Skip to main content
NCBI home page
PLOS One logoLink to PLOS One
. 2019 Dec 27;14(12):e0226295. doi: 10.1371/journal.pone.0226295

Prediction of poor outcome after hypoxic-ischemic brain injury by diffusion-weighted imaging: A systematic review and meta-analysis

Ruili Wei 1,#, Chaonan Wang 2,#, Fangping He 1, Lirong Hong 3, Jie Zhang 4, Wangxiao Bao 1, Fangxia Meng 1, Benyan Luo 1,*
Editor: Chiara Lazzeri5
PMCID: PMC6934311  PMID: 31881032

Abstract

Accurate prediction of the neurological outcome following hypoxic–ischemic brain injury (HIBI) remains difficult. Diffusion-weighted imaging (DWI) can detect acute and subacute brain abnormalities following global cerebral hypoxia. Therefore, DWI can be used to predict the outcomes of HIBI. To this end, we searched the PubMed, EMBASE, and Cochrane Library databases for studies that examine the diagnostic accuracy of DWI in predicting HIBI outcomes in adult patients between January1995 and September 2019. Next, we conducted a comprehensive meta-analysis using the Meta-DiSc and several complementary techniques. Following the application of inclusion and exclusion criteria, a total of 28 studies were included with 98 data subsets. The overall sensitivity and specificity, with 95% confidence interval, were 0.613(0.599–0.628) and 0.958(0.947–0.967), respectively, and the area under the curve was 0.9090. Significant heterogeneity among the included studies and a threshold effect were observed (p<0.001). Different positive indices were the major sources for the heterogeneity, followed by the anatomical region examined, both of which significantly affected the prognostic accuracy. In conclusion, we demonstrated that DWI can be an instrumental modality in predicting the outcome of HIBI with good prognostic accuracy. However, the lack of clear and generally accepted positive indices limits its clinical application. Therefore, using more reliable positive indices and combining DWI with other clinical predictors may improve the diagnostic accuracy of HIBI.

Introduction

Hypoxic–ischemic brain injury (HIBI) occurs secondary to multiple events that cause hypoxia or hypoperfusion like cardiac arrest, respiratory failure, hanging, drowning or severe hypotension as a result of oxygen and nutrient deprivation [1]. Despite recent heath care advances, HIBI remains one of the principle causes of death and long-term disability worldwide. Specifically, the toll of the neurological recovery, possible complications and rehabilitation imposes a huge socioeconomic burden on individuals as well as the health care system as a whole [2, 3]. Therefore, identifying patients who can likely achieve a favorable or poor neurological outcome will significantly impact the patient prognosis and facilitate informed health care decisions.

Diminished brain-stem or extensor reflex, day three motor response, and day one cortical somatosensory evoked potentials (SSEPs), as well as serum neuron specific enolase (NSE) during the first three days and early myoclonic status epilepticus were used to predict poor HIBI outcome [4–6]. However, the emerging use of therapeutic hypothermia for the management of comatose cardiac arrest patients has decreased the utility of the above mentioned markers[7–10]. Particularly, therapeutic hypothermia involves the use of sedatives and neuromuscular blockers during the induction and normothermia phases which render the prognostic predictors less reliable, especially those based on clinical examination [7, 11]. Therefore, developing a more accurate assessment of early-stage HIBI patients is urgently needed.

Neuroimaging approaches like magnetic resonance imaging (MRI), Diffusion-weighted imaging (DWI), and computed tomography (CT) are commonly used diagnostic techniques for exploring brain structure and function [12]. Nevertheless, CT and conventional MRI frequently underestimate the degree of brain injury in acute HIBI [13, 14]. On the other hand, DWI provides a more accurate diagnostic alternative in acute or subacute HIBI [15] and enables precise estimation of disease degree by calculating the apparent diffusion coefficient (ADC) [16, 17]. Moreover, DWI has been proven valuable in therapeutic hypothermia or sedated patients [18, 19]. Previous studies have investigated the diagnostic and prognostic value of DWI in HIBI; however, the sensitivity and specificity of DWI as a clinical tool were inconsistent among the different studies [14, 20–22]. In this study, we performed a meta-analysis of previously published literature to re-evaluate the diagnostic value of DWI in predicting HIBI outcomes.

Methods

Study design

In this study, we performed a comprehensive literature research in PubMed, EMBASE, and the Cochrane Library databases for DWI from January 1995 to September 2019. We examined the diagnostic value of DWI in predicting HIBI outcomes using the following keywords: (“diffusion-weighted magnetic resonance images” or “diffusion magnetic resonance” or “DW-MRI” or “DW magnetic resonance images” or “diffusion-weighted imaging” or “diffusion MRI” or diffusion-weighted MRI”) and (“anoxia” or “ischemia” or “hypoxia” or “heart arrest” or “cardiac arrest” or “postoperative complication” or “respiratory insufficiency” or “resuscitation” or “drowning”) and (“prognosis” or “outcome”). In addition, we also examined the reference section of all examined articles for additional reports. In some cases, we had to contact the corresponding authors to seek the original data sets if the necessary information could not be extracted online. From each study, we gathered and analyzed the following information: patients’ baseline demographic characteristics (gender, age, hypothermia treatment and outcome assessment), study design (prospective or retrospective), experimental protocol, elapsed interval between HIBI and brain MRI, DWI imaging protocol (magnetic field strength, b-value, and positive indices), and the diagnostic results (i.e. the true-positive, false-positive, false-negative, and true-negative results).

In order to investigate the predictive power of DWI on HIBI outcome, we only analyzed studies that examined the neurological outcome in terms of the five cerebral performance categories (CPCs) or an equivalent [5, 23]. A CPC score of 1 indicated full recovery; 2 indicated moderate disability, 3 indicated severe neurological disability with preserved consciousness, 4 indicated comatose or vegetative state patients, 5 indicated death. Next, the outcome was classified into poor and good according to the CPC scores (3–4 or 4–5 versus1-2 or 1–3, respectively).

Inclusion and exclusion criteria

The inclusion criteria included English language clinical prognostic DWI articles that were published in indexed journals and studies investigating adult HIBI patients (>/ = 14 years). Also, we included studies reporting various causes of HIBI, provided that each condition resulted in the common endpoint of generalized cerebral hypoxia or global hypoperfusion. Finally, only studies with complete data sets (i.e.the number of true/false negatives and positives for poor outcome prediction) were included. This was essential to enable the calculation of outcome variables with confidence intervals (CIs). Exclusion criteria included published abstracts, case reports, review articles and studies involving 10 patients or less, as well as patients with HIBI secondary to stroke, trauma, intracranial infection, sepsis, and/or metabolic dysfunction.

We confirmed the quality of the included studies using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool as detailed previously [24]. Two primary investigators were responsible for data collection and quality assessment in an independent manner.

Statistical analysis

A Chi-square test and the inconsistency index (I2) were used to estimate the heterogeneity between enrolled studies. A P < 0.1 or I2 > 50% indicated the presence of heterogeneity[25]. If heterogeneity was recorded, a binary regression model with random coefficients was used to determine the diagnostic performance [26]. The summary receiver operating characteristic (SROC) curve, and area under the curve (AUC) were used to predict the outcome of HIBI [27].

The threshold effect was determined from the “shoulder-arm” shape of the ROC curve [28]. A correlation between the logit of sensitivity and the logit of (1—specificity) was computed by the Spearman correlation coefficient to assess the existence of a threshold effect, and a P < 0.05 indicated a positive threshold effect [29]. Next, we performed a meta-regression analysis and subgroup analysis to investigate factors that could possibly lead to heterogeneity and explored their possible impact on diagnostic accuracy [30].

The analysis of heterogeneity test, the threshold effect and the diagnostic performance, as well as meta-regression and subgroup analyses were all carried out by Meta-DiSc (version 1.4) [31]. On the other hand, publication bias was assessed by an asymmetry test and Deeks’ funnel plot using Stata (version 12.0). An inverted symmetrical funnel plot with P > 0.05 indicated the lack of publication bias [32].

Results

From January 1995 to September 2019, we collected a total of 4042 records from the different data bases. After applying the inclusion and exclusion criteria, a total of 28 studies were included in this meta-analysis (Fig 1).

Fig 1. Flow chart representing the scheme of our study design.

Fig 1

Open in a new tab

Study features and quality assessment

The clinical features and baseline characters of patients in each study examined are presented in Table 1. A total of 1,645 patients (age range between 14 and 89 years) were enrolled from the 28 studies. The average number of patients in each included study was 59 (range 14–172). Among the investigated studies, 14 were conducted prospectively and the remaining 14 studies were retrospective. Five studies collected data from out-of-hospital cardiac arrest (OHCA) patients, while the other 23 studies included OHCA and in-hospital cardiac arrest (IHCA) patients. Hypothermia treatment was administered to all patients in 11 studies (n = 600), and only some patients in the other 13 studies (n = 551). In the remaining 4 studies, hypothermia treatment was not offered or not mentioned to patients (n = 90). Outcome was assessed at hospital discharge, death or within several weeks in 8 studies; at 3 months in 2 studies, and at 6 months or more in 18 studies. A poor outcome was defined as CPC 3–5 in 14 studies or as CPC 4–5 in another 10 studies, and according to other scoring systems in the remaining 4 studies.

Table 1. Study designs and baseline patient characteristics.

Author, year, reference Type IHCA or OHCA No. of patients Males, % Mean age, years [±SD] or median (IQR range) Treatment with
hypothermia		 Definition of poor outcome Timing of outcome assessment
Barrett,2007[33] Retro Mix 18 10(56%) 62 (49~73) no CPC3-5 Death or hospital discharge
Bevers,2018[34] Retro Mix 78 49(63%) 53 ± 17 yes CPC4,5 hospital discharge
Choi,2010 [35] Pro OHCA 39 28(71.8%) 49.1(18~89) 15/39 CPC3-5 3 months
Choi,2018 [36] Pro Mix 14 10(71.4%) 43.4 ± 15.6 8/14 CPC 3–5 at discharge.
Cronberg,2011 [37] Pro Mix 22 N/A N/A yes CPC4-5 6 months
Els,2004 [14] Pro Mix 12 N/A 53 (27~71) no CPC4-5 6 months
Greer,2012 [38] Retro Mix 80 49(61%) 57±16 14/80 mRS5 3 months
Greer,2013 [39] Pro Mix 80 49(61%) 62 (IQR 46–70) 14/80 mRS4-5 6 months
Hirsch,2015 [40] Pro Mix 68 44(64.7%) 56 ± 15 37/68 CPC4-5 6 months
Hirsch,2016[22] Retro Mix 125 82(66%) 58 ± 16 77/125 CPC4-5 Day 14 or
at discharge
Jarnum,2009 [18] Pro Mix 20 11(55%) 57.8(14~81) yes CPC3-5 6 months
Jeon,2017 [41] Retro Mix 39 27 (69%) 52.2±16.5 yes CPC3-5 6 months
Kim,2012[42] Retro OHCA 43 29(67.4%) 57±17.6
 yes CPC3-5 6 months
Kim,2013[43] Retro OHCA 51 38 (74.5) 63(IQR, 42–72) 45/51 CPC3-5 6 months
Kim,2016[44] Retro OHCA 110 83(75.5%) 59 (47–70) 100/110 CPC3-5 6 months
Luyt,2012[45] Pro Mix 57 40 (70%) 52 ± 18 36 /57 GOS-E1-4 12 months
Mettenburg,2016[46] Retro Mix 33 N/A 54(24–80) yes CPC4,5 at discharge
Mlynash,2010[47] Pro Mix 32 23(72%) 55.5±17.3 21/32 CPC4-5 6 months
Moon,2018[48] Pro Mix 96 66 (68%) 52±16 yes CPC3-5 6 months
Oren,2019[49] Retro Mix 38 20(52.6%) 52.8(18–87) N/A CPC4-5 6 months
Park,2015[50] Pro Mix 19 16(84.2%) 54.6±18.7 yes CPC3-5 at discharge
Reynolds,2017[51] Retro Mix 69 37(54%) 60 (IQR 50, 73) 60/69 CPC3-5 at discharge
Ryoo,2015[52] Retro OHCA 172 117(68.0%) 54.7 ± 16.0 yes CPC3-5 at discharge
Topcuoglu,2009[53] Retro Mix 22 14(61%) 56±16.9 no CPC4-5 6 months
Velly,2018[54] Pro Mix 150 97 (65%) 51 ±16 110/150 CPC3-5 6 months
Wallin,2018[55] Pro Mix 46 31 (67%) 68 (IQR 59–76) yes CPC3-5 6 months
Wijman,2009[19] Pro Mix 32 N/A N/A yes CPC4-5 6-month
Wu,2009[21] Retro Mix 80 49(61%) 57±16 14/80 mRS4-5 6 months
Open in a new tab

Note: N/A = data unavailable; Retro = retrospective study; Pro = prospective study; OHCA = out-of-hospital cardiac arrest; IHCA = in-hospital cardiac arrest; Mix = OHCA or IHCA; CPC = cerebral performance categories; GOS-E = expand the Glasgow outcome scale score; mRS = Modified Rankin Scale. IQR = interquartile range.

MRI parameters of each study are presented in Table 2. Briefly, a 1.5-T MRI scanner was used in 15 studies, a 3.0-T MRI scanner was used in 3 studies and both scanners were used in another 9 studies (Table 2). In the final study, the type of scanner was unclear. With respect to b-values in the DWI, a single b-value of 1000 s/mm2 was used in 9 studies; b-values of both 0 s/mm2and 1000 s/mm2 were used in 15 studies. The b-value(s) used were unclear in the remaining 4 studies. The mean elapsed interval between MRI and HIBI ranged from 2 hours to 13 days (Table 2). Further, among the 28 studies, 15 studies used qualitive MRI-positive indices for their analysis, 10 studies used a quantitative index, 1 study used both and the final 3 studies used a semi-quantitative index [40, 50, 54]. Within the same study, multiple sets of data were considered as different DWI-positive indices. Therefore, we had 98 data subsets for meta-analysis (Table 2).

Table 2. Characteristics of the imaging protocol of the enrolled studies.

Study Elapsed interval Field
strength		 b-value
(s/mm2)		 Positive Index Sensitivity Specificity
Barrett,2007 72 h (IQR,22–229) 1.5T 1000s DWI abnormalities 0.700 0.750
Bevers,2018 4 (IQR 3–5) N/A N/A whole brain ADC signal intensity 0.191 1.000
15% total brain volume with ADC signal intensity < 650 mm2/s 0.362 1.000
Choi,2010 52.9h ± 37.5 1.5T 0 /1000s mixed pattern of brain injury 0.769 0.923
mean ADC value of
frontal cortex		 0.714 1.000
parietal cortex 0.857 1.000
temporal cortex 0.643 1.000
occipital cortex 0.929 1.000
precentral cortex 0.857 1.000
postcentral cortex 0.714 1.000
caudate nucleus 0.643 1.000
putamen 0.929 1.000
thalamus 0.857 1.000
Choi,2018 3h 1.5 T 0/1000s HSI on early DWI 0.909 1.000
Cronberg,2011 106h(IQR93-118) 1.5T or 3T 0 /1000s extensive brain injury 0.579 1.000
Els,2004 16 h (4–32) 1.5T N/A multiple cortical areas abnormalities 1.000 1.000
Greer,2012 48 h (IQR 0–10h) 1.5T 0 /1000s any imaging abnormality 0.985 0.462
basal ganglia abnormalities 0.791 0.692
cortical abnormalities 0.955 0.462
cerebellar abnormalities 0.612 0.538
Greer,2013 48 h (IQR 0–10h) 1.5T 0 /1000s bilateral hippocampal hyperintensities 0.273 1.000
Heradstveit,2011 3h 1.5T 0 /1000s DWI abnormalities 0.000 1.000
32h DWI abnormalities 1.000 1.000
96h DWI abnormalities 1.000 1.000
Hirsch,2015 77h (IQR58-144h) 1.5T 0/ 1000s qualitative MRI scoring system 0.600 1.000
DWI score (25~192h) 0.725 1.000
Hirsch,2016 69 h± 25 1.5T 0 /1000s >10% Brain volume with ADC<650x10−6 mm2/s 0.717 0.909
>22% Brain volume with ADC<650x10−6 mm2/s 0.522 1.000
Jarnum,2009 123 h (39–251h) 1.5T or 3T 0 /1000s diffuse signal abnormalities 0.824 1.000
Jeon,2017 175(117.5–240)min 1.5 T 1000s positive high signal on DW-MRI 0.813 1.000
Kim,2012 45.8h(IQR,36.8–52.4) 3.0T 1000s ADC value of frontal cortex 0.625 1.000
parietal cortex 0.656 1.000
temporal cortex 0.563 1.000
occipital cortex 0.906 1.000
precentral cortex 0.656 1.000
postcentral cortex 0.719 1.000
caudate nucleus 0.469 1.000
putamen 0.781 1.000
thalamus 0.625 1.000
cerebellum 0.563 1.000
pons 0.469 1.000
Kim,2013 46 h (IQR,37–52) 3.0T 1000s MCS of frontal region 0.700 1.000
occipital region 0.900 1.000
parietal region 0.825 1.000
rolandic region 0.800 1.000
temporal region 0.625 1.000
BG region 0.750 1.000
LMEAN of frontal region 0.650 1.000
occipital region 0.625 1.000
parietal region 0.625 1.000
rolandic region 0.725 1.000
temporal region 0.550 1.000
BG region 0.500 1.000
LMIN of frontal region 0.725 1.000
occipital region 0.750 1.000
parietal region 0.825 1.000
rolandic region 0.675 1.000
temporal region 0.625 1.000
BG region 0.425 1.000
Kim,2016 53 h(46–72) 1.5T or 3T N/A mean ADC of the entire brain 0.506 1.000
median ADC of the entire brain 0.494 1.000
LADCV 0.747 1.000
DC-LADCV 0.892 1.000
Luyt,2012 11 d(7–17) 1.5 or 3.0T N/A mean diffusivity values in nine grey regions 0.837 0.875
Mettenburg,2016 4d 1.5T 1000s diffuse pattern of restricted diffusion (diffuse brain injury) 0.238 0.917
diffuse pattern of gyral edema 0.429 0.917
restricted diffusion in basal ganglia (any) 0.667 0.917
restricted diffusion in the hippocampi 0.286 1.000
Mlynash,2010 80 h(IQR, 55–117) 1.5T 0 /1000s extensive cortical lesion pattern 0.800 1.000
abnormalities in basal ganglia 0.867 0.500
abnormalities in brainstem 0.200 1.000
Moon,2018 17±14h 3.0T 1000s PV500 > 6.25% 0.720 1.000
17±14h PV400 >2.50% 0.640 1.000
17±14h Mean ADC< = 726× 10−6 mm2/s 0.440 1.000
77±23h PV400>1.66% 0.792 1.000
77±23h Mean ADC< = 627× 10−6 mm2/s 0.208 1.000
Oren,2019 2.9d (1~5d) 1.5 or 3.0T 0 /1000s abnormalities on DWI/ADC 0.815 0.545
Park,2015 2h (1.5–3.3h) 1.5T 0/1000s overall qualitative DWI scores 1.000 1.000
DWI scores of Cortex 0.917 1.000
DWI scores of Cortex + DGN 1.000 1.000
Reynolds,2017 4d (IQR3-6) 1.5 or 3.0T 1000s ≥2.8% diffusion restriction of the entire brain at an ADC of ≤650 × 10−6 mm2/s 0.682 1.000
ADC changes in the thalamus at an ADC threshold of ≤650 × 10−6 mm2/s 0.183 1.000
Ryoo,2015 2.0d [1.0–3.0] 1.5 or 3.0T 1000s positive DWI finding or regional brain injury of frontal cortex 0.729 0.963
parietal 0.814 0.963
temporal 0.686 0.981
occipital 0.771 0.963
basal ganglia or thalamus 0.466 1.000
cerebellum 0.314 1.000
brain stem 0.025 1.000
MRI positive finding 0.864 0.926
Topcuoglu,2009 136.8h±108 1.5T 1000s extensive cortical lesion pattern 0.875 1.000
Velly,2018 13d(7-18d) 1.5 T or 3T 0 /1000s FLAIR-DWI overall score 0.402 1.000
FLAIR-DWI cortex score 0.333 1.000
FLAIR-DWI cortex plus deep grey nuclei score 0.368 1.000
Wallin,2018 4 d(IQR,4–5) 1.5 T or 3T 0 /1000s acute hypoxic-ischemic lesions 0.773 0.625
Wijdicks,2001 144h (24~360) 1.5T 0 /1000s diffuse signal abnormalities 1.000 1.000
Wijman,2009 49–108h 1.5T 0 /1000s >10% brain volume with ADC<650x10−6 mm2/s 0.810 1.000
Wu,2009 2d (IQR 0–10d) 1.5T 0 / 1000s whole-brain median ADC 0.409 1.000
Open in a new tab

Note: DC-LADCV = the relative volume of the dominant (biggest) cluster of the low-ADC voxels. HIS = high signal intensity; LADCV = the relative volume of voxels with ADC values less than the predefined ADC threshold; LMEAN = lowest mean ADC; LMIN = lowest minimum ADC; MCS = Maximum cluster size; PV = % voxels with ADC values below the predefined ADC thresholds.

In 21 studies, MRI analyses were performed in a blinded manner. In one study, the investigators were not blind to the examined groups and it was not indicated in the remaining 6 studies. For ethical reasons, the image analysts were blinded to clinical information and outcome in 21 studies, but the clinical treatment team was blinded to the imaging analysis results in only 3 studies [22, 45, 54]. Further, it is worth mentioning that all of the examined studies had a relatively small study population which may affect the reliability of the results obtained in the current work. Therefore, we performed a quality assessment test using the QUADAS-2 tool (Fig 2, S1 Fig). Among the 28 studies, 13 studies demonstrated patient selection bias risk and applicability concerns. With regards to the index test, a total of 8 studies had bias risk; while 8 studies had applicability concerns (Fig 2, S1 Fig).

Fig 2. Evaluation of the included studies using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool.

Fig 2

Open in a new tab

Bias risk and applicability concerns were analyzed in all studies and categorized into high (red), low (green) and unclear (yellow).

Diagnostic performance

The overall sensitivity and specificity were 0.613 (95% CI, 0.599–0.628) and 0.958 (95% CI, 0.947–0.967), respectively (Fig 3A and 3B). In the SROC analysis, the AUC and Q-index were 0.9090 and 0.8410, respectively, thereby, indicating a good diagnostic accuracy (Fig 3C). For individual studies, the sensitivity ranged from 2.5% to 100%, and their specificity ranged from 46% to 100%. These results indicate a significant heterogeneity among the examined studies (Fig 3A and 3B).

Fig 3. Diagnostic performance of the included studies.

Fig 3

Open in a new tab

A,B: Forest plot demonstrating the sensitivity and specificity of individual studies arranged in alphabetical order. C Summary receiver operating characteristic (SROC) curve, D Receiver operating characteristic (ROC) plane respectively. CI: confidence intervals.

Assessment of study heterogeneity

A significant heterogeneity was detected in the sensitivities and specificities of the included studies (P < 0.001). The ROC curve demonstrated a “shoulder-arm” shape indicative of a threshold effect (Fig 3D). Additional analysis revealed a significant linear correlation between the logit of sensitivity and the logit of (1—specificity) (r = 0.539, P < 0.001), thereby, confirming a threshold effect which resulted in the notable heterogeneity. This led us to hypothesize that different positive indices (cutoff values) were the major source of heterogeneity.

Next, we explored other factors that can cause heterogeneity via meta-regression analysis using the following predictor variables: study type (prospective or retrospective), patient category (OHCA or IHCA), hypothermia treatment (present/absent), poor outcome definition, timeframe for outcome assessment, study bias (blinding), elapsed time until brain MRI, field strength, b-value, test index (qualitative or quantitative), and the examined brain region. The patient category, test index and the examined brain region were selected by multivariate meta-regression analysis as significant predictor variables that can affect heterogeneity.

Subgroup analysis

Next, we carried out a subgroup analysis on the different study subsets (Table 3). Among the examined brain regions, the cortical region had the highest diagnostic accuracy, followed by the basal ganglia region (moderate diagnostic accuracy). While, the other brain regions (cerebellum, brain stem, and hippocampus) showed low diagnostic accuracy (P = 0.0049). Interestingly, the diagnostic accuracy was similar when scanning the cortical regions only and the whole brain.

Table 3. Subgroup analysis among the different study subsets.

Study characteristics No of subsets Pooled sensitivity(95% CI) Pooled specificity(95% CI) P
Total 98 0.613(0.599–0.628) 0.958(0.947–0.967)
Region measured 0.0049
Global 33 0.611(0.587–0.636) 0.951(0.93–0.966)
Cortex 41 0.712(0.689–0.733) 0.973(0.958–0.984)
Basal Ganglia 16 0.582(0.541–0.623) 0.928(0.888–0.958)
Others 8 0.301(0.259–0.344) 0.968(0.931–0.988)
Index test 0.0018
(0.0009)
qualitive 32 0.635(0.612–0.659) 0.904(0.880–0.925)
quantitive 56 0.628(0.608–0.648) 0.995(0.987–0.999)
Semi-quantitive(all) 10 0.472(0.427–0.518) 1.000(0.984–1.000)
Semi-quantitive(<7d) 7 0.739(0.658–0.810) 1.000(0.973–1.000)
Time of MRI examination 0.1426
~1d 9 0.767(0.694–0.829) 1.000(0.960–1.000)
2~6d 85 0.627(0.611–0.642) 0.953(0.941–0.963)
>6d 4 0.425(0.376–0.475) 0.991(0.949–1.000)
OHCA or IHCA 0.0001
OHCA 53 0.646(0.627–0.665) 0.984(0.973–0.991)
IHCA or MIX 45 0.566(0.543–0.589) 0.925(0.903–0.942)
Co-index 0.0008
DWI 98 0.613(0.599–0.628) 0.958(0.947–0.967)
co-index 6 0.862(0.823–0.895) 1.000(0.977–1.000)
Open in a new tab

Note: OHCA = out-of-hospital cardiac arrest; Mix = OHCA or IHCA (in-hospital cardiac arrest)

The pooled data revealed that qualitative and quantitative analysis methods had a similar diagnostic accuracy, while the semi-quantitative analysis had lower diagnostic accuracy (P = 0.0018) [40, 50, 54]. Interestingly, the elimination of Velly et al.[54], in which the DWI examination was performed 6 days after onset (7–18 day), from semi-quantitative group can change the significance of the results. Specifically, the diagnostic accuracy of semi-quantitative group would have been significantly better than that of the qualitive or quantitative index within the first 7 days after HIBI (p = 0.0008).

Further, the MRI examination time is also an important factor affecting the diagnostic accuracy except for test index. The analysis of different time points demonstrated that the diagnostic accuracy of DWI within 6 days of onset was higher than that of after 6 days, but it did not reach statistical significances (p = 0.1426). Moreover, the imaging diagnostic accuracy was higher in the OHCA patients than the IHCA or mixed (OHCA/IHCA) patients (P = 0.0001).

Furthermore, we observed that DWI imaging indices combined with other predictors (co-index) like brain CT[41], EEG[34], motor response[34, 40] or other MRI modalities [54] produced significantly improved diagnostic accuracy (Table 3; P = 0.0008).

Publication bias

There was no evidence of publication bias (P = 0.19) as revealed by the symmetric distribution of diagnostic odds ratio against (effective sample size)-1/2(S2 Fig).

Discussion

In this study, we analyzed the efficiency of DWI in predicting a poor outcome of HIBI. Our meta-analysis results demonstrated that DWI is an accurate imaging tool for predicting HIBI outcome with high specificity (95.9%). On the other hand, individual studies showed significant heterogeneity in terms of sensitivity and specificity. This heterogeneity was primarily attributed to the threshold effect, in addition to the test index, the region imaged and the patients’ categorization (OHCA or IHCA). The different imaging protocols, signal characteristics and anatomic regions measured accounted for different positive indices which affected the overall imaging diagnostic accuracy.

Our meta-analysis results indicated that the diagnostic accuracy varied substantially according to the region being assessed. The cortical region demonstrated the highest diagnostic accuracy, followed by the basal ganglia with moderate accuracy. Therefore, DWI signal abnormalities or ADC reduction can be significantly influenced by the anatomical region examined. Several studies showed that DWI signal abnormalities or ADC reduction were also time dependent [21, 35, 47]. In poor-outcome patients, Mlynash et al. confirmed that cortical structures exhibited the most profound ADC reductions, which were observed as early as 1–2 days after the HIBI and reached a nadir 3–5 days after the HIBI. Therefore, Wijman et al proposed that the ideal prognostic window is between 49 and 108 hours after HIBI [19]. Our subgroup analysis also showed that diagnostic accuracy during the 6 days that follows HIBI was higher than that after the 6 days period. Interestingly, the diagnostic accuracy during the first 24 hours after HIBI was not less stringent than other studies acquiring the results of DWI during the ideal prognostic window. This could be attributed to the use of sensitive indicators, like abnormal high signal presence on DW-MRI during the acute window rather than extensive abnormality.

Since MRI signal is affected by the region and time of detection, it is particularly important to select an appropriate diagnostic strategy and index. Typically, post-ischemic MRI images display cortical or basal ganglia hyperintensity in DWI sequences [56, 57]. Following HIBI, the presence of large or extensive multilobar alterations on DWI MRI images has been correlated with poor outcome [58]. The apparent diffusion coefficient (ADC) value has been widely used to quantitatively assess the progression of ischemia when using DWI. In HIBI, several ADC methods, such as determining the whole-brain ADC value, quantifying the region with low ADC, or calculating the lowest ADC value in a specific brain area have been previously used to predict patient outcomes [21, 43, 47]. However, we observed that different research centers used different predictors. Therefore, there was a lack of clear and generally accepted positive indices, especially for the quantitative indices. Consequently, it was difficult for those indicators to be widely applied among the different research centers. In agreement, the 2015 guidelines of the European Society of Intensive Care Medicine and European Resuscitation Council also highlighted the limitations of studies using MRI to prognosticate following HIBI; noting the lack of homogeneity in radiological definitions of imaging findings [11]. Thus, there is a need for a specific DWI index that can be used concisely in the clinic.

The semi-quantitative method (qualitative MRI scoring system) has been successfully developed as a tool to predict the outcome following perinatal asphyxia, and has been reported to provide an accurate index for HIBI severity following postanoxic coma [40, 59]. Our study showed that qualitative brain MRI scoring system was also good for predicting the outcome of the HIBI and may be an ideal DWI index for clinical use. Future well-designed, large-scale studies should be carried out to confirm the best positive index. The combination of qualitative and quantitative methods, or machine-based auto-analysis can be potential directions for future studies.

Although DWI could predict the outcome of HIBI with good prognostic accuracy, there are still several limitations of solely depending on it. Guidelines from professional societies advocated neuroimaging was recommended only in combination with other predictors [11]. Our pooled data also showed that DWI examination combined with other predictors could improve diagnostic accuracy [34, 40, 41, 54]. Therefore, the integration of DWI data with other prognostic markers such as serial neurological assessments, physiological tests, serum marker levels or other model MRI examination in the future could be instrumental for the prediction of HIBI outcome. This model will ultimately affect the patients’ care strategies.

In conclusion, in this study we performed a systemic review and meta-analysis to assess the ability of DWI in predicting poor outcome in HIBI. Our results indicated that DWI can accurately predict the poor outcome of HIBI. Nevertheless, this meta-analysis had various limitations. First, patients with implanted devices like pacemakers or implantable cardioverter defibrillators (ICDs), or other metallic objects could not undergo the conventional MRI. For example, only 21/514 (4.1%) cardiopulmonary arrest survivors underwent subsequent brain MRIs, which may reflect a patient selection bias [33]. However, this issue was resolved in more recent studies [19, 40].

Second, our meta-analysis included different populations, like OHCA or IHCA patients, or patients who did or did not undergo hypothermia treatment (and the reporting of outcome assessment and the timeframe thereof), and reflects a wide variability in case characteristics. In addition, the strategies for active treatment withdrawal differed between studies. These differences can partly be responsible for the heterogeneity of our results.

Third, the retrospective nature of 14 from the 28 included studies, the relatively small sample size in each individual study and the absence of proper blinding measures in almost all studies (25/28) could have led to studies with a low quality of evidence. Further, the exclusion of non-English articles could have limited the strength of our meta-analysis. Therefore, future research should include more studies to confirm our results and evaluate the predictive value of DWI in global brain anoxia.

Conclusion

Our meta-analysis demonstrated that DWI can accurately predict the outcome of HIBI. However, the diagnostic accuracy is influenced by the region measured and time of MRI acquisition. Furthermore, the lack of clear and generally accepted positive indexes limits its clinical application. The use of a more reliable positive index and combining DWI with other predictors may help to improve the accuracy of diagnosis.

Supporting information

S1 PRISMA Checklist. PRISMA checklist.

(DOC)

Click here for additional data file. (64KB, doc)
S1 Fig. Risk of bias and applicability concerns summary.

Review authors' judgements about each domain for each included study.

(TIF)

Click here for additional data file. (2.4MB, tif)
S2 Fig. DEEKS funnel analysis to assess the publication bias.

(TIFF)

Click here for additional data file. (25.6KB, tiff)

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

This work was financially supported by the Administration of traditional Chinese medicine project of Zhejiang Province (2018ZA067) and National Natural Science Foundation of China (81671143).

References

  • 1.Busl KM, Greer DM. Hypoxic-ischemic brain injury: pathophysiology, neuropathology and mechanisms. NeuroRehabilitation. 2010;26(1):5–13. 10.3233/NRE-2010-0531 . [DOI] [PubMed] [Google Scholar]
  • 2.Becker LB, Ostrander MP, Barrett J, Kondos GT. Outcome of CPR in a large metropolitan area—where are the survivors? Annals of emergency medicine. 1991;20(4):355–61. 10.1016/s0196-0644(05)81654-3 . [DOI] [PubMed] [Google Scholar]
  • 3.Blackhall LJ, Ziogas A, Azen SP. Low survival rate after cardiopulmonary resuscitation in a county hospital. Archives of internal medicine. 1992;152(10):2045–8. . [PubMed] [Google Scholar]
  • 4.Wijdicks EF, Hijdra A, Young GB, Bassetti CL, Wiebe S, Quality Standards Subcommittee of the American Academy of N. Practice parameter: prediction of outcome in comatose survivors after cardiopulmonary resuscitation (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology. 2006;67(2):203–10. 10.1212/01.wnl.0000227183.21314.cd . [DOI] [PubMed] [Google Scholar]
  • 5.Sandroni C, Cariou A, Cavallaro F, Cronberg T, Friberg H, Hoedemaekers C, et al. Prognostication in comatose survivors of cardiac arrest: an advisory statement from the European Resuscitation Council and the European Society of Intensive Care Medicine. Resuscitation. 2014;85(12):1779–89. 10.1016/j.resuscitation.2014.08.011 . [DOI] [PubMed] [Google Scholar]
  • 6.Sandroni C, D'Arrigo S. Neurologic Prognostication: Neurologic Examination and Current Guidelines. Seminars in neurology. 2017;37(1):40–7. 10.1055/s-0036-1593857 . [DOI] [PubMed] [Google Scholar]
  • 7.Samaniego EA, Mlynash M, Caulfield AF, Eyngorn I, Wijman CA. Sedation confounds outcome prediction in cardiac arrest survivors treated with hypothermia. Neurocritical care. 2011;15(1):113–9. 10.1007/s12028-010-9412-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Blondin NA, Greer DM. Neurologic prognosis in cardiac arrest patients treated with therapeutic hypothermia. The neurologist. 2011;17(5):241–8. 10.1097/NRL.0b013e318224ee0e . [DOI] [PubMed] [Google Scholar]
  • 9.Samaniego EA, Persoon S, Wijman CA. Prognosis after cardiac arrest and hypothermia: a new paradigm. Current neurology and neuroscience reports. 2011;11(1):111–9. 10.1007/s11910-010-0148-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.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 hypothermia. The New England journal of medicine. 2002;346(8):557–63. 10.1056/NEJMoa003289 . [DOI] [PubMed] [Google Scholar]
  • 11.Nolan JP, Soar J, Cariou A, Cronberg T, Moulaert VR, Deakin CD, et al. European Resuscitation Council and European Society of Intensive Care Medicine Guidelines for Post-resuscitation Care 2015: Section 5 of the European Resuscitation Council Guidelines for Resuscitation 2015. Resuscitation. 2015;95:202–22. 10.1016/j.resuscitation.2015.07.018 . [DOI] [PubMed] [Google Scholar]
  • 12.Bandettini PA. What's new in neuroimaging methods? Annals of the New York Academy of Sciences. 2009;1156:260–93. 10.1111/j.1749-6632.2009.04420.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Kim SH, Choi SP, Park KN, Youn CS, Oh SH, Choi SM. Early brain computed tomography findings are associated with outcome in patients treated with therapeutic hypothermia after out-of-hospital cardiac arrest. Scand J Trauma Resusc Emerg Med. 2013;21:57 10.1186/1757-7241-21-57 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Els T, Kassubek J, Kubalek R, Klisch J. Diffusion-weighted MRI during early global cerebral hypoxia: A predictor for clinical outcome? Acta neurologica Scandinavica. 2004;110(6):361–7. 10.1111/j.1600-0404.2004.00342.x [DOI] [PubMed] [Google Scholar]
  • 15.Gonzalez RG, Schaefer PW, Buonanno FS, Schwamm LH, Budzik RF, Rordorf G, et al. Diffusion-weighted MR imaging: diagnostic accuracy in patients imaged within 6 hours of stroke symptom onset. Radiology. 1999;210(1):155–62. 10.1148/radiology.210.1.r99ja02155 . [DOI] [PubMed] [Google Scholar]
  • 16.Haku T, Miyasaka N, Kuroiwa T, Kubota T, Aso T. Transient ADC change precedes persistent neuronal death in hypoxic-ischemic model in immature rats. Brain research. 2006;1100(1):136–41. 10.1016/j.brainres.2006.05.018 . [DOI] [PubMed] [Google Scholar]
  • 17.Desmond PM, Lovell AC, Rawlinson AA, Parsons MW, Barber PA, Yang Q, et al. The value of apparent diffusion coefficient maps in early cerebral ischemia. AJNR American journal of neuroradiology. 2001;22(7):1260–7. Epub 2001/08/11. . [PMC free article] [PubMed] [Google Scholar]
  • 18.Järnum H, Knutsson L, Rundgren M, Siemund R, Englund E, Friberg H, et al. Diffusion and perfusion MRI of the brain in comatose patients treated with mild hypothermia after cardiac arrest: a prospective observational study. Resuscitation [Internet]. 2009; 80(4):[425–30 pp.]. Available from: http://cochranelibrary-wiley.com/o/cochrane/clcentral/articles/621/CN-00702621/frame.html. 10.1016/j.resuscitation.2009.01.004 [DOI] [PubMed] [Google Scholar]
  • 19.Wijman CAC, Mlynash M, Caulfield AF, Hsia AW, Eyngorn I, Bammer R, et al. Prognostic value of brain diffusion- after weighted imaging cardiac arrest. Annals of neurology. 2009;65(4):394–402. 10.1002/ana.21632 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Wijdicks EF, Campeau NG, Miller GM. MR imaging in comatose survivors of cardiac resuscitation. AJNR American journal of neuroradiology. 2001;22(8):1561–5. Epub 2001/09/18. . [PMC free article] [PubMed] [Google Scholar]
  • 21.Wu O, Sorensen AG, Benner T, Singhal AB, Furie KL, Greer DM. Comatose patients with cardiac arrest: Predicting clinical outcome with diffusion-weighted MR imaging. Radiology. 2009;252(1):173–81. 10.1148/radiol.2521081232 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Hirsch KG, Mlynash M, Eyngorn I, Pirsaheli R, Okada A, Komshian S, et al. Multi-Center Study of Diffusion-Weighted Imaging in Coma After Cardiac Arrest. Neurocritical care. 2016;24(1):82–9. 10.1007/s12028-015-0179-9 [DOI] [PubMed] [Google Scholar]
  • 23.Brain Resuscitation Clinical Trial ISG. Randomized clinical study of thiopental loading in comatose survivors of cardiac arrest. The New England journal of medicine. 1986;314(7):397–403. 10.1056/NEJM198602133140701 . [DOI] [PubMed] [Google Scholar]
  • 24.Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529–36. 10.7326/0003-4819-155-8-201110180-00009 . [DOI] [PubMed] [Google Scholar]
  • 25.Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Statistics in medicine. 2002;21(11):1539–58. 10.1002/sim.1186 . [DOI] [PubMed] [Google Scholar]
  • 26.Vamvakas EC. Meta-analyses of studies of the diagnostic accuracy of laboratory tests: a review of the concepts and methods. Archives of pathology & laboratory medicine. 1998;122(8):675–86. . [PubMed] [Google Scholar]
  • 27.Honest H, Khan KS. Reporting of measures of accuracy in systematic reviews of diagnostic literature. BMC health services research. 2002;2:4 10.1186/1472-6963-2-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Kim KW, Lee J, Choi SH, Huh J, Park SH. Systematic Review and Meta-Analysis of Studies Evaluating Diagnostic Test Accuracy: A Practical Review for Clinical Researchers-Part I. General Guidance and Tips. Korean J Radiol. 2015;16(6):1175–87. 10.3348/kjr.2015.16.6.1175 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Arends LR, Hamza TH, van Houwelingen JC, Heijenbrok-Kal MH, Hunink MG, Stijnen T. Bivariate random effects meta-analysis of ROC curves. Medical decision making: an international journal of the Society for Medical Decision Making. 2008;28(5):621–38. 10.1177/0272989X08319957 . [DOI] [PubMed] [Google Scholar]
  • 30.Dinnes J, Deeks J, Kirby J, Roderick P. A methodological review of how heterogeneity has been examined in systematic reviews of diagnostic test accuracy. Health Technol Assess. 2005;9(12):1–113, iii. 10.3310/hta9120 . [DOI] [PubMed] [Google Scholar]
  • 31.Zamora J, Abraira V, Muriel A, Khan K, Coomarasamy A. Meta-DiSc: a software for meta-analysis of test accuracy data. BMC medical research methodology. 2006;6:31 10.1186/1471-2288-6-31 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Song F, Khan KS, Dinnes J, Sutton AJ. Asymmetric funnel plots and publication bias in meta-analyses of diagnostic accuracy. International journal of epidemiology. 2002;31(1):88–95. 10.1093/ije/31.1.88 . [DOI] [PubMed] [Google Scholar]
  • 33.Barrett KM, Freeman WD, Weindling SM, Brott TG, Broderick DF, Heckman MG, et al. Brain injury after cardiopulmonary arrest and its assessment with diffusion-weighted magnetic resonance imaging. Mayo Clinic proceedings. 2007;82(7):828–35. 10.4065/82.7.828 [DOI] [PubMed] [Google Scholar]
  • 34.Bevers MB, Scirica BM, Avery KR, Henderson GV, Lin AP, Lee JW. Combination of Clinical Exam, MRI and EEG to Predict Outcome Following Cardiac Arrest and Targeted Temperature Management. Neurocritical care. 2018;29(3):396–403. Epub 2018/07/02. 10.1007/s12028-018-0559-z . [DOI] [PubMed] [Google Scholar]
  • 35.Choi SP, Park KN, Park HK, Kim JY, Youn CS, Ahn KJ, et al. Diffusion-weighted magnetic resonance imaging for predicting the clinical outcome of comatose survivors after cardiac arrest: A cohort study. Critical Care. 2010;14(1). 10.1186/cc8874 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Choi DW, Lee SW, Jeong SH, Park JS, Kim H. Early diffusion-weighted imaging and outcome prediction of comatose survivors after suicidal hanging. American Journal of Emergency Medicine. 2018. 10.1016/j.ajem.2018.04.027 [DOI] [PubMed] [Google Scholar]
  • 37.Cronberg T, Rundgren M, Westhall E, Englund E, Siemund R, Rosen I, et al. Neuron-specific enolase correlates with other prognostic markers after cardiac arrest. Neurology. 2011;77(7):623–30. 10.1212/WNL.0b013e31822a276d . [DOI] [PubMed] [Google Scholar]
  • 38.Greer D, Scripko P, Bartscher J, Sims J, Camargo E, Singhal A, et al. Clinical MRI interpretation for outcome prediction in cardiac arrest. Neurocritical care. 2012;17(2):240–4. 10.1007/s12028-012-9716-y [DOI] [PubMed] [Google Scholar]
  • 39.Greer DM, Scripko PD, Wu O, Edlow BL, Bartscher J, Sims JR, et al. Hippocampal magnetic resonance imaging abnormalities in cardiac arrest are associated with poor outcome. Journal of stroke and cerebrovascular diseases: the official journal of National Stroke Association. 2013;22(7):899–905. Epub 2012/09/22. 10.1016/j.jstrokecerebrovasdis.2012.08.006 . [DOI] [PubMed] [Google Scholar]
  • 40.Hirsch KG, Mlynash M, Jansen S, Persoon S, Eyngorn I, Krasnokutsky MV, et al. Prognostic Value of A Qualitative Brain MRI Scoring System After Cardiac Arrest. Journal of Neuroimaging. 2015;25(3):430–7. 10.1111/jon.12143 [DOI] [PubMed] [Google Scholar]
  • 41.Jeon CH, Park JS, Lee JH, Kim H, Kim SC, Park KH, et al. Comparison of brain computed tomography and diffusion-weighted magnetic resonance imaging to predict early neurologic outcome before target temperature management comatose cardiac arrest survivors. Resuscitation. 2017;118:21–6. 10.1016/j.resuscitation.2017.06.021 [DOI] [PubMed] [Google Scholar]
  • 42.Kim J, Choi BS, Kim K, Jung C, Lee JH, Jo YH, et al. Prognostic performance of diffusion-weighted MRI combined with NSE in comatose cardiac arrest survivors treated with mild hypothermia. Neurocritical care. 2012;17(3):412–20. 10.1007/s12028-012-9773-2 [DOI] [PubMed] [Google Scholar]
  • 43.Kim J, Kim K, Hong S, Kwon B, Yun ID, Choi BS, et al. Low apparent diffusion coefficient cluster-based analysis of diffusion-weighted MRI for prognostication of out-of-hospital cardiac arrest survivors. Resuscitation. 2013;84(10):1393–9. 10.1016/j.resuscitation.2013.04.011 [DOI] [PubMed] [Google Scholar]
  • 44.Kim J, Kim K, Suh GJ, Kwon WY, Kim KS, Shin J, et al. Prognostication of cardiac arrest survivors using low apparent diffusion coefficient cluster volume. Resuscitation. 2016;100:18–24. Epub 2016/01/18. 10.1016/j.resuscitation.2015.12.013 . [DOI] [PubMed] [Google Scholar]
  • 45.Luyt CE, Galanaud D, Perlbarg V, Vanhaudenhuyse A, Stevens RD, Gupta R, et al. Diffusion tensor imaging to predict long-term outcome after cardiac arrest: a bicentric pilot study. Anesthesiology. 2012;117(6):1311–21. Epub 2012/11/09. 10.1097/ALN.0b013e318275148c . [DOI] [PubMed] [Google Scholar]
  • 46.Mettenburg JM, Agarwal V. Discordant Observation of Brain Injury by MRI and Malignant Electroencephalography Patterns in Comatose Survivors of Cardiac Arrest following Therapeutic Hypothermia. 2016. 10.3174/ajnr.A4839 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Mlynash M, Campbell DM, Leproust EM, Fischbein NJ, Bammer R, Eyngorn I, et al. Temporal and spatial profile of brain diffusion-weighted MRI after cardiac arrest. Stroke. 2010;41(8):1665–72. 10.1161/STROKEAHA.110.582452 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Moon HK, Jang J, Park KN, Kim SH, Lee BK, Oh SH, et al. Quantitative analysis of relative volume of low apparent diffusion coefficient value can predict neurologic outcome after cardiac arrest. Resuscitation. 2018;126:36–42. 10.1016/j.resuscitation.2018.02.020 [DOI] [PubMed] [Google Scholar]
  • 49.Oren NC, Chang E, Yang CW, Lee SK. Brain Diffusion Imaging Findings May Predict Clinical Outcome after Cardiac Arrest. Journal of neuroimaging: official journal of the American Society of Neuroimaging. 2019;29(4):540–7. Epub 2019/05/21. 10.1111/jon.12626 . [DOI] [PubMed] [Google Scholar]
  • 50.Park JS, Lee SW, Kim H, Min JH, Kang JH, Yi KS, et al. Efficacy of diffusion-weighted magnetic resonance imaging performed before therapeutic hypothermia in predicting clinical outcome in comatose cardiopulmonary arrest survivors. Resuscitation. 2015;88:132–7. 10.1016/j.resuscitation.2014.11.031 [DOI] [PubMed] [Google Scholar]
  • 51.Reynolds AS, Guo X, Matthews E, Brodie D, Rabbani LE, Roh DJ, et al. Post-anoxic quantitative MRI changes may predict emergence from coma and functional outcomes at discharge. Resuscitation. 2017;117:87–90. 10.1016/j.resuscitation.2017.06.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Ryoo SM, Jeon SB, Sohn CH, Ahn S, Han C, Lee BK, et al. Predicting Outcome With Diffusion-Weighted Imaging in Cardiac Arrest Patients Receiving Hypothermia Therapy: Multicenter Retrospective Cohort Study. Critical care medicine. 2015;43(11):2370–7. Epub 2015/08/19. 10.1097/CCM.0000000000001263 . [DOI] [PubMed] [Google Scholar]
  • 53.Topcuoglu MA, Oguz KK, Buyukserbetci G, Bulut E. Prognostic value of magnetic resonance imaging in post-resuscitation encephalopathy. Internal Medicine. 2009;48(18):1635–45. 10.2169/internalmedicine.48.2091 [DOI] [PubMed] [Google Scholar]
  • 54.Velly L, Perlbarg V, Boulier T, Adam N, Delphine S, Luyt CE, et al. Use of brain diffusion tensor imaging for the prediction of long-term neurological outcomes in patients after cardiac arrest: a multicentre, international, prospective, observational, cohort study. The Lancet Neurology. 2018;17(4):317–26. 10.1016/S1474-4422(18)30027-9 [DOI] [PubMed] [Google Scholar]
  • 55.Wallin E, Larsson IM, Kristofferzon ML, Larsson EM, Raininko R, Rubertsson S. Acute brain lesions on magnetic resonance imaging in relation to neurological outcome after cardiac arrest. Acta anaesthesiologica Scandinavica. 2018;62(5):635–47. 10.1111/aas.13074 [DOI] [PubMed] [Google Scholar]
  • 56.Keijzer HM, Hoedemaekers CWE, Meijer FJA, Tonino BAR, Klijn CJM, Hofmeijer J. Brain imaging in comatose survivors of cardiac arrest: Pathophysiological correlates and prognostic properties. Resuscitation. 2018;133:124–36. 10.1016/j.resuscitation.2018.09.012 . [DOI] [PubMed] [Google Scholar]
  • 57.Howard RS, Holmes PA, Siddiqui A, Treacher D, Tsiropoulos I, Koutroumanidis M. Hypoxic-ischaemic brain injury: imaging and neurophysiology abnormalities related to outcome. QJM: monthly journal of the Association of Physicians. 2012;105(6):551–61. 10.1093/qjmed/hcs016 . [DOI] [PubMed] [Google Scholar]
  • 58.Bazarian JJ, Freeman WD, Chiota NA, Cronberg T, Rundgren M, Westhall E, et al. Neuron-specific enolase correlates with other prognostic markers after cardiac arrest. Neurology. 2011;77(20):1856–7. 10.1212/WNL.0b013e31823c1141 [DOI] [PubMed] [Google Scholar]
  • 59.Haataja L, Mercuri E, Guzzetta A, Rutherford M, Counsell S, Flavia Frisone M, et al. Neurologic examination in infants with hypoxic-ischemic encephalopathy at age 9 to 14 months: use of optimality scores and correlation with magnetic resonance imaging findings. The Journal of pediatrics. 2001;138(3):332–7. 10.1067/mpd.2001.111325 . [DOI] [PubMed] [Google Scholar]

Decision Letter 0

Chiara Lazzeri

2 Sep 2019

PONE-D-19-17572

Prediction of neurological outcome after hypoxic-ischemic brain injury by diffusion-weighted imaging: A systematic review and meta-analysis

PLOS ONE

Dear Dr. Luo,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

We would appreciate receiving your revised manuscript by Oct 17 2019 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Chiara Lazzeri

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: I Don't Know

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors investigated an interesting topic that can have relevant clinical meaning. The methodology is accurate and rigorous. But I have concerns regarding many points that I will detail below.

The first point is that I am very puzzled in the statistical analysis, because if each author has chosen different cut-offs to get 100% specificity hat is FPR = 0 it cannot be said that the specificity of the method is this and not very variable. The analysis shows in fact that there is no shared index and that each author has decided his own, therefore the negativity of other cofactors in conditioning the specificity and a false deduction The conclusion of the work that can be deduced is that each work used different indices, whereas I would prefer to see cumulative analysis of homogeneous set of data. For example It is well known that MRI findings changes truough the time, so diffent index can be usefult at different time, so the specificity is not of the MRI but of index and Is time dependent.

More in details

Title: according to the results please add in the title “poor”

Abstracts: “explicit” please find a synonymous.

Please use prognostic instead of diagnostic

Introduction:

pg 3:

“disease modifying agents” this definition is not appropriate for HIE,

“normothermia phase which render the prognostic predictors less reliable[7].” This is not true, please have a loo to recent Bibliography, such as Scarpino et al., 2018, Resuscitation

pg 4: developing a more accurate assessment of acute-stage HIBI patients is urgently needed.: I do not agree with these, because performing MRI is not possible usually in the first 24 hours

Nevertheless, CT and conventional MRI frequently underestimate the degree of brain injury in acute HIBI [12: being a systematic review I think not appropriate referring to a reference of 1999.

Methods

Pg 4:

In this study, we performed a comprehensive literature research in PubMed, EMBASE, and the Cochrane Library databases for DWI from January 1995 to December 2018.: this point seems to be very illogical for two mean reasons 1) TTM have been introduced about 2002 2) DWI is not a technique introduced so early. Actually only one reference is dated 2001.

Pg 5: The outcome was classified into poor and good according to the CPC scores (3-4 or 4-5 versus1-2 or 1-3, respectively). Alternatively, we classified the outcomes into CPC 4–5 versus 1–3, if the CPC thresholds were not defined.

Please clarify this point, the authors extrapolate the data from the table presented by authors according to the cut off they used. I do not agree to attribute a priori a cut off if this is not specified and the studies not reporting clear indication should be not considered in the analysis.

Table 2: I’m very surprised to find in the table studies in which idex test has specificity and sensitivity of 100%. He authors reviewed the quality of the study and how can include these studies in their computation?

DISCUSSION

PG 17

“Given this finding, the semi-quantitative method was used by only 2 studies”

Please rephrase this sentence, this is a systematic review, not an original paper reporting data about a sample of patients.

PG 18

“Further, during brain damage, Wu et al., demonstrated an initial ADC reduction in the striatum and thalamus, followed by the cortex and the subcortical white matter. This DWI pattern could be an indication of ongoing tissue damage due to secondary apoptotic processes, and thus various brain structures can respond differently to ischemic injury [22].”

This sentence is not useful, in this kind of paper the authors should report consideration about cumulative data, is not a narrative report.

Pg 18-19

“However, our pooled analysis did not demonstrate that sensitivity and specificity of DWI were time dependent. The meta-regression analysis showed that the elapsed time between HIBI and brain MRI examination was not a factor in heterogeneityInterestingly, their diagnostic accuracy was not less stringent than other studies acquiring the DWI data during the ideal time window. This suggests that the time of the MRI is not an important factor in determining diagnostic accuracy.

This is an example of what I have underlined in the first part of my comments; this results is true if we evaluated the value of specificity, in this case 100%, but this finding would be right only if in all the paper the authors had used the same parameters to reach the best predictive power at every window. In fact the the studies reported by the authors [20, 38, 44, 48, 51, 52] all used different measure od MRI , so the message that time dependence of MRI is not a factor is not true. MRI is a time dependent test and for every time frame require different measure to reach the best predicitve power.

PG 19

“Therefore, DWI examination could be carried out in a wider timeframe than other prognostic strategies like clinical examination, myoclonus and status myoclonus, electroencephalogram, or biomarkers”

Again this is not completely true, EEG and SEP can be performed in any time windows, and offer also the advantage to be more available in every clinical setting, can be recorded bed-side and repeated more time.

CONCLUSION

They need to be completely rephrased according to the revised version of the manuscript according to the point raised.

Reviewer #2: The authors gave a review and meta-analysis of different diffusion weighted imaging studies predicting neurological recovery in HIBI patients. They find that DWI has a high diagnostic accuracy, but clinical application is limited due to the high variety in study design of the analyses articles.

Although diagnostic properties for HIBI patients are of great interest to clinicians, the manuscript in its current format has, in my opinion, limited added value for clinical practice.

In my opinion, the authors have performed many statistical tests, but with limited adjustments to the clinical, technical and pathofysiological background of the DWI analyses. Although the tests may be carried out correctly, their applicability is limited in the current form of the manuscript. I therefor asked the authors to adjust the manuscript more towards clinical use.

The authors chose to pool studies of different study desings in one meta-analyses. I answerd "partly" on question one, since I am not convinced that this is appropriate for the current study.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

Attachment

Submitted filename: Response to authors.docx

Click here for additional data file. (15.7KB, docx)
PLoS One. 2019 Dec 27;14(12):e0226295. doi: 10.1371/journal.pone.0226295.r002

Author response to Decision Letter 0

17 Nov 2019

Response to reviewer # 1

1- Title: according to the results please add in the title “poor”

Response to reviewer: Thanks for your suggestion. We revised the title as recommended “Prediction of poor outcome after hypoxic-ischemic brain injury by diffusion-weighted imaging: A systematic review and meta-analysis”

2- Abstracts: “explicit” please find a synonymous.

Response to reviewer: Thanks for your comment. We replaced “explicit” by “clear and generally accepted” in the revised abstract.

3- Please use prognostic instead of diagnostic

Response to reviewer: Thanks for your comment. We used “prognostic” instead of “diagnostic” in the revised manuscript.

4- Introduction:

A- pg 3:

“disease modifying agents” this definition is not appropriate for HIE,

Response to reviewer: Thanks for your comment. We deleted this phrase in the revised manuscript (page 3, lines20-21).

B- “normothermia phase which render the prognostic predictors less reliable[7].” This is not true, please have a loo to recent Bibliography, such as Scarpino et al., 2018, Resuscitation

Response to reviewer: Thanks for your comment. We agree, low doses of sedatives and therapeutic hypothermia (TTM) had limited effects on cortical SEP components and/or the EEG (Scarpino, Lanzo et al. 2018). However, according to European Resuscitation Council and European Society of Intensive Care Medicine Guidelines for Post-resuscitation Care 2015, both TTM itself and sedatives or neuromuscular blocking drugs used to maintain it may potentially interfere with prognostication indices, especially those based on clinical examination (Nolan JP et al, 2015, Resuscitation). We rewrote this statement to make it clearer in the revised version of our manuscript (page 3, lines20-21).

C- pg 4: developing a more accurate assessment of acute-stage HIBI patients is urgently needed.: I do not agree with these, because performing MRI is not possible usually in the first 24 hours.

Response to reviewer: Thanks for your comment. Obtaining a brain MRI in critically-ill patients with potential cardiac instability may be challenging during the acute-stage (Wijdicks, Campeau et al. 2001). We agree that using the word “acute”may be not appropriate. Therefore, we used the term “early-stage” instead of “acute-stage”. However, we believe that the development of advanced equipment will enable the use of MRI examination at earlier HIBI stages.

D- Nevertheless, CT and conventional MRI frequently underestimate the degree of brain injury in acute HIBI [12: being a systematic review I think not appropriate referring to a reference of 1999.

Response to reviewer: Thanks for your suggestion. We updated the relevant literature.

5- Methods

A- Pg 4:

In this study, we performed a comprehensive literature research in PubMed, EMBASE, and the Cochrane Library databases for DWI from January 1995 to December 2018.: this point seems to be very illogical for two mean reasons 1) TTM have been introduced about 2002 2) DWI is not a technique introduced so early. Actually only one reference is dated 2001.

Response to reviewer: Thanks for your comment. The clinical application of DWI for ischemic brain injury began in the 1990s (Fisher, Prichard et al. 1995, Schabitz and Fisher 1995). In our preliminary search, we found that the first study reporting the application of DWI in global cerebral ischemia was published in 1999 (Arbelaez, Castillo et al. 1999) Therefore, for the comprehensiveness of our search, we set the beginning time as 1995 and this did not influence our results.

Regarding TTM treatment, subjects included in our analysis were not limited to patients receiving TTM treatment. In fact, all eligible subjects with hypoxic–ischemic brain injury were included in this study regardless they received TTM treatment or no. I hope this explanation clarifies our point of view.

B- Pg 5: The outcome was classified into poor and good according to the CPC scores (3-4 or 4-5 versus1-2 or 1-3, respectively). Alternatively, we classified the outcomes into CPC 4–5 versus 1–3, if the CPC thresholds were not defined.

Please clarify this point, the authors extrapolate the data from the table presented by authors according to the cut off they used. I do not agree to attribute a priori a cut off if this is not specified and the studies not reporting clear indication should be not considered in the analysis.

Response to reviewer: Thanks for your comment. Among the enrolled studies, the cutoff value was not defined in only one study (Wijdicks, Campeau et al. 2001). In that study, the outcome for each patient was recorded for analysis. On the other hand, cut off values were defined in all the remaining studies. Therefore, we were able to define the prognostic results for each patient and thus our definition of CPC 4–5 as poor outcome was based on the results reported by the original authors.

In order to obtain more reliable results, we set more stringent inclusion criteria for this research by excluding studies that involved less than 10 patients. Therefore, we excluded three small studies and thus the above statement was deleted. (p5, line 21)

C- Table 2: I’m very surprised to find in the table studies in which idex test has specificity and sensitivity of 100%. He authors reviewed the quality of the study and how can include these studies in their computation?

Response to reviewer: Thanks for your comment. According to our inclusion and exclusion criteria, we initially enrolled 29 observational cohort studies for our final analysis, which included 13 prospective studies and 16 retrospective studies. All 16 retrospective studies were cohort studies and the data of 6 out of those 16 studies were prospectively collected and retrospectively analyzed.

To update our data, we re-searched the databases (PubMed, EMBASE, and the Cochrane Library databases) from January 1995 to September 2019, and set more stringent inclusion criteria for our analysis (i.e., studies with less than 10 patients were excluded). Accordingly, we found two additional studies (Velly, Perlbarg et al. 2018, Oren, Chang et al. 2019) and removed three original studies from the literature research due to the small number of cases (Wijdicks, Campeau et al. 2001, Heradstveit, Larsson et al. 2011, Choi, Youn et al. 2012). Next, we analyzed the diagnostic results (i.e. the true-positive, false-positive, false-negative, and true-negative results) for calculating their sensitivity and specificity. Finally, we observed that the main conclusion was still the same with our previous analysis. This further confirms the consistency of our results. The updated analysis has been included in the revised manuscript.

7- DISCUSSION

A- PG 17

“Given this finding, the semi-quantitative method was used by only 2 studies”

Please rephrase this sentence, this is a systematic review, not an original paper reporting data about a sample of patients.

Response to reviewer: We updated the data and revised this sentence (page 17 lines 13-20 of the revised manuscript).

B- PG 18

“Further, during brain damage, Wu et al., demonstrated an initial ADC reduction in the striatum and thalamus, followed by the cortex and the subcortical white matter. This DWI pattern could be an indication of ongoing tissue damage due to secondary apoptotic processes, and thus various brain structures can respond differently to ischemic injury [22].”

This sentence is not useful, in this kind of paper the authors should report consideration about cumulative data, is not a narrative report.

Response to reviewer: Thanks for your suggestion, we deleted this sentence and rewritten the relevant paragraph (page 16, lines 1-4 of the revised manuscript).

C- Pg 18-19

“However, our pooled analysis did not demonstrate that sensitivity and specificity of DWI were time dependent. The meta-regression analysis showed that the elapsed time between HIBI and brain MRI examination was not a factor in heterogeneity Interestingly, their diagnostic accuracy was not less stringent than other studies acquiring the DWI data during the ideal time window. This suggests that the time of the MRI is not an important factor in determining diagnostic accuracy.

This is an example of what I have underlined in the first part of my comments; this results is true if we evaluated the value of specificity, in this case 100%, but this finding would be right only if in all the paper the authors had used the same parameters to reach the best predictive power at every window. In fact the studies reported by the authors [20, 38, 44, 48, 51, 52] all used different measure od MRI , so the message that time dependence of MRI is not a factor is not true. MRI is a time dependent test and for every time frame require different measure to reach the best predicitve power.

Response to reviewer: Thanks for your important comment. We re-searched the databases (PubMed, EMBASE, and the Cochrane Library databases) from January 1995 to September 2019. We included 2 new studies (Velly, Perlbarg et al. 2018, Oren, Chang et al. 2019), and excluded three original because of the small number of cases (Wijdicks, Campeau et al. 2001, Heradstveit, Larsson et al. 2011, Choi, Youn et al. 2012). Our subgroup analysis showed that diagnostic accuracy during 6 days after HIBI was higher than that after 6 days, although our meta-regression analysis showed that the elapsed time between HIBI and brain MRI examination was not a factor in heterogeneity (Table 1). Interestingly, the diagnostic accuracy during the first 24 hours after HIBI was not less stringent than the other studies acquiring the DWI data during days 2 to 6. This does not contradict the previous conclusion that DWI signal abnormalities were time dependent, because those studies used different diagnostic strategy and criteria (Hirsch, Mlynash et al. 2015, Park, Lee et al. 2015). Although DWI signals were time-dependent but, different diagnostic strategies could be used to improve the accuracy of diagnosis. It is worth mentioning that the diagnostic accuracy of DWI will significantly decrease if the examination period exceeds 7 days. We revised the manuscript accordingly (page16, lines 4-15).

Table 1. Subgroup analysis among the different study subsets.

Study characteristics No of subsets Pooled sensitivity(95 % CI) Pooled specificity(95 % CI) P

Total 98 0.613(0.599-0.628) 0.958(0.947-0.967)

Time of MRI examination 0.1426

~1d 9 0.767(0.694-0.829) 1.000(0.960-1.000)

2~6d 85 0.627(0.611-0.642) 0.953(0.941-0.963)

>6d 4 0.425(0.376-0.475) 0.991(0.949-1.000)

D- PG 19

“Therefore, DWI examination could be carried out in a wider timeframe than other prognostic strategies like clinical examination, myoclonus and status myoclonus, electroencephalogram, or biomarkers”

Again this is not completely true, EEG and SEP can be performed in any time windows, and offer also the advantage to be more available in every clinical setting, can be recorded bed-side and repeated more time.

Response to reviewer: Thanks a lot. We agree, and we rewritten the paragraph accordingly (page 16, lines 9-21; page 17, lines 1-12 )

8- CONCLUSION

They need to be completely rephrased according to the revised version of the manuscript according to the point raised.

Response to reviewer: Thanks for your valuable suggestions. We rewritten this paragraph accordingly in the revised version (pages 19, lines 9-13).

Response to reviewer #2

1- In my opinion, the authors have performed many statistical tests, but with limited adjustments to the clinical, technical and pathofysiological background of the DWI analyses. Although the tests may be carried out correctly, their applicability is limited in the current form of the manuscript. I therefor asked the authors to adjust the manuscript more towards clinical use.

Response to the reviewer：Thanks for suggestion. In order to make this manuscript more comprehensive and reliable for clinicians, we re-searched the databases (PubMed, EMBASE, and the Cochrane Library databases) from January 1995 to September 2019, and set more stringent inclusion criteria for our analysis. Studies that involved less than 10 patients were excluded. Accordingly, we included two additional studies (Velly, Perlbarg et al. 2018, Oren, Chang et al. 2019) and removed three original studies from the literature research due to the small number of cases (Wijdicks, Campeau et al. 2001, Heradstveit, Larsson et al. 2011, Choi, Youn et al. 2012). We re-analyzed the included data, then added new statements and revised the discussion section to make the manuscript more informative for clinicians.

In 2015 , the European Society of Intensive Care Medicine and European Resuscitation Council (Nolan JP, et al，Resuscitation, 2015.) highlighted the limitations of studies using MRI after HIBI. The lack of homogeneity in radiological definitions of imaging findings and neuroimaging caused this drawback. Our review focused on DWI research and it included recently published studies to help clinicians make informed decisions based on the summary of studies performed in the last 25 years. Further, we added meta-regression and subgroup analysis to explain the limitations of the enrolled studies thus, providing a more accurate theoretical basis for the clinical analysis and judgment.

2- The authors chose to pool studies of different study desings in one meta-analyses. I answerd "partly" on question one, since I am not convinced that this is appropriate for the current study.

Response to reviewer：Thanks for suggestion again. According to our inclusion and exclusion criteria, we initially enrolled 29 observational cohort studies for our final analysis which included 13 prospective studies and 16 retrospective studies. All 16 retrospective studies were cohort studies, and data of 6/16 studies were prospectively collected and retrospectively analyzed. Our preliminary analysis suggested the absence of significant difference in the diagnostic accuracy between the prospective group and retrospective group. Therefore, we included all these observational cohort studies in our analysis.

To update our data, we also re-searched the databases and set more stringent inclusion criteria for our analysis. In addition, we also carefully checked and explored the study heterogeneity (the variability across studies). We evaluated the threshold effect of included studies at first and then we performed a meta-regression analysis and subgroup analysis to investigate factors that could possibly lead to heterogeneity and explored their possible impact on diagnostic accuracy. This is also critical for our clinical analysis and further research. (page 8, lines 4-5; p16-19).

References

Arbelaez, A., M. Castillo and S. K. Mukherji (1999). "Diffusion-weighted MR imaging of global cerebral anoxia." American Journal of Neuroradiology 20(6): 999-1007.

Choi, S. P., C. S. Youn, K. N. Park, J. H. Wee, J. H. Park, S. H. Oh, S. H. Kim and J. Y. Kim (2012). "Therapeutic hypothermia in adult cardiac arrest because of drowning." Acta Anaesthesiol Scand 56(1): 116-123.

Fisher, M., J. W. Prichard and S. Warach (1995). "New magnetic resonance techniques for acute ischemic stroke." Jama 274(11): 908-911.

Heradstveit, B. E., E. M. Larsson, H. Skeidsvoll, S. M. Hammersborg, T. Wentzel-Larsen, A. B. Guttormsen and J. K. Heltne (2011). "Repeated magnetic resonance imaging and cerebral performance after cardiac arrest--a pilot study." Resuscitation 82(5): 549-555.

Hirsch, K. G., M. Mlynash, S. Jansen, S. Persoon, I. Eyngorn, M. V. Krasnokutsky, C. A. Wijman and N. J. Fischbein (2015). "Prognostic value of a qualitative brain MRI scoring system after cardiac arrest." J Neuroimaging 25(3): 430-437.

Oren, N. C., E. Chang, C. W. Y. Yang and S. K. Lee (2019). "Brain Diffusion Imaging Findings May Predict Clinical Outcome after Cardiac Arrest." Journal of Neuroimaging 29(4): 540-547.

Park, J. S., S. W. Lee, H. Kim, J. H. Min, J. H. Kang, K. S. Yi, K. H. Park and B. K. Lee (2015). "Efficacy of diffusion-weighted magnetic resonance imaging performed before therapeutic hypothermia in predicting clinical outcome in comatose cardiopulmonary arrest survivors." Resuscitation 88: 132-137.

Scarpino, M., G. Lanzo, F. Lolli, R. Carrai, M. Moretti, M. Spalletti, M. Cozzolino, A. Peris, A. Amantini and A. Grippo (2018). "Neurophysiological and neuroradiological multimodal approach for early poor outcome prediction after cardiac arrest." Resuscitation 129: 114-120.

Schabitz, W. R. and M. Fisher (1995). "Diffusion weighted imaging for acute cerebral infarction." Neurol Res 17(4): 270-274.

Velly, L., V. Perlbarg, T. Boulier, N. Adam, S. Delphine, C. E. Luyt, V. Battisti, G. Torkomian, C. Arbelot, R. Chabanne, B. Jean, C. Di Perri, S. Laureys, G. Citerio, A. Vargiolu, B. Rohaut, N. Bruder, N. Girard, S. Silva, V. Cottenceau, T. Tourdias, O. Coulon, B. Riou, L. Naccache, R. Gupta, H. Benali, D. Galanaud, L. Puybasset, J. M. Constantin, J. Chastre, J. Amour, C. Vezinet, J. J. Rouby, M. Raux, O. Langeron, V. Degos, F. Bolgert, N. Weiss, T. Similowski, A. Demoule, A. Duguet, E. Tollard, B. Veber, J. A. Lotterie, P. Sanchez-Pena, M. Génestal and M. Patassini (2018). "Use of brain diffusion tensor imaging for the prediction of long-term neurological outcomes in patients after cardiac arrest: a multicentre, international, prospective, observational, cohort study." The Lancet Neurology 17(4): 317-326.

Wijdicks, E. F., N. G. Campeau and G. M. Miller (2001). "MR imaging in comatose survivors of cardiac resuscitation." AJNR Am J Neuroradiol 22(8): 1561-1565.

Thanks again for your meticulous reviews. All new amendments are written in red font color in the revised version of our manuscript. I hope the revised version is now acceptable for publication.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file. (61KB, docx)

Decision Letter 1

Chiara Lazzeri

25 Nov 2019

Prediction of poor outcome after hypoxic-ischemic brain injury by diffusion-weighted imaging: A systematic review and meta-analysis

PONE-D-19-17572R1

Dear Dr. Luo,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

With kind regards,

Chiara Lazzeri

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Chiara Lazzeri

10 Dec 2019

PONE-D-19-17572R1

Prediction of poor outcome after hypoxic-ischemic brain injury by diffusion-weighted imaging: A systematic review and meta-analysis

Dear Dr. Luo:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Chiara Lazzeri

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 PRISMA Checklist. PRISMA checklist.

    (DOC)

    Click here for additional data file. (64KB, doc)
    S1 Fig. Risk of bias and applicability concerns summary.

    Review authors' judgements about each domain for each included study.

    (TIF)

    Click here for additional data file. (2.4MB, tif)
    S2 Fig. DEEKS funnel analysis to assess the publication bias.

    (TIFF)

    Click here for additional data file. (25.6KB, tiff)
    Attachment

    Submitted filename: Response to authors.docx

    Click here for additional data file. (15.7KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    Click here for additional data file. (61KB, docx)

    Data Availability Statement

    All relevant data are within the manuscript and its Supporting Information files.

    Articles from PLoS ONE are provided here courtesy of PLOS

    RESOURCES