepared by ILCOR and is 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”.

Conflict of Interest Declaration

The ILCOR Continuous Evidence Evaluation process is guided by a rigorous ILCOR Conflict of Interest policy. The following Task Force members and other authors were recused from the discussion as they declared a conflict of interest: none

The following Task Force members and other authors declared an intellectual conflict of interest and this was acknowledged and managed by the Task Force Chairs and Conflict of Interest committees: MAS and GDP funded by the NIHR (UK) to review and update the UK termination of resuscitation guideline used by UK ambulance services.

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 34 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; Grunau 2019 61; Haukoos 2004 145; Jordan 2017 75; Kajino 2013 54; Kashiura 2016 49; Kim 2015 104; Lee 2019 e134; Marsden 1995 49; Morrison 2007 266; Morrison 2009 324; Morrison 2014 486; Ong 2006 337; Ong 2007 244; Petrie 2001 186; Ruygrok 2008 239; Sasson 2008 1432; Shibahashi 2018 28; Skrifvars 2010 679; SOS-Kanto 2017 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 12 non-randomised studies {Bonnin 1993 1457; Chiang 2016 39; Glober 2019 8; Goto 2019 240; Haukoos 2004 145; Lee 2019 e134; Marsden 1995 49; Morrison 2007 266; Petrie 2001 186; SOS-Kanto 2017 345; Verbeek 2002 671; Yoon 2019 73} deriving and internally validating 15 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 (no-ROSC TOR){Bonnin 19931457}.

0.77 [0.74, 0.79]

0.93 [0.86, 0.98]

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

0.17 [0.15, 0.20]

1.00 [0.91, 1.00]

Glober et al 2019 (Glob1 TOR){Glober 2019 8}

0.14 [0.13, 0.16]

1.00 [0.98, 1.00]

Goto et al 2019 (Goto1 TOR){Goto 2018, 240}

0.11 [0.11, 0.11]

1.00 [0.99, 1.00]

Haukoos et al 2004 (Haukoos1 TOR){Haukoos 2004 145}

0.68 [0.64, 0.71]

0.92 [0.78, 0.98]

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]

Marsden et al 1995 (Marsden TOR){Marsden 1995 49}

0.58 [0.53, 0.63]

1.00 [0.03, 1.00]

Morrison et al 2007 (ALS TOR){Morrison 2007 266}

0.51 [0.50, 0.53]

1.00 [0.98, 1.00]

Petrie et al 2001 (Petrie TOR){Petrie 2001 186}

0.39 [0.38, 0.40]

0.98 [0.97, 0.99]

SOS-Kanto 2017 (SOS_Kanto1 TOR){SOS-Kanto 2017 345}

0.50 [0.49, 0.50]

0.95 [0.93, 0.96]

Verbeek et al 2002 (BLS TOR){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 24 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; Grunau 2019 61; 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 2017 345; Verhaert 2016 60; Yates 2018 21; Yoon 2019 73} externally validating 14 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 (NAEMSP TOR){Cone 2005 276}

0.58 [0.54, 0.63]

1.00 [0.74, 1.00]

Diskin et al 2014 (ALS TOR){Diskin 2014 910}

0.27 [0.21, 0.32]

1.00 [0.91, 1.00]

Drennan et al 2014 (uTOR){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 (BLS TOR){Goto 2019, 240}

0.91 [0.91, 0.91]

0.62 [0.60, 0.63]

Grunau et al 2017 (uTOR){Grunau 2017 374}

0.72 [0.71, 0.73]

0.91 [0.89, 0.93]

Grunau et al 2019 (Shib1 TOR){Grunau 2019 61}

0.23 [0.22, 0.23]

0.97 [0.97, 0.98]

Jordan et al 2017 (uTOR){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 (BLS TOR){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 (Goto1 TOR){Lee 2019 e134}

0.39 [0.37, 0.40]

0.95 [0.93, 0.97]

Lee et al 2019 (SOS-Kanto1 TOR){Lee 2019 e134}

0.27 [0.26, 0.28]

0.98 [0.97, 0.99]

Morrison et al 2007 (BLS TOR){Morrison 2007 266}

0.51 [0.50, 0.53]

1.00 [0.98, 1.00]

Morrison et al 2009 (ALS 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 (ALS TOR){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 2017 (BLS TOR){SOS-Kanto 2017 345}

0.78 [0.77, 0.79]

0.89 [0.86, 0.91]

SOS-Kanto 2017 (Goto2 TOR){SOS-Kanto 2017 345}

0.50 [0.49, 0.51]

0.95 [0.93, 0.96]

SOS-Kanto 2013 (SOS-Kanto2 TOR){SOS-Kanto 2012 345}

0.44 [0.43, 0.45]

0.97 [0.96, 0.98]

SOS-Kanto 2013 (SOS-Kanto3 TOR){SOS-Kanto 2012 345}

0.41 [0.40, 0.42]

0.99 [0.97, 0.99]

Verhaert et al 2016 (ALS TOR){Verhaert 2016 60}

0.07 [0.05, 0.10]

1.00 [0.96, 1.00]

Yates et al 2018 (uTOR){Yates 2018 21}

0.34 [0.27, 0.41]

0.17 [0.04, 0.41]

Yoon et al 2019 (uTOR){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 13 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 2017 345} 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 11 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; Skrifvars 2010 679; 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 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 3.

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; Yates 2018 21, Yoon 2019 73} 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 97.64 % {Drennan 2014 1488 the estimate of false positives (TOR rule predicts death but patient will survive) per 1000 patients tested ranged from 0 to 19.

  • 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 6 non-randomised studies {Glober 2019 8; Goto 2019 240; Yoon 2019 73; Lee 2019 e134; Haukoos 2004 145; Shibahashi 2018 28} deriving and internally validating 12 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)

Glober et al 2019 (Glob2 TOR){Glober 2019 8}

0.19 [0.17, 0.21]

1.00 [0.98, 1.00]

Goto et al 2019 (Goto1 TOR){Goto 2018, 240}

0.11 [0.10, 0.11]

1.00 [1.00, 1.00]

Haukoos et al 2004 (Haukoos2 TOR){Haukoos 2004 145}

0.57 [0.54, 0.61]

1.00 [0.79, 1.00]

Haukoos et al 2004 (Haukoos3 TOR){Haukoos 2004 145}

0.69 [0.66, 0.72]

1.00 [0.78, 1.00]

Haukoos et al 2004 (Haukoos4 TOR){ Haukoos 2004 145}

0.69 [0.65, 0.72]

1.00 [0.48, 1.00]

Lee et al 2019 (KOCARC4 TOR){Lee 2019 e134}

0.30 [0.28, 0.31]

1.00 [0.99, 1.00]

Lee et al 2019 (KOCARC5 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 9 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 11 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 (BLS TOR){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]

Lee et al 2019 (Goto1 TOR){Lee 2019 e134}

0.27 [0.26, 0.28]

0.98 [0.97, 0.99]

Lee et al 2019 (SOS-Kanto1 TOR){Lee 2019 e134}

0.39 [0.37, 0.40]

0.95 [0.93, 0.97]

SOS-Kanto 2017 (BLS TOR){SOS-Kanto 2017 345}

0.77 [0.76, 0.78]

0.96 [0.94, 0.98]

SOS-Kanto 2017 (ALS TOR){SOS-Kanto 2017 345}

0.49 [0.48, 0.50]

0.98 [0.96, 0.99]

SOS-Kanto 2017 (SOS-Kanto 1){SOS-Kanto 2017 345}

0.49 [0.48, 0.50]

0.97 [0.95, 0.99]

SOS-Kanto 2017 (SOS-Kanto 2){SOS-Kanto 2017 345}

0.44 [0.43, 0.44]

0.99 [0.97, 1.00]

SOS-Kanto 2017 (SOS-Kanto 3){SOS-Kanto 2017 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 (Haukoos3 TOR){Ruygrok 2008 239}

0.06 [0.04, 0.08]

1.00 [0.92, 1.00]

Skrifvars et al 2010 (ALS TOR){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 (uTOR){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 6 non-randomised studies {Cheong 2016 623; Kajino 2013 54; Kashiura 2016 49; Kim 2015 104; Lee 2019 e134; SOS-Kanto 2017 345} 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% {Lee 2019 e134} 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% {Cheong 2016 623} 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 6 non-randomised studies {Cheong 2016 623; Kajino 2013 54; Kashiura 2016 49; Lee 2019 e134; Ruygrok 2008 239; Skrifvars 2010 679} 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% {Lee 2019 e134} 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 99.00% {Skrifvars 2010 679} 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. Where an evidence-based TOR rule is included to inform practice, the EMS system should consider the training needs of EMS crews to communicate bad news and support the relatives of the recently deceased, in addition to consideration of the generalizability of the chosen TOR rule to its healthcare 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-heart beating 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
  • Risk associated with emergent transport of futile cases with ongoing resuscitation

Attachment: EIT-642-OHCA-To R_Et D-5May20

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