REBEL Cast

Dive into the critical world of omphalitis, a serious infection affecting newborns. Discover the alarming signs of erythema and warmth around the umbilicus that necessitate immediate action. Learn why early diagnosis is crucial and the role of antibiotics in treatment. Hear about the importance of pediatric surgery consultations to prevent life-threatening complications. The discussion highlights the severe progression of this condition and the aggressive approach needed when symptoms escalate.

Explore the critical role of orogastric lavage in treating drug overdoses. Discover when this procedure is appropriate, especially for highly toxic substances. Learn about the risks associated with lavage and the importance of airway management to prevent aspiration. Timing is essential, and not every case warrants this intervention. Tune in for a deep dive into this rare but vital emergency care technique!

Sanjay Mohan, an in-house toxicologist, dives into the perilous world of toxic alcohols like methanol and ethylene glycol. He discusses how these substances can present symptoms mimicking ethanol intoxication, complicating diagnosis. The conversation highlights key diagnostic challenges, stressing that a normal osmolar gap doesn't rule out ingestion. Mohan also covers critical management strategies, including the use of fomepizole and the importance of early intervention, particularly in cases of severe metabolic acidosis or renal dysfunction.

Take Home Points Anticipate anatomically challenging airways and consider early intubation prior to loss of airway anatomy. Skip the zones of the neck and focus on hard signs of vascular (Shock w/o another source, Pulsatile bleeding, Expanding hematoma, Audible bruit, Signs of stroke) or aerodigestive (Airway compromise, Bubbling wound, Extensive SubQ air, Stridor, Significant hemoptysis/hematemesis). The presence of hard signs indicates the need to go to the OR or for angiographic intervention. Control hemorrhage with a single finger and direct pressure. REBEL Core Cast 127.0 – Penetrating Neck Injuries Click here for Direct Download of the Podcast. Post Peer Reviewed By: Salim R. Rezaie, MD (Twitter/X: @srrezaie) The post REBEL Core Cast 127.0 – Penetrating Neck Injuries appeared first on REBEL EM - Emergency Medicine Blog.

Background: Cath lab activation based on ST-elevation myocardial infarction (STEMI) criteria is founded on aging data and requires evolution. In the “Occlusive Myocardial Infarction (OMI) Manifesto,” emergency physicians Dr. Steve Smith, Dr. Pendell Meyers, and Dr. Scott Weingart introduced a new paradigm —OMI vs. non-occlusive myocardial infarction (NOMI). The OMI/NOMI paradigm focuses on the presence of coronary occlusion, while STEMI/NSTEMI categorizes myocardial infarctions based on electrocardiogram (ECG) findings. Patients with OMI exhibit higher mortality and worse left ventricular function compared to those with NOMI.1, 2, 3 Detecting OMI is more difficult and necessitates scrutiny of the ECG, which is challenging in a busy emergency department where ED clinicians are interrupted more than ten times per hour.4, 5 Some OMI ECG signs include ST elevation in only one lead, subtle ST elevation with minimal reciprocal changes, isolated ST depressions, and hyperacute T waves. To meet this challenge, Dr. Steve Smith, Dr. Pendell Meyers (Dr. Smith’s ECG Blog), and their team developed The Queen of Hearts, a machine-learning AI model that has the potential to aid in the early detection of subtle OMI ECG changes. Accurately identifying OMI changes in ECG that STEMI criteria might otherwise miss would allow for more timely intervention, potentially salvaging more myocardium. An AI model that is highly sensitive in detecting OMI while maintaining a high degree of specificity would be an ideal tool to support emergency physicians’ clinical decision-making. The performance of this tool is unknown. Click here for Direct Download of the Podcast. Paper: Herman R, Meyers HP, Smith SW, et al. International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction. Eur Heart J Digit Health. 2023;5(2):123-133. Published 2023 Nov 28. PMID: 38505483 Clinical question: “Can an AI model detect an OMI lesion using a single 12-lead ECG?” What They Did: Investigators performed a retrospective derivation study followed by validation on an internal data set from the same Acute Coronary Syndrome (ACS) database. Cases eligible for inclusion were randomly assigned to a model development training set (derivation set) and testing set (validation set).   The training set included ECG feature extraction and classification Feature extraction used 60,000 parameters The classification component combined all extracted features and used an additional 150,000 parameters. The validation data set was used for hyperparameter tuning and threshold selection.  Investigators then tested the AI model on two data sets An internal European data set (internal validation set) A separate US data set (external validation set) from the DOMI ARIGATO database. They compared the AI model with the existing criteria for detecting OMI on 12-lead ECGs and analyzed the AI model in various subgroups. Population: Derivation Set: Random selection of ACS patients from the Cardiovascular Centre Aalst in Belgium and ACS patients from an international image database patient. EU Internal Test Set: Random Selection of ACS patients from the Cardiovascular Centre Aalst in Belgium and ACS patients from an international image database patient. US External Test Set: Patients from the DOMI ARIGATO database. Exclusion: ECGs >24 h before CAG and post-CAG ECGs with poor signal quality  ECGs with missing Expert Annotation, undigitizable ECGs, Baseline ECGs (additionally excluded from the US External Database) Intervention: AI-powered ECG model implemented on ECGs from the internal EU and external US datasets. Comparator: Blinded physician annotations of the standard ‘STEMI criteria’ on ECG Blinded subjective ECG expert annotations of OMI Angiographic clinical outcome data Outcomes: Primary Outcome: AI model’s ability to identify patients with angiographically confirmed OMI using only the 12-lead ECG. Secondary Outcomes: OMI AI model performance across demographic and ECG subgroups A comparison of the AI model performance against the existing STEMI criteria for detecting acute coronary occlusion from 12-lead ECGs A sensitivity analysis of AI model performance using various angiographic and laboratory cut-offs of OMI An evaluation of misclassified cases Results: The derivation set used in the AI model development included 18,616 ECGs from 10,543 patients with clinically validated outcomes. The overall test set included 3254 ECGs from 2222 patients   The internal EU testing cohort 2016 ECGs from 1630 patients  The US testing cohort 1238 ECGs from 633 patients  The prevalence of OMI differed between the internal EU and the external US test sets, 16% compared with 36.2%, respectively ( < 0.001). The patients in the US test set were younger, had more ECGs recorded before catheterization, and were more likely to present with a STEMI-positive ECG. AI Model Performance: Achieved an Area Under the ROC Curve (AUC) of 0.938 [95% CI: 0.924–0.951]. Accuracy: 90.9% [95% CI: 89.7–92.0]. Sensitivity: 80.6% [95% CI: 76.8–84.0]. Specificity: 93.7% [95% CI: 92.6–94.8]. STEMI Criteria Performance: STEMI criteria accuracy: 83.6% [95% CI: 82.1–85.1]. Sensitivity: 32.5% [95% CI: 28.4–36.6]. Specificity: 97.7% [95% CI: 97.0–98.3]. ECG Experts Performance: Accuracy of ECG experts was 90.8% [95% CI: 89.5–91.9]. Sensitivity: 73.0% [95% CI: 68.7–77.0]. Specificity: 95.7% [95% CI: 94.7–96.6]. OMI AI Model vs. STEMI Criteria: The OMI AI model performs significantly better than the STEMI criteria in sensitivity, Negative Predictive Value (NPV), Matthews correlation coefficient (MCC), and AUC. However, it has lower specificity and Positive Predictive Value (PPV) compared to the STEMI criteria. OMI AI Model vs. ECG Experts: The OMI AI model has higher sensitivity and NPV than ECG experts. It shows equal performance in AUC and is adjudicated as equal overall to ECG experts. Specificity and PPV are lower than ECG experts, and MCC is neutral. ECG Experts vs. STEMI Criteria: ECG experts have higher sensitivity, NPV, MCC, and AUC than STEMI criteria. They perform the same in specificity and PPV compared to STEMI criteria, leading to significantly better adjudication. Strengths: Rigorous Methodological Approach: The study follows a comprehensive methodological approach, encompassing stages of development, validation, and comparison. Large and Diverse Dataset: The model was trained and tested on a substantial dataset of 18,616 ECGs from 10,543 patients with ACS across multiple international cohorts. This diversity enhances the model’s generalizability and robustness. Expert Interpretation and Validation: All cases in the derivation set included expert ECG interpretations alongside clinically validated angiographic outcome data, ensuring high accuracy in the model’s training process. High Agreement Among Experts: Two authors, serving as ECG experts, annotated all tracings for the presence of OMI while being blinded to all clinical data. They achieved a 94% agreement (kappa = 0.849), demonstrating the reliability of the expert annotations. Independent Review: Blinded independent clinical reviewers adjudicated all angiographic data in the EU internal testing set, adding an extra layer of objectivity and reliability to the validation process. Comprehensive Performance Comparison: The study compares the AI model’s performance with existing STEMI criteria and expert ECG interpretations. This sets a quantifiable humanistic standard, highlighting the AI model’s enhanced performance. Limitations: Applicability Limited to ACS Patients: The AI model was developed using patients and ECGs exclusively from ACS databases, restricting its applicability to only those with ACS. Disease-Oriented Outcomes: The outcomes in this study are disease-oriented. While facilitating the diagnosis of OMI may lead to improved patient-oriented outcomes, this was not directly studied. Limited Generalizability to Asymptomatic Patients: The study is not generalizable to a broader population of asymptomatic patients and was not designed to quantify other relevant clinical endpoints such as mortality, in-hospital complications, or major adverse cardiovascular events (MACE). Lack of Prospective Validation: The validation set was analyzed retrospectively, lacking prospective validation to confirm the model’s effectiveness in real-world clinical settings. Randomization Process Not Mentioned: The randomization process used to allocate cases to the derivation or validation set is not mentioned, which may impact the robustness of the findings. Comparison Limited to 12-Lead ECG: The AI model was compared to the 12-lead ECG alone. Some patients undergo emergency angiography without clear STEMI criteria based on the full clinical picture. Therefore, the interpretation of the overall gain is limited without a pragmatic comparison to real-world clinical practices and patient-oriented outcomes. Limited Generalizability to Younger Population and Women: Approximately 10% of ECGs were from patients ≤45 years of age, and three-quarters of the cases were from males, limiting the generalizability to younger populations and women. Inappropriate Use of P-Values: The inclusion of p-values in Tables 1 and 2 is puzzling, as this is not a randomized controlled trial (RCT). Demographic differences between validation sets are expected and desired for external validity. Variability in Care Standards: Significant differences in clinical presentation and management between patients in Europe and the USA (e.g., the USA had younger patients and more ECGs before catheterization) could affect the model’s performance across different healthcare systems. Subjective Outcome Verification: The detection of OMI relied on visual verification of TIMI flow on angiograms, which may be subjective. Conflict of Interest: The lead author disclosed a conflict of interest as the co-founder and Chief Medical Officer of Powerful Medical. Other authors have disclosed employee or shareholder status in Powerful Medical. Discussion: Inside the Numbers: The data for this AI model is impressive, showcasing a remarkable capability in early and accurate detection of OMI on ECGs, demonstrating a sensitivity of 80.6% (76.8–84.0) and specificity of 93.7% (92.6–94.8). The AI model not only surpassed the standard STEMI ECG criteria [sensitivity 32.5% (28.4–36.6) and specificity 97.7% (97.0–98.3)] but also matched the performance of Dr. Steve Smith and Dr. Pendell Meyers, two well known ECG experts [sensitivity 73.0% (68.7–77.0) and specificity 95.7% (94.7–96.6)]. Additionally, when considering the existing evidence, the AI model would likely outperform ED physicians’ and cardiologists’ ability to detect ischemia on ECG, who achieved sensitivities of approximately 65% and specificities ranging from 65–79% in multiple studies.7, 8, 9 This high accuracy demonstrates AI’s potential to improve diagnostic processes and patient outcomes in emergency settings. The AI model’s PPV in this study was 0.780 (0.742–0.816), and the NPV was 0.946 (0.935–0.957) for the primary outcome. PPV and NPV are heavily influenced by disease prevalence, and a high prevalence increases the PPV, indicating that a positive test result is more likely to be a true positive. The 16% and 36.2% prevalence of OMI in the internal and external validation sets are likely much higher than expected from a random group of patients assessed for ACS in the ED on any given day. Consequently, the PPV and NPV are likely lower in a less risky population with a lower prevalence for ACS.   The AI model’s AUC for detecting OMI was 0.938 (0.924–0.951), with an optimal threshold of 0.1106. The optimal threshold refers to the chosen point that maximizes the AI model’s accuracy. The point is a probability that ranges from 0–1. However, investigators must choose the value (optimal threshold) at which the model determines whether the ECG is positive or negative. Therefore, the optimal threshold converts a continuous variable (probability) into a binary decision, such as distinguishing between the presence or absence of OMI on ECG. If the threshold is set too low, it might result in high sensitivity but low specificity, leading to many false positives. The ROC curve is a graphical plot that illustrates the diagnostic ability of a binary classifier as its discrimination threshold is varied. In this instance, a ROC curve with an AUC of 0.938 is outstanding and highlights the potential of the AI model to optimize clinical decision-making processes. Critical Biases and Considerations: The primary flaw in this paper is selection bias. All patients included in the derivation and validation sets were selected from ACS databases. As mentioned, the prevalence of OMI in the internal and external validation sets is very high. Physicians should exercise caution when applying this data more broadly (i.e., all patients with an ECG in the ED).  The AI model detected OMI in 979 cases total, 267 of which also met the STEMI criteria on ECG. Therefore, 27% of the OMIs detected by the AI model might have been more obvious and less noteworthy to an emergency physician aiming to improve their diagnostic capabilities. However, the remaining 73% of AI-detected OMIs are particularly interesting because they require meticulous ECG scrutiny for accurate diagnosis. While not all these AI-detected OMI cases met the primary outcome criteria, technology can fill a void in identifying patients who may benefit from emergent intervention despite the lack of STEMI-specific criteria on ECG. “Time is myocardium,” and the primary goal in ACS treatment is to detect OMI on ECG as early as possible to prevent myocardial necrosis. Utilization of STEMI criteria missed 330 OMI patients —false negatives. Among these, 133 had a median revascularization time of 9.3 hours but were correctly identified by the AI model on the first ECG. Early detection can potentially improve patient outcomes, especially in cases with real-world median angiography time of 9 hours. While this data is compelling, it highlights the need for prospective evaluation of the AI model compared to the performance of the average emergency physician to fully assess its clinical effectiveness. The Future and Transformative Potential of AI: This AI model’s development and validation process mirrors that of a clinical decision instrument, beginning with retrospective derivation followed by internal and external validation. Before widespread implementation, prospective validation in various clinical settings with diverse populations is necessary. Additionally, utilization studies should confirm that the AI model achieves its intended goals, such as earlier detection of OMI and improved patient-oriented outcomes. While the idea of AI taking over the world might be an exaggeration, its transformative impact cannot be overstated. The continuous advancement and integration of AI technologies can lead to more efficient, accurate, and personalized solutions. Moreover, AI’s continuous refinement through machine learning suggests its performance will only improve over time. As the AI model is exposed to more data and varied cases, it can refine its algorithms, enhance its accuracy, and adapt to new patterns, making it an invaluable tool in the medical field. And, unlike human counterparts, AI will not fatigue and will maintain high accuracy levels, even after the 12th hour on duty and dozens of ECG interpretations. The possibilities for AI applications in healthcare are virtually limitless.  Author’s conclusion: “AI model outperformed gold-standard STEMI criteria in the diagnosis of OMI, but further prospective clinical studies are needed to define the role of the OMI AI model in guiding ACS triage and the timely referral of patients benefiting from immediate revascularization.” Clinical Bottom Line: The Queen of Hearts AI model demonstrates impressive accuracy, surpassing STEMI criteria and matching expert interpretation for detecting OMI on ECG. However, the high prevalence of OMI in the study’s datasets may overestimate AI’s ability to detect OMI in a general ED population with a lower disease prevalence. Ultimately, the model requires prospective validation in diverse clinical settings before widespread adoption— but this could be a winning hand. Follow Me:          References: Wang TY, Zhang M, Fu Y, et al. Incidence, distribution, and prognostic impact of occluded culprit arteries among patients with non-ST-elevation acute coronary syndromes undergoing diagnostic angiography. Am Heart J. 2009;157(4):716-723. PMID: 19332201 Pride YB, Tung P, Mohanavelu S, et al. Angiographic and clinical outcomes among patients with acute coronary syndromes presenting with isolated anterior ST-segment depression: a TRITON-TIMI 38 (Trial to Assess Improvement in Therapeutic Outcomes by Optimizing Platelet Inhibition With Prasugrel-Thrombolysis In Myocardial Infarction 38) substudy. JACC Cardiovasc Interv. PMID: 20723851 Khan AR, Golwala H, Tripathi A, et al. Impact of total occlusion of culprit artery in acute non-ST elevation myocardial infarction: a systematic review and meta-analysis. Eur Heart J. 2017;38(41):3082-3089. PMID: 29020244 Ratwani RM, Fong A, Puthumana JS, Hettinger AZ. Emergency Physician Use of Cognitive Strategies to Manage Interruptions. Ann Emerg Med. 2017;70(5):683-687. PMID: 28601266 Chisholm CD, Dornfeld AM, Nelson DR, Cordell WH. Work interrupted: a comparison of workplace interruptions in emergency departments and primary care offices. Ann Emerg Med. 2001;38(2):146-151. PMID: 11468609 Herman R, Meyers HP, Smith SW, et al. International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction. Eur Heart J Digit Health. 2023;5(2):123-133. Published 2023 Nov 28. PMID: 38505483 Veronese G, Germini F, Ingrassia S, et al. Emergency physician accuracy in interpreting electrocardiograms with potential ST-segment elevation myocardial infarction: Is it enough?. Acute Card Care. 2016;18(1):7-10. PMID: 27759433 McCabe JM, Armstrong EJ, Ku I, et al. Physician accuracy in interpreting potential ST-segment elevation myocardial infarction electrocardiograms. J Am Heart Assoc. 2013;2(5):e000268. Published 2013 Oct 4. PMID: 24096575 Paez Perez Y, Rimm S, Bove J, et al. Does the Electrocardiogram Machine Interpretation Affect the Ability to Accurately Diagnose ST-Elevation Myocardial Infarction by Emergency Physicians?. Crit Pathw Cardiol. 2023;22(1):8-12. PMID: 36812338 Guest Post By: Marco Propersi, DO FAAEM Vice-Chair, Emergency Medicine Assistant Emergency Medicine Residency Program Director Vassar Brothers Hospital, Poughkeepsie, New York Twitter/X: @marco_propersi Joseph Bove, DO FAAEM Associate Director Emergency Medicine Co-Director of the EM Residency Clerkship St. Joseph’s University Medical Center Email: bovej@sjhmc.org Post Peer Reviewed By: Anand Swaminathan, MD (Twitter/X: @EMSwami) The post A Winning Hand in Cardiology: Queen of Hearts AI Model Enhances OMI Detection appeared first on REBEL EM - Emergency Medicine Blog.

Joanna Piero, a pediatric hematologist oncologist at Staten Island University Hospital, shares her expertise in managing critical pediatric hematology and oncology emergencies. She emphasizes the life-saving importance of administering antibiotics within an hour for patients with fever and neutropenia. Joanna stresses that fever in sickle cell patients is always an emergency, requiring immediate cultures and treatment. She also discusses crucial red flags in pediatric headaches that may suggest a brain tumor, highlighting the need for swift specialist consultations.

Explore the critical steps for managing hyperkalemia in emergency care. Learn why an EKG is essential upon patient presentation, especially for those with renal issues. Discover the common causes, including medications and massive cell death, that lead to elevated potassium levels. Hear about the alarming cardiac effects and neuromuscular symptoms that can arise. The discussion emphasizes timely interventions like administering calcium salts for unstable patients, showcasing the urgency in tackling this common electrolyte disorder.

Take Home Points Management of severe beta-blocker and calcium-channel blocker toxicity should occur in a stepwise fashion: potential gastric decontamination, multiple lines of access, judicious fluids, calcium, glucagon, and vasopressors as needed. Initiation of high dose insulin therapy requires a tremendous amount of logistical and cognitive resources as it requires cross-disciplinary collaboration and is prone to mismanagement. If the patient doesn’t respond to maximum pharmacologic therapy, venous-arterial ECMO should be considered. REBEL Core Cast 124.0 – Hyperinsulinemia Euglycemia Therapy Click here for Direct Download of the Podcast. Background and Physiology Shock secondary to beta-blocker (BB) or calcium-channel blocker (CCB) toxicity bears a tremendous degree of morbidity and mortality. According to the 2022 Annual Report of the National Poison Data System from America’s Poison Center, CCBs and BBs account for the sixth and seventh largest number of fatalities from overdose.1 Recall that cardiac output is a function of both stroke volume and heart rate. The natural response to diminishing stroke volume is a compensatory rise in heart rate (tachycardia). Keep a low threshold to search a patient’s medication list for BB/CCBs, when a hypotension is seen with a “normal heart rate.” Clinical Manifestations Both BBs and CCBs ultimately cause reduced levels of intracellular calcium within myocytes. Depending on the degree of toxicity, subsequent effects include: decreased systemic vascular resistance, vasodilation, bradycardia, various conduction delays, and ultimately hypotension and cardiogenic shock. In addition to abnormal vital signs, look for surrogates of poor clinical perfusion: acidemia, lactate, decreasing urinary output Traditional Management Consider GI decontamination to reduce systemic absorption: 1g/kg up to 50g of activated charcoal. Patient must be alert or the airway must be secured as to avoid aspiration. Obtain multiple lines of intravenous access (3 PIVs or triple lumen CVC) and provide a judicious amount of fluids. (more on this below) Pharmacotherapy Calcium Gluconate: 1-3g intravenous Glucagon: 3mg-5mg slow intravenous push. Rapid administration may induce nausea and emesis. Vasopressors as a bridge to… HIET Mechanism of action is still not fully elucidated however several factors are implicated: Insulin augments cardiac contractility by activating “reverse-mode” Na-Ca exchange and subsequently increasing calcium concentration in the sarcoplasmic reticulum. 2 At a resting physiologic state, the heart utilize free fatty acids as its primary energy course. Under stressed conditions, glucose is used instead. Insulin helps to facilitate glucose metabolism. HIET Dosing: 1 unit/kg IV bolus. Then infusion starting at 1 unit/kg/hr infusion and titrate q30-60 minutes, keeping in mind that effects are not instant. Relative maximum is ~10 unit/kg/hr. If glucose <250 mg/dL, administer a bolus of dextrose 25-50 g (or 0.5-1 g/kg) IV. Ask pharmacy to concentrate insulin from 1 unit/mL to 10 units/ml. Patients often succumb to volume overload given pre-existing cardiac disease and the volume of medical resuscitation through their hospital stay. Once HIET is initiated, dextrose and potassium infusions should simultaneously be started to obviate hypoglycemia and hypokalemia Dextrose: 0.5-1 g/kg/hr via D50/D20 Replete potassium to a minimum of 3.5mEq/L A central venous catheter (often a triple lumen) is often needed to emergently replete potassium and provide D50/D20 safely (given its high osmolarity) Serial monitoring of dextrose (q15-30 minutes) and potassium (q1 hour) is critical HIET has been demonstrated to improve perfusion without necessarily increasing SVR/MAP – while MAPs may not markedly increase dramatically in the short term, obtain serial blood gases, lactate, and track urinary output to track perfusion. 3 Hyperinsulinemia Euglycemia Therapy (HIET) for BB/CCB Toxicity Management of severe beta-blocker and calcium-channel blocker toxicity should occur in a stepwise fashion: potential gastric decontamination, multiple lines of access, judicious fluids, calcium, glucagon, and vasopressors as needed. Initiation of high dose insulin therapy requires a tremendous amount of logistical and cognitive resources as it requires cross-disciplinary collaboration and is prone to mismanagement. HIET Dosing: 1 unit/kg IV bolus. Then infusion starting at 1 unit/kg/hr infusion and titrate q30-60 minutes, keeping in mind that effects are not instant. Relative maximum is ~10 unit/kg/hr. HIET therapy requires simultaneous dextrose and potassium infusions as insulin will induce hypoglycemia and shift potassium intracellularly. If the patient doesn’t respond to maximum pharmacologic therapy, venous-arterial ECMO should be considered. References Gummin DD, Mowry JB, Beuhler MC, et al. 2022 Annual Report of the National Poison Data System® (NPDS) from America’s Poison Centers®: 40th Annual Report. Clin Toxicol (Phila). 2023;61(10):717-939. doi:10.1080/15563650.2023.226898 von Lewinski D, Bruns S, Walther S, Kögler H, Pieske B. Insulin causes [Ca2+]i-dependent and [Ca2+]i-independent positive inotropic effects in failing human myocardium. Circulation. 2005;111(20):2588-2595. doi:10.1161/CIRCULATIONAHA.104.497461 Holger JS, Engebretsen KM, Fritzlar SJ, Patten LC, Harris CR, Flottemesch TJ. Insulin versus vasopressin and epinephrine to treat beta-blocker toxicity. Clin Toxicol (Phila). 2007;45(4):396-401. doi:10.1080/15563650701285412 Post Peer Reviewed By: Salim R. Rezaie, MD (Twitter/X: @srrezaie) The post REBEL Core Cast 124.0 – Hyperinsulinemia Euglycemia Therapy appeared first on REBEL EM - Emergency Medicine Blog.

Discover the rare yet life-threatening condition of posterior epistaxis. The discussion focuses on its unique symptoms and the crucial differences from anterior epistaxis. Learn about effective diagnosis and management protocols, as well as treatment options. The speakers emphasize rapid control techniques and the necessity of surgical intervention. They also highlight the importance of involving ENT specialists and ensuring proper monitoring for affected patients.

The discussion kicks off with an exploration of the implications of andexanet alfa in treating factor 10A inhibitor-related brain hemorrhages, stressing the need for more research. It critically assesses the drug’s hemostatic efficacy and raises concerns about biases in clinical trials funded by its manufacturer. Key criticisms focus on patient outcomes, with a striking emphasis on the risk of thrombotic events. Finally, the conversation highlights the importance of transparency and ethical standards in medical research.