Argyrodite configuration determination for DFT and AIMD calculations using an integrated optimization strategy.

When constructing a partially occupied model structure for use in density functional theory (DFT) and ab initio molecular dynamics (AIMD) calculations, the selection of appropriate configurations has been a vexing issue. Random sampling and the ensuing low-Coulomb-energy entry selection have been routine. Here, we report a more efficient way of selecting low-Coulomb-energy configurations for a representative solid electrolyte, Li6PS5Cl. Metaheuristics (genetic algorithm, particle swarm optimization, cuckoo search, and harmony search), Bayesian optimization, and modified deep Q-learning are utilized to search the large configurational space. Ten configuration candidates that exhibit relatively low Coulomb energy values and thereby lead to more convincing DFT and AIMD calculation results are pinpointed along with computational cost savings by the assistance of the above-described optimization algorithms, which constitute an integrated optimization strategy. Consequently, the integrated optimization strategy outperforms the conventional random sampling-based selection strategy.

Optimal Composition of Li Argyrodite with Harmonious Conductivity and Chemical/Electrochemical Stability: Fine-Tuned Via Tandem Particle Swarm Optimization.

A tandem (two-step) particle swarm optimization (PSO) algorithm is implemented in the argyrodite-based multidimensional composition space for the discovery of an optimal argyrodite composition, i.e., with the highest ionic conductivity (7.78 mS cm-1 ). To enhance the industrial adaptability, an elaborate pellet preparation procedure is not used. The optimal composition (Li5.5 PS4.5 Cl0.89 Br0.61 ) is fine-tuned to enhance its practical viability by incorporating oxygen in a stepwise manner. The final composition (Li5.5 PS4.23 O0.27 Cl0.89 Br0.61 ), which exhibits an ionic conductivity (σion ) of 6.70 mS cm-1 and an activation barrier of 0.27 eV, is further characterized by analyzing both its moisture and electrochemical stability. Relative to the other compositions, the exposure of Li5.5 PS4.23 O0.27 Cl0.89 Br0.61 to a humid atmosphere results in the least amount of H2 S released and a negligible change in structure. The improvement in the interfacial stability between the Li(Ni0.9 Co0.05 Mn0.05 )O2 cathode and Li5.5 PS4.23 O0.27 Cl0.89 Br0.61 also results in greater specific capacity during fast charge/discharge. The structural and chemical features of Li5.5 PS4.5 Cl0.89 Br0.61 and Li5.5 PS4.23 O0.27 Cl0.89 Br0.61 argyrodites are characterized using synchrotron X-ray diffraction, Raman spectroscopy, and X-ray photoelectron spectroscopy. This work presents a novel argyrodite composition with favorably balanced properties while providing broad insights into material discovery methodologies with applications for battery development.

Unravelling the Nature of the Intrinsic Complex Structure of Binary-Phase Na-Layered Oxides.

The layered sodium transition metal oxide, NaTMO2 (TM = transition metal), with a binary or ternary phases has displayed outstanding electrochemical performance as a new class of strategy cathode materials for sodium-ion batteries (SIBs). Herein, an in-depth phase analysis of developed Na1-x TMO2 cathode materials, Na0.76 Ni0.20 Fe0.40 Mn0.40 O2 with P2- and O3-type phases (NFMO-P2/O3) is offered. Structural visualization on an atomic scale is also provided and the following findings are unveiled: i) the existence of a mixed-phase intergrowth layer distribution and unequal distribution of P2 and O3 phases along two different crystal plane indices and ii) a complete reversible charge/discharge process for the initial two cycles that displays a simple phase transformation, which is unprecedented. Moreover, first-principles calculations support the evidence of the formation of a binary NFMO-P2/O3 compound, over the proposed hypothetical monophasic structures (O3, P3, O'3, and P2 phases). As a result, the synergetic effect of the simultaneous existence of P- and O-type phases with their unique structures allows an extraordinary level of capacity retention in a wide range of voltage (1.5-4.5 V). It is believed that the insightful understanding of the proposed materials can introduce new perspectives for the development of high-voltage cathode materials for SIBs.

Blood-brain barrier disruption as a cause of various serum neuron-specific enolase cut-off values for neurological prognosis in cardiac arrest patients.

We compared the cut-off and prognostic value of serum neuron-specific enolase (NSE) between groups with and without severe blood-brain barrier (BBB) disruption to reveal that a cause of various serum NSE cut-off value for neurological prognosis is severe BBB disruption in out-of-hospital cardiac arrest (OHCA) patients underwent target temperature management (TTM). This was a prospective, single-centre study conducted from January 2019 to June 2021. Severe BBB disruption was indicated using cerebrospinal fluid-serum albumin quotient values > 0.02. The area under the receiver operating characteristic curve of serum NSE obtained on day 3 of hospitalisation to predict poor outcomes was used. In patients with poor neurologic outcomes, serum NSE in those with severe BBB disruption was higher than in those without (P = 0.006). A serum NSE cut-off value of 40.4 µg/L for poor outcomes in patients without severe BBB disruption had a sensitivity of 41.7% and a specificity of 96.0%, whereas a cut-off value of 34.6 µg/L in those with severe BBB disruption had a sensitivity of 86.4% and a specificity of 100.0%. We demonstrated that the cut-off and prognostic value of serum NSE were heterogeneous, depending on severe BBB disruption in OHCA patients treated with TTM.

Relationships between serum levels of lactate dehydrogenase and neurological outcomes of patients who underwent targeted temperature management after out-of-hospital cardiac arrest.

ABSTRACT: This study aimed to evaluate times for measuring serum lactate dehydrogenase levels (SLLs) to predict neurological prognosis among out-of-hospital cardiac arrest (OHCA) survivors.This retrospective study examined patients who experienced OHCA treated with targeted temperature management (TTM). The SLLs were evaluated at the return of spontaneous circulation (ROSC) and at 24, 48, and 72 hours later. Neurological outcomes after 3 months were evaluated for relationships with the SLL measurement times.A total of 95 comatose patients with OHCA were treated using TTM. Seventy three patients were considered eligible, including 31 patients (42%) who experienced good neurological outcomes. There were significant differences between the good and poor outcome groups at most time points (P < .001), except for ROSC (P = .06). The ROSC measurement had a lower area under the receiver operating characteristic curve (AUC: 0.631, 95% confidence interval [CI]: 0.502-0.761) than at 48 hours (AUC: 0.830, 95% CI: 0.736-0.924), at 24 hours (AUC: 0.786, 95% CI: 0.681-0.892), and at 72 hours (AUC: 0.821, 95% CI: 0.724-0.919).A higher SLL seemingly predicted poor neurological outcomes, with good prognostic values at 48 hours and 72 hours. Prospective studies should be conducted to confirm these results.

A machine-learning-based alloy design platform that enables both forward and inverse predictions for thermo-mechanically controlled processed (TMCP) steel alloys.

Predicting mechanical properties such as yield strength (YS) and ultimate tensile strength (UTS) is an intricate undertaking in practice, notwithstanding a plethora of well-established theoretical and empirical models. A data-driven approach should be a fundamental exercise when making YS/UTS predictions. For this study, we collected 16 descriptors (attributes) that implicate the compositional and processing information and the corresponding YS/UTS values for 5473 thermo-mechanically controlled processed (TMCP) steel alloys. We set up an integrated machine-learning (ML) platform consisting of 16 ML algorithms to predict the YS/UTS based on the descriptors. The integrated ML platform involved regularization-based linear regression algorithms, ensemble ML algorithms, and some non-linear ML algorithms. Despite the dirty nature of most real-world industry data, we obtained acceptable holdout dataset test results such as R2 > 0.6 and MSE < 0.01 for seven non-linear ML algorithms. The seven fully trained non-linear ML models were used for the ensuing 'inverse design (prediction)' based on an elitist-reinforced, non-dominated sorting genetic algorithm (NSGA-II). The NSGA-II enabled us to predict solutions that exhibit desirable YS/UTS values for each ML algorithm. In addition, the NSGA-II-driven solutions in the 16-dimensional input feature space were visualized using holographic research strategy (HRS) in order to systematically compare and analyze the inverse-predicted solutions for each ML algorithm.

Using Out-of-Hospital Cardiac Arrest (OHCA) and Cardiac Arrest Hospital Prognosis (CAHP) Scores with Modified Objective Data to Improve Neurological Prognostic Performance for Out-of-Hospital Cardiac Arrest Survivors.

This study aimed to determine whether accuracy and sensitivity concerning neurological prognostic performance increased for survivors of out-of-hospital cardiac arrest (OHCA) treated with targeted temperature management (TTM), using OHCA and cardiac arrest hospital prognosis (CAHP) scores and modified objective variables. We retrospectively analyzed non-traumatic OHCA survivors treated with TTM. The primary outcome was poor neurological outcome at 3 months after return of spontaneous circulation (cerebral performance category, 3-5). We compared neurological prognostic performance using existing models after adding objective data obtained before TTM from computed tomography (CT), magnetic resonance imaging (MRI), and biomarkers to replace the no-flow time component of the OHCA and CAHP models. Among 106 patients, 61 (57.5%) had poor neurologic outcomes. The area under the receiver operating characteristic (AUROC) curve for the OHCA and CAHP models was 0.89 (95% confidence interval (CI) 0.81-0.94) and 0.90 (95% CI 0.82-0.95), respectively. The prediction of poor neurological outcome improved after replacing no-flow time with a grey/white matter ratio measured using CT, high-signal intensity (HSI) on diffusion-weighted MRI (DWI), percentage of voxel using apparent diffusion coefficient value, and serum neuron-specific enolase levels. When replaced with HSI on DWI, the AUROC and sensitivity of the OHCA and CAHP models were 0.96 and 74.5% and 0.97 and 83.8%, respectively (100% specificity). Prognoses concerning neurologic outcomes improved compared with existing OHCA and CAHP models by adding new objective variables to replace no-flow time. External validation is required to generalize these results in various contexts.

Cyan-Light-Emitting Chalcogenometallate Phosphor, KGaS2:Eu2+, for Phosphor-Converted White Light-Emitting Diodes.

A novel KGaS2 phosphor host that emits a cyan light was discovered to fill the cyan gap in the visible spectrum of phosphor-converted white light-emitting diodes (pc-wLEDs). KGaS2, belonging to the chalcogenometallates of the type ABQ2, was synthesized via a solid-state route with compositions optimized to achieve a phosphor host that would achieve the best photoluminescence (PL) properties. The activation with Eu2+ gave rise to PL in the cyan region of the spectrum with a PL maximum at ∼498 nm, as measured under the near-UV (420 nm) and blue (450 nm) excitations. The PL properties at the near-UV excitation are found to be much better, as compared to those obtained at the blue excitation. The Rietveld analysis, using high resolution synchrotron X-ray diffraction calibrated at a wavelength of 1.522 Å and selected area electron diffraction (SAED) pattern analysis of the composition optimized with the highest PL intensity, revealed a centrosymmetric monoclinic structure in the C2/c space group. The stoichiometry of the optimized composition, as estimated using Rietveld refinement, was revealed as KGa0.921S1.882:Eu2+. The decay curve measurement, using time-resolved spectroscopy, yielded a 10% decay time of 0.41 µs, which is much smaller compared with the decay time of the commercially available ß-SIALON phosphor that has a 10% decay time of 1.71 µs. The white pc-LED, fabricated with a cyan phosphor, had a higher value on the color rendering index and a lower value for color correlated temperatures, as compared with the version fabricated without a cyan phosphor, which makes this novel phosphor suitable for applications as a pc-wLED.

Negative pressure wound therapy for skin necrosis prevention after snakebite in the emergency department: A retrospective cohort study.

ABSTRACT: This retrospective cohort study aimed to compare the effectiveness of conventional treatment and ultra-early application of negative pressure wound therapy (NPWT) in patients with snakebites.Patients who visited the emergency department within 24 hours after a snakebite were assigned to the non- NPWT or NPWT group. Swelling resolution time and rates of necrosis, infection, and operations were compared between the 2 groups. The Stony Brook Scar Evaluation Scale was used to measure short- and long-term wound healing results.Among the included 61 patients, the swelling resolution time was significantly shorter in the NPWT group than in non- NPWT group (P = .010). The NPWT group showed lower necrosis (4.3% versus 36.8%; P = .003) and infection (13.2% and 4.3%; P = .258) rates than the non- NPWT group. The median Stony Brook Scar Evaluation Scale scores were higher in the NPWT group than in the non- NPWT group (P< .001).These findings suggest that ultra-early application of NPWT reduces edema, promotes wound healing, and prevents necrosis in patients with snakebites.

Dirty engineering data-driven inverse prediction machine learning model.

Most data-driven machine learning (ML) approaches established in metallurgy research fields are focused on a build-up of reliable quantitative models that predict a material property from a given set of material conditions. In general, the input feature dimension (the number of material condition variables) is much higher than the output feature dimension (the number of material properties of concern). Rather than such a forward-prediction ML model, it is necessary to develop so-called inverse-design modeling, wherein required material conditions could be deduced from a set of desired material properties. Here we report a novel inverse design strategy that employs two independent approaches: a metaheuristics-assisted inverse reading of conventional forward ML models and an atypical inverse ML model based on a modified variational autoencoder. These two unprecedented approaches were successful and led to overlapped results, from which we pinpointed several novel thermo-mechanically controlled processed (TMCP) steel alloy candidates that were validated by a rule-based thermodynamic calculation tool (Thermo-Calc.). We also suggested a practical protocol to elucidate how to treat engineering data collected from industry, which is not prepared as independent and identically distributed (IID) random data.

The cut-off value of a qualitative brain diffusion-weighted image (DWI) scoring system to predict poor neurologic outcome in out-of-hospital cardiac arrest (OHCA) patients after target temperature management.

AIM: We presented the cut-off value of a diffusion-weighted image (DWI) scoring system to predict poor neurologic outcome using DWI taken 72-96 h after out-of-hospital cardiac arrest (OHCA) patients underwent target temperature management (TTM). METHODS: This was a prospective single-centre observational study, conducted from March 2018 to April 2020 in OHCA patients after TTM. Neurological status was assessed 6 months after return of spontaneous circulation (ROSC) using the Glasgow-Pittsburgh cerebral performance categories (CPC) scale. CPC of 1-2 demonstrated good neurologic outcomes whilst a CPC of 3-5 was related to poor neurologic outcomes. The receiver operating characteristic curves and DeLong method were used to evaluate the cut-off value of the DWI scoring system to predict poor neurologic outcome. RESULTS: The good and poor neurologic outcome groups consisted of 38 (54.3%) and 32 (45.7%) patients, respectively. The area under the receiver operating characteristic curve (AUROC) of the overall, cortex, deep grey nuclei, and cortex plus deep grey nuclei scores, white matter, brainstem, and cerebellum measured 72-96 h after ROSC were 0.96, 0.96, 0.97, 0.96, 0.95, 0.95, and 0.93 respectively. For 100.0% specificity to predict poor neurologic outcome, the overall scores of the DWI scoring system measured 72-96 h after ROSC with a cut-off value of 52 had a sensitivity of 81.3% (95% CI: 63.6-92.8). CONCLUSION: This study demonstrated that the DWI scoring systems measured between 72 and 96 h after ROSC were valuable tools to predict poor neurologic outcome in post-OHCA patients treated with TTM.

Impact of low and high partial pressure of carbon dioxide on neuron-specific enolase derived from serum and cerebrospinal fluid in patients who underwent targeted temperature management after out-of-hospital cardiac arrest: A retrospective study.

AIM: In a previous study, low and high-normal arterial carbon dioxide tension (PaCO2) were not associated with serum neuron-specific enolase (NSE) in cardiac arrest survivors. We assessed the effect of PaCO2 on NSE in cerebrospinal fluid (CSF) and serum. METHODS: This was a retrospective study. PaCO2 for the first 24 h was analysed in four means, qualitative exposure state (qES), time-weighted average (TWA), median, and minimum-maximum (Min-Max). These subgroups were divided into low (LCO2) and high PaCO2 (HCO2) groups defined as PaCO2 ≤ 35.3 and PaCO2 > 43.5 mmHg, respectively. NSE was measured at 24, 48, and 72 h (sNSE24,48,72 and cNSE24,48,72) from return of spontaneous circulation (ROSC). The primary outcome was the association between PaCO2 and the NSE measured at 24 h after ROSC. RESULTS: Forty-two subjects (male, 33; 78.6%) were included in total cohort. PaCO2 in TWA subgroup was associated with cNSE24,48,72, while PaCO2 in the other subgroup were only associated with cNSE24. PaCO2 and cNSE in qES subgroup showed good correlation (r = -0.61; p < 0.01), and in TWA, median, and Min-Max subgroup showed moderate correlations (r = -0.57, r = -0.48, and r = -0.60; p < 0.01). Contrastively, sNSE was not associated and correlated with PaCO2 in all analysis. Poor neurological outcome in LCO2 was significantly higher than HCO2 in qES, TWA, and median subgroups (p < 0.01, p < 0.01, and p = 0.02). CONCLUSION: Association was found between NSE and PaCO2 using CSF, despite including normocapnic ranges; TWA of PaCO2 may be most strongly associated with CSF NSE levels. A prospective, multi-centre study is required to confirm our results.

The value of cerebrospinal fluid ubiquitin C-terminal hydrolase-L1 protein as a prognostic predictor of neurologic outcome in post-cardiac arrest patients treated with targeted temperature management.

AIM: We evaluated the prognostic value of serum- and cerebrospinal fluid (CSF)-ubiquitin carboxyl-terminal esterase L1 protein (UCHL1) measurements in post- post-out of hospital cardiac arrest (OHCA) patients treated with target temperature management (TTM), to predict neurologic outcome. METHODS: This was a prospective single-centre observational cohort study, conducted from April 2018 to September 2019. Serum- and CSF-UCHL1 were obtained immediately (UCHL1initial), 24 h (UCHL124), 48 h (UCHL148), and 72 h (UCHL172) after return of spontaneous circulation (ROSC). The area under the receiver operating characteristic curves (AUROC) and Delong method were used to identify cut-off values of serum- and CSF-UCHL1initial, UCHL124, UCHL148, UCHL172 for predicting neurologic outcomes. RESULTS: Of 38 patients enrolled, 16 comprised the poor outcome group. The AUROCs for serum- and CSF-UCHL1initial were 0.71 and 0.93 in predicting poor neurological outcomes, respectively (p = 0.01). The AUROCs for serum- and CSF-UCHL124 were 0.85 and 0.91 (p = 0.24). The AUROCs for serum- and CSF-UCHL148 were 0.90 and 0.97 (p = 0.07). The AUROCs for serum- and CSF-UCHL172 were 0.94 and 0.98 (p = 0.25). CONCLUSION: Findings of this study demonstrate that CSF-UCHL1 measured immediately, 24, 48, and 72 h after ROSC is a valuable predictor for evaluating neurologic outcomes, whereas serum-UCHL1 measured at 24, 48, and 72 h after ROSC showed a significant performance in the prognostication of poor outcomes in post-OHCA patients treated with TTM.

Cerebrospinal fluid lactate dehydrogenase as a potential predictor of neurologic outcomes in cardiac arrest survivors who underwent target temperature management.

PURPOSE: Cerebrospinal fluid (CSF) lactate dehydrogenase (LDH) levels increase in patients with brain injury. We investigated neurologic outcomes associated with CSF LDH levels in out-of-hospital cardiac arrest (OHCA) survivors who underwent target temperature management (TTM). MATERIALS AND METHODS: This was a prospective single-centre observational study from April 2018 to May 2019 on a cohort of 41 patients. CSF and serum LDH samples were obtained immediately (LDH0) and at 24 (LDH24), 48 (LDH48), and 72 h (LDH72) after return of spontaneous circulation (ROSC). Neurologic outcomes were assessed at 3 months after ROSC using the Cerebral Performance Category scale. RESULTS: Twenty-one patients had a poor neurologic outcome. CSF LDH levels were significantly higher in the poor neurologic outcome group at each time point. The area under the curve (AUC) of CSF LDH48 was 0.941 (95% confidence interval [CI], 0.806-0.992). With a cut off value of 250 U/L, CSF LDH48 had a high sensitivity (94.1%; 95% CI, 71.3-99.9) at 100% specificity. CONCLUSIONS: CSF LDH level at 48 h was a highly specific and sensitive marker for 3-month poor neurologic outcome. This may constitute a useful predictive marker for neurologic outcome in OHCA survivors treated with TTM.

Publisher Correction: A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns.

Here we report a facile, prompt protocol based on deep-learning techniques to sort out intricate phase identification and quantification problems in complex multiphase inorganic compounds. We simulate plausible powder X-ray powder diffraction (XRD) patterns for 170 inorganic compounds in the Sr-Li-Al-O quaternary compositional pool, wherein promising LED phosphors have been recently discovered. Finally, 1,785,405 synthetic XRD patterns are prepared by combinatorically mixing the simulated powder XRD patterns of 170 inorganic compounds. Convolutional neural network (CNN) models are built and eventually trained using this large prepared dataset. The fully trained CNN model promptly and accurately identifies the constituent phases in complex multiphase inorganic compounds. Although the CNN is trained using the simulated XRD data, a test with real experimental XRD data returns an accuracy of nearly 100% for phase identification and 86% for three-step-phase-fraction quantification.

Liver Donation After Brain Death Following Intentional Ingestion of 99% E-Cigarette Liquid Nicotine 10 mL.

Incidences of brain death due to nicotine overdose by e-cigarettes have been increasing. In such cases, liver donation has been not reported because of the secondary damage to the liver due to metabolism of large amounts of nicotine. However, kidneys have been considered acceptable for transplant. Here, we present a successful case of liver transplant from a brain-dead donor due to intentional nicotine ingestion who did not have extensive steatosis. To the best of our knowledge, this is the first report of such a case.

Aliovalent-doped sodium chromium oxide (Na0.9Cr0.9Sn0.1O2 and Na0.8Cr0.9Sb0.1O2) for sodium-ion battery cathodes with high-voltage characteristics.

NaCrO2 with high rate-capability is an attractive cathode material for sodium-ion batteries (NIBs). However, the amount of reversibly extractable Na+ ions is restricted by half, which results in relatively low energy density for practical NIB cathodes. Herein, we describe aliovalent-doped O3-Na0.9[Cr0.9Sn0.1]O2 (NCSnO) and O3-Na0.8[Cr0.9Sb0.1]O2 (NCSbO), both of which show high-voltage characteristics that translate to an increase in energy density. In contrast to NaCrO2, NCSnO and NCSbO can be reversibly charged to 3.80 and 3.95 V, respectively, delivering 0.5 Na+ along with Cr3+/4+ redox alone. The reversible chargeability to Na0.4[Cr0.9Sn0.1]O2 and Na0.3[Cr0.9Sb0.1]O2 is not associated with the suppression of Cr6+ formation. Both compounds show concentrations of Cr6+ that are higher than that of Na0.3CrO2, with an absence of O3' phases. This implies that aliovalent-doping contributes to a suppression of the Cr6+ migration into tetrahedral sites in the interslab space, which reduces the possibility of irreversible comproportionation. NCSnO and NCSbO deliver capacities comparable to that of NaCrO2, but show a higher average discharge voltage (2.94 V for NaCrO2; 3.14 V for NCSnO; 3.21 V for NCSbO), which leads to a noticeable increase in energy densities. The high-voltage characteristics of NCSnO and NCSbO are also validated via density-functional-theory calculations.

Relationship between optic nerve sheath diameter measured by magnetic resonance imaging, intracranial pressure, and neurological outcome in cardiac arrest survivors who underwent targeted temperature management.

AIM: Studies on the prognostic performance of optic nerve sheath diameter (ONSD) in out-of-hospital cardiac arrest survivors (OHCA) have reported conflicting results. We aimed to investigate the usefulness of ONSD measured using magnetic resonance imaging (MRI) to estimate its association with intracranial pressure (ICP) and 6-month neurological outcomes in CA survivors treated with targeted temperature management (TTM). METHOD: This retrospective study included 37 CA survivors who underwent TTM from January 2018 to December 2018. ICP was measured by lumbar catheter during TTM on Days 0, 1, 2, and 3. ONSD was measured using MRI on Days 0 and 3. The primary outcome was the correlation between ONSD and ICP associated with neurological outcomes obtained after 6 months. RESULTS: The median (interquartile range [IQR]) ONSD was not significantly different between the good and poor neurological outcome group on Day 0 (5.2 mm [4.8-5.8] vs 5.2 mm [4.8-5.6]; p = 0.948) and Day 3 (5.0 mm [4.8-5.2] vs 5.5 mm [4.4-5.9]; p = 0.105). ONSD and ICP had excellent correlation on Day 3 (r = 0.90, p < 0.001). ONSD showed excellent correlation with increased ICP (IICP) defined as ICP above 20 mmHg (r = 0.89, p < 0.001). ONSD cut-off of 5.99 mm was used with a sensitivity of 90.0% and specificity of 98.0% to identify IICP. CONCLUSION: The ONSD on Days 0 or 3 did not show differences in neurological outcomes in OHCA patients treated with TTM. However, ONSD had an excellent correlation with ICP on Day 3 and with IICP. Further studies are required to confirm our results.