OBJECTIVE:Gastrointestinal bleeding (GIB) is a common complication associated with warfarin use. However, the optimal approach for anticoagulation reversal-whether with prothrombin complex concentrate (PCC), fresh frozen plasma (FFP), vitamin K (intravenous or oral), or no reversal-remains unclear as current literature and society guidelines do not provide definitive recommendations. METHODS:A retrospective analysis of emergency department patients with warfarin-associated GIB who presented to an academic health system was performed, comparing reversal with PCC, FFP, vitamin K only, or no reversal agent. The primary outcome was 30-day all-cause mortality. Secondary outcomes included rebleeding events and 30-day thrombotic events. Standardized guidelines for reporting were followed (STROBE). RESULTS:Of 815 patients, within 12 h of presentation, 10.7 % received reversal with PCC (with or without vitamin K), 12.9 % with FFP (with or without vitamin K), and 34.9 % with vitamin K alone; 41.5 % of patients received no reversal agent. Compared to all other groups, patients receiving PCC had significantly higher 30-day mortality (18.4 % [PCC] vs 5.7 % [FFP] vs 4.6 % [vitamin K] vs 5.6 % [no reversal], p < 0.001), which remained significant after adjusting for hemodynamic instability and ICU admission. There were no significant differences in rates of thrombotic event within 30 days (3.4 % vs 3.8 % vs 1.4 % vs 1.2 %, p = 0.20). CONCLUSIONS:Patients who received PCC had a threefold increase in mortality compared to FFP, vitamin K alone, or no reversal, even after adjusting for severity of the bleeding. Further research is necessary to understand factors leading to this observed mortality difference among patients with warfarin related GIB.

Importance Diagnostic errors represent a major patient safety concern, with the potential to significantly impact patient outcomes. To address this, various trigger-based strategies have been developed to identify diagnostic errors, aiming to enhance clinical decision-making and improve patient safety.Objective To evaluate the performance of three pre-established triggers (T) in the emergency department (ED) setting and assess their effectiveness in detecting diagnostic errors.Design Consecutive cohort, retrospective observational design.Setting Academic ED with 80 000 annual visits.Participants Adults and children presenting to a single ED in the USA between 1 May 2018 and 1 January 2020.Intervention/outcomes Electronic health records (EHRs) were retrieved and categorised into trigger-positive and trigger-negative cases using the following criteria: T1—unscheduled returnvisits to the ED with admission within 7–10 days of theinitial visit; T2—care escalation from the inpatient unitto the intensive care unit (ICU) within 6, 12 or 24 hoursof ED admission; and T3—all deaths in the ED or within24 hours of ED admission, excluding palliative care. A random sample of trigger-positive cases was reviewed using the SaferDx tool to determine the presence or absence of a diagnostic error.Results A total of 5791 trigger-positive and 118262 trigger-negative cases were identified. Among trigger-positive cases, 4159 (72%) were associated with T1, 1415 (24%) with T2, and 217 (4%) with T3. A preliminary chart review of 462 trigger-positive and 251 trigger-negative cases showed most were error-negative (279 and 217, respectively). Detailed reviews found 32 diagnostic errors among 183 trigger-positive cases, yielding PPVs of 5.4% (T1), 8.9% (T2), and 6.9% (T3). No errors were found in 34 reviewed trigger-negative cases, resulting in a 100% NPV. Sepsis was the most common diagnosis among error-positive cases (n=11, 34.4%). Those with non-specific chief complaints like altered mental status or shortness of breath had higher diagnostic error risk.Conclusion and relevance While previously proposed EHR-based triggers can identify some diagnostic errors, they are insufficient for detecting all cases. To improve error detection performance, we recommend exploring data-driven strategies, such as machine learning techniques, to more effectively identify underlying contributing factors to diagnostic errors and enhance detection accuracy in the ED.

OBJECTIVES:Electronic health records (EHR)-based triggers (eTriggers) have been used to study diagnostic errors in the emergency department (ED), often with suboptimal performance. Our objective was to investigate incremental value of multi-factor machine learning (ML) approaches to improve eTrigger performance. METHODS:Patients presenting to an academic ED were categorized into trigger-positive and trigger-negative using standard trigger (T) definitions: (T1) ED return visits resulting in admission within 10 days; (T2) care escalation from the inpatient unit to the ICU within 24 hours; and (T3) deaths within 24 hours of admission. We trained and evaluated 6 supervised ML models. RESULTS:A total of 124,053 consecutive encounters (5791 T-positive and 118,262 T-negative) were included. Among the T-positive, 4159 (72%) were associated with T1, 1415 (24%) with T2, and 217 (4%) with T3. The T-based positive predictive values (PPV) were 5.2% for T1, 8.2% for T2, and 6.5% for T3. ML models trained and evaluated on balanced training dataset and imbalanced test set had low classification performances (accuracy: 0.72-0.95; PPV: 0.00-0.16; F1-score: 0.00-0.23). Higher performances were observed in balanced test sets (accuracy: 0.80-0.97; PPV: 0.82-1.00; F1-score: 0.79-0.97). Comparing models trained on clinically annotated data with models trained on T-based labels identified other important factors. CONCLUSIONS:Utilizing machine learning to refine e-triggers slightly improves the identification of diagnostic errors, as evidenced by an increase in PPV values. We identified new potential factors contributing to ED diagnostic errors. These findings open new avenues to construct or modify more accurate e-triggers for diagnostic error identification.

PURPOSE:Rapid sequence intubation (RSI) is a common emergency department (ED) procedure with an associated complication of postintubation hypotension (PIH). It has not been clearly established whether the selection and dose of induction agent affect risk of PIH. The objective of this study was to determine the incidence of PIH in patients receiving full-dose compared to reduced-dose induction agent for RSI in the ED. METHODS:This was a health system-wide, retrospective cohort study comparing incidence of PIH based on the induction medication and dose given for RSI in the ED. Patients were included if they underwent RSI from July 1, 2018, through December 31, 2020, were 18 years of age or older, and received etomidate or ketamine. A reduced dose was defined as a ketamine dose of 1.25 mg/kg or less and an etomidate dose of 0.2 mg/kg or less. RESULTS:A total of 909 patients were included in the final analysis, with most receiving etomidate (n = 764; 84%) and a smaller number receiving ketamine (n = 145; 16%). Patients who received ketamine had a higher mean pre-intubation shock index (full dose, 1.08; reduced dose, 1.04) than those who received etomidate (full dose, 0.89; reduced dose, 0.92) (P ≤ 0.001). Reduced doses of induction agent were observed for 107 patients receiving etomidate (14.0%) and 60 patients receiving ketamine (41.4%). Patients who received full-dose ketamine for induction had the highest rate of PIH (n = 31; 36.5%), and the difference was statistically significant compared to patients receiving reduced-dose ketamine (16.7%; P = 0.021) and full-dose etomidate (22.8%; P = 0.010). CONCLUSION:We observed that full-dose ketamine was associated with the highest rate of PIH; however, this group had the poorest baseline hemodynamics, confounding interpretation. Our results do not support broad use of a reduced-dose induction agent.

BACKGROUND:Massive pulmonary embolism (PE) causing obstructive shock can lead to circulatory failure and cardiac arrest. There is a paucity of data describing current practice around thrombolytic use and outcomes in this patient population. OBJECTIVE:The objective of this study was to describe the characteristics and outcomes of patients who received a thrombolytic agent during cardiac arrest due to suspected PE, including efficacy and safety. METHODS:This study was a retrospective, descriptive cohort of 32 adult patients who received alteplase or tenecteplase in the emergency department during active cardiac arrest. Agent selection and dosing were at the discretion of the primary provider. RESULTS:Most patients presented with a witnessed out-of-hospital cardiac arrest with a non-shockable rhythm. The mean age was 63 years. Dyspnea was most commonly reported prior to cardiac arrest. The median dose for alteplase was 50 mg and for tenecteplase was 45 mg. Eleven patients achieved ROSC after thrombolytic administration; seven of these patients survived to hospital admission. All but one patient experienced a major bleeding event during admission. Ultimately, only two patients survived to hospital discharge. A subgroup analysis compared patients administered alteplase to those administered tenecteplase. Nine of the eleven patients that achieved ROSC were administered alteplase, five of which survived to hospital admission. All five patients experienced a major bleeding event. Two of the eleven patients that achieved ROSC were administered tenecteplase, both of which survived to hospital admission. One patient experienced a major bleeding event. Ultimately, only one patient in each group survived to hospital discharge. CONCLUSION:This study provides new data regarding the outcomes of thrombolytic therapy in patients experiencing cardiac arrest due to suspected massive PE. Despite administration of thrombolytics, survival to hospital admission and subsequent survival to hospital discharge were seen in only a very small proportion of patients. Further research is necessary to optimize the management of this life-threatening condition.

Background:Precision medicine, sometimes referred to as personalized medicine, is rapidly changing the possibilities for how people will engage health care in the near future. As technology to support precision medicine exponentially develops, there is an urgent need to proactively improve our understanding of precision medicine and pose important research questions (RQs) related to its inclusion in the education and training of future emergency physicians. Methods:A seven-step process was employed to develop a research agenda exploring the intersection of precision and emergency medicine education/training. A literature search of articles about precision medicine was conducted first, which informed the creation of future four scenarios in which trainees and practicing physicians regularly discuss and incorporate precision medicine tools into their discussions and work. Based on these futurist narratives, potential education RQs were generated by an expert panel. A total of 59 initial questions were subsequently categorized and refined to a priority list through a nominal group voting method. The top/priority questions were presented at the 2023 SAEM Consensus Conference on Precision Medicine, Austin, Texas, for further input. Results:Eight high-value education RQs were developed, reflecting a holistic view of the challenges and opportunities for precision medicine education in the knowledge, skills, and attitudes relevant to emergency medicine. These questions contend with topics such as most effective pedagogical methods; intended resulting outcomes and behaviors; the generational differences between practicing emergency physicians, educators, and future trainees; and the desires and expectations of patients. Conclusions:Emergency medicine and emergency physicians must be prepared to understand precision medicine and incorporate this information into their "toolbox" of thinking, problem solving, and communication with patients and colleagues. This research agenda on how best to educate future emergency physicians in the use of personalized data to provide optimal health care is the focus of this article.

BACKGROUND:Diltiazem is an effective rate control agent for atrial fibrillation with rapid ventricular rate (AF RVR). However, its negative inotropic effects may increase the risk for worsening heart failure in patients with a reduced ejection fraction (EF). OBJECTIVES:This observational study aims to describe the incidence of worsening heart failure in patients who receive intravenous diltiazem for acute atrial fibrillation management. METHODS:Adult patients that received diltiazem in the emergency department (ED) for AF RVR (heart rate ≥ 100 beats/min) from 2021 to 2022 and had a prior documented EF were included. The primary outcome is worsening heart failure within 24 h of diltiazem administration. Secondary outcomes include return ED visits and death within 7 days. EF percentage was compared across outcomes using Wilcoxon rank-sum tests. Outcomes were compared by reduced EF (< 50%) and preserved EF (≥ 50%). Continuous data were summarized with medians and interquartile ranges, and categorical features were summarized with frequency counts and percentages. Wilcoxon rank-sum tests were used for numeric outcomes and chi-squared tests or Fisher's exact tests for categorical outcomes, with a p-value < 0.05 considered statistically significant. RESULTS:There were 674 patients with AF RVR that received diltiazem, and 386 patients met the inclusion criteria for analysis. Baseline demographics included a median age of 72 (64-81) years, with 14.5% of patients having a prior diagnosis of congestive heart failure. EF < 50% was identified in 13.7% of patients (n = 53), of which approximately 30% of these patients safely discharged home after receiving i.v. diltiazem. The primary outcome of worsening heart failure occurred in 7/41 (17%) and 10/207 (4.8%) patients with reduced and preserved ejection fractions, respectively, who were admitted to the hospital (p = 0.005). CONCLUSION:The development of worsening heart failure is multifactorial and may include the use of diltiazem in critically ill patients requiring hospital admission.

Chilaiditi sign is an incidental radiological finding where the intestine is interposed between the diaphragm and liver. Chilaiditi syndrome (CS), characterized by gastrointestinal symptoms and Chilaiditi sign on imaging, is of important clinical significance despite its rarity given associated complications including intestinal obstruction, bowel ischemia, and perforation. While most cases involve the large intestine, we report a rare case of CS with ileal involvement complicated by small bowel obstruction, managed conservatively. Failure to recognize Chilaiditi sign or CS may prompt unnecessary surgical interventions, emphasizing the need for physician awareness to ensure accurate timely diagnosis and appropriate management.

Wieruszewski, Erin1; Mattson, Alicia1; Manuel, Francis2; Mara, Kristin1; Bellolio, Fernanda3; Cabrera, Daniel2; Brown, Caitlin2 Author Information

OBJECTIVE:To identify and assess artificial intelligence (AI)-enabled products reviewed by the U.S. Food and Drug Administration (FDA) that are potentially applicable to emergency medicine (EM). METHODS:The FDA AI-enabled products website was accessed to identify all marketed products as of March 2024. Board-certified EM physicians analyzed all products for applicability to EM practice. Inclusion criteria included products used by EM physicians directly or non-EM physicians participating directly in the evaluation and management of patients in an acute care setting. The Clinical and Economic Review (ICER) Evidence Rating Matrix was used to rate the net health benefit of applicable products. RESULTS:A total of 882 AI-enabled products have been reviewed by the FDA from 1995 to 2024. There were 272 products that were updates of prior products that were excluded, leaving 610 unique products. Products were most commonly evaluated by Radiology (454/610), Cardiovascular (59/610), and Neurology (25/610) panels. We found 154 (25 %) products applicable to EM that were approved through Radiology (121/154), Cardiovascular (24/154), Neurology (5/154), Anesthesiology (3/154), and Ophthalmology (1/154) panels. There were 30 products that were rated as having a comparable or incremental net health benefit with moderate certainty (a C+ rating). CONCLUSION:An increasing number of AI-enabled products are available and regulated by the FDA. We have identified 154 that are applicable to EM, primarily related to assisting with diagnosis on various imaging modalities. There remain many opportunities for EM to assist in product reviews and meaningful translation of products into clinical practice.

Artificial intelligence (AI) tools are becoming more prevalent in healthcare settings, particularly for diagnostic and therapeutic recommendations, with an expected surge in the incoming years. The bedside use of this technology for clinicians opens the possibility of disagreements between the recommendations from AI algorithms and clinicians' judgment. There is a paucity in the literature analyzing the nature and possible outcomes of these potential conflicts, particularly related to ethical considerations. The goal of this scoping review is to identify, analyze and classify current themes and potential strategies addressing ethical conflicts originating from the conflict between AI and human recommendations. A protocol was written prior to the initiation of the study. Relevant literature was searched by a medical librarian for the terms of artificial intelligence, healthcare and liability, ethics, or conflict. Search was run in 2021 in Ovid Cochrane Central Register of Controlled Trials, Embase, Medline, IEEE Xplore, Scopus, and Web of Science Core Collection. Articles describing the role of AI in healthcare that mentioned conflict between humans and AI were included in the primary search. Two investigators working independently and in duplicate screened titles and abstracts and reviewed full-text of potentially eligible studies. Data was abstracted into tables and reported by themes. We followed methodological guidelines for Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Of 6846 titles and abstracts, 225 full texts were selected, and 48 articles included in this review. 23 articles were included as original research and review papers. 25 were included as editorials and commentaries with similar themes. There was a lack of consensus in the included articles on who would be held liable for mistakes incurred by following AI recommendations. It appears that there is a dichotomy of the perceived ethical consequences depending on if the negative outcome is a result of a human versus AI conflict or secondary to a deviation from standard of care. Themes identified included transparency versus opacity of recommendations, data bias, liability of outcomes, regulatory framework, and the overall scope of artificial intelligence in healthcare. A relevant issue identified was the concern by clinicians of the "black box" nature of these recommendations and the ability to judge appropriateness of AI guidance (Table 1). AI clinical tools are being rapidly developed and adopted, and the use of this technology will create conflicts between AI algorithms and healthcare workers with various outcomes. In turn, these conflicts may have legal, and ethical considerations. There is limited consensus about the focus of ethical and liability for outcomes originated from disagreements. This scoping review identified the importance of framing the problem in terms of conflict between standard of care or not, and informed by the themes of transparency/opacity, data bias, legal liability, absent regulatory frameworks and understanding of the technology. Finally, limited recommendations to mitigate ethical conflicts between AI and humans have been identified. Further work is necessary in this field.

Background: Emergency departments (EDs) care for many patients nearing the end of life with advanced serious illnesses. Simulation training offers an opportunity to teach physicians the interpersonal skills required to manage end-of-life care. Objective: We hypothesized a gaming simulation of an imminently dying patient using the LIVE. DIE. REPEAT (LDR) format, would be perceived as an effective method to teach end-of-life communication and palliative care management skills. Methods: This was a gaming simulation replicating the experience of caring for a dying patient with advanced serious illness in the ED. The scenario involved a patient with pancreatic cancer presenting with sepsis and respiratory distress, with a previously established goal of comfort care. The gaming simulation game was divided into 4 stages, and at each level, learners were tasked with completing 1 critical action. The gaming simulation was designed using the LDR serious game scheme in which learners are allowed infinite opportunities to progress through defined stages depicting a single patient scenario. If learners successfully complete the predetermined critical actions of each stage, the game is paused, and there is a debriefing to reinforce knowledge or skills before progressing to the next stage of the gaming simulation. Conversely, if learners do not achieve the critical actions, the game is over, and learners undergo debriefing before repeating the failed stage with an immediate transition into the next. We used the Simulation Effectiveness Tool-Modified survey to evaluate perceived effectiveness in teaching end-of-life management. Results: Eighty percent (16/20) of residents completed the Simulation Effectiveness Tool-Modified survey, and nearly 100% (20/20) either strongly or somewhat agreed that the gaming simulation improved their skills and confidence at the end of life in the following dimensions: (1) better prepared to respond to changes in condition, (2) more confident in assessment skills, (3) teaching patients, (4) reporting to the health care team, (5) empowered to make clinical decisions, and (6) able to prioritize care and interventions. All residents felt the debriefing contributed to learning and provided opportunities to self-reflect. All strongly or somewhat agree that they felt better prepared to respond to changes in the patient's condition, had a better understanding of pathophysiology, were more confident on their assessment skills, and had a better understanding of the medications and therapies after the gaming simulation. A total of 88% (14/16) of them feel more empowered to make clinical decisions. After completing the gaming simulation, 88% (14/16) of residents strongly agreed that they would feel more confident communicating with a patient and prioritizing care interventions in this context. Conclusions: This palliative gaming simulation using the LDR format was perceived by resident physicians to improve confidence in end-of-life communication and palliative care management.

Diagnostic decision-making in the emergency department (ED) is a complex cognitive process involving high uncertainty, making it susceptible to diagnostic errors. The use of data-centric approaches can aid with the identification of factors contributing to diagnostic errors. One of these approaches is Classification and Regression Tree (CART). Our objective in this project is to apply previously validated diagnostic error triggers to patient ED encounters and use machine learning to compare trigger-positive and trigger-negative cases and help identify factors that influence diagnostic safety. We evaluated a cohort of ED visits (all ages, 2017-2019) to identify factors contributing to ED diagnostic error in two phases. In phase one we used the SAS HPSPLIT procedure (CART) to identify important features related to a visit being trigger-positive or trigger-negative. The model was initially based on all data to identify important features using a 10-fold cross-validation method. In the second phase we built a model using the features from phase one and applied the trained model to the manually reviewed cases. We predicted being trigger-positive or trigger-negative and compared them against cases with confirmed errors (Error Yes/No) to compute diagnostic test accuracy and positive and negative predictive values (PPV, NPV). There were 125,342 unique ED encounters including 119,456 trigger-negative and 5,886 trigger-positive. A total of 720 events were manually reviewed (291 trigger-negative, 429 trigger-positive), and 32 of these cases had an identified diagnostic error. The overall rate of diagnostic error for these reviewed cases was 4.4% (95% CI 3.2 to 6.2%). After performing the first phase, an accuracy of 0.953, an F1 score of 0.036, and an AUC of 0.677 was achieved. The second phase resulted in an accuracy of 0.672, F1 score of 0.737, and AUC of 0.713. After the second phase, our PPV was 5.1% (CI 3.2 to 7.7%), NPV was 96.6% (CI 93.8 to 98.3%), sensitivity was 68.8%, and specificity was 40.8%. Top predictive factors are presented in Figure. Our proposed classifier based on phase two had an accuracy of 42% in separating the error-positive and error-negative cases. It was successful in highlighting predictive performance of multiple factors including ICD codes, chief complaints, age, and number of labs and lab panels completed in the ED. Diagnostic errors were uncommon, and application of predicted features did not improve the PPV. Transparency in the reporting of the methods is key for future implementation of science and dissemination of findings. Future work would be to evaluate the performance of this model on a larger set of clinically annotated data.

BACKGROUND:Andexanet alfa (AA) is approved for reversal of factor Xa inhibitor (FXaI) bleeds; however, there are limited reports of its use for gastrointestinal bleeding (GIB) in real-world populations. The objective of this study was to report real-world utilization and evaluation of the effectiveness of AA for FXaI-associated GIB. METHODS:This retrospective cohort study including consecutive patients receiving AA for FXaI-associated GIB (7/2018-2/2021). Demographics, blood product administration, hemostatic efficacy, rebleeding, thrombosis, and mortality rates were collected. Hemostatic efficacy (HE), based on corrected hemoglobin at 12 h compared to baseline, was categorized as excellent (<10% decrease), good (≤ 20% decrease), or poor (>20% decrease, > 2 units of additional coagulation intervention or death prior to repeat hemoglobin). Comparative transfusion requirements between efficacy groups was assessed by Wilcoxon-Rank test. RESULTS:Twenty-two patients were included (64% male, median (IQR) age 76 years (67, 80). Most patients (59%, n = 13) were on apixaban, and the primary anticoagulation indication was atrial fibrillation (64%, n = 14). Median initial hemoglobin was 7.5 g/dL (IQR 6.4, 8.8) and 50% (n = 11) were upper GIB. Hemostatic efficacy was excellent in 46% (n = 10), good in 23% (n = 5), and poor in 32% (n = 7). There was no statistically significant difference in red blood cells (RBCs) received between those with excellent/good hemostasis (median 2, IQR 1 to 2) and those with poor hemostasis (median 4, IQR 1.5 to 4.5). Two patients (9%) had arterial thrombotic events within 30 days of reversal. CONCLUSION:In this multicenter, single arm, real-world observational analysis of patients with factor Xa inhibitor associated GIB most patients achieved good hemostasis following administration of AA. There was a 9% 30-day thrombotic event rate. The lack of a control group limits the strength of the conclusions that can be drawn from this study.

Background: Systemic lupus erythematosus is a chronic mul tisystemic autoimmune disease with diverse clinical mani festations. Women are the most vulnerable population and have the greatest neurological involvement with a higher risk of seizures. Neuropsychiatric manifestations occur in early stages of the disease and diagnosis since they can occur together with systemic manifestations or not. The frequency of neuropsychiatric manifestations in systemic lupus erythe matosus has been described from 14 to 75%; being cognitive alterations one of the major symptoms to highlight. Which, in the same way can be accompanied by affective disorders such as depression and anxiety. Since psychosis, secondary to SLE, stands out for its low prevalence (10%), laboratory studies usually guide us towards a definitive diagnosis, being ribosomal P antibodies the ones that have been more spe cifically related to lupus psychosis. MRI is the test of choice and brain lesions are dominated by punctate white matter hyperintensities. In the following case report, we present a 20-year-old pa tient who had a history of diagnosed hepatic steatosis, MODY type diabetes and resection of the right ovary for mature teratoma of 9 years of evolution; but with no psychiatric his tory of importance at the time of her evaluation. However, she acutely presented a psychotic outbreak characterized by delusions of grandiosity and reference; as well as behavioral, cognitive, and affective alterations. For which she had to go to a 3rd level hospital during the period of health contin gency in 2020. After a history of SARS-CoV-2 infection three months before her neuropsychiatric pathology, neurological symptoms secondary to COVID-19 infection were suspected, as well as isolated psychiatric pathology. Therefore, a study approach of the first psychotic outbreak was performed, diagnosing systemic lupus erythematosus with neuropsy chiatric manifestations. Treatment was based on a bolus of methylprednisolone and antipsychotics; later modified by therapy with oral corticosteroids and depot antipsychotic. Conclusion: Systemic lupus erythematosus with neuropsy chiatric manifestations is an infrequent presentation of the disease, because of the wide variation in its appearance, pa tients with psychiatric symptoms in a general hospital setting should be considered for extensive approaches. In the same way, having this knowledge of this case may broaden our knowledge about the complications of this rheumatologic pathology. And one of its most serious complications such as lupus psychosis to be able to make a better approach to the first psychotic outbreak in general hospitals, where the assessment of a specialist can be more complicated. Keywords: Psychotic break; hallucinations; anti-NMDA antibod ies; neurolupus (NPLSE); COVID-19; case report