<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd">
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article">
 <front>
  <journal-meta>
   <journal-id journal-id-type="publisher-id">
    jbbs
   </journal-id>
   <journal-title-group>
    <journal-title>
     Journal of Behavioral and Brain Science
    </journal-title>
   </journal-title-group>
   <issn pub-type="epub">
    2160-5866
   </issn>
   <issn publication-format="print">
    2160-5874
   </issn>
   <publisher>
    <publisher-name>
     Scientific Research Publishing
    </publisher-name>
   </publisher>
  </journal-meta>
  <article-meta>
   <article-id pub-id-type="doi">
    10.4236/jbbs.2025.156007
   </article-id>
   <article-id pub-id-type="publisher-id">
    jbbs-143787
   </article-id>
   <article-categories>
    <subj-group subj-group-type="heading">
     <subject>
      Articles
     </subject>
    </subj-group>
    <subj-group subj-group-type="Discipline-v2">
     <subject>
      Biomedical 
     </subject>
     <subject>
       Life Sciences, Medicine 
     </subject>
     <subject>
       Healthcare
     </subject>
    </subj-group>
   </article-categories>
   <title-group>
    Neuroprognostication after Cardiac Arrest
   </title-group>
   <contrib-group>
    <contrib contrib-type="author" xlink:type="simple">
     <name name-style="western">
      <surname>
       Briana
      </surname>
      <given-names>
       Lacy
      </given-names>
     </name> 
     <xref ref-type="aff" rid="aff1"> 
      <sup>1</sup>
     </xref>
    </contrib>
    <contrib contrib-type="author" xlink:type="simple">
     <name name-style="western">
      <surname>
       Pankhuri
      </surname>
      <given-names>
       Banerjee
      </given-names>
     </name> 
     <xref ref-type="aff" rid="aff2"> 
      <sup>2</sup>
     </xref>
    </contrib>
    <contrib contrib-type="author" xlink:type="simple">
     <name name-style="western">
      <surname>
       Nithisha
      </surname>
      <given-names>
       Thatikonda
      </given-names>
     </name> 
     <xref ref-type="aff" rid="aff3"> 
      <sup>3</sup>
     </xref>
    </contrib>
    <contrib contrib-type="author" xlink:type="simple">
     <name name-style="western">
      <surname>
       Todd
      </surname>
      <given-names>
       Masel
      </given-names>
     </name> 
     <xref ref-type="aff" rid="aff3"> 
      <sup>3</sup>
     </xref>
    </contrib>
   </contrib-group> 
   <aff id="aff1">
    <addr-line>
     aJohn Sealy School of Medicine, University of Texas Medical Branch, Galveston, USA
    </addr-line> 
   </aff> 
   <aff id="aff2">
    <addr-line>
     aDepartment of Neurology, The Mount Sinai Hospital, New York, USA
    </addr-line> 
   </aff> 
   <aff id="aff3">
    <addr-line>
     aDepartment of Neurology, University of Texas Medical Branch, Galveston, USA
    </addr-line> 
   </aff> 
   <pub-date pub-type="epub">
    <day>
     30
    </day> 
    <month>
     06
    </month>
    <year>
     2025
    </year>
   </pub-date> 
   <volume>
    15
   </volume> 
   <issue>
    06
   </issue>
   <fpage>
    115
   </fpage>
   <lpage>
    126
   </lpage>
   <history>
    <date date-type="received">
     <day>
      24,
     </day>
     <month>
      April
     </month>
     <year>
      2025
     </year>
    </date>
    <date date-type="published">
     <day>
      27,
     </day>
     <month>
      April
     </month>
     <year>
      2025
     </year> 
    </date> 
    <date date-type="accepted">
     <day>
      27,
     </day>
     <month>
      June
     </month>
     <year>
      2025
     </year> 
    </date>
   </history>
   <permissions>
    <copyright-statement>
     © Copyright 2014 by authors and Scientific Research Publishing Inc. 
    </copyright-statement>
    <copyright-year>
     2014
    </copyright-year>
    <license>
     <license-p>
      This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/
     </license-p>
    </license>
   </permissions>
   <abstract>
    Neuroprognostication is one of the most controversial and sensitive examinations in the field of neurology. Neuroprognostication after cardiac arrest is a particularly important evaluation to complete as it is closely tied to the pathology of cardiac arrest and the time to return of spontaneous circulation (ROSC). With the improvement in healthcare, there are more survivors of cardiac arrest. These survivors have variable outcomes, and often, a difficult prognosis to interpret based on their clinical presentation with hypoxic brain injury. Many patients experience moderate to severe neurological impairment in the form of severe cognitive disability or persistent vegetative state. Withdrawal of life-sustaining measures inevitably leads to death, thus propagating neuroprognostication as a controversial tool used to guide these difficult decisions. Thorough evaluation and tests are required for prognostication, with new guidelines consistently being updated. The non-exhaustive list of tests includes pupillary light response, neurological motor response, computed tomography of the brain, magnetic resonance imaging of the brain, electro-encephalogram, somatosensory evoked potentials, and more. Testing variables and outcomes are also dependent on the patient’s clinical picture, including potential hypothermic status. Overall, neuroprognostication after cardiac arrest holds great value in guiding clinical decision-making with the help of physical exam skills, updated algorithmic decision-making guidelines, and technology.
   </abstract>
   <kwd-group> 
    <kwd>
     Neuroprognostication
    </kwd> 
    <kwd>
      Hypoxic Brain Injury
    </kwd> 
    <kwd>
      Cardiac Arrest
    </kwd> 
    <kwd>
      Clinical Decision-Making
    </kwd> 
    <kwd>
      Neurology
    </kwd>
   </kwd-group>
  </article-meta>
 </front>
 <body>
  <sec id="s1">
   <title>1. Introduction</title>
   <p>Survival after cardiac arrest is steadily improving, yet most survivors face a second battle: hypoxic‑ischemic brain injury. Contemporary U.S. registry data show that roughly 60% of adults who regain spontaneous circulation develop measurable neurological deficits, and up to one‑third remain severely disabled at discharge <xref ref-type="bibr" rid="scirp.143787-1">
     <a href="#ref1">[1]</a>
    </xref>. Many of these survivors experience moderate to severe impairments, such as severe cognitive disabilities or persistent vegetative states <xref ref-type="bibr" rid="scirp.143787-1">
     [1]
    </xref>.</p>
   <p>Cognitive disability spans a broad spectrum, from mild executive‑function deficits to persistent vegetative state. The U.S. Federal Communications Commission, for instance, classifies intellectual disability, autism‑spectrum disorders, severe mental illness, brain injury, stroke, and Alzheimer’s disease under the umbrella of cognitive disability <xref ref-type="bibr" rid="scirp.143787-2">
     [2]
    </xref>. This review focuses on impairments that arise specifically from post‑arrest hypoxic‑ischemic injury.</p>
   <p>Neuroprognostication—the structured, multimodal prediction of long‑term neurological outcome—guides decisions on continuing or withdrawing life‑sustaining therapy (WLST). 80% of post-cardiac arrest fatalities are due to withdrawal of life-sustaining treatment (WLST). Post-cardiac arrest withdrawal of life-sustaining treatment reflects a complex interplay of regional practices, ethical frameworks, and cultural influences that significantly impact mortality outcomes.</p>
   <p>Stark regional variations exist, with North America and Northern Europe showing high WLST rates (30% - 65%). At the same time, Eastern European countries rarely practice WLST <xref ref-type="bibr" rid="scirp.143787-3">
     [3]
    </xref>, revealing how intensely geographic and cultural contexts shape end-of-life decisions. Hospital culture creates further variation, with studies demonstrating that early WLST (before 72 hours) negatively impacts survival rates and differs markedly between institutions within the same country <xref ref-type="bibr" rid="scirp.143787-4">
     [4]
    </xref>. Religious perspectives profoundly influence these decisions, with most major faiths permitting withdrawal of non-beneficial treatments while others prohibit it entirely. The disconnect between physician and public perspectives—illustrated by a Swedish study showing 82.3% of doctors would withhold treatment in a specific case compared to only 40.2% of the general population—reveals potential ethical misalignments between medical professionals and society <xref ref-type="bibr" rid="scirp.143787-5">
     <a href="#ref5">[5]</a>
    </xref>. Shared decision-making processes vary widely, with inconsistent family involvement and poorly documented goals-of-care discussions despite their crucial role in determining outcomes. Early WLST, based on perceived poor neurological prognosis, though common, contravenes current guidelines recommending waiting at least 72 hours, potentially resulting in preventable deaths through premature termination of care before accurate prognostication is possible <xref ref-type="bibr" rid="scirp.143787-5">
     [5]
    </xref> <xref ref-type="bibr" rid="scirp.143787-6">
     [6]
    </xref>. These factors collectively demonstrate why standardized protocols balancing respect for patient autonomy with evidence-based neurological assessment are essential to ensure ethical decision-making in this vulnerable patient population.</p>
   <p>Cardiac arrest abruptly halts effective cardiac output and cerebral perfusion. Etiologies divide broadly into cardiac causes (e.g., hypertension‑ or hyperlipidemia‑driven arrhythmia, myocardial infarction) and non‑cardiac causes (respiratory failure, electrolyte derangement, sepsis, trauma). Shared vascular risk factors—hypertension, hyperlipidemia, age, diabetes—link neurological disease with arrest risk. Regardless of the trigger, global hypoperfusion deprives the brain of oxygen and glucose; the extent of injury is governed chiefly by the arrest etiology, the duration of no‑flow/low‑flow, and the time to return of spontaneous circulation (ROSC) <xref ref-type="bibr" rid="scirp.143787-7">
     [7]
    </xref> <xref ref-type="bibr" rid="scirp.143787-8">
     [8]
    </xref>.</p>
  </sec><sec id="s2">
   <title>2. Study Aims</title>
   <p>To guide clinicians, patients, and families toward evidence‑based decisions, this review pursues three objectives:</p>
   <p>1) Summarize the accuracy and limitations of clinical examination, electrophysiology, neuroimaging, and biochemical markers used for neuroprognostication after adult cardiac arrest.</p>
   <p>2) Evaluate how current prognostic practices influence WLST timing and functional outcome, emphasizing the risk of self‑fulfilling prophecies.</p>
   <p>3) Propose a practical, phased algorithm that integrates multimodal data and specifies when prognostic certainty is sufficient to inform WLST versus continued supportive care.</p>
   <p>By articulating these aims, we provide a roadmap for clinicians seeking to balance prognostic accuracy with therapeutic optimism.</p>
  </sec><sec id="s3">
   <title>3. Pathophysiology Cellular Mechanisms (Primary Injury)</title>
   <p>The sudden cessation of cerebral perfusion during cardiac arrest halts oxidative phosphorylation, rapidly depleting adenosine triphosphate (ATP) and disabling ATP‑dependent ion pumps. Loss of ionic homeostasis permits an uncontrolled influx of calcium, sodium, and chloride, depolarizing neuronal membranes and triggering activation of calcium‑dependent proteases, lipases, and endonucleases <xref ref-type="bibr" rid="scirp.143787-9">
     <a href="#ref9">[9]</a>
    </xref>. Excessive glutamate release and mitochondrial membrane permeabilization generate large quantities of reactive oxygen species (ROS) <xref ref-type="bibr" rid="scirp.143787-9">
     [9]
    </xref>. The ROS drives both apoptotic and necrotic cell death. Selectively vulnerable neuronal populations—hippocampal CA1 pyramidal neurons, Purkinje cells of the cerebellum, and basal ganglia neurons—are the first to succumb, explaining the characteristic cognitive and motor deficits observed after resuscitation <xref ref-type="bibr" rid="scirp.143787-9">
     [9]
    </xref>-<xref ref-type="bibr" rid="scirp.143787-11">
     [11]
    </xref>.</p>
  </sec><sec id="s4">
   <title>4. Reperfusion Injury (Secondary Injury after ROSC)</title>
   <p>Restoration of circulation re‑oxygenates metabolically primed mitochondria, precipitating an abrupt ROS surge that oxidizes lipids, proteins, and nucleic acids <xref ref-type="bibr" rid="scirp.143787-9">
     [9]
    </xref>. Endothelial swelling, leukocyte adhesion, and microthrombi create a microvascular “no‑reflow” phenomenon, so regional hypoxia persists despite seemingly normal systemic flow. Blood–brain‑barrier disruption and failure of cerebral autoregulation promote vasogenic edema and further compromise perfusion <xref ref-type="bibr" rid="scirp.143787-10">
     <a href="#ref10">[10]</a>
    </xref>. These secondary processes evolve over hours to days, providing a therapeutic window for interventions such as targeted temperature management and antioxidant or anti‑inflammatory strategies <xref ref-type="bibr" rid="scirp.143787-10">
     [10]
    </xref>.</p>
  </sec><sec id="s5">
   <title>5. Neurological Assessment after ROSC</title>
   <sec id="s5_1">
    <title>5.1. Clinical Examination</title>
    <p>Because many resuscitated patients remain comatose, history must often be gleaned from family, bystanders, or emergency personnel. Initial evaluation centers on establishing the time and quality of ROSC and objectively grading arousal. The Glasgow Coma Scale (GCS) permits serial, standardized assessment of consciousness, distinguishing coma, unresponsive wakefulness (vegetative state), and the minimally conscious state <xref ref-type="bibr" rid="scirp.143787-12">
      [12]
     </xref>‑<xref ref-type="bibr" rid="scirp.143787-21">
      <a href="#ref21">[21]</a>
     </xref>. Higher admission GCS scores generally portend better neurological recovery <xref ref-type="bibr" rid="scirp.143787-21">
      [21]
     </xref>.</p>
   </sec>
   <sec id="s5_2">
    <title>5.2. Advanced Neurodiagnostic Tools</title>
    <p>Electroencephalography (EEG) detects electrographic seizures, quantifies background reactivity, and identifies malignant patterns (e.g., burst suppression) associated with unfavorable outcome. Non‑contrast computed tomography (CT) rapidly rules out intracranial hemorrhage, mass effect, or severe cerebral edema. At the same time, diffusion‑weighted magnetic‑resonance imaging (DW-MRI) or functional MRI (fMRI) reveals early ischemic injury and assesses functional connectivity. Finally, carotid duplex ultrasonography evaluates extracranial carotid flow and guides hemodynamic management when cerebral perfusion is in doubt <xref ref-type="bibr" rid="scirp.143787-21">
      [21]
     </xref>. Together, these complementary modalities integrate structural and functional data to refine prognostication.</p>
   </sec>
   <sec id="s5_3">
    <title>5.3. Limitations of Neurodiagnostic Tools</title>
    <p>Interpretation of ancillary tests requires care. Sedatives, neuromuscular blockade, and targeted temperature management can suppress cortical activity on EEG, creating patterns that mimic electrocerebral silence, and inter‑observer variability further limits specificity <xref ref-type="bibr" rid="scirp.143787-22">
      [22]
     </xref> <xref ref-type="bibr" rid="scirp.143787-23">
      [23]
     </xref>. Non‑contrast CT may overlook subtle early hypoxic‑ischaemic changes <xref ref-type="bibr" rid="scirp.143787-24">
      <a href="#ref24">[24]</a>
     </xref>. Although quantitative grey‑to‑white‑matter ratio analysis improves sensitivity, repeated radiation exposure complicates serial imaging <xref ref-type="bibr" rid="scirp.143787-24">
      [24]
     </xref>. MRI and fMRI are logistically challenging in haemodynamically unstable patients, and ferromagnetic implants can preclude their use; prolonged acquisition times necessitate additional sedation that may itself alter blood‑oxygen‑level‑dependent (BOLD) signals and delay care <xref ref-type="bibr" rid="scirp.143787-25">
      [25]
     </xref>. Even when feasible, fMRI data are hampered by motion artifact, hemodynamic lag, and the absence of standardized connectivity thresholds <xref ref-type="bibr" rid="scirp.143787-26">
      [26]
     </xref>. Carotid duplex ultrasonography is operator dependent and provides no insight into the intracranial microcirculation, where the “no‑reflow” phenomenon predominates <xref ref-type="bibr" rid="scirp.143787-27">
      [27]
     </xref>. Consequently, reliable neuroprognostication relies on a multimodal paradigm correlating serial clinical examinations with at least two independent ancillary investigations.</p>
   </sec>
  </sec><sec id="s6">
   <title>6. Evaluation</title>
   <p>Neuroprognostication following cardiac arrest involves a comprehensive, multimodal approach that includes a detailed examination under specific conditions. The patient should be evaluated without sedation, and temperature regulation should be maintained with a goal temperature below 36.5 ˚C <xref ref-type="bibr" rid="scirp.143787-28">
     <a href="#ref28">[28]</a>
    </xref>. Essential assessments include arterial blood gas analysis, metabolic and hematological blood tests, and serial serum neuron‑specific enolase (NSE) levels. Additional diagnostic modalities may encompass somatosensory evoked potentials, brain computed tomography, continuous electroencephalography, brain magnetic resonance imaging, carotid duplex or cerebral angiography, and, when indicated, advanced functional imaging such as positron‑emission tomography or single‑photon emission computed tomography <xref ref-type="bibr" rid="scirp.143787-28">
     [28]
    </xref>. In rare circumstances, an apnea test can evaluate brainstem function when other prognostic markers remain inconclusive. This specialised, stepwise testing strategy equips clinicians with a more definitive prognosis and enhances the overall accuracy of neuroprognostication. The recommended algorithm for evaluating comatose cardiac‑arrest survivors illustrates the day‑by‑day steps and expectations for patient care (<xref ref-type="fig" rid="fig1">
     Figure 1
    </xref>).</p>
   <fig id="fig1" position="float">
    <label>Figure 1</label>
    <caption>
     <title>Figure 1. Algorithm for neuroprognostication in adult comatose cardiac arrest survivors. The evidence-based algorithm determines functional outcome after 3 months.</title>
    </caption>
    <graphic mimetype="image" position="float" xlink:type="simple" xlink:href="https://html.scirp.org/file/3901173-rId21.jpeg?20250630052637" />
   </fig>
  </sec><sec id="s7">
   <title>7. Treatment/Management</title>
   <p>The latest Neurocritical Care Society (NCS) guidelines have identified eleven clinical variables to support clinician decision‑making and family discussions. The American Heart Association guidelines recommend delaying neurological prognostication until at least 72 hours after return to normothermia in post-cardiac arrest patients based on substantial evidence demonstrating the risks of premature assessment. This crucial waiting period accounts for the delayed clearance of sedatives and neuromuscular blockers in hypothermia-treated patients, which can confound clinical examinations <xref ref-type="bibr" rid="scirp.143787-29">
     [29]
    </xref>. Multiple studies reveal that false-positive rates for poor outcome prediction decrease significantly when assessments occur after this timeframe, with evidence that 15% - 30% of patients with good outcomes may not awaken until 48 hours to 12 days after sedation cessation <xref ref-type="bibr" rid="scirp.143787-6">
     [6]
    </xref>. Prospective research reveals that approximately one-third of patients had life-sustaining therapy withdrawn within 72 hours of admission, potentially contributing to 2300 preventable deaths annually in the U.S. <xref ref-type="bibr" rid="scirp.143787-30">
     <a href="#ref30">[30]</a>
    </xref>. This recommendation reflects the understanding that approximately 80% of patients destined for good outcomes recover consciousness within 3.5 days post-ROSC, while delayed awakening commonly occurs until 7 days, with documented cases extending beyond this timeframe <xref ref-type="bibr" rid="scirp.143787-30">
     [30]
    </xref>. Current guidelines emphasize a multimodal prognostication approach conducted no earlier than 72 hours after normothermia to maximize accuracy and allow sufficient neurological recovery time <xref ref-type="bibr" rid="scirp.143787-31">
     [31]
    </xref>.</p>
   <p>Brain MRI performed 2 - 7 days post-cardiac arrest provides optimal prognostic information as supported by multiple lines of evidence. This timing window coincides with the evolution of apparent diffusion coefficient (ADC) changes, which reach nadir between 3 - 5 days after arrest <xref ref-type="bibr" rid="scirp.143787-32">
     [32]
    </xref>, allowing for maximal detection of hypoxic-ischemic injury. Current guidelines formally recommend brain MRI at 2 - 5 days after return of spontaneous circulation based on established studies, though recent evidence validates the utility of imaging through day 7 <xref ref-type="bibr" rid="scirp.143787-33">
     [33]
    </xref>. This extended window is particularly valuable as quantitative thresholds have been validated, showing that &gt; 10% of brain tissue with an ADC &lt; 650 × 10<sup>−</sup><sup>6</sup> mm<sup>2</sup>/s identifies poor outcomes with high specificity <xref ref-type="bibr" rid="scirp.143787-34">
     [34]
    </xref>. The 2 - 7 day period allows for clearance of sedative medications and completion of therapeutic temperature management, reducing confounding factors and improving prognostic accuracy. Multimodal prognostication guidelines from neurological societies support this timing, emphasizing that diffusion restriction patterns across bilateral anterior and posterior circulation, involving both cortex and deep gray matter, are most predictive when assessed during this interval <xref ref-type="bibr" rid="scirp.143787-28">
     [28]
    </xref>. This timing also aligns with the broader recommendation to delay definitive prognostication until at least 72 hours after normothermia, creating a comprehensive approach to neurological assessment following cardiac arrest.</p>
   <p>Neuromuscular blocking agents (NMBAs) present a double-edged sword in post-cardiac arrest neuroprognostication. They mask critical clinical examination findings like motor responses and brainstem reflexes, while significantly enhancing the reliability of somatosensory evoked potentials (SSEPs). When administered during SSEP testing, NMBAs eliminate muscular artifacts that could otherwise contaminate recordings and compromise signal quality. Current guidelines specifically recommend their use to improve signal-to-noise ratios below the critical threshold of 0.25 µV <xref ref-type="bibr" rid="scirp.143787-35">
     [35]
    </xref>. Unlike sedatives and analgesics that can alter cortical waveforms, NMBAs act exclusively at the neuromuscular junction without affecting central signal transmission, preserving the integrity of cortical N20 responses critical for prognostication <xref ref-type="bibr" rid="scirp.143787-28">
     [28]
    </xref>. This selective peripheral action makes SSEPs particularly valuable within a multimodal assessment framework, as they remain relatively resistant to the confounding effects of both hypothermia and sedation compared to EEG patterns or clinical examination findings. The Canadian Cardiovascular Society’s position statement emphasizes this advantage, recommending NMBAs during SSEP recording to minimize artifacts and false positives, while cautioning that their use necessitates delaying clinical examination until complete clearance <xref ref-type="bibr" rid="scirp.143787-3">
     [3]
    </xref>. Prudent practice involves scheduling SSEP assessments when NMBAs are clinically indicated for shivering control during targeted temperature management, followed by adequate time for drug clearance before clinical examination, supporting the standard 72-hour post-normothermia timeline for comprehensive neuroprognostication <xref ref-type="bibr" rid="scirp.143787-36">
     [36]
    </xref>.</p>
   <p>When prognosis remains indeterminate, clinicians should communicate the possibility of a prolonged and uncertain recovery trajectory. Shared decision‑making demands that the wishes of the patient and their surrogate be incorporated; in many cases, an extended observation period may be appropriate and should be discussed transparently.</p>
   <p>Predictive markers in the NCS framework are stratified as reliable, moderately reliable, or not reliable based on three‑month functional outcomes. These markers, summarized in <xref ref-type="table" rid="table1">
     Table 1
    </xref>, feed into an evidence‑driven algorithm (<xref ref-type="fig" rid="fig2">
     Figure 2
    </xref>) that guides day‑to‑day prognostic expectations and therapeutic choices.</p>
   <p>Alongside the collected patient history, physical examination findings, imaging results, and neuroprognostic indicators, the provided algorithms also support clinicians in navigating the neuroprognostication process for suitable patients <xref ref-type="bibr" rid="scirp.143787-18">
     [18]
    </xref> <xref ref-type="bibr" rid="scirp.143787-19">
     [19]
    </xref>.</p>
  </sec><sec id="s8">
   <title>8. Complications and Conclusions</title>
   <p>These guidelines are essential for navigating neurologically complex situations that frequently intertwine medical, ethical, and legal considerations <xref ref-type="bibr" rid="scirp.143787-28">
     [28]
    </xref>. Every case is unique and warrants comprehensive, ongoing dialogue with the family. Such communication ensures that care aligns with patient values and clarifies what constitutes a meaningful quality of life. Most hospitals provide dedicated ethics and legal consult services when additional complexities emerge.</p>
   <table-wrap id="table1">
    <label>
     <xref ref-type="table" rid="table1">
      Table 1
     </xref></label>
    <caption>
     <title>
      <xref ref-type="bibr" rid="scirp.143787-"></xref>Table 1. Reliable and moderate predictors of functional outcomes in patients with cardiac arrest at 3 months or later.</title>
    </caption>
    <table class="MsoTableGrid custom-table" border="0" cellspacing="0" cellpadding="0"> 
     <tr> 
      <td class="custom-bottom-td custom-top-td acenter" width="35.29%"><p style="text-align:center">Category of predictors</p></td> 
      <td class="custom-bottom-td custom-top-td acenter" width="64.71%"><p style="text-align:center">Interpretation</p></td> 
     </tr> 
     <tr> 
      <td class="custom-bottom-td custom-top-td aleft" width="100.00%" colspan="2"><p style="text-align:left">Reliable predictors of poor functional outcome at 3 months or later</p></td> 
     </tr> 
     <tr> 
      <td class="custom-top-td aleft" width="35.29%"><p style="text-align:left">Pupillary light response (PLR) ≥ 72 h from ROSC</p></td> 
      <td class="custom-top-td aleft pli" width="64.71%"><p style="text-align:left">Bilateral absence of the PLR.</p><p style="text-align:left">Use quantitative pupillometry where available.</p><p style="text-align:left">Where a pupillometer is unavailable and the PLR is thought to be absent, consult ophthalmology or use a magnifying glass.</p><p style="text-align:left">Consider potential confounders such as medications (mydriatic ophthalmic drops, nebulized bronchodilators) and prior ophthalmic surgery.</p></td> 
     </tr> 
     <tr> 
      <td class="custom-bottom-td aleft" width="35.29%"><p style="text-align:left">Somatosensory Evoked Potentials (SSEP)</p><p style="text-align:left">≥ 48 h from ROSC</p></td> 
      <td class="aleft pli" width="64.71%"><p style="text-align:left">Target-measured N20 amplitude is &lt; 4 μV at 48 - 72 h from ROSC; it indicates a poor outcome.</p><p style="text-align:left">Responses must be present at Erb’s point and the cervical spine, as a prerequisite to prognostication.</p><p style="text-align:left">Consider routine use of neuromuscular blockade during testing to minimize artifact.</p><p style="text-align:left">Studies should be interpreted as indeterminate in the presence of significant background noise, which may obscure the N20 response.</p><p style="text-align:left">Severe hypothermia may abolish the N20 response.</p></td> 
     </tr> 
     <tr> 
      <td class="custom-bottom-td custom-top-td aleft" width="100.00%" colspan="2"><p style="text-align:left">Moderately reliable predictors of poor functional outcome at 3 months or later</p></td> 
     </tr> 
     <tr> 
      <td class="custom-top-td aleft" width="35.29%"><p style="text-align:left">CT Head, non-contrast ≥  48 h following ROSC</p></td> 
      <td class="custom-top-td aleft pli" width="64.71%"><p style="text-align:left">Diffuse pattern—loss of gray–white differentiation and sulcal effacement should be present across vascular distributions in the bilateral anterior and posterior circulation, with involvement of cerebral cortex and deep gray matter.</p><p style="text-align:left">Do not use in the presence of artifact from sources such as EEG electrodes, patient movement, or beam hardening from bone.</p></td> 
     </tr> 
     <tr> 
      <td class="aleft" width="35.29%"><p style="text-align:left">MRI brain with DWI sequence, 2 - 7 days following ROSC</p></td> 
      <td class="aleft pli" width="64.71%"><p style="text-align:left">Diffuse pattern- restricted diffusion should be present across vascular distributions in the bilateral anterior and posterior circulation, with cerebral cortex involvement and deep gray matter.</p><p style="text-align:left">Do not use in the presence of artifact from sources such as patient movement.</p><p style="text-align:left">Seizures and their potential etiologies of restricted diffusion must be ruled out.</p></td> 
     </tr> 
     <tr> 
      <td class="custom-bottom-td aleft" width="35.29%"><p style="text-align:left">Electroencephalography (EEG)</p></td> 
      <td class="aleft pli" width="64.71%"><p style="text-align:left">Presence of suppression or burst suppression.</p><p style="text-align:left">Suppression is defined as a background voltage &lt; 10 µV for &gt; 99% of the record.</p><p style="text-align:left">Burst suppression is defined as a suppressed (&lt; 10 µV) pattern present for 50% - 99% of the record.</p><p style="text-align:left">Exclude confounders such as sedation, toxic-metabolic encephalopathy, and hypothermia.</p></td> 
     </tr> 
     <tr> 
      <td class="custom-bottom-td custom-top-td aleft" width="100.00%" colspan="2"><p style="text-align:left">Not reliable predictors of poor functional outcome at 3 months or later</p></td> 
     </tr> 
     <tr> 
      <td class="custom-bottom-td custom-top-td aleft" width="35.29%"><p style="text-align:left">Neurological exam</p></td> 
      <td class="custom-bottom-td custom-top-td aleft pli" width="64.71%"><p style="text-align:left">An absent or extensor motor response or absent withdrawal, localization, or command-following at any time.</p></td> 
     </tr> 
    </table>
   </table-wrap>
   <fig id="fig2" position="float">
    <label>Figure 2</label>
    <caption>
     <title>Figure 2. Algorithm for neuroprognostication in adult comatose cardiac arrest survivors: predictors &amp; prognosis.</title>
    </caption>
    <graphic mimetype="image" position="float" xlink:type="simple" xlink:href="https://html.scirp.org/file/3901173-rId22.jpeg?20250630052638" />
   </fig>
   <p>As clinician expertise in neuroprognostication grows—and as algorithms and ancillary tools become more widespread—favourable outcomes for cardiac‑arrest survivors should continue to rise. Nonetheless, the effectiveness of these strategies remains contingent on each institution’s resources and interdisciplinary collaboration <xref ref-type="bibr" rid="scirp.143787-28">
     [28]
    </xref>. As clinician expertise in neuro prognostication grows and algorithms and ancillary tools become more widespread, favorable outcomes for cardiac‑arrest survivors should continue to rise. Nonetheless, the effectiveness of these strategies remains contingent on each institution’s resources and interdisciplinary collaboration <xref ref-type="bibr" rid="scirp.143787-28">
     [28]
    </xref>. In Rajajee’s study, resource utilization varied amongst models, thus representing the contrast in financial resources between low- and high-resource healthcare settings. Given the importance of thoughtful conversation regarding a patient’s goals of care, resource-intensive testing such as MRI and NSE might not be necessary. Instead, utilizing low-cost methods of prognostication, including clinical exam in concordance with a patient’s discussed wishes, offers significant predictive value. However, without proper discussion and investigation according to predictive algorithms, there is a possibility for long-term expenditure on equipment, facilities, and resource teams, including transport. That being said, limiting the scope of practice to testing only in ways that will alter or enhance clinical treatment is encouraged, particularly in low-resource settings.</p>
   <p>Neuroprognostication sits at the intersection of medicine, ethics, and law. Transparent, ongoing dialogue with families aligns care with patient values and clarifies what constitutes an acceptable quality of life. Where disputes arise, institutional ethics or legal services may be required <xref ref-type="bibr" rid="scirp.143787-28">
     [28]
    </xref>.</p>
   <p>As clinicians become familiar with multimodal prognostication and protocols, tools, and algorithms mature, cardiac‑arrest survivors’ outcomes should continue to improve. Nevertheless, success depends on local resources and interdisciplinary collaboration <xref ref-type="bibr" rid="scirp.143787-28">
     [28]
    </xref>.</p>
  </sec>
 </body><back>
  <ref-list>
   <title>References</title>
   <ref id="scirp.143787-ref1">
    <label>1</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Lacerte, M., Hays Shapshak,  A. and Mesfin, F.B. (2024) Hypoxic Brain Injury. StatPearls Publishing.
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref2">
    <label>2</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Federal Communications Commission (2024) Cognitive Disabilities. &gt;https://www.fcc.gov/cognitive-disabilities 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref3">
    <label>3</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Fordyce, C.B., Kramer, A.H., Ainsworth, C., Christenson, J., Hunter, G., Kromm, J., et al. (2023) Neuroprognostication in the Post Cardiac Arrest Patient: A Canadian Cardiovascular Society Position Statement. Canadian Journal of Cardiology, 39, 366-380. &gt;https://doi.org/10.1016/j.cjca.2022.12.014 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref4">
    <label>4</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Elmer, J., Torres, C., Aufderheide, T.P., Austin, M.A., Callaway, C.W., Golan, E., et al. (2016) Association of Early Withdrawal of Life-Sustaining Therapy for Perceived Neurological Prognosis with Mortality after Cardiac Arrest. Resuscitation, 102, 127-135. &gt;https://doi.org/10.1016/j.resuscitation.2016.01.016 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref5">
    <label>5</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Rydvall, A. and Lynöe, N. (2008) Withholding and Withdrawing Life-Sustaining Treatment: A Comparative Study of the Ethical Reasoning of Physicians and the General Public. Critical Care, 12, R13. &gt;https://doi.org/10.1186/cc6786 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref6">
    <label>6</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Sandroni, C., Skrifvars, M.B. and Taccone, F.S. (2023) Brain Monitoring after Cardiac Arrest. Current Opinion in Critical Care, 29, 68-74. &gt;https://doi.org/10.1097/mcc.0000000000001023 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref7">
    <label>7</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Patel,  K. and Hipskind,  J.E. (2023) Cardiac Arrest. StatPearls Publishing. 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref8">
    <label>8</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Sekhon, M.S., Ainslie, P.N. and Griesdale, D.E. (2017) Clinical Pathophysiology of Hypoxic Ischemic Brain Injury after Cardiac Arrest: A “Two-Hit” Model. Critical Care, 21, Article No. 90. &gt;https://doi.org/10.1186/s13054-017-1670-9
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref9">
    <label>9</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Adigun,  R., Basit,  H. and Murray, J. (2023) Cell Liquefactive Necrosis. StatPearls Publishing. 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref10">
    <label>10</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Elmer,  J. and Callaway,  C.W. (2019) The Brain after Cardiac Arrest. Seminars in Neurology, 37, 19-24. &gt;https://doi.org/10.1055/s-0036-1597833 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref11">
    <label>11</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Hirschberg, R. and Giacino, J.T. (2011) The Vegetative and Minimally Conscious States: Diagnosis, Prognosis and Treatment. Neurologic Clinics, 29, 773-786. &gt;https://doi.org/10.1016/j.ncl.2011.07.009
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref12">
    <label>12</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Laureys, S., Celesia, G.G., Cohadon, F., Lavrijsen, J., León-Carrión, J., Sannita, W.G., et al. (2010) Unresponsive Wakefulness Syndrome: A New Name for the Vegetative State or Apallic Syndrome. BMC Medicine, 8, Article No. 68. &gt;https://doi.org/10.1186/1741-7015-8-68 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref13">
    <label>13</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Posner, J.B., Saper, C.B., Schiff, N. and Plum, F. (2008). Plum and Posner’s Diagnosis of Stupor and Coma. Oxford University Press. &gt;https://doi.org/10.1093/med/9780195321319.001.0001 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref14">
    <label>14</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Giacino, J.T., Schnakers, C., Rodriguez-Moreno, D., Kalmar, K., Schiff, N. and Hirsch, J. (2009) Behavioral Assessment in Patients with Disorders of Consciousness: Gold Standard or Fool’s Gold? In: Progress in Brain Research, Elsevier, 33-48. &gt;https://doi.org/10.1016/s0079-6123(09)17704-x 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref15">
    <label>15</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Giacino, J.T. and Kalmar, K. (2005) Diagnostic and Prognostic Guidelines for the Vegwive and Minimally Conscious States. Neuropsychological Rehabilitation, 15, 166-174. &gt;https://doi.org/10.1080/09602010443000498 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref16">
    <label>16</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Giacino, J.T., Kalmar, K. and Whyte, J. (2004) The JFK Coma Recovery Scale-Revised: Measurement Characteristics and Diagnostic Utility11no Commercial Party Having a Direct Financial Interest in the Results of the Research Supporting This Article Has or Will Confer a Benefit upon the Authors or upon Any Organization with Which the Authors Are Associated. Archives of Physical Medicine and Rehabilitation, 85, 2020-2029. &gt;https://doi.org/10.1016/j.apmr.2004.02.033 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref17">
    <label>17</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Deng,  R., Liu,  Y.C., Li,  J.Q., Xu, J. and Chen,  G. (2020) The Role of Carbon Dioxide in Acute Brain Injury. Brain Circulation, 6, 199‑208. 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref18">
    <label>18</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Fischer, D., Edlow, B.L., Giacino, J.T. and Greer, D.M. (2022) Neuroprognostication: A Conceptual Framework. Nature Reviews Neurology, 18, 419-427. &gt;https://doi.org/10.1038/s41582-022-00644-7 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref19">
    <label>19</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Sandroni, C., Cronberg, T. and Sekhon, M. (2021) Brain Injury after Cardiac Arrest: Pathophysiology, Treatment, and Prognosis. Intensive Care Medicine, 47, 1393-1414. &gt;https://doi.org/10.1007/s00134-021-06548-2 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref20">
    <label>20</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Faugeras, F., Rohaut, B., Valente, M., Sitt, J., Demeret, S., Bolgert, F., et al. (2017) Survival and Consciousness Recovery Are Better in the Minimally Conscious State than in the Vegetative State. Brain Injury, 32, 72-77. &gt;https://doi.org/10.1080/02699052.2017.1364421 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref21">
    <label>21</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Westhall, E., Rossetti, A.O., van Rootselaar, A.F., et  al. (2017) Electroencephalography for Prognostication after Cardiac Arrest: A Prospective Cohort Study. Neurology, 88, 701‑708. 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref22">
    <label>22</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Sivaraju, A, Gilmore, E.J., Mundlamuri, R.C., et al. (2015) Influence of Sedative Medications on EEG Background Patterns in Post‑Cardiac‑Arrest Patients. Clinical Neurophysiology, 126, 151‑158. 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref23">
    <label>23</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Metting, Z., Wilczak, N., de Kruijff, M., et al. (2009) Grey‑to‑White Matter Ratio on CT for Outcome Prediction after Anoxic Brain Injury. Critical Care Medicine, 37, 1179‑1184. 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref24">
    <label>24</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Hirsch, L.J., Abend, N.S., Arnson, H.A., et al. (2020) Practical Issues in Obtaining MRI in Critically Ill Haemodynamically Unstable Patients. Journal of Neuroimaging, 30, 131‑140. 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref25">
    <label>25</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Rosazza, C., Palmas, G., Meneghello, F., et al. (2018) Resting‑State fMRI Connectivity Alterations in Post‑Anoxic Coma. Neuro Image: Clinical, 17, 233‑242. 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref26">
    <label>26</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Iadecola, C. (2013) Pathobiology of the Cerebral Microcirculation after Global Ischaemia. Annals of the New York Academy of Sciences, 1290, 34‑47. 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref27">
    <label>27</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Fugate, J.E., Wijdicks, E.F.M., White, R.D. and Rabinstein, A.A. (2011) Does Therapeutic Hypothermia Affect Time to Awakening in Cardiac Arrest Survivors? Neurology, 77, 1346-1350. &gt;https://doi.org/10.1212/wnl.0b013e318231527d 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref28">
    <label>28</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Rajajee, V., Muehlschlegel, S., Wartenberg, K.E., Alexander, S.A., Busl, K.M., Chou, S.H.Y., et al. (2023) Guidelines for Neuroprognostication in Comatose Adult Survivors of Cardiac Arrest. Neurocritical Care, 38, 533-563. &gt;https://doi.org/10.1007/s12028-023-01688-3 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref29">
    <label>29</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Callaway, C.W., Donnino, M.W., Fink, E.L., Geocadin, R.G., Golan, E., Kern, K.B., et al. (2015) Part 8: Post–Cardiac Arrest Care. Circulation, 132, 465-482. &gt;https://doi.org/10.1161/cir.0000000000000262 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref30">
    <label>30</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Geocadin, R.G., Callaway, C.W., Fink, E.L., Golan, E., Greer, D.M., Ko, N.U., et al. (2019) Standards for Studies of Neurological Prognostication in Comatose Survivors of Cardiac Arrest: A Scientific Statement from the American Heart Association. Circulation, 140, 517-542. &gt;https://doi.org/10.1161/cir.0000000000000702 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref31">
    <label>31</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Merchant, R.M., Topjian, A.A., Panchal, A.R., Cheng, A., Aziz, K., Berg, K.M., et al. (2020) Part 1: Executive Summary: 2020 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation, 142, 337-357. &gt;https://doi.org/10.1161/cir.0000000000000918 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref32">
    <label>32</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Mlynash, M., Campbell, D.M., Leproust, E.M., Fischbein, N.J., Bammer, R., Eyngorn, I., et al. (2010) Temporal and Spatial Profile of Brain Diffusion-Weighted MRI after Cardiac Arrest. Stroke, 41, 1665-1672. &gt;https://doi.org/10.1161/strokeaha.110.582452 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref33">
    <label>33</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Hirsch, K.G., Mlynash, M., Eyngorn, I., Pirsaheli, R., Okada, A., Komshian, S., et al. (2015) Multi-Center Study of Diffusion-Weighted Imaging in Coma after Cardiac Arrest. Neurocritical Care, 24, 82-89. &gt;https://doi.org/10.1007/s12028-015-0179-9 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref34">
    <label>34</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Hirsch, K.G., Fischbein, N., Mlynash, M., Kemp, S., Bammer, R., Eyngorn, I., et al. (2020) Prognostic Value of Diffusion-Weighted MRI for Post-Cardiac Arrest Coma. Neurology, 94, 1684-1692. &gt;https://doi.org/10.1212/wnl.0000000000009289 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref35">
    <label>35</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Benghanem, S., Pruvost-Robieux, E., Bouchereau, E., Gavaret, M. and Cariou, A. (2022) Prognostication after Cardiac Arrest: How EEG and Evoked Potentials May Improve the Challenge. Annals of Intensive Care, 12, Article No. 111. &gt;https://doi.org/10.1186/s13613-022-01083-9 
    </mixed-citation>
   </ref>
   <ref id="scirp.143787-ref36">
    <label>36</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Kromm, J., Davenport, A. and Wilcox, M.E. (2024) Neuroprognostication after Cardiac Arrest. Chest Critical Care, 2, Article 100074. &gt;https://doi.org/10.1016/j.chstcc.2024.100074
    </mixed-citation>
   </ref>
  </ref-list>
 </back>
</article>