Neurofilament light as an outcome predictor after cardiac arrest: a post hoc analysis of the COMACARE trial

Wihersaari, Lauri; Ashton, Nicholas J.; Reinikainen, Matti; Jakkula, Pekka; Pettilä, Ville; Hästbacka, Johanna; Tiainen, Marjaana; Loisa, Pekka; Friberg, Hans; Cronberg, Tobias; Blennow, Kaj; Zetterberg, Henrik; Skrifvars, Markus B.

doi:10.1007/s00134-020-06218-9

Cited by 99 publications

(103 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast to that study, the current findings suggest that even at 24 h following cardiac arrest the prognostic performance is excellent with an AUROC of 95% (95% CI: 92-98%) and sensitivity above 60% while retaining 100% specificity upon adding research-grade biomarkers. These results are mainly driven by the biomarker NFL and are similar to two previous studies, of which one is based on the same data (TTM-trial) [5,6]. In fact, level B on day 3 (72 h) as well as level C for all time points had a sensitivity above 60% while retaining 100% specificity, which is comparable with the performance reported using the ERC/ESICM guidelines, which included important prognostics such as SSEP, EEG and neuroradiological imaging.…”

Section: Discussionsupporting

confidence: 88%

“…Today, only neuron-specific enolase (NSE) is included in the guidelines [4]. Amongst many novel biomarkers that have been studied for brain injury after cardiac arrest, the most promising so far is neurofilament light (NFL), with an area under the receiver operating characteristic curve (AUROC) of 94-98% for discrimination of longterm neurological outcome as early as 24 h after ROSC [5,6]. Other biomarkers of brain injury, including S100 calcium-binding protein B (S100B), tau protein, glial fibrillary acidic protein (GFAP), and ubiquitin C-terminal hydrolase-L1 (UCHL1), have also shown potential in cardiac arrest prognostication [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting neurological outcome after out-of-hospital cardiac arrest with cumulative information; development and internal validation of an artificial neural network algorithm

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background Prognostication of neurological outcome in patients who remain comatose after cardiac arrest resuscitation is complex. Clinical variables, as well as biomarkers of brain injury, cardiac injury, and systemic inflammation, all yield some prognostic value. We hypothesised that cumulative information obtained during the first three days of intensive care could produce a reliable model for predicting neurological outcome following out-of-hospital cardiac arrest (OHCA) using artificial neural network (ANN) with and without biomarkers. Methods We performed a post hoc analysis of 932 patients from the Target Temperature Management trial. We focused on comatose patients at 24, 48, and 72 h post-cardiac arrest and excluded patients who were awake or deceased at these time points. 80% of the patients were allocated for model development (training set) and 20% for internal validation (test set). To investigate the prognostic potential of different levels of biomarkers (clinically available and research-grade), patients’ background information, and intensive care observation and treatment, we created three models for each time point: (1) clinical variables, (2) adding clinically accessible biomarkers, e.g., neuron-specific enolase (NSE) and (3) adding research-grade biomarkers, e.g., neurofilament light (NFL). Patient outcome was the dichotomised Cerebral Performance Category (CPC) at six months; a good outcome was defined as CPC 1–2 whilst a poor outcome was defined as CPC 3–5. The area under the receiver operating characteristic curve (AUROC) was calculated for all test sets. Results AUROC remained below 90% when using only clinical variables throughout the first three days in the ICU. Adding clinically accessible biomarkers such as NSE, AUROC increased from 82 to 94% (p < 0.01). The prognostic accuracy remained excellent from day 1 to day 3 with an AUROC at approximately 95% when adding research-grade biomarkers. The models which included NSE after 72 h and NFL on any of the three days had a low risk of false-positive predictions while retaining a low number of false-negative predictions. Conclusions In this exploratory study, ANNs provided good to excellent prognostic accuracy in predicting neurological outcome in comatose patients post OHCA. The models which included NSE after 72 h and NFL on all days showed promising prognostic performance.

show abstract

Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Predicting neurological outcome after out-of-hospital cardiac arrest with cumulative information; development and internal validation of an artificial neural network algorithm

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, while CSF t-tau is considered in the CSF framework, plasma t-tau was omitted due to the previously published evidence demonstrating limited validity as an AD biomarker [25,29,30,115]. Furthermore, while plasma NfL is the most published blood biomarker related to AD, it was also not considered due to its global association to neurodegeneration [26,27] and neurological injury [28,116] (e.g. nonspecificity for AD).…”

Section: Discussionmentioning

confidence: 99%

“…Concentrations of blood (plasma or serum) NfL have been shown to be robustly, albeit moderately, increased in MCI and AD [21][22][23][24][25]. Yet, NfL is a well-established measure of global neuronal injury in many neurodegenerative diseases [26,27] and acute neurological disorders [28]. Thus, NfL does not have the required specificity to be classified as an "AD biomarker".…”

Section: Introductionmentioning

confidence: 99%

The validation status of blood biomarkers of amyloid and phospho-tau assessed with the 5-phase development framework for AD biomarkers

Ashton

Leuzy

Karikari

et al. 2021

Eur J Nucl Med Mol Imaging

View full text Add to dashboard Cite

Purpose The development of blood biomarkers that reflect Alzheimer’s disease (AD) pathophysiology (phosphorylated tau and amyloid-β) has offered potential as scalable tests for dementia differential diagnosis and early detection. In 2019, the Geneva AD Biomarker Roadmap Initiative included blood biomarkers in the systematic validation of AD biomarkers. Methods A panel of experts convened in November 2019 at a two-day workshop in Geneva. The level of maturity (fully achieved, partly achieved, preliminary evidence, not achieved, unsuccessful) of blood biomarkers was assessed based on the Biomarker Roadmap methodology and discussed fully during the workshop which also evaluated cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers. Results Plasma p-tau has shown analytical validity (phase 2 primary aim 1) and first evidence of clinical validity (phase 3 primary aim 1), whereas the maturity level for Aβ remains to be partially achieved. Full and partial achievement has been assigned to p-tau and Aβ, respectively, in their associations to ante-mortem measures (phase 2 secondary aim 2). However, only preliminary evidence exists for the influence of covariates, assay comparison and cut-off criteria. Conclusions Despite the relative infancy of blood biomarkers, in comparison to CSF biomarkers, much has already been achieved for phases 1 through 3 – with p-tau having greater success in detecting AD and predicting disease progression. However, sufficient data about the effect of covariates on the biomarker measurement is lacking. No phase 4 (real-world performance) or phase 5 (assessment of impact/cost) aim has been tested, thus not achieved.

show abstract

“…8 NfL does not, however, perform well when measured in blood at admission after cardiac arrest. 9 Among widely available biomarkers in blood, lactate and glucose at admission have shown limited prognostic value. 10,11 Other novel blood biomarkers include proenkephalin A 119À159 (penKid), a surrogate for endogenous opioid pentapeptides enkephalins widely expressed in the central nervous system (as revealed by its name À ἐgkέwalo § is the Greek name for brain) and in other tissues including kidney, muscle and the heart.…”

Section: Introductionmentioning

confidence: 99%

Plasma proenkephalin A 119–159 and dipeptidyl peptidase 3 on admission after cardiac arrest help predict long-term neurological outcome

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background: A large proportion of adult survivors of cardiac arrest have a poor neurological outcome. Guidelines recommend multimodal neuroprognostication no earlier than 72À96 h after cardiac arrest. There is great interest in earlier prognostic markers, including very early markers at admission. The novel blood biomarkers proenkephalin A 119À159 (penKid), bioactive adrenomedullin (bio-ADM) and circulating dipeptidyl peptidase 3 (cDPP3) have not been previously investigated for the early prognosis of cardiac arrest survivors.Methods: This multicentre observational study included adult survivors of cardiac arrest admitted to intensive care at four Swedish intensive care units (ICUs) during 2016. Blood samples were collected at ICU admission and batch analysed. The association between admission plasma penKid, bio-ADM and cDPP3 and poor long-term neurological outcome, according to the Cerebral Performance Category (CPC) scale, was assessed by binary logistic regression. Their prognostic performance was assessed using the area under the receiver operating characteristic curve (AUC).Results: A total of 190 patients were included, of which 136 patients had suffered out-of-hospital and 54 patients in-hospital cardiac arrest. Poor longterm neurological outcome was associated with elevated admission plasma concentrations of penKid and cDPP3, but not with bio-ADM. The association for penKid, but not for cDPP3, remained after adjusting for clinical cardiac arrest variables with prognostic value (time to return of spontaneous circulation (ROSC), initial rhythm, admission Glasgow coma scale (GCS) motor score and absence of pupillary reflexes). The prognostic performance of above mentioned clinical cardiac arrest variables alone was very good with an AUC of 0.90 (95% confidence interval, CI, 0.86À0.95), but improved further with the addition of penKid resulting in an AUC of 0.93 (95% CI 0.89À0.97, p < 0.026). Plasma penKid and cDPP3 alone provided moderate long-term prognostic information with AUCs of 0.70 and 0.71, respectively. Conclusion:After cardiac arrest, admission plasma levels of penKid and cDPP3, but not bio-ADM, predicted long-term neurological outcome. When added to clinical cardiac arrest variables, penKid further improved prognostic performance.

show abstract

Neurofilament light as an outcome predictor after cardiac arrest: a post hoc analysis of the COMACARE trial

Cited by 99 publications

References 34 publications

Predicting neurological outcome after out-of-hospital cardiac arrest with cumulative information; development and internal validation of an artificial neural network algorithm

Predicting neurological outcome after out-of-hospital cardiac arrest with cumulative information; development and internal validation of an artificial neural network algorithm

The validation status of blood biomarkers of amyloid and phospho-tau assessed with the 5-phase development framework for AD biomarkers

Plasma proenkephalin A 119–159 and dipeptidyl peptidase 3 on admission after cardiac arrest help predict long-term neurological outcome

Contact Info

Product

Resources

About