Kashyap, Rahul

Objectives: In the United States, training for physicians who manage critically ill adult patients (intensivists) evolved through parallel subspecialty critical care medicine (CCM) pathways with significant commonality. The Society of Critical Care Medicine Adult Critical Care Physician Core Knowledge and Skills Task Force aimed to delineate the common core knowledge and skills required of all intensivists. Design: A master list of content areas and procedural skills was compiled from all CCM subspecialty program requirements and blueprints of the certification examinations. Using a modified Delphi approach, participants were asked to categorize the knowledge items as “advanced knowledge is essential,” “general, but not advanced, knowledge is essential,” or “knowledge is not essential.” Procedures were categorized as “intensivist performs routinely,” “intensivist only performs in an emergency,” or “intensivist knows” about the procedure. Setting: Representatives from CCM stakeholder organizations, including accreditation and certification organizations, critical care societies, and program directors’ societies, were invited to participate. SUBJECTS: Members of the Adult Critical Care Physician Core Knowledge and Skills Task Force of the Society of Critical Care Medicine. Interventions: For the first two rounds of the modified Delphi process, Research Electronic Data Capture was used. For the third and fourth rounds, the process was completed through online meetings with Zoom (Zoom Video Corporations, San Jose, CA) utilizing Zoom’s polling feature. Measurements and Main Results: A total of 541 items were determined to be essential, with 145 requiring advanced knowledge and 323 requiring general knowledge. For 73 items, consensus regarding advanced vs. general could not be achieved, but they remained essential. Only eight items were felt to be nonessential. Of the 16 procedures, most were categorized as “intensivist performs.” Conclusions: The large number of items included in the list of essential knowledge and skills demonstrates the complexity of modern CCM. Utilization of a common framework across the subspecialties of CCM could lead to greater harmonization among the fellowship program requirements and certification examinations.

Background Adherence to evidence-based care processes and patient outcomes in intensive care units (ICUs) can be influenced by staffing and resource availability. We aimed to evaluate if there is a weekend effect on adherence to evidence-based care processes, and hospitalization outcomes and whether a checklist implementation could mitigate potential differences. Methods Post hoc analysis of the Checklist for Early Recognition and Treatment of Acute Illness and Injury (CERTAIN) study dataset collected before and after checklist implementation in 34 ICUs across 15 countries (2013-2017). Admission days were classified as ‘weekend/holidays’ or ‘weekdays’ according to local work schedules and public holidays. The primary outcome was the omission of 10 evidence-based care processes addressed in the checklist. Mortality and length of stay differences between weekend/holiday and weekday admissions were evaluated as secondary outcomes. Results 4256 patients contributed 1141 weekend versus 3501 weekday observation days pre-intervention, and 2014 versus 6507 post-intervention. Pre-intervention, peptic ulcer prophylaxis was omitted more frequently on weekends/holidays than weekdays (adjusted rate ratio [aRR], 0.58 [95%–confidence interval [CI] 0.38-0.88), whereas head-of-bed elevation was omitted more often on weekdays than on weekends/holidays (aRR, 3.17 [1.14-8.86]). Post-intervention, peptic ulcer prophylaxis omission rates became similar (aRR, 1.03 [0.68-1.56], but head-of-bed elevation became omitted more often on weekends than on weekdays (aRR, 0.63 [0.45-0.88]). Post-intervention, oral care was omitted more frequently on weekends/holidays than in weekdays (aRR, 0.63 [0.45-0.9]), and central catheter removal was more frequent on weekdays than in weekends/holidays (aRR, 1.11 [1.02-1.21]). No significant differences in mortality or length of stay were found. Conclusion A weekend effect influenced adherence to some care processes. While checklist implementation improved overall adherence, some disparities diminished, while new ones emerged. Organizational, cultural, and temporal factors should be further studied to optimize care delivery across all times and settings. Clinical Trial Registration Number NCT01973829

Background/Objectives: Sodium glucose co-transporter 2 inhibitors (SGLT2is) have demonstrated a reduction in heart failure (HF) hospitalizations in HF patients and decreased recurrence of atrial fibrillation (AF), including in those who have undergone catheter ablation (CA). The effects of SGLT2i are likely due to suppression of the renin–angiotensin–aldosterone system, reduction in oxidative stress with subsequent improvement in myocardial efficiency, and attenuation of cardiac remodeling. We aim to present the effects of SGLT2i on AF recurrence in patients who have undergone CA for AF. Methods: This is a systematic review and meta-analysis of randomized controlled trials (RCTs) and retrospective studies evaluating the effect of SGLT2i on AF recurrence following CA compared with non-SGLT2i. The primary outcome was the recurrence of AF by the final follow-up reported in each study. Secondary outcomes include AF recurrence by the first follow-up within 12 to 24 months and follow-up intervals (6, 12, 18, 24, and 36 to 42 months) post-ablation, multivariate risk of AF recurrence, and the effect on left atrial diameter (LAD) (less than 45 mm vs. greater than or equal to 45 mm). For risk of bias (ROB) analysis, the NIH ROB and Cochrane ROB2 tool were used. All statistical, heterogeneity, and sensitivity analyses were conducted using Cochrane Review Manager. A random-effect model was employed for all pooled statistical analyses. Results: A total of nine studies, two RCTs and seven retrospective studies, were included (N = 6874) for the primary outcome. Compared to non-SGLT2i (N = 3693), SGLT2i (N = 3181) significantly decreased AF recurrence by the final follow-up (OR = 0.62; 95% CI: 0.45–0.85; p = 0.008). For secondary outcomes, SGLT2i significantly reduced AF recurrence by the first follow-up within 12 to 24 months post-ablation (OR = 0.58; p = 0.0001) and by the different follow-up periods, 6-month (OR = 0.53; p = 0.02), 12-month (OR = 0.56; p = 0.0001), 18-month (OR = 0.55; p = 0.01), and 24-month (OR = 0.60; p = 0.12) follow-up periods. On the other hand, by 36 to 42 months, SGLT2i was associated with increased risk of AF recurrence (OR = 1.41; p = 0.004). Conclusions: We conclude that SGLT2i demonstrated a reduction in AF recurrence following CA, particularly by 12 to 18 months post-ablation.

Background Artificial intelligence (AI) and large language models (LLMs), are potential tools for enhancing healthcare delivery and clinical research. Presently, there is a scarcity of research regarding the viewpoints of medical students toward LLMs. This study aims to explore the perceptions of LLMs and their applications among this demographic. Methods A cross-sectional study was done using an online survey. It was designed using Google Forms and circulated from July 2023 to August 2023. The target population included medical students from Ecuador, Ethiopia, India, Mauritius, Pakistan, United Arab Emirates (UAE), and the United States (USA). Results A total of 1180 responses were collected from 10 medical colleges across 7 countries. The UAE had the largest number of responses (31.7%), followed by Ecuador (17.6%), and Mauritius (13.7%). And 77.4% respondents were already aware about LLMs before attempting the survey, most popularly, ChatGPT. Participants were most familiar with the specific use of LLMs in research (46%), and the lowest familiarity was seen in the use of LLMs in healthcare (37%). More than half of the participants (52%) support the use of LLMs healthcare, yet a significant number of them remained neutral (37%) or disagreed (31%) on LLMs being safe in this context. Conclusion While most of the medical students are aware of LLMs and their applications, the multinational survey demonstrated hesitancy among medical students in fully adopting LLMs into healthcare practice and clinical research. This study prompts further exploration of medical students’ attitudes and acceptance concerning the integration of LLMs for research methodologies, ethical considerations, and healthcare practice.

Background: Procalcitonin (PCT) is recognized as an inflammatory biomarker, often elevated in COVID-19 pneumonia alongside other biomarkers. Understanding its association with severe outcomes and comparing its predictive ability with other biomarkers is crucial for clinical management. Objectives: This retrospective multicenter observational study aimed to investigate the association between PCT levels and adverse outcomes in hospitalized COVID-19 patients. Additionally, it sought to compare the predictive performance of various biomarkers. Design: The study analyzed data from the Society of Critical Care Medicine (SCCM) Viral Infection and Respiratory Illness Universal Study (VIRUS) registry, comprising COVID-19 patients hospitalized across multiple Mayo Clinic sites between March 2020 and June 2022. Methods: A total of 7851 adult COVID-19 patients were included. Patients were categorized into 6 groups based on the worst WHO ordinal scale. Multivariate models were constructed using peak biomarker levels within 72 hours of admission, adjusted for confounders. Results: Elevated PCT levels were independently associated with increased odds of adverse outcomes, including ICU admission (adjusted odds ratio [aOR] 1.32, 95%CI 1.27-1.38), IMV requirement (aOR 1.35, 95%CI: 1.28-1.42), and in-hospital mortality (aOR 1.30, 95%CI: 1.22-1.37). A 3.48-fold increase in IMV requirement and 3.55 times increase in in-hospital mortality were noted with peak PCT ⩾ 0.25 ng/ml. Similar associations were observed with other biomarkers like NLR (AUC 0.730), CRP, IL-6, LDH (AUC 0.800), and D-dimer (AUC 0.719). Models incorporating NLR, LDH, D-dimer, and PCT demonstrated the highest predictive accuracy, with a combined model exhibiting an area under the curve (AUC) of 0.826 (95%CI 0.803-0.849). Conclusions: Higher PCT levels were significantly linked to worse outcomes in COVID-19 patients, emphasizing its potential as a prognostic marker. Biomarker-based predictive models, particularly those including PCT, showed promising utility for risk assessment and clinical decision-making. Further prospective studies are warranted to validate these findings on a larger scale.

The AI-MIRACLE Study investigates the efficacy of using ChatGPT 4.0, a large language model (LLM), for translating and simplifying radiology reports into multiple languages, aimed at enhancing patient comprehension. The study assesses the model’s performance across the most spoken languages in the U.S., emphasizing the accuracy and clarity of translated and simplified radiology reports for non-medical readers. This study employed ChatGPT 4.0 to translate and simplify selected radiology reports into Vietnamese, Tagalog, Spanish, Mandarin, and Arabic. Hindi was used as a preliminary test language for validation of the questionnaire. Performance was assessed via Google form surveys distributed to bilingual physicians, which assessed the translation accuracy and clarity of simplified texts provided by ChatGPT 4. Responses from 24 participants showed mixed results. The study underscores the model’s varying success across different languages, emphasizing both potential applications and limitations. ChatGPT 4.0 shows promise in breaking down language barriers in healthcare settings, enhancing patient comprehension of complex medical information. However, the performance is inconsistent across languages, indicating a need for further refinement and more inclusive training of AI models to handle diverse medical contexts and languages. The study highlights the role of LLMs in improving healthcare communication and patient comprehension, while indicating the need for continued advancements in AI technology, particularly in the translation of low-resource languages.

Aim: AI research, development, and implementation are expanding at an exponential pace across healthcare. This paradigm shift in healthcare research has led to increased demands for clinical outcomes, all at the expense of a significant gap in AI literacy within the healthcare field. This has further translated to a lack of tools in creating a framework for literature in the AI in medicine domain. We propose HUMANE (Harmonious Understanding of Machine Learning Analytics Network), a checklist for establishing an international consensus for authors and reviewers involved in research focused on artificial intelligence (AI) or machine learning (ML) in medicine. Methods: This study was conducted using the Delphi method by devising a survey using the Google Forms platform. The survey was developed as a checklist containing 8 sections and 56 questions with a 5-point Likert scale. Results: A total of 33 survey respondents were part of the initial Delphi process with the majority (45%) in the 36–45 years age group. The respondents were located across the USA (61%), UK (24%), and Australia (9%) as the top 3 countries, with a pre-dominant healthcare background (42%) as early-career professionals (3–10 years’ experience) (42%). Feedback showed an overall agreeable consensus (mean ranges 4.1–4.8, out of 5) as cumulative scores throughout all sections. The majority of the consensus was agreeable with the Discussion (Other) section of the checklist (median 4.8 (interquartile range (IQR) 4.8-4.8)), whereas the least agreed section was the Ground Truth (Expert(s) review) section (median 4.1 (IQR 3.9–4.2)) and the Methods (Outcomes) section (median 4.1 (IQR 4.1–4.1)) of the checklist. The final checklist after consensus and revision included a total of 8 sections and 50 questions. Conclusions: The HUMANE international consensus has reflected on further research on the potential of this checklist as an established consensus in improving the reliability and quality of research in this field.