| 1. |
- Arctaedius, Isabelle, et al.
(författare)
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Plasma glial fibrillary acidic protein and tau: predictors of neurological outcome after cardiac arrest.
- 2024
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Ingår i: Critical care (London, England). - 1364-8535 .- 1466-609X. ; 28:1
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Tidskriftsartikel (refereegranskat)abstract
- The purpose was to evaluate glial fibrillary acidic protein (GFAP) and total-tau in plasma as predictors of poor neurological outcome after out-of-hospital (OHCA) and in-hospital cardiac arrest (IHCA), including comparisons with neurofilament light (NFL) and neuron-specific enolase (NSE).Retrospective multicentre observational study of patients admitted to an intensive care unit (ICU) in three hospitals in Sweden 2014-2018. Blood samples were collected at ICU admission, 12h, and 48h post-cardiac arrest. Poor neurological outcome was defined as Cerebral Performance Category 3-5 at 2-6months after cardiac arrest. Plasma samples were retrospectively analysed for GFAP, tau, and NFL. Serum NSE was analysed in clinical care. Prognostic performances were tested with the area under the receiver operating characteristics curve (AUC).Of the 428 included patients, 328 were OHCA, and 100 were IHCA. At ICU admission, 12h and 48h post-cardiac arrest, GFAP predicted neurological outcome after OHCA with AUC (95% CI) 0.76 (0.70-0.82), 0.86 (0.81-0.90) and 0.91 (0.87-0.96), and after IHCA with AUC (95% CI) 0.77 (0.66-0.87), 0.83 (0.74-0.92) and 0.83 (0.71-0.95). At the same time points, tau predicted outcome after OHCA with AUC (95% CI) 0.72 (0.66-0.79), 0.75 (0.69-0.81), and 0.93 (0.89-0.96) and after IHCA with AUC (95% CI) 0.61 (0.49-0.74), 0.68 (0.56-0.79), and 0.77 (0.65-0.90). Adding the change in biomarker levels between time points did not improve predictive accuracy compared to the last time point. In a subset of patients, GFAP at 12h and 48 h, as well as tau at 48h, offered similar predictive value as NSE at 48h (the earliest time point NSE is recommended in guidelines) after both OHCA and IHCA. The predictive performance of NFL was similar or superior to GFAP and tau at all time points after OHCA and IHCA.GFAP and tau are promising biomarkers for neuroprognostication, with the highest predictive performance at 48h after OHCA, but not superior to NFL. The predictive ability of GFAP may be sufficiently high for clinical use at 12h after cardiac arrest.
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| 2. |
- Blennow Nordström, Erik, et al.
(författare)
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Serum neurofilament light levels are correlated to long-term neurocognitive outcome measures after cardiac arrest
- 2022
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Ingår i: Brain Injury. - : Informa UK Limited. - 0269-9052 .- 1362-301X. ; 36:6, s. 800-809
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Tidskriftsartikel (refereegranskat)abstract
- Objective To explore associations between four methods assessing long-term neurocognitive outcome after out-of-hospital cardiac arrest and early hypoxic-ischemic neuronal brain injury assessed by the biomarker serum neurofilament light (NFL), and to compare the agreement for the outcome methods. Methods An explorative post-hoc study was conducted on survivor data from the international Target Temperature Management after Out-of-hospital Cardiac Arrest trial, investigating serum NFL sampled 48/72-hours post-arrest and neurocognitive outcome 6 months post-arrest. Results Among the long-term surviving participants (N = 457), serum NFL (n = 384) was associated to all outcome instruments, also when controlling for demographic and cardiovascular risk factors. Associations between NFL and the patient-reported Two Simple Questions (TSQ) were however attenuated when adjusting for vitality and mental health. NFL predicted results on the outcome instruments to varying degrees, with an excellent area under the curve for the clinician-report Cerebral Performance Category (CPC 1-2: 0.90). Most participants were classified as CPC 1 (79%). Outcome instrument correlations ranged from small (Mini-Mental State Examination [MMSE]-TSQ) to strong (CPC-MMSE). Conclusions The clinician-reported CPC was mostly related to hypoxic-ischemic brain injury, but with a ceiling effect. These results may be useful when selecting methods and instruments for clinical follow-up models.
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| 3. |
- Ebner, Florian, et al.
(författare)
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Serum GFAP and UCH-L1 for the prediction of neurological outcome in comatose cardiac arrest patients
- 2020
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Ingår i: Resuscitation. - : Elsevier BV. - 0300-9572. ; 154, s. 61-68
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Neurological outcome prediction is crucial early after cardiac arrest. Serum biomarkers released from brain cells after hypoxic-ischaemic injury may aid in outcome prediction. The only serum biomarker presently recommended in the European Resuscitation Council prognostication guidelines is neuron-specific enolase (NSE), but NSE has limitations. In this study, we therefore analyzed the outcome predictive accuracy of the serum biomarkers glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase-L1 (UCH-L1) in patients after cardiac arrest. Methods: Serum GFAP and UCH-L1 were collected at 24, 48 and 72 h after cardiac arrest. The primary outcome was neurological function at 6-month follow-up assessed by the cerebral performance category scale (CPC), dichotomized into good (CPC1-2) and poor (CPC3-5). Prognostic accuracies were tested with receiver-operating characteristics by calculating the area under the receiver-operating curve (AUROC) and compared to the AUROC of NSE. Results: 717 patients were included in the study. GFAP and UCH-L1 discriminated between good and poor neurological outcome at all time-points when used alone (AUROC GFAP 0.88–0.89; UCH-L1 0.85–0.87) or in combination (AUROC 0.90–0.91). The combined model was superior to GFAP and UCH-L1 separately and NSE (AUROC 0.75–0.85) at all time-points. At specificities ≥95%, the combined model predicted poor outcome with a higher sensitivity than NSE at 24 h and with similar sensitivities at 48 and 72 h. Conclusion: GFAP and UCH-L1 predicted poor neurological outcome with high accuracy. Their combination may be of special interest for early prognostication after cardiac arrest where it performed significantly better than the currently recommended biomarker NSE.
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| 4. |
- Grindegård, Linnéa, et al.
(författare)
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Association Between EEG Patterns and Serum Neurofilament Light After Cardiac Arrest: A Post Hoc Analysis of the TTM Trial.
- 2022
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Ingår i: Neurology. - 1526-632X .- 0028-3878. ; 98:24
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Tidskriftsartikel (refereegranskat)abstract
- Electroencephalography (EEG) is widely used for prediction of neurological outcome after cardiac arrest. To better understand the relationship between EEG and neuronal injury, we explore the association between EEG and neurofilament light (NFL) as a marker of neuroaxonal injury. We evaluate whether highly malignant EEG patterns are reflected by high NFL levels. Additionally, we explore the association of EEG backgrounds and EEG discharges with NFL.Post-hoc analysis of the Target Temperature Management after out-of-hospital cardiac arrest (TTM)-trial. Routine EEGs were prospectively performed after the temperature intervention ≥36 hours post-arrest. Patients who awoke or died prior to 36 hours post-arrest were excluded. EEG-experts blinded to clinical information classified EEG background, amount of discharges and highly malignant EEG patterns according to the standardized American Clinical Neurophysiology Society terminology. Prospectively collected serum samples were analyzed for NFL after trial completion. The highest available concentration at 48 or 72-hours post-arrest was used.262/939 patients with EEG and NFL data were included. Patients with highly malignant EEG patterns had 2.9 times higher NFL levels than patients with malignant patterns and NFL levels were 13 times higher in patients with malignant patterns than those with benign patterns (95% CI: 1.4-6.1 and 6.5-26.2 respectively, effect size 0.47, p<0.001). Both background and the amount of discharges were independently strongly associated with NFL levels (p<0.001). The EEG background had a stronger association with NFL levels than EEG discharges (R2=0.30 and R2=0.10, respectively). NFL levels in patients with a continuous background were lower than for any other background (95% CI for discontinuous, burst-suppression and suppression, respectively: 2.26-18.06, 3.91-41.71 and 5.74-41.74, effect size 0.30 and p<0.001 for all). NFL levels did not differ between suppression and burst-suppression. Superimposed discharges were only associated with higher NFL levels if the EEG background was continuous.Benign, malignant, and highly malignant EEG patterns reflect the extent of brain injury as measured by NFL in serum. The extent of brain injury is more strongly related to the EEG background than superimposed discharges. Combining EEG and NFL may be useful to better identify patients misclassified by single methods.clinicaltrials.gov, NCT01020916.
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| 5. |
- Lagebrant, Alice, et al.
(författare)
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Brain injury markers in blood predict signs of hypoxic ischaemic encephalopathy on head computed tomography after cardiac arrest
- 2023
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Ingår i: Resuscitation. - : Elsevier. - 0300-9572 .- 1873-1570. ; 184
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Tidskriftsartikel (refereegranskat)abstract
- Background/Aim: Signs of hypoxic ischaemic encephalopathy (HIE) on head computed tomography (CT) predicts poor neurological outcome after cardiac arrest. We explore whether levels of brain injury markers in blood could predict the likelihood of HIE on CT.Methods: Retrospective analysis of CT performed at 24-168 h post cardiac arrest on clinical indication within the Target Temperature Management after out-of-hospital cardiac arrest-trial. Biomarkers prospectively collected at 24-and 48 h post-arrest were analysed for neuron specific enolase (NSE), neurofilament light (NFL), total-tau and glial fibrillary acidic protein (GFAP). HIE was assessed through visual evaluation and quantitative grey-white-matter ratio (GWR) was retrospectively calculated on Swedish subjects with original images available.Results: In total, 95 patients were included. The performance to predict HIE on CT (performed at IQR 73-116 h) at 48 h was similar for all biomark-ers, assessed as area under the receiving operating characteristic curve (AUC) NSE 0.82 (0.71-0.94), NFL 0.79 (0.67-0.91), total-tau 0.84 (0.74- 0.95), GFAP 0.79 (0.67-0.90). The predictive performance of biomarker levels at 24 h was AUC 0.72-0.81. At 48 h biomarker levels below Youden Index accurately excluded HIE in 77.3-91.7% (negative predictive value) and levels above Youden Index correctly predicted HIE in 73.3-83.7% (positive predictive value). NSE cut-off at 48 h was 48 ng/ml. Elevated biomarker levels irrespective of timepoint significantly correlated with lower GWR.Conclusion: Biomarker levels can assess the likelihood of a patient presenting with HIE on CT and could be used to select suitable patients for CT-examination during neurological prognostication in unconscious cardiac arrest patients.
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| 6. |
- Levin, Helena, et al.
(författare)
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Plasma neurofilament light is a predictor of neurological outcome 12 h after cardiac arrest
- 2023
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Ingår i: Critical Care. - : Springer Science and Business Media LLC. - 1364-8535. ; 27:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundPrevious studies have reported high prognostic accuracy of circulating neurofilament light (NfL) at 24-72 h after out-of-hospital cardiac arrest (OHCA), but performance at earlier time points and after in-hospital cardiac arrest (IHCA) is less investigated. We aimed to assess plasma NfL during the first 48 h after OHCA and IHCA to predict long-term outcomes.MethodsObservational multicentre cohort study in adults admitted to intensive care after cardiac arrest. NfL was retrospectively analysed in plasma collected on admission to intensive care, 12 and 48 h after cardiac arrest. The outcome was assessed at two to six months using the Cerebral Performance Category (CPC) scale, where CPC 1-2 was considered a good outcome and CPC 3-5 a poor outcome. Predictive performance was measured with the area under the receiver operating characteristic curve (AUROC).ResultsOf 428 patients, 328 (77%) suffered OHCA and 100 (23%) IHCA. Poor outcome was found in 68% of OHCA and 55% of IHCA patients. The overall prognostic performance of NfL was excellent at 12 and 48 h after OHCA, with AUROCs of 0.93 and 0.97, respectively. The predictive ability was lower after IHCA than OHCA at 12 and 48 h, with AUROCs of 0.81 and 0.86 (p <= 0.03). AUROCs on admission were 0.77 and 0.67 after OHCA and IHCA, respectively. At 12 and 48 h after OHCA, high NfL levels predicted poor outcome at 95% specificity with 70 and 89% sensitivity, while low NfL levels predicted good outcome at 95% sensitivity with 71 and 74% specificity and negative predictive values of 86 and 88%.ConclusionsThe prognostic accuracy of NfL for predicting good and poor outcomes is excellent as early as 12 h after OHCA. NfL is less reliable for the prediction of outcome after IHCA.
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| 7. |
- Moseby-Knappe, Marion, et al.
(författare)
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Performance of a guideline-recommended algorithm for prognostication of poor neurological outcome after cardiac arrest
- 2020
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Ingår i: Intensive Care Medicine. - : Springer Science and Business Media LLC. - 0342-4642 .- 1432-1238. ; 46:10, s. 1852-62
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Tidskriftsartikel (refereegranskat)abstract
- © 2020, The Author(s). Purpose: To assess the performance of a 4-step algorithm for neurological prognostication after cardiac arrest recommended by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM). Methods: Retrospective descriptive analysis with data from the Target Temperature Management (TTM) Trial. Associations between predicted and actual neurological outcome were investigated for each step of the algorithm with results from clinical neurological examinations, neuroradiology (CT or MRI), neurophysiology (EEG and SSEP) and serum neuron-specific enolase. Patients examined with Glasgow Coma Scale Motor Score (GCS-M) on day 4 (72–96h) post-arrest and available 6-month outcome were included. Poor outcome was defined as Cerebral Performance Category 3–5. Variations of the ERC/ESICM algorithm were explored within the same cohort. Results: The ERC/ESICM algorithm identified poor outcome patients with 38.7% sensitivity (95% CI 33.1–44.7) and 100% specificity (95% CI 98.8–100) in a cohort of 585 patients. An alternative cut-off for serum neuron-specific enolase, an alternative EEG-classification and variations of the GCS-M had minor effects on the sensitivity without causing false positive predictions. The highest overall sensitivity, 42.5% (95% CI 36.7–48.5), was achieved when prognosticating patients irrespective of GCS-M score, with 100% specificity (95% CI 98.8–100) remaining. Conclusion: The ERC/ESICM algorithm and all exploratory multimodal variations thereof investigated in this study predicted poor outcome without false positive predictions and with sensitivities 34.6–42.5%. Our results should be validated prospectively, preferably in patients where withdrawal of life-sustaining therapy is uncommon to exclude any confounding from self-fulfilling prophecies.
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| 8. |
- Moseby-Knappe, Marion, et al.
(författare)
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Serum markers of brain injury can predict good neurological outcome after out-of-hospital cardiac arrest
- 2021
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Ingår i: Intensive Care Medicine. - : Springer Science and Business Media LLC. - 0342-4642 .- 1432-1238. ; 47, s. 984-994
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Tidskriftsartikel (refereegranskat)abstract
- Purpose The majority of unconscious patients after cardiac arrest (CA) do not fulfill guideline criteria for a likely poor outcome, their prognosis is considered "indeterminate". We compared brain injury markers in blood for prediction of good outcome and for identifying false positive predictions of poor outcome as recommended by guidelines. Methods Retrospective analysis of prospectively collected serum samples at 24, 48 and 72 h post arrest within the Target Temperature Management after out-of-hospital cardiac arrest (TTM)-trial. Clinically available markers neuron-specific enolase (NSE) and S100B, and novel markers neurofilament light chain (NFL), total tau, ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) and glial fibrillary acidic protein (GFAP) were analysed. Normal levels with a priori cutoffs specified by reference laboratories or defined from literature were used to predict good outcome (no to moderate disability, Cerebral Performance Category scale 1-2) at 6 months. Results Seven hundred and seventeen patients were included. Normal NFL, tau and GFAP had the highest sensitivities (97.2-98% of poor outcome patients had abnormal serum levels) and NPV (normal levels predicted good outcome in 87-95% of patients). Normal S100B and NSE predicted good outcome with NPV 76-82.2%. Normal NSE correctly identified 67/190 (35.3%) patients with good outcome among those classified as "indeterminate outcome" by guidelines. Five patients with single pathological prognostic findings despite normal biomarkers had good outcome. Conclusion Low levels of brain injury markers in blood are associated with good neurological outcome after CA. Incorporating biomarkers into neuroprognostication may help prevent premature withdrawal of life-sustaining therapy.
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| 9. |
- Ahmad, Shahzad, et al.
(författare)
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CDH6 and HAGH protein levels in plasma associate with Alzheimer’s disease in APOE ε4 carriers
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Many Alzheimer’s disease (AD) genes including Apolipoprotein E (APOE) are found to be expressed in blood-derived macrophages and thus may alter blood protein levels. We measured 91 neuro-proteins in plasma from 316 participants of the Rotterdam Study (incident AD = 161) using Proximity Extension Ligation assay. We studied the association of plasma proteins with AD in the overall sample and stratified by APOE. Findings from the Rotterdam study were replicated in 186 AD patients of the BioFINDER study. We further evaluated the correlation of these protein biomarkers with total tau (t-tau), phosphorylated tau (p-tau) and amyloid-beta (Aβ) 42 levels in cerebrospinal fluid (CSF) in the Amsterdam Dementia Cohort (N = 441). Finally, we conducted a genome-wide association study (GWAS) to identify the genetic variants determining the blood levels of AD-associated proteins. Plasma levels of the proteins, CDH6 (β = 0.638, P = 3.33 × 10−4) and HAGH (β = 0.481, P = 7.20 × 10−4), were significantly elevated in APOE ε4 carrier AD patients. The findings in the Rotterdam Study were replicated in the BioFINDER study for both CDH6 (β = 1.365, P = 3.97 × 10−3) and HAGH proteins (β = 0.506, P = 9.31 × 10−7) when comparing cases and controls in APOE ε4 carriers. In the CSF, CDH6 levels were positively correlated with t-tau and p-tau in the total sample as well as in APOE ε4 stratum (P < 1 × 10−3). The HAGH protein was not detected in CSF. GWAS of plasma CDH6 protein levels showed significant association with a cis-regulatory locus (rs111283466, P = 1.92 × 10−9). CDH6 protein is implicated in cell adhesion and synaptogenesis while HAGH protein is related to the oxidative stress pathway. Our findings suggest that these pathways may be altered during presymptomatic AD and that CDH6 and HAGH may be new blood-based biomarkers.
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| 10. |
- Arvidsson, Ida, et al.
(författare)
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Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer’s disease in patients with mild cognitive symptoms
- 2024
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Ingår i: Alzheimer's Research and Therapy. - 1758-9193. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Predicting future Alzheimer’s disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) is an important task for healthcare. Structural brain imaging as measured by magnetic resonance imaging (MRI) could potentially contribute when making such predictions. It is unclear if the predictive performance of MRI can be improved using entire brain images in deep learning (DL) models compared to using pre-defined brain regions. Methods: A cohort of 332 individuals with SCD/MCI were included from the Swedish BioFINDER-1 study. The goal was to predict longitudinal SCD/MCI-to-AD dementia progression and change in Mini-Mental State Examination (MMSE) over four years. Four models were evaluated using different predictors: (1) clinical data only, including demographics, cognitive tests and APOE ε4 status, (2) clinical data plus hippocampal volume, (3) clinical data plus all regional MRI gray matter volumes (N = 68) extracted using FreeSurfer software, (4) a DL model trained using multi-task learning with MRI images, Jacobian determinant images and baseline cognition as input. A double cross-validation scheme, with five test folds and for each of those ten validation folds, was used. External evaluation was performed on part of the ADNI dataset, including 108 patients. Mann-Whitney U-test was used to determine statistically significant differences in performance, with p-values less than 0.05 considered significant. Results: In the BioFINDER cohort, 109 patients (33%) progressed to AD dementia. The performance of the clinical data model for prediction of progression to AD dementia was area under the curve (AUC) = 0.85 and four-year cognitive decline was R2 = 0.14. The performance was improved for both outcomes when adding hippocampal volume (AUC = 0.86, R2 = 0.16). Adding FreeSurfer brain regions improved prediction of four-year cognitive decline but not progression to AD (AUC = 0.83, R2 = 0.17), while the DL model worsened the performance for both outcomes (AUC = 0.84, R2 = 0.08). A sensitivity analysis showed that the Jacobian determinant image was more informative than the MRI image, but that performance was maximized when both were included. In the external evaluation cohort from ADNI, 23 patients (21%) progressed to AD dementia. The results for predicted progression to AD dementia were similar to the results for the BioFINDER test data, while the performance for the cognitive decline was deteriorated. Conclusions: The DL model did not significantly improve the prediction of clinical disease progression in AD, compared to regression models with a single pre-defined brain region.
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