How to cite this article: Hajijama S, Juneja D, Nasa P. Large Language Model in Critical Care Medicine: Opportunities and Challenges. Indian J Crit Care Med 2024;28(6):523-525.

Although the incidence of systemic thromboembolism in valvular heart disease has been reported to be as high as 10% to 35%, embolization to the coronary arteries is uncommon. We present a case of a patient with acute myocardial infarction caused by coronary thromboemboli associated with combined valvular heart disease and atrial fibrillation. The thromboemboli were documented in the left descending artery. Coronary interventions including thromboaspiration and percutaneous coronary balloon angioplasty were attempted.

Trimethoprim-sulfamethoxazole (TMP-SMX) acute respiratory distress syndrome (ARDS) is a rare, but severe complication of a commonly prescribed antibiotic. TMP-SMX typically affects young, otherwise well patients with a specific human leukocyte antigen type (HLA-B*07:02 and HLA-C*07:02). The condition is poorly understood with a unique pathological appearance and mechanism that remains unclear. Mortality rate is greater than one third. We describe the case of a previously well 18-year-old woman treated with a prolonged course of TMP-SMX for a complex urinary tract infection who developed rapidly progressive respiratory failure requiring prolonged intensive care admission, extra-corporeal membranous oxygenation, and eventual lung transplantation. No targeted treatment exists, further research is required to better understand disease pathogenetic mechanisms and potential therapeutic interventions.

UNASSIGNED: Critical care, emergency medicine, and surgical trainees frequently perform surgical and Seldinger-technique tube thoracostomy, thoracentesis, and thoracic ultrasound. However, approaches to teaching these skills are highly heterogeneous. Over 10 years, we have developed a standardized, multidisciplinary curriculum to teach these procedures.
UNASSIGNED: Emergency medicine residents, surgical residents, and critical care fellows, all in the first year of their respective programs, underwent training in surgical and Seldinger chest tube placement and securement, thoracentesis, and thoracic ultrasound. The curriculum included preworkshop instructional videos and 45-minute in-person practice stations (3.5 hours total). Sessions were co-led by faculty from emergency medicine, thoracic surgery, and pulmonary/critical care who performed real-time formative assessment with standardized procedural steps. Postcourse surveys assessed learners\' confidence before versus after the workshop in each procedure, learners\' evaluations of faculty by station and specialty, and the workshop overall.
UNASSIGNED: One hundred twenty-three trainees completed course evaluations, demonstrating stable and positive responses from learners of different backgrounds taught by a multidisciplinary group of instructors, as well as statistically significant improvement in learner confidence in each procedure. Over time, we have made incremental changes to our curriculum based on feedback from instructors and learners.
UNASSIGNED: We have developed a unique curriculum designed, revised, and taught by a multidisciplinary faculty over many years to teach a unified approach to the performance of common chest procedures to surgical, emergency medicine, and critical care trainees. Our curriculum can be readily adapted to the needs of institutions that desire a standardized, multidisciplinary approach to thoracic procedural education.

Delirium is a significant public health concern, with tremendous implications for patient outcomes. Intensive care unit (ICU)-related delirium is gaining attention due to the higher prevalence of delirium in ICU-admitted patients. The most common negative outcomes of ICU delirium include cognitive impairments, functional dependence, high incidence of mortality, extended stay in the ICU, and high costs. So far, no single etiological factor has been identified as the sole cause of delirium. Several functional, neurotransmitter, or injury-causing hypotheses have been proposed for ICU delirium. Several risk factors contribute to the development of delirium in patients admitted to the ICU. These are age, gender, types of sedation, physical restraints, medical and surgical interventions, pain, and extended stay in the ICU. The most commonly used assessment modules for ICU delirium are the PREdiction of DELIRium in ICu patients (PRE-DELIRIC), Early PREdiction model for DELIRium in ICu patients (E-PRE-DELERIC), and Lanzhou Model, Confusion Assessment Method for the Intensive Care Unit (CAM-ICU), Intensive Care Delirium Screening Checklist (ICDSC), and Delirium Rating Scale (DRS). There is no proper treatment for ICU delirium; however, it can be managed through various pharmacological and non-pharmacological interventions. Healthcare providers should receive constant education and training on delirium recognition, prevention, and management to enhance patient care and outcomes in the ICU. Further research is needed on the effective prevention and management of ICU delirium.

Acute kidney injury (AKI) is a major complication following liver transplantation (LT), which utilizes grafts from donors after cardiac death (DCD). We developed a machine-learning-based model to predict AKI, using data from 894 LT recipients (January 2015-March 2021), split into training and testing sets. Five machine learning algorithms were employed to construct the prediction models using 17 clinical variables. The performance of the models was assessed by the area under the receiver operating characteristic curve (AUC), accuracy, F1-score, sensitivity and specificity. The best-performing model was further validated in an independent cohort of 195 LT recipients who received DCD grafts between April 2021 and December 2021. The Shapley additive explanations method was utilized to elucidate the predictions and identify the most crucial features. The gradient boosting machine (GBM) model demonstrated the highest AUC (0.76, 95% CI: 0.70-0.82), F1-score (0.73, 95% CI: 0.66-0.79) and sensitivity (0.74, 95% CI: 0.66-0.80) in the testing set and a comparable AUC (0.75, 95% CI: 0.67-0.81) in the validation set. The GBM model identified high preoperative indirect bilirubin, low intraoperative urine output, prolonged anesthesia duration, low preoperative platelet count and graft steatosis graded NASH Clinical Research Network 1 and above as the top five important features for predicting AKI following LT using DCD grafts. The GBM model is a reliable and interpretable tool for predicting AKI in recipients of LT using DCD grafts. This model can assist clinicians in identifying patients at high risk and providing timely interventions to prevent or mitigate AKI.

We present the case of a 27-year-old pregnant woman, newly diagnosed with Systemic Lupus Erythematosus (SLE) during pregnancy. The patient delivered a newborn at 38 weeks gestation, who, on the first day of life, manifested complete heart block. This case underscores the clinical challenges associated with neonatal lupus, emphasizing the need for collaborative, multidisciplinary management.

Anti-MDA-5 dermatomyositis (DM) is a subtype of idiopathic inflammatory myopathy, commonly presenting as clinically amyopathic dermatomyositis. It is associated with rapidly progressive interstitial lung disease and a poor prognosis. Here, we present two cases of anti-MDA-5 DM and discuss the challenges associated with timely diagnosis, and the importance of early and aggressive treatment.

Artificial intelligence (AI) is a technique that attempts to replicate human intelligence, analytical behavior, and decision-making ability. This includes machine learning, which involves the use of algorithms and statistical techniques to enhance the computer\'s ability to make decisions more accurately. Due to AI\'s ability to analyze, comprehend, and interpret considerable volumes of data, it has been increasingly used in the field of healthcare. In critical care medicine, where most of the patient load requires timely interventions due to the perilous nature of the condition, AI\'s ability to monitor, analyze, and predict unfavorable outcomes is an invaluable asset. It can significantly improve timely interventions and prevent unfavorable outcomes, which, otherwise, is not always achievable owing to the constrained human ability to multitask with optimum efficiency. AI has been implicated in intensive care units over the past many years. In addition to its advantageous applications, this article discusses its disadvantages, prospects, and the changes needed to train future critical care professionals. A comprehensive search of electronic databases was performed using relevant keywords. Data from articles pertinent to the topic was assimilated into this review article.

Emergency department (ED) care teams face challenges in providing timely, high-quality care to critically ill patients because of competing patient care priorities and a multitude of system strains, including patient boarding. Patients who are boarding in the ED experience increased morbidity and mortality, and this is particularly true for those who are critically ill. Geography-based models for critical care delivery in the ED range from resuscitation bays to full-fledged ED intensive care units. Studies have shown that such models can improve patient survival without affecting cost. Here, we describe how we reappropriated limited fixed resources to create a critical care resuscitation unit in a busy, urban, academic ED. Our objective is to provide a blueprint for similar models, paying particular attention to operations, clinical care, education, and financial stability.