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Introduction In the USA, minoritised communities (racial and ethnic) have suffered disproportionately from COVID-19 compared with non-Hispanic white communities. In a large cohort of patients hospitalised for COVID-19 in a healthcare system spanning five adult hospitals, we analysed outcomes of patients based on race and ethnicity.Methods This was a retrospective cohort analysis of patients 18 years or older admitted to five hospitals in the mid-Atlantic area between 4 March 2020 and 27 May 2022 with confirmed COVID-19. Participants were divided into four groups based on their race/ethnicity: non-Hispanic black, non-Hispanic white, Latinx and other. Propensity score weighted generalised linear models were used to assess the association between race/ethnicity and the primary outcome of in-hospital mortality.Results Of the 9651 participants in the cohort, more than half were aged 18–64 years old (56%) and 51% of the cohort were females. Non-Hispanic white patients had higher mortality (p

BackgroundThere is significant heterogeneity in disease progression among hospitalized patients with COVID-19. The pathogenesis of SARS-CoV-2 infection is attributed to a complex interplay between virus and host immune response that in some patients unpredictably and rapidly leads to “hyperinflammation” associated with increased risk of mortality. The early identification of patients at risk of progression to hyperinflammation may help inform timely therapeutic decisions and lead to improved outcomes. ObjectiveThe primary objective of this study was to use machine learning to reproducibly identify specific risk-stratifying clinical phenotypes across hospitalized patients with COVID-19 and compare treatment response characteristics and outcomes. A secondary objective was to derive a predictive phenotype classification model using routinely available early encounter data that may be useful in informing optimal COVID-19 bedside clinical management. MethodsThis was a retrospective analysis of electronic health record data of adult patients (N=4379) who were admitted to a Johns Hopkins Health System hospital for COVID-19 treatment from 2020 to 2021. Phenotypes were identified by clustering 38 routine clinical observations recorded during inpatient care. To examine the reproducibility and validity of the derived phenotypes, patient data were randomly divided into 2 cohorts, and clustering analysis was performed independently for each cohort. A predictive phenotype classifier using the gradient-boosting machine method was derived using routine clinical observations recorded during the first 6 hours following admission. ResultsA total of 2 phenotypes (designated as phenotype 1 and phenotype 2) were identified in patients admitted for COVID-19 in both the training and validation cohorts with similar distributions of features, correlations with biomarkers, treatments, comorbidities, and outcomes. In both the training and validation cohorts, phenotype-2 patients were older; had elevated markers of inflammation; and were at an increased risk of requiring intensive care unit–level care, developing sepsis, and mortality compared with phenotype-1 patients. The gradient-boosting machine phenotype prediction model yielded an area under the curve of 0.89 and a positive predictive value of 0.83. ConclusionsUsing machine learning clustering, we identified and internally validated 2 clinical COVID-19 phenotypes with distinct treatment or response characteristics consistent with similar 2-phenotype models derived from other hospitalized populations with COVID-19, supporting the reliability and generalizability of these findings. COVID-19 phenotypes can be accurately identified using machine learning models based on readily available early encounter clinical data. A phenotype prediction model based on early encounter data may be clinically useful for timely bedside risk stratification and treatment personalization.

A 58-year-old woman was admitted with symptoms of coronavirus disease-2019. She subsequently developed mixed shock, and an echocardiogram showed mid-distal left ventricular hypokinesis and apical ballooning, findings typical of stress, or takotsubo, cardiomyopathy. Over the next few days her left ventricular function improved, the further supporting the reversibility of acute stress cardiomyopathy. (Level of Difficulty: Beginner.)

Introduction End-of-life (EOL) care is an essential skill for most physicians and health care providers, yet there continues to be an educational gap in medical education literature for these skills. The Johns Hopkins School of Medicine developed the Transition to Residency, Internship, and Preparation for Life Events (TRIPLE) curriculum with the primary goal of preparing graduating medical students for life after medical school. Methods The EOL module was one of many within the TRIPLE curriculum and consisted of two half-day sessions that targeted EOL care, death, dying, and communication skills. The first half-day session focused on a standardized patient encounter where learners initiated and completed an EOL care goals conversation around a living will. The second half-day session focused on death and dying. It included didactic sessions on organ donation, autopsy/death certificates, a simulation-based learning session on ending a resuscitation, and a standardized patient encounter where learners disclosed the death of a loved one. End-of-day and end-of-course evaluations were collected via anonymous online surveys. Results In 2019, 120 students and 26 instructors participated in TRIPLE. Students rated the EOL module overall as 4.6 of 5 (SD = 0.6) and rated instructors overall as 4.6 of 5 (SD = 0.6). Discussion By implementing a thorough and diverse curriculum with a variety of modalities and targeted skills, learners may be better prepared to care for patients dealing with EOL care issues. Further, the generalization of these skills may assist learners in a variety of other aspects of patient and family care.

Purpose: In recent years, several well-known medical schools in the United States have partnered with foreign institutions to promote the post-graduate model of medical education and the concept of the academic medical center. One such example is Perdana University Graduate School of Medicine (PUGSOM) in Malaysia, founded in collaboration with the Johns Hopkins University School of Medicine in 2010 with the hope of setting a new standard for biomedical research and patient-centered care in Southeast Asia. One issue for educators at PUGSOM was how best to integrate radiology into the clinical curriculum. Given radiology׳s global importance, a core clerkship was proposed; however, a major challenge lay in creating content that was rigorous and relevant to the Malaysian setting. Methods: After interviewing practicing Malaysian radiologists, attending medicine ward rounds, and performing a literature review of successful radiology curricula, two senior American medical students from Johns Hopkins developed a two-week introductory clerkship combining experiential learning with online, case-based lectures featuring diseases and technology pertinent to the Malaysian environment. Results/discussion: To our knowledge, this is the first example of a course developed by medical students in the United States for medical students in the developing world. The project serves as an example of the type of mutually beneficial international exchange intended by global partnerships in medical education.