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Background: Neurological outcomes after out-of-hospital cardiac arrest (OHCA) depend on multiple factors, including the patient's baseline condition and post-arrest management. The SLANT, developed specifically for OHCA survivors treated with targeted temperature management (TTM), requires further validation, particularly in Asian populations. Methods: This multicenter retrospective cohort study analyzed data from 2016 to 2023, examining demographics, pre-arrest conditions, resuscitation events, and laboratory biomarkers following TTM. The primary outcome was defined as a poor neurological outcome at hospital discharge. Model performance was assessed using the area under the receiver operating characteristic curve. Multivariate logistic regression analysis was used to analyze the included variables. Results: A total of 448 eligible adult patients were included, of whom 77.9% experienced poor neurological outcomes at discharge. The performance of the current cohort was comparable to that of the original SLANT cohort, achieving an area under the curve of 0.797 (95% confidence interval: 0.746–0.849). All five factors of the SLANT score remained statistically significant in predicting poor neurological outcomes. At a cutoff of ≥ 6.5, the SLANT score demonstrated a specificity of 53.5% and positive predictive value (PPV) of 86.9%. Increasing the cutoff value to 8.5 improved the specificity to 66.7% and the PPV to 89.6%. Conclusion: The SLANT showed high PPV for predicting poor neurological outcomes at discharge in patients with OHCA undergoing TTM across a multicenter Asian cohort. Combining the score with other neurological assessments is recommended for improved neuroprognostication. [ABSTRACT FROM AUTHOR]

Background: Despite advances in resuscitation practice, patient survival following cardiac arrest remains poor. The utilization of MRI in neurological outcome prognostication post-cardiac arrest is growing and various classifications has been proposed; however a consensus has yet to be established. MRI, though valuable, is resource-intensive, time-consuming, costly, and not universally available. This study aims to validate a MRI lesion pattern score in a cohort of out of hospital cardiac arrest patients at a tertiary referral hospital in Switzerland. Methods: This cohort study spanned twelve months from February 2021 to January 2022, encompassing all unconscious patients aged ≥ 18 years who experienced out-of-hospital cardiac arrest of any cause and were admitted to the intensive care unit (ICU) at Inselspital, University Hospital Bern, Switzerland. We included patients who underwent the neuroprognostication process, assessing the performance and validation of a MRI scoring system. Results: Over the twelve-month period, 137 patients were admitted to the ICU, with 52 entering the neuroprognostication process and 47 undergoing MRI analysis. Among the 35 MRIs indicating severe hypoxic brain injury, 33 patients (94%) experienced an unfavourable outcome (UO), while ten (83%) of the twelve patients with no or minimal MRI lesions had a favourable outcome. This yielded a sensitivity of 0.94 and specificity of 0.83 for predicting UO with the proposed MRI scoring system. The positive and negative likelihood ratios were 5.53 and 0.07, respectively, resulting in an accuracy of 91.49%. Conclusion: We demonstrated the effectiveness of the MLP scoring scheme in predicting neurological outcome in patients following cardiac arrest. However, to ensure a comprehensive neuroprognostication, MRI results need to be combined with other assessments. While neuroimaging is a promising objective tool for neuroprognostication, given the absence of sedation-related confounders—compared to electroencephalogram (EEG) and clinical examination—the current lack of a validated scoring system necessitates further studies. Incorporating standardized MRI techniques and grading systems is crucial for advancing the reliability of neuroimaging for neuroprognostication. Trial Registration: Registry of all Projects in Switzerland (RAPS) 2020-01761. [ABSTRACT FROM AUTHOR]

Background: We aimed to investigate the diagnostic performance of S100 as an outcome predictor after out-of-hospital cardiac arrest (OHCA) and the potential influence of two target temperatures (33 °C and 36 °C) on serum levels of S100.Methods: This is a substudy of the Target Temperature Management after Out-of-Hospital Cardiac Arrest (TTM) trial. Serum levels of S100 were measured a posteriori in a core laboratory in samples collected at 24, 48, and 72 h after OHCA. Outcome at 6 months was assessed using the Cerebral Performance Categories Scale (CPC 1-2 = good outcome, CPC 3-5 = poor outcome).Results: We included 687 patients from 29 sites in Europe. Median S100 values were higher in patients with a poor outcome at 24, 48, and 72 h: 0.19 (IQR 0.10-0.49) versus 0.08 (IQR 0.06-0.11) μg/ml, 0.16 (IQR 0.10-0.44) versus 0.07 (IQR 0.06-0.11) μg/L, and 0.13 (IQR 0.08-0.26) versus 0.06 (IQR 0.05-0.09) μg/L (p < 0.001), respectively. The ability to predict outcome was best at 24 h with an AUC of 0.80 (95% CI 0.77-0.83). S100 values were higher at 24 and 72 h in the 33 °C group than in the 36 °C group (0.12 [0.07-0.22] versus 0.10 [0.07-0.21] μg/L and 0.09 [0.06-0.17] versus 0.08 [0.05-0.10], respectively) (p < 0.02). In multivariable analyses including baseline variables and the allocated target temperature, the addition of S100 improved the AUC from 0.80 to 0.84 (95% CI 0.81-0.87) (p < 0.001), but S100 was not an independent outcome predictor. Adding S100 to the same model including neuron-specific enolase (NSE) did not further improve the AUC.Conclusions: The allocated target temperature did not affect S100 to a clinically relevant degree. High S100 values are predictive of poor outcome but do not add value to present prognostication models with or without NSE. S100 measured at 24 h and afterward is of limited value in clinical outcome prediction after OHCA.Trial Registration: ClinicalTrials.gov identifier: NCT01020916 . Registered on 25 November 2009. [ABSTRACT FROM AUTHOR]