Neuroprognostic accuracy of blood biomarkers for post-cardiac arrest patients: A systematic review and meta-analysis

Journal Pre-proof Neuroprognostic accuracy of blood biomarkers for post-cardiac arrest patients: A systematic review and meta-analysis Chih-Hung Wang, Wei-Tien Chang, Ke-Ing Su, Chien-Hua Huang, Min-Shan Tsai, Eric Chou, Tsung-Chien Lu, Wen-Jone Chen, Chien-Chang Lee, Shyr-Chyr Chen

PII:

S0300-9572(20)30026-5

DOI:

https://doi.org/10.1016/j.resuscitation.2020.01.006

Reference:

RESUS 8368

To appear in:

Resuscitation

Received Date:

16 September 2019

Revised Date:

26 December 2019

Accepted Date:

9 January 2020

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Neuroprognostic Accuracy of Blood Biomarkers for Post-cardiac Arrest Patients: A Systematic Review and Meta-analysis

Chih-Hung Wang, MD, PhD1,2 ; Wei-Tien Chang, MD, PhD1,2; Ke-Ing Su, MS1; ChienHua Huang, MD, PhD1,2 ; Min-Shan Tsai, MD, PhD1,2; Eric Chou, MD3; Tsung-Chien

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Lu, MD, PhD1,2; Wen-Jone Chen, MD, PhD1,2,4; Chien-Chang Lee, MD, ScD1,2,*; Shyr-

1

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Chyr Chen, MD1,2,*

Department of Emergency Medicine, National Taiwan University Hospital, Taipei,

Department of Emergency Medicine, College of Medicine, National Taiwan

3

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University, Taipei, Taiwan

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2

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Taiwan

Department of Emergency Medicine, Baylor Scott&White All Saints Medical Center,

Division of Cardiology, Department of Internal Medicine, National Taiwan University

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Fort Worth, Texas, USA

Hospital and National Taiwan University College of Medicine, Taipei, Taiwan

Address for correspondence and reprints: *Dr. Chien-Chang Lee 1

Address: Health Economic Outcomes Research Group, Department of Emergency Medicine, National Taiwan University Hospital, No. 7, Chung-Shan South Road, Taipei 100, Taiwan. E-mail: [email protected]/ [email protected] TEL: +886-2-23123456 ext 62831

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FAX: +886-2-23223150

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*Dr. Shyr-Chyr Chen

Address: Department of Emergency Medicine, National Taiwan University Hospital,

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E-mail: [email protected]

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No. 7, Chung-Shan South Road, Taipei 100, Taiwan.

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TEL: +886-2-23123456 ext 62831

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FAX: +886-2-23223150

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The two corresponding authors contributed equally to this work. Both corresponding authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.

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Abstract Aim: To summarise and compare the prognostic accuracy of the blood biomarkers of brain injury, including NSE and S-100B, for neurological outcomes in adult postcardiac arrest patients.

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Methods: We systematically searched PubMed and Embase databases from their inception to March 2019. We selected studies providing sufficient data of prognostic

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values of NSE or S-100B to predict neurological outcomes in adult post-cardiac arrest

patients. We adopted QUADAS-2 to assess risk of bias and a Bayesian bivariate

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random-effects meta-analysis model to synthesise the prognostic data. The study

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protocol was registered with PROSPERO (CRD42018084933).

Results: We included 42 studies involving 4806 patients in the meta-analysis. The NSE

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was associated with a pooled sensitivity of 0.56 (95% credible interval [CrI], 0.47–0.65)

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and pooled specificity of 0.99 (95% CrI, 0.98–1.00). The S-100B was associated with a pooled sensitivity of 0.63 (95% CrI, 0.46–0.78) and pooled specificity of 0.97 (95% CrI, 0.92–1.00). The heterogeneity for NSE (I2, 22.4%) and S-100B (I2, 16.1%) was low and publication bias was not significant. In subgroup analyses, both biomarkers were associated with high specificity across all subgroups with regard to different 3

populations (i.e. whether patients were out-of-hospital cardiac arrest or whether patients received targeted temperature management), different timings of measurement, and different timings of outcome assessment.

Conclusions: The prognostic performance was comparable between NSE and S-100B.

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Both biomarkers may be integrated into a multimodal neuroprognostication algorithm for post-cardiac arrest patients and institution-specific cut-off points for both

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biomarkers should be established.

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100B; Neurological outcome

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Keywords: Cardiac arrest; Cardiopulmonary resuscitation; Neuron-specific enolase; S-

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Background

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Cardiac arrests, either out-of-hospital cardiac arrest (OHCA) [1] or in-hospital cardiac arrest (IHCA) [2], are critical events with poor neuroprognosis. It was reported

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that approximately 65% of OHCA patients died from neurological injuries and most died following withdrawal of life sustaining therapy due to poor neuroprognosis [3]. Therefore, accurate neuroprognostication, especially testing with high specificity (i.e. low false-positive rate), is important for these patients.

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Recent guidelines [4] recommend a multimodal strategy of neuroprognostication for post-cardiac arrest patients. Among those recommended predictors of neurological outcome, Neuron-specific enolase (NSE) and S-100B are commonly examined blood biomarkers, both of which are proteins released following injury to neurons and glial cells, respectively. Blood concentrations of NSE and S-100B are thus likely to correlate

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with the extent of hypoxic-ischaemic brain injury following cardiac arrest and the potential of neurological recovery.

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The advantages of using biomarkers in neuroprognostication included objective

results and likely independence from the influence of targeted temperature or sedatives.

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To maximise the value of existing evidence in the literature, we performed this

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systematic review and meta-analysis to assess and compare the prognostic accuracy of

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NSE and S-100B for prediction of neurological outcomes in post-cardiac arrest patients.

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Materials and Methods

This systematic review and meta-analysis were performed in accordance with the

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PRISMA-DTA (Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies ) statement [5], the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy [6], and recommendations for reviews of diagnostic accuracy [7]. The study protocol was registered with PROSPERO

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(CRD42018084933).

Data Sources and Searches Two investigators (CHW and WTC) independently searched the PubMed and Embase databases from their inception through to March 2019. The search strings were

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as follows: (1) PubMed: ((neuron specific enolase) OR s100 OR s100b OR s100-b) AND ((cardiac arrest) OR cardiopulmonary resuscitation); (2) Embase: ('neuron

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specific enolase'/exp OR 'neuron specific enolase' OR 's100' OR 's100b' OR 's100-b') AND ('cardiac arrest'/exp OR 'cardiac arrest' OR 'cardiopulmonary resuscitation'/exp

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OR 'cardiopulmonary resuscitation'). No restrictions were set on publication year or

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language. To ensure completeness, we screened the bibliographies of the selected

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strategy.

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publications and relevant review articles for references not captured by our search

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Study Selection

Two investigators (CHH and MST) independently scanned the titles and abstracts

of all retrieved articles to determine whether the articles were pertinent to this review. We used the following prespecified inclusion criteria: (a) population included adult post-cardiac arrest patients, (b) evaluation of NSE or S-100B, (c) results included 6

neurological outcome or death (death was classified as the worst neurological status), and (d) sufficient data to construct a 2×2 contingency table of true-positive, falsepositive, true-negative, and false-negative counts [8]. During study selection, investigators were not blinded to authors and institutions of the publications. We excluded studies that included trauma patients. We also excluded studies having

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significant overlap (more than 50%) of study patients with the selected studies. We attempted to contact authors if studies provided insufficient information to construct a

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2×2 table. Full-text articles were retrieved if either of the reviewers considered the abstract potentially suitable. After retrieving the full reports of potentially relevant

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studies, two reviewers (CHH and MST) independently assessed each study’s eligibility

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on the basis of the inclusion criteria. Differences of opinion regarding study eligibility

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were settled by consultation with another investigator (WJC).

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Data Extraction and Quality Assessment

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Two investigators (CHW and WTC) independently extracted qualitative and quantitative data, and a third investigator (SCC) adjudicated discordant assessments. Data were extracted for author information, publication year, study design, study setting, patient number, patient characteristics, quantitative data required for building a 2×2 contingency table [8], information regarding the details for NSE and S-100B tests, and 7

outcomes. For each biomarker, we selected a single data set of prognostic accuracy values from each study for meta-analysis. If more than one data set was reported in a single study (e.g. different sensitivity and specificity at different time points), we selected the representative data set according to the following hierarchy of priority: (a) data of highest specificity, (b) data of highest sensitivity, (c) data with the longest

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interval between cardiac arrest and timing of measurement, (d) data with the longest follow-up time for neurological outcomes, and (e) data with the largest patient number.

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The rationale for this hierarchy was to reflect the use of these biomarkers in clinical use

for post-cardiac arrest patients.

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pursuing a test with maximum specificity and accuracy in predicting long-term outcome

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Populations were categorised according to site of cardiac arrest (OHCA vs IHCA)

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and implementation of targeted temperature management (TTM) (TTM vs No TTM). In order to maximize the number of studies included in the subgroup analysis, we would

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attempt to categorize the studies according to the quartiles of the proportions of OHCA

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or TTM. The patients receiving target temperature between 32 and 36℃ [9] were categorized into TTM group. Also, because the TTM was first recommended in the 2005 resuscitation guidelines [9], publications before 2006 were categorised as No TTM subgroup if the proportion of TTM was not specifically reported. For timing of test performance, early and late tests were defined as measurement time within and 8

beyond 24 hours of cardiac arrest, respectively. If the timing of test performance was a period rather than a single time point, the end of the time interval was used for categorisation. For timing of outcome assessment, early and late outcomes were defined as neurological outcomes assessed before and after hospital discharge (or one month following cardiac arrest), respectively.

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Two investigators (CHH and MST) independently assessed risk of bias of the included studies using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy

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Studies 2) tool [10]. Disagreements were resolved by consensus or consultation with a

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Data Synthesis and Analysis

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third investigator (CCL).

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We calculated the pooled sensitivity, specificity, likelihood ratios (LRs), and areas under the receiver-operating characteristic curve (AUCs) across studies with 95%

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credible intervals (CrIs) using a Bayesian bivariate random-effects meta-analysis model

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[11]. We used the Bayesian bivariate model instead of frequentist methods because the latter may have unstable estimates when the study number is small. The Bayesian metaanalyses were implemented using non-informative priors. Results are presented separately for NSE and S-100B. The bivariate approach models the logit-transformed sensitivity and specificity, and adjusts for the negative correlation between the 9

sensitivity and specificity of the index test that may arise from different thresholds used in different studies [7,12]. We also used the model to construct a hierarchical summary receiver-operating characteristic (HSROC) curve with credible and prediction regions as a way to summarise the true- and false-positive rates from different diagnostic studies [13]. We quantified the extent of between-study heterogeneity by calculating I2

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statistics. We used Deek’s test to assess possible publication bias [7]. Subgroup analyses were planned a priori to investigate potential sources of heterogeneity.

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Subgroups that included at least five studies per stratum were eligible for quantitative synthesis.

with

the

meta4diag

package

(https://CRAN.R-

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www.r-project.org)

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Bayesian statistical analyses were conducted using R 3.2.1 software (R Foundation;

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project.org/package=meta4diag/), while I2 and test of publication bias were done using STATA version 14 (StataCorp, College Station, TX, USA) and OpenMeta [Analyst]

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(http://www. cebm.brown.edu/openmeta/). All statistical tests were two-sided, with

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significance defined as p < 0.05.

Results

Study characteristics and quality assessment We included 42 studies [14-55] involving 4806 patients (Supplemental Figure 1).

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As shown in Table 1 and Supplemental Table 1, the median cohort size across included studies was 68 patients. The first quartile of the OHCA proportions was 69% and the second quartile of the TTM proportions was 71%. Therefore, we determined that if the proportion of OHCA or TTM exceeded 70% in a given cohort, the feature of OHCA or TTM was attributed to the cohort. Therefore, 22 studies (52%) were categorized as

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OHCA cohort and 19 studies (45%) as TTM cohort (Table 1 Supplemental Table 1). Most of the cut-off values for NSE and S-100B were calculated statistically to identify

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the threshold having the highest specificity rather than being prespecified.

Quality assessments using QUADAS-2 criteria are summarised in Supplemental

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Figure 2. Only 9 studies [15, 29, 31, 36, 38, 39, 46, 47, 49] used pre-determined cut-off

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values of NSE or S-100B to calculate the prognostic accuracy (Supplemental Table 1).

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Despite that the measurements of these biomarkers were objective tests, absence of prespecified cut-off points may still introduce risk of bias according to the QUADAS-

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2 criteria. Therefore, in the domain of index test, the risk of bias was judged as high

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risk for the studies without prespecifying cut-off points (Supplemental Figure 2). Twenty-three studies (54.8%) had unclear risk of bias in use of the reference standard (neurological outcomes) because whether neurological outcomes were assessed without knowledge of the results of the index tests was not explicitly stated.

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Quantitative synthesis Figures 1 and 2 demonstrate forest plots of the sensitivity and specificity of NSE and S-100B reported in the 42 included studies, respectively. Summary estimates of all prognostic accuracy measures are shown in Table 2. The overall pooled sensitivity of NSE was 0.56 (95% CrI, 0.47–0.65) and specificity was 0.99 (95% CrI, 0.98–1.00).

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The overall pooled sensitivity of S-100B was 0.63 (95% CrI, 0.46–0.78) and specificity was 0.97 (95% CrI, 0.92–1.00). The HSROC curves, together with bivariate summary

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points of sensitivity and specificity with their 95% credible regions for both NSE and

S-100B, are shown in Figure 3. The heterogeneity for the overall summary estimates of

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NSE and S-100B was low and publication bias was not significant.

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The results of subgroup analyses examining the prognostic accuracy of NSE and

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S-100B in selected populations are presented in Table 2. Basically, there were no obvious differences between NSE and S-100B in specificity across different subgroups.

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Because there were less than five studies in some subgroups, the data were not pooled.

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Discussion

Main findings

This systematic review identified 42 studies with 4806 patients examining the prognostic accuracy of NSE and S-100B for neurological outcomes among post-cardiac

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arrest patients. The meta-analysis indicated that the overall prognostic performance for NSE and S-100B was similarly excellent (AUC: 0.91 for both biomarkers) with high specificity (NSE: 0.99; S-100B: 0.97) for predicting poor neurological outcomes. Furthermore, the subgroup analyses revealed that for both biomarkers, the specificity was consistently high for different populations, different timings of test performance,

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and different timings of outcome assessment.

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Comparisons with previous studies

A previous meta-analysis [57] revealed that the pooled sensitivity and specificity

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of NSE level >33 µg/L were 0.51 and 0.88, respectively, for post-cardiac arrest patients

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receiving TTM. The cut-off point of 33 µg/L for NSE was mainly identified before the

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era of TTM [58]. Because TTM had been shown to improve neurological outcomes [59,60], the prognostic performance of a specific cut-off point for NSE or S-100B may

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change over time. As shown in Supplemental Table 1, the used cut-off points for NSE

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and S-100B varied considerably because these cut-off points were calculated post-hoc through statistical analysis with an aim to maximise specificity, which accordingly contributed to the substantial variability in sensitivity across included studies (Figures 1 and 2) and might artificially inflate the specificity. The nature of the current metaanalysis is based on reported aggregated results, so we cannot derive an optimal cut-off 13

point. This work can only be done in an individual patient data meta-analysis. Before the availability of such data, each hospital should establish its own cut-off values of these biomarkers according to the test kits used, as recommended by guidelines [4]. Furthermore, because S-100B was less documented than NSE in previous studies, European

guidelines

[4]

suggest

NSE

as

the

preferred

biomarkers

in

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neuroprognostication. Nevertheless, our meta-analysis indicated that the prognostic performance of S-100B was comparable to NSE. Because of the relatively small

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number of included studies assessing S-100B, we chose a Bayesian bivariate model to synthesise the evidence. Traditionally, diagnostic test meta-analysis is performed using

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frequentist methods; however, these methods are generally considered less robust than

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Bayesian techniques at accounting for study heterogeneity. Bayesian random-effects

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models are superior to frequentist random-effects models with respect to estimating between-study variance, because they do not ignore the imprecision of variance

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estimates. Therefore, S-100B can be considered as an alternative to NSE if NSE is not

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available. However, this should be verified in future studies.

Interpretation of subgroup analyses Most studies investigated the prognostic performance of NSE or S-100B among OHCA patients (Table 2); for IHCA patients, more studies should be conducted to 14

validate their use. In comparison with clinical examination and electroencephalography, tests of biomarkers are less likely to be affected by targeted temperatures or sedatives. Therefore, prognostic performance did not differ considerably between patients receiving and not receiving TTM. The half-life for NSE and S-100B is approximately 24 and 2 hours, respectively.

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Therefore, it is expected that S-100B peaks earlier in the circulation after hypoxicischaemic brain injury than NSE [61-64] and most studies investigated the prognostic

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performance of early S-100B or late NSE measurement for post-cardiac arrest patients.

Regardless of the timing of measurement for NSE or S-100B, the specificity was

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consistently high (Table 2), except that the number of studies assessing late S-100B

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measurement was not large enough for quantitative synthesis. Studies [65,66] have

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indicated that increased S-100B level was implicated in the process of neuronal apoptosis at the early post-cardiac arrest phase while NSE may serve as a marker of

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neuronal injury or death as a consequence of hypoxic-ischaemic damage [67]. Early S-

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100B measurement and late NSE measurement may be complementary in neuroprognosis, and should be further examined. The pooled specificity of NSE and S-100B for prediction of late neurological

outcomes was quite high with low heterogeneity (Table 2). However, heterogeneity was moderately high for prediction of early outcomes. It was reported that approximately 15

50% of OHCA patients who were discharged with unfavourable neurological outcomes after TTM did recover favourable neurological status at 6–12 months after discharge [68]. Optimal timing for evaluating neurological outcome after cardiac arrest has yet to be established. It may be suitable to assess neurological outcome at a longer follow-up

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timing [56], which allows opportunity for observing improvement.

Clinical applications

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There are no tests with 100% specificity (false-positive rate of 0) in clinical practice. False-positive rates lower than 5% with narrow confidence intervals are

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generally accepted as reliable [69]. As shown by the Fagan nomogram (Supplemental

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Figure 3), when pre-test probability could be objectively estimated, both NSE and S-

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100B tests could efficiently help identify patients with high probability of poor neurological recovery.

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It should also be emphasised that although the specificity of both NSE and S-100B

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tests is high, these tests should not be used alone for prognostication, as recommended by American [70] and European [4] guidelines. However, the optimal combination of prognostic modalities has not been verified. Various risk-stratification scores have been developed and validated for post-cardiac arrest patients, such as cardiac arrest hospital prognosis (CAHP) score for OHCA patients [71,72] and cardiac arrest survival 16

postresuscitation in-hospital (CASPRI) score for IHCA patients [73,74]. These riskstratification scores may also help define the pre-test probability and be used along with NSE and S-100B tests for neuroprognostication.

Study limitations

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First, many of the studies excluded during the literature search were due to insufficient information to build a 2×2 contingency table. Most studies examining the

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prognostic performance of serial NSE or S-100B tests provided only serial sensitivity and specificity, without case number and total participant number provided at

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corresponding time points. Studies reporting diagnostic test accuracy should still follow

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the STARD guidelines [75] to explicitly report contingency tables of the index test

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results by the results of the reference standard at each time point. Second, the metaanalysis could only examine the overall prognostic performance of NSE and S-100B

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tests and their performance across different subgroups. A specific cut-off point for NSE

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or S-100B could not be identified through meta-analysis. Rather, as guideline recommendations [4,70], each hospital should establish its own normal values and cutoff points for these biomarkers. Third, like most classification systems for neurological function or brain injury, such as cerebral performance category score [76], Glasgow outcome score [77], modified Glasgow outcome score [78], modified Rankin scale[79], 17

death was graded as the worst outcome on the scale in our study. Also, many of the included studies did not explicitly indicate blinding of outcome assessors to the results of index tests when evaluating the reference standard of poor neurologic outcome. Both factors could have biased the assessment, introducing the bias of ‘self-fulfilling prophecies.’ The prognostic performance of these biomarkers may thus been falsely

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exaggerated. Fourth, the proportion of 70% used to categorize a study as OHCA vs IHCA or TTM vs No TTM was determined by distribution of the proportions of a

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certain character. This classification was used to maximize the number of studies

included in the subgroup analysis, which may introduce selection bias. Therefore, the

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results of subgroup analysis may best be viewed as being exploratory rather than

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definite. Fifth, despite that there was no significant statistical heterogeneity for the

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overall synthesized results, there was obvious methodological and clinical heterogeneity across the included studies, such as different timings of measurement or

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definitions of neurological outcomes. Although subgroup analyses had been conducted

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to reduce the confounding effect of each variable, a large-scale prospective study may still be necessary to clarify the prognostic accuracy of these biomarkers in a specific group. Finally, in the process of calculating the pooled prognostic estimates, the data sets with the highest specificity were prioritised to select. We selected to present the pooled accuracy estimate in favour of specificity with an aim to reflect the use of these 18

biomarkers in clinical practice [4, 70]. A marker with high specificity and low false negative can help clinician to make a difficult clinical decision.

Conclusions Prognostic performance was comparable between NSE and S-100B, both of which

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demonstrated high specificity in predicting poor neurological outcome for post-cardiac arrest patients. The prognostic performance may not be influenced by different

populations, different timings of test performance, and different timings of outcome

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assessment. Both NSE and S-100B may be integrated into a multimodal

Conflicts of Interests

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neuroprognostication algorithm for post-cardiac arrest patients.

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The authors declare that they have no conflict of interest.

Acknowledgments

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We thank the staff of the 3rd Core Lab, Department of Medical Research, National Taiwan University Hospital for technical support. Author Chih-Hung Wang recieved a grant (108-S4091) from the National Taiwan University Hospital. National Taiwan University Hospital had no involvement in designing the study, collecting, analysing or

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47. Rossetti AO, Tovar Quiroga DF, Juan E, Novy J, White RD, Ben-Hamouda N, et al. Electroencephalography Predicts Poor and Good Outcomes After Cardiac Arrest: A Two-Center Study. Crit Care Med 2017;45:e674-82. 27

48. Streitberger KJ, Leithner C, Wattenberg M, Tonner PH, Hasslacher J, Joannidis M, et al. Neuron-Specific Enolase Predicts Poor Outcome After Cardiac Arrest and Targeted Temperature Management: A Multicenter Study on 1,053 Patients. Crit Care Med 2017;45:1145-51. 49. Tsetsou S, Novy J, Pfeiffer C, Oddo M, Rossetti AO. Multimodal Outcome

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52. Jang JH, Park WB, Lim YS, Choi JY, Cho JS, Woo JH, et al. Combination of

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S100B and procalcitonin improves prognostic performance compared to either alone in patients with cardiac arrest: A prospective observational study. Medicine (Baltimore) 2019;98:e14496. 53. Kim JH, Kim MJ, You JS, Lee HS, Park YS, Park I, et al. Multimodal approach for neurologic prognostication of out-of-hospital cardiac arrest patients undergoing 28

targeted temperature management. Resuscitation 2019;134:33-40. 54. Park JH, Wee JH, Choi SP, Oh JH, Cheol S. Assessment of serum biomarkers and coagulation/fibrinolysis markers for prediction of neurological outcomes of out of cardiac arrest patients treated with therapeutic hypothermia. Clin Exp Emerg Med 2019;6:9-18.

Neuron-specific-enolase

as

a

predictor

of

the

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outcome

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56. Becker LB, Aufderheide TP, Geocadin RG, Callaway CW, Lazar RM, Donnino

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MW, et al. Primary outcomes for resuscitation science studies: a consensus statement

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57. Golan E, Barrett K, Alali AS, Duggal A, Jichici D, Pinto R, et al. Predicting neurologic outcome after targeted temperature management for cardiac arrest:

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58. Zandbergen EG, Hijdra A, Koelman JH, Hart AA, Vos PE, Verbeek MM, et al. Prediction of poor outcome within the first 3 days of postanoxic coma. Neurology 2006;66:62-8.

59. Bernard SA, Gray TW, Buist MD, Jones BM, Silvester W, Gutteridge G, et al. Treatment of comatose survivors of out-of-hospital cardiac arrest with induced 29

hypothermia. N Engl J Med 2002;346:557-63. 60. Hypothermia after Cardiac Arrest Study Group. Mild therapeutic hypothermia to improve the neurologic outcome after cardiac arrest. N Engl J Med 2002;346:549-56. 61. Bottiger BW, Mobes S, Glatzer R, Bauer H, Gries A, Bärtsch P, et al. Astroglial protein S-100 is an early and sensitive marker of hypoxic brain damage and outcome

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63. Mortberg E, Zetterberg H, Nordmark J, Blennow K, Rosengren L, Rubertsson S.

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S-100B is superior to NSE, BDNF and GFAP in predicting outcome of resuscitation from cardiac arrest with hypothermia treatment. Resuscitation 2011;82:26-31.

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64. Minami T, Sainte S, De Praetere H, Rega F, Flameng W, Verbrugghe P, et al.

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Hospital cost savings and other advantages of sutureless vs stented aortic valves for intermediate-risk elderly patients. Surg Today 2017;47:1268-73. 65. Bianchi R, Adami C, Giambanco I, Donato R. S100B binding to RAGE in microglia stimulates COX-2 expression. J Leukoc Biol 2007;81:108-18. 66. Van Eldik LJ, Wainwright MS. The Janus face of glial-derived S100B: beneficial 30

and detrimental functions in the brain. Restor Neurol Neurosci 2003; 21:97-108. 67. Marangos PJ, Schmechel DE. Neuron specific enolase, a clinically useful marker for neurons and neuroendocrine cells. Annu Rev Neurosci 1987;10:269-95. 68. Terman SW, Hume B, Meurer WJ, Silbergleit R. Impact of presenting rhythm on short- and long-term neurologic outcome in comatose survivors of cardiac arrest treated

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71. Maupain C, Bougouin W, Lamhaut L, Deye N, Diehl JL, Geri G, et al. The CAHP (Cardiac Arrest Hospital Prognosis) score: a tool for risk stratification after out-ofhospital cardiac arrest. Eur Heart J 2016;37:3222-8. 72. Wang CH, Huang CH, Chang WT, Tsai MS, Yu PH, Wu YW, et al. Prognostic performance of simplified out-of-hospital cardiac arrest (OHCA) and cardiac arrest 31

hospital prognosis (CAHP) scores in an East Asian population: a prospective cohort study. Resuscitation 2019;137:133-9. 73. Chan PS, Spertus JA, Krumholz HM, Berg RA, Li Y, Sasson C, et al: A validated prediction tool for initial survivors of in-hospital cardiac arrest. Arch Intern Med 2012;172:947-53.

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75. Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, et al.

Becker LB, Aufderheide TP, Geocadin RG, Callaway CW, Lazar RM, Donnino

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76.

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studies. BMJ 2015;351:h5527.

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STARD 2015: an updated list of essential items for reporting diagnostic accuracy

MW, et al. Primary outcomes for resuscitation science studies: a consensus statement

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from the American Heart Association. Circulation 2011;124: 2158-77.

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77. Jennett B, Bond M. Assessment of outcome after severe brain damage. Lancet 1975; 1: 480-4. 78. Rana OR, Schröder JW, Kühnen JS, Saygili E, Gemein C, Zink MDH, et al. The Modified Glasgow Outcome Score for the prediction of outcome in patients after cardiac arrest: a prospective clinical proof of concept study. Clin Res Cardiol 32

2012;101:533-43. 79. Wilson JTL, Hareendran A, Grant M, Baird T, Schulz UGR, Muir KW, et al. Improving the assessment of outcomes in stroke: use of a structured interview to assign grades on the modified Rankin Scale. Stroke 2002;33:2243-6.

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Figure 1. Forest plots of sensitivity (top) and specificity (bottom) for NSE. CrI, credible interval; FN, false-negative; FP, false-positive; TN, true-negative; TP, true-positive.

Jo

ur

na

lP

re

-p

(TIFF)

33

ro of -p re lP na ur Jo

Figure 2. Forest plots of sensitivity (top) and specificity (bottom) for S-100B. CrI, credible interval; FN, false-negative; FP, false-positive; TN, true-negative; TP, truepositive. (TIFF)

34

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-p

Figure 3. Hierarchical summary receiver-operating characteristic (HSROC) curves and

Jo

ur

na

lP

NSE (top) and S-100B (bottom).

re

bivariate summary points of (specificity, sensitivity) and their 95% credible regions for

35

36

ro of

-p

re

lP

na

ur

Jo

Table 1. Summary characteristics of the included 42 studies Frequency, n (%)b

Description Continent America

3 (7)

Europe

27 (64)

Asia

7 (17)

Australia/ Oceania

3 (7)

Cross-continent

2 (5)

Before 2005

10 (24)

2006-2010

5 (12)

2011-2015

14 (33)

2016-2019

13 (31)

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Study design Prospective cohort

26 (57)

Retrospective cohort

12 (29)

68 (43-127)

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Median sample size (IQR), n Population

22 (52)

IHCA (IHCA patients >70% of study cohort)

2 (5)

TTM (TTM performed in >70% of study

19 (45)

na

lP

OHCA (OHCA patients >70% of study cohort)

cohort)

No TTM (TTM performed in <30% of study

ur

cohort)

ro of

Year of publication

15 (36)

Blood biomarkders tested

29 (69)

S-100B

6 (14)

Both NSE and S-100B

7 (17)

Jo

NSE

Timing of measurement after cardiac arresta Early NSE measurement

6 (14)

Late NSE measurement

30 (71)

Early S-100B measurement

10 (24)

Late S-100B measurement

3 (7) 37

Cut-off value (IQR), µg/L NSE

45.95 (28.95-68.05)

S-100B

0.7 (0.3-1.25)

Cerebral performance category scale

28 (67)

Modified Rankin scale

1 (2)

Glasgow outcome score

8 (19)

Modified Glasgow outcome score

1 (2)

Glasgow coma scale

1 (2)

No formal scale used

3 (7)

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Definition of poor neurological outcome

Timing of outcome assessment after arrest Early outcome

19 (45)

Late outcome

23 (55)

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IHCA: in-hospital cardiac arrest; IQR: interquartile range; NSE: neuron specific enolase; OHCA: outof-hospital cardiac arrest; TTM:targeted temperature management a

Early and late measurements were defined as measurement time within and beyond 24 hours of

Unless otherwise indicated.

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b

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cardiac arrest, respectively.

Table 2. Summary Estimates of Overall and Subgroup Analyses for NSE and S-100B Studies

Sensitivit

Specificit

AUC

Positive

Negative

I2

Publicatio

(patients

y

y

(95%

likelihoo

likelihoo

(%)

n

),

(95%

(95%

CrI)

d ratio

d ratio

CrI)

CrI)

(95%

(95%

CrI)

CrI)

na

Variable

ur

n (n)

bias*

Overall

36

0.56

0.99

0.91

102.73

0.44

22.

(4508)

(0.47-

(0.98-

(0.9-

(34.69-

(0.34-

4

0.65)

1.00)

0.91)

648.75)

0.54)

13

0.63

0.97

0.91

23.88

0.39

16.

(677)

(0.46-

(0.92-

(0.89

(7.23-

(0.21-

1

0.78)

1.00)

-

212.62)

0.56)

Jo

NSE (14-

16, 20-32,

34-36, 38-

0.06

51, 53-55) S-100B (15, 17-19, 21-23, 27,

0.69

38

33, 37, 43,

0.93)

52, 54) Population NSE for OHCA (14,

17

0.53

0.99

0.97

78.00

0.48

(2494)

(0.34-

(0.98-

(0.95

(19.98-

(0.27-

0.71)

1.00)

-

769.48)

0.69)

16, 21, 2729, 34, 39,

0

NA

0.98)

40, 42, 45, 46, 48-50,

for OHCA

8

0.72

0.97

0.95

27.65

(388)

(0.52-

(0.91-

(0.92

(7.16-

0.87)

1.00)

-

441.00)

(17-19, 21, 27, 33, 52,

0.99)

54) 0.52

1.00

0.97

(2586)

(0.32-

(0.99-

(0.97

0.71)

1.00)

29, 32, 3436, 38, 40-

51, 53-55) 5

(329)

(15, 17-19, 21-23)

(0.28-

48791.7)

0.71)

5.3

NA

NA

42.96

0.44

33.

(0.30-

(0.91-

(0.87

(5.56-

(0.18-

4

0.80)

1.00)

-

3019.48)

0.72)

0.97) 0.99

0.91

41.70

0.47

(844)

(0.44-

(0.96-

(0.89

(12.64-

(0.36-

0.64)

1.00)

-

367.52)

0.58)

26, 28, 49)

for No TTM

(48.14-

0.54

(14-16, 20-

S-100B

0.48

12

Jo

No TTM

0.53)

0.92

52, 54)

NSE for

6

0.99

ur

(27, 33, 37,

0.57

na

for TTM

(0.10-

NA

0.97)

42, 48, 50,

S-100B

-

38.

410.94

re

TTM (27,

16

lP

NSE for

0.29

-p

S-100B

ro of

53, 54)

0

NA

0

NA

0.92)

7

0.62

0.97

0.97

19.59

0.40

(318)

(0.37-

(0.91-

(0.88

(6.08-

(0.15-

0.84)

0.99)

-

125.08)

0.66)

1.00) 39

Timing of test performan ce Early NSE

6

0.56

0.97

0.64

19.53

0.46

50.

(342)

(0.45-

(0.82-

(0.59

(2.81-

(0.33-

4

0.67)

1.00)

-

500.42)

0.61)

measureme nt (14, 15,

NA

0.68)

21, 22, 43,

Early S100B

10

0.70

0.98

0.93

36.36

(403)

(0.52-

(0.92-

(0.91

(7.67-

0.85)

1.00)

-

827.55)

measureme

0.95)

19, 21, 22,

52) 0.56

1.00

(4166)

(0.44-

(0.99-

0.66)

1.00)

nt (16, 20, 23-32, 34-

3

0.53)

134.14

0.45

(0.92

(35.41-

(0.33-

-

1244.63)

0.57)

0

NA

na

44-51, 54,

ur

55)

100B

(0.13-

NA

0.93)

36, 38-42,

Late S-

16.

0.92

lP

measureme

30

re

27, 33, 43,

Late NSE

0.30

-p

nt (15, 17-

ro of

53)

3

NAe

NA

NA

NA

NA

NA

NA

0.59

0.99

0.93

53.32

0.42

44.

NA

(274)

Jo

measureme nt (23, 37, 54)

Timing of outcome assessment NSE for

16

40

early

(2434)

outcome

(0.40-

(0.96-

(0.92

(14.99-

(0.25-

0.75)

1.00)

-

539.37)

0.63)

(14, 20, 23,

5

0.94)

25, 28, 35, 36, 39, 4345, 48, 50, 53-55)

for early

6

0.80

0.88

0.91

6.44

0.23

45.

(335)

(0.53-

(0.67-

(0.85

(2.19-

(0.05-

3

0.95)

0.97)

-

29.69)

0.57)

outcome (17, 19, 23,

0.97)

33, 43, 54)

late

20

0.54

1.00

0.88

230.89

(2074)

(0.44-

(0.99-

(0.87

(34.80-

0.63)

1.00)

-

outcome (15, 16, 21,

0.88)

34, 38, 4042, 46, 47, 49, 51)

(342)

NA

0

NA

(0.36-

10514.9)

0.56)

1.00

1.00

133.40

0.52

(0.32-

(0.97-

(0.9-

(12.30-

(0.34-

0.65)

1.00)

1.00)

8534.33)

0.69)

ur

outcome

0.49

na

for late

7

lP

27, 29-32,

S-100B

0

re

22, 24, 26,

0.46

-p

NSE for

NA

ro of

S-100B

(15, 18, 21, 22, 27, 37,

Jo

52)

AUC: area under the receiver operating characteristic curve; CrI: credible interval; NA: not available; NSE: neuron specific enolase; OHCA: out-of-hospital cardiac arrest; TTM:targeted temperature management *

Deek’s test p-value

41