Standardised and automated assessment of head computed tomography reliably predicts poor functional outcome after cardiac arrest: a prospective multicentre study

According to the European Resuscitation Council (ERC) and the European Society for Intensive Care Medicine (ESICM), “diffuse and extensive anoxic injury” on neuroimaging is predictive of poor functional outcome after cardiac arrest [1]. Head computed tomography (CT) is widely available and is frequently used for neuroprognostication [2‐4]. Recent meta-analyses conclude that the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) level of evidence for CT to predict outcome after cardiac arrest is very low [5‐7]. Signs of “diffuse and extensive anoxic injury” seen as a reduced differentiation between grey and white matter and/or effacement of the cerebral sulci on CT correlate well with elevated levels of neuronal injury markers [8] and histopathological severity of hypoxic–ischaemic encephalopathy (HIE) [9].

In clinical practice, CTs are usually assessed qualitatively using a non-standardised approach [10]. Some specialised centres use non-standardised quantitative methods such as grey–white-matter ratio (GWR) placing regions of interest at the basal ganglia and/or in (sub)cortical regions to quantify oedema [7]. Manual assessments carry the risk of interrater variability and standardisation and/or automatisation may be necessary to ensure a safe translation from research to clinical routine [11‐14]. In stroke imaging, automated quantification of non-contrast CTs is routinely used [15]. This has not yet been achieved for neuroprognostication and a few single-centre retrospective studies with automated quantification of GWR after cardiac arrest have been published [16, 17].

Based on our retrospective studies with adult out-of-hospital cardiac arrest patients, we established Standard Operating Procedures (SOPs) for qualitative (visual interpretation) and quantitative (GWR) CT assessments for clinical use [13, 18‐20]. In line with the previous studies [18, 19, 21‐24], our recent pilot study from the Target Temperature Management after Out-of-hospital Cardiac Arrest (TTM)-trial confirmed that GWR was most accurate at the basal ganglia level where a cutoff < 1.10 predicted poor functional outcome with 100% specificity [18].

We here present the results of a prospective observational substudy of an international multicentre trial in which we applied our previously published criteria for standardised qualitative and quantitative CT assessment as well as an atlas-based automated GWR method for neuroprognostication after cardiac arrest [14]. Our main hypotheses were that “Definite signs of severe HIE”, by standardised assessment and manually or automatically obtained GWR < 1.10, would predict poor functional outcome without false positives in CTs performed 48 h–7 days after cardiac arrest [13].

In this prospective multicentre study, evaluating three different methods of diagnosing severe hypoxic ischaemic injury on CT for prediction of poor functional outcome after cardiac arrest, we validate pre-published standardised criteria and evaluate GWR cutoff < 1.10 for manual and automated assessments [13]. We conclude that CT is a highly specific prognostic tool for neuroprognostication, regardless of assessment method, with highest sensitivities for poor outcome prediction when performed 48–96 h post-arrest. GWR determination at the basal ganglia level < 1.10 performed either manually or automated offer a more objective measure of HIE with reduced interrater variability.

CT is a guideline-recommended predictor of outcome after cardiac arrest with very low quality of evidence [1, 5, 7, 40]. The main concerns raised by ERC/ESICM include the lack of multicentre validation and standardised assessments of both qualitative and quantitative methods [1]. Our study provides a framework that is easy to use in clinical practice and addresses several concerns raised in recent publications [5‐7, 10, 41].

Our standardised qualitative criteria define signs of severe HIE as a “complete or nearly complete loss of grey-white matter differentiation in the basal ganglia and in the frontoparietal cortex with additional evidence of brain swelling/sulcal effacement” [13]. A visual interpretation according to a checklist with mandatory evaluation at several levels of the brain had to be completed before reaching a conclusion. This qualitative assessment predicted poor outcome with 0% false-positive rate in 980 blinded ratings overall. In line with the previous qualitative studies, sensitivities for individual raters ranged between 11 and 61% for imaging performed 48 h–7 days post-arrest [18, 20, 41].

Both the ERC/ESICM and the American Neurocritical Care Society recommendations use similar, undefined terminology to describe signs of severe HIE on CT; "diffuse", "extensive" or "bilaterally across vascular territories", with a "loss of grey-white-matter differentiation" [1, 5]. While our standardised qualitative criteria may offer a more precise definition of severe HIE than those given in the current guidelines, it achieved only moderate interrater reliability. The CT evaluation in our study was mostly performed by experienced raters (3–15 years with CTs of cardiac arrest patients). Rater experience may impact both sensitivity and specificity of CT evaluation using our SOP. This should be kept in mind when implementing our CT analysis in clinical routine. Future improvements to improve interrater reliability are necessary and may include a better standardisation of windowing during visual analysis, standards regarding decision in case of residual grey–white differentiation and awareness of the effects from residual contrast agents. In contrast to clinical practice, our raters only had one CT available for analysis and did not have access to pre-cardiac arrest CTs. We plan a subsequent study using serial CTs to evaluate whether an analysis of changes in grey–white-matter differentiation and brain volume over time improves prognostic accuracy.

GWR is the only guideline-recommended method to quantify the extent of HIE on CT and can be applied with routine radiological software, but there is no consensus on the number, size, and exact location of regions of interest [1, 10, 22, 42‐44]. Based on previous investigations and our retrospective pilot study, we chose to validate manually placed 0.1 cm² regions of interest at the basal ganglia level [18]. Importantly, we included the instruction to place the regions of interest in a subregion with a radiodensity representative of the entire anatomical target region and to avoid potential confounders (artefacts, calcifications, lacunar infarcts, etc.). We confirmed that both manual GWR models had a maximal specificity at GWR < 1.10, which is in accordance with the previous studies [6, 7, 18]. As expected, sensitivities increased at cutoff < 1.15 at cost of a slightly decreased specificity. None of the false positives through quantitative measurements fulfilled criteria for "severe HIE" with the qualitative assessment, underlining the potential value of combining both approaches. As in our pilot study, GWR-8 was consistently superior to GWR-4 concerning prognostic accuracies, intra- and interrater agreements [18]. A possible explanation for the higher accuracy of GWR-8 is the reduction of noise due to the larger number of regions of interest [18, 45]. GWR-8 was superior to the qualitative assessment for some raters and the interrater reliability for GWR-8 was superior to that of qualitative assessments—highlighting a potential advantage of quantification. We presume that the interrater reliability of manual GWR could be further improved by applying stricter instructions for measurements within anatomical regions or using non-circular and/or larger regions of interest.

Automated atlas-based GWR measurements offer an alternative to manual measurements unaffected by interrater variability and could increase the availability of GWR for hospitals without on-site neuroradiologic expertise [17]. A few previous studies evaluate automated GWR quantification and they are limited by single-centre, retrospective designs, and early assessment of functional outcome [16, 17]. The prognostic accuracy of automated GWR < 1.10 in our prospective cohort was as good as the manually assessed GWR with 40% sensitivity at 100% specificity. This performance is also in the range of manual and automated GWR from CTs performed > 24 h post-arrest in the previous studies [18, 20, 24, 46], routine EEG, and SSEP [7]. Except for one study on early CTs, 1.10 was the lowest reported GWR cut-off with 100% specificity thus far [47]. Overall, automated GWR < 1.10 performed within 7 days post-arrest had one false-positive prediction of poor outcome in a patient with a subcortical low attenuating area close to putamen, most likely an old lacunar infarction or perivascular space, but with intact overall grey–white-matter differentiation. The use of automated GWR relies on anatomic landmarks and its use must include a quality check for co-registration and exclusion of artefacts or acute brain pathologies potentially interfering with measurements [10]. Future studies on larger cohorts should investigate whether machine learning can predict outcome from CTs after cardiac arrest with superior accuracy compared to our human rater-based approach [48].

Our results on optimal timing support guideline recommendations [1, 5] that CTs performed 48–96 h have higher sensitivity for predicting poor outcome than examinations performed within the first hours post-arrest [5, 17, 18, 20, 23, 24, 49]. Examinations performed on hospital admission are often routinely used to exclude cerebral causes of unconsciousness and may be too early to detect HIE for most patients. The increase in sensitivity within the first days corresponds with developing HIE. The higher sensitivity of later examinations is in line with previous observations and supports the notion that an optimal time window of a few days exists for neuroprognostic CT’s [17]. We found no clinically relevant effect of temperature allocation on prognostic accuracies for prediction of poor functional outcome. When performed at an optimal timepoint and analysed using standardised interpretation, combining CT with other prognostic methods with higher sensitivities such as EEG or NSE could increase the number of correctly identified poor outcome patients.

The combination of a structured qualitative assessment of severe HIE with a quantitative assessment at the basal ganglia level (GWR-8 or automated GWR < 1.10) allows the prediction of poor functional outcome after cardiac arrest with high specificity and moderate sensitivity. CT should be considered in patients unconscious later than 48 h after cardiac arrest using a multimodal approach to neuroprognostication. Automated GWR could help avoid errors compared to manual ratings and make head CT quantification more accessible.