Early head CT in post-cardiac arrest patients: A helpful tool or contributor to self-fulfilling prophecy?

Beekman, Rachel; Maciel, Carolina B.; Ormseth, Cora; Zhou, Sonya E.; Galluzzo, Daniela; Miyares, Laura; Torres-Lopez, Victor; Payabvash, Sam; Mak, Adrian; Greer, David M.; Gilmore, Emily J.

doi:10.1016/j.resuscitation.2021.06.004

Cited by 25 publications

(14 citation statements)

References 27 publications

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“…In this study investigating the Inter-rater variability of quantitative head CT assessment in patients after cardiac arrest, Inter-rater agreement was considerably better than in studies investigating descriptive, qualitative assessment (9,10). Previous evidence on the subject had been inconclusive with one study reporting moderate agreement between three human raters and another study reporting good agreement between four human raters (23,24).…”

Section: Figurementioning

confidence: 67%

“…International guidelines recommend using imaging findings of “diffuse and extensive anoxic injury” for prediction of poor neurological outcome without providing a clear definition of this finding ( 3 , 7 , 8 ). Studies on the inter-rater reliability of qualitative assessment of head CT have consequently found poor to moderate agreement on the presence and/or severity of HIE between different raters, sites, and specialties ( 9 , 10 ).…”

Section: Introductionmentioning

confidence: 99%

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Inter-rater agreement between humans and computer in quantitative assessment of computed tomography after cardiac arrest

et al. 2022

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BackgroundHead computed tomography (CT) is used to predict neurological outcome after cardiac arrest (CA). The current reference standard includes quantitative image analysis by a neuroradiologist to determine the Gray-White-Matter Ratio (GWR) which is calculated via the manual measurement of radiodensity in different brain regions. Recently, automated analysis methods have been introduced. There is limited data on the Inter-rater agreement of both methods.MethodsThree blinded human raters (neuroradiologist, neurologist, student) with different levels of clinical experience retrospectively assessed the Gray-White-Matter Ratio (GWR) in head CTs of 95 CA patients. GWR was also quantified by a recently published computer algorithm that uses coregistration with standardized brain spaces to identify regions of interest (ROIs). We calculated intraclass correlation (ICC) for inter-rater agreement between human and computer raters as well as area under the curve (AUC) and sensitivity/specificity for poor outcome prognostication.ResultsInter-rater agreement on GWR was very good (ICC 0.82–0.84) between all three human raters across different levels of expertise and between the computer algorithm and neuroradiologist (ICC 0.83; 95% CI 0.78–0.88). Despite high overall agreement, we observed considerable, clinically relevant deviations of GWR measurements (up to 0.24) in individual patients. In our cohort, at a GWR threshold of 1.10, this did not lead to any false poor neurological outcome prediction.ConclusionHuman and computer raters demonstrated high overall agreement in GWR determination in head CTs after CA. The clinically relevant deviations of GWR measurement in individual patients underscore the necessity of additional qualitative evaluation and integration of head CT findings into a multimodal approach to prognostication of neurological outcome after CA.

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Section: Figurementioning

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Inter-rater agreement between humans and computer in quantitative assessment of computed tomography after cardiac arrest

et al. 2022

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“…Unklar ist, welche prognostische Aussagekraft eine frühe Bildgebung mittels kranialer Computertomographie nach CA hat. Eine retrospektive Studie untersuchte deshalb, wie sich die Analyse einer frühen CT-Bildgebung innerhalb von 6 h nach CA auf therapeutische Entscheidungen auswirkte [ 1 ]. Dabei beurteilten Neurointensivmediziner die CTs hinsichtlich einer möglichen hypoxisch-ischämischen Hirnschädigung („hypoxic ischemic brain injury“, HIBI).…”

Section: Neues Zur Bildgebung Nach Herz-kreislauf-stillstandunclassified

Das ist neu in der Neurointensiv- und Notfallmedizin: die wichtigsten Studien des Jahres im Rück- und Überblick

2022

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Die vorliegende Übersichtsarbeit fasst wichtige klinische Studien der neurologischen Notfall- und Intensivmedizin zwischen 2020 und 2021 zusammen zu den Themen: rekanalisierende Therapie beim ischämischen Schlaganfall, Anwendbarkeit und Auswirkung eines zerebralen Sauerstoffgewebemonitorings bei Subarachnoidalblutung, Wirksamkeit induzierter Hypothermie bei Patienten mit „cardiac arrest“ (CA), Wertigkeit früher kranialer Bildgebung nach CA, Relevanz eines schnellen Managements und medikamentöser Therapie beim Status epilepticus sowie Inzidenz von Critical-illness-Polyneuropathie-Myopathie bei intensivpflichtigen COVID-Patienten.

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“…However, the findings indicating HIBI on early head CT are highly variable. There are no guidelines for a clear, consistent definition of HIBI 12 , and there is a low interrater agreement rate 13 , although this depends on the expertise of the radiologists 13 , 14 . Therefore, we selected the GWR, which is a quantitative measure of HIBI, for comparison with machine learning.…”

Section: Discussionmentioning

confidence: 99%

Explainable artificial intelligence-based prediction of poor neurological outcome from head computed tomography in the immediate post-resuscitation phase

Kawai

Kogeichi

Yamamoto

et al. 2023

Sci Rep

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Predicting poor neurological outcomes after resuscitation is important for planning treatment strategies. We constructed an explainable artificial intelligence-based prognostic model using head computed tomography (CT) scans taken immediately within 3 h of resuscitation from cardiac arrest and compared its predictive accuracy with that of previous methods using gray-to-white matter ratio (GWR). We included 321 consecutive patients admitted to our institution after resuscitation for out-of-hospital cardiopulmonary arrest with circulation resumption over 6 years. A machine learning model using head CT images with transfer learning was used to predict the neurological outcomes at 1 month. These predictions were compared with the predictions of GWR for multiple regions of interest in head CT using receiver operating characteristic (ROC)-area under curve (AUC) and precision recall (PR)-AUC. The regions of focus were visualized using a heatmap. Both methods had similar ROC-AUCs, but the machine learning model had a higher PR-AUC (0.73 vs. 0.58). The machine learning-focused area of interest for classification was the boundary between gray and white matter, which overlapped with the area of focus when diagnosing hypoxic– ischemic brain injury. The machine learning model for predicting poor outcomes had superior accuracy to conventional methods and could help optimize treatment.

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Early head CT in post-cardiac arrest patients: A helpful tool or contributor to self-fulfilling prophecy?

Cited by 25 publications

References 27 publications

Inter-rater agreement between humans and computer in quantitative assessment of computed tomography after cardiac arrest

Inter-rater agreement between humans and computer in quantitative assessment of computed tomography after cardiac arrest

Das ist neu in der Neurointensiv- und Notfallmedizin: die wichtigsten Studien des Jahres im Rück- und Überblick

Explainable artificial intelligence-based prediction of poor neurological outcome from head computed tomography in the immediate post-resuscitation phase

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