Mitochondrial dysfunction is a key contributor to septic cardiomyopathy. Although recent literature implicates dynamin related protein 1 (Drp1) and its mitochondrial adaptor fission 1 (Fis1) in the development of pathologic fission and mitochondrial failure in neurodegenerative disease, little is known about the role of Drp1/Fis1 interaction in the context of sepsis-induced cardiomyopathy. Our study tests the hypothesis that Drp1/Fis1 interaction is a major driver of sepsis-mediated pathologic fission, leading to mitochondrial dysfunction in the heart. Methods: H9C2 cardiomyocytes were treated with lipopolysaccharide (LPS) to evaluate changes in mitochondrial membrane potential, oxidative stress, cellular respiration, and mitochondrial morphology. Balb/c mice were treated with LPS, cardiac function was measured by echocardiogaphy, and mitochondrial morphology determined by electron microscopy (EM). Drp1/ Fis1 interaction was inhibited by P110 to determine whether limiting mitochondrial fission can reduce LPS-induced oxidative stress and cardiac dysfunction.Results: LPS-treated H9C2 cardiomyocytes demonstrated a decrease in mitochondrial respiration followed by an increase in mitochondrial oxidative stress and a reduction in membrane potential. Inhibition of Drp1/Fis1 interaction with P110 attenuated LPS-mediated cellular oxidative stress and preserved membrane potential. In vivo, cardiac dysfunction in LPS-treated mice was associated with increased mitochondrial fragmentation. Treatment with P110 reduced cardiac mitochondrial fragmentation, prevented decline in cardiac function, and reduced mortality.

Thoracic aortic aneurysm and dissection (TAAD) affects many patients globally and has high mortality rates if undetected. Once thought to be solely a degenerative disease that afflicted the aorta due to high pressure and biomechanical stress, extensive investigation of the heritability and natural history of TAAD has shown a clear genetic basis for the disease. Here, we review both the cellular mechanisms and clinical manifestations of syndromic and non-syndromic TAAD. We particularly focus on genes that have been linked to dissection at diameters <5.0 cm, the current lower bound for surgical intervention. Genetic screening tests to identify patients with TAAD associated mutations that place them at high risk for dissection are also discussed.

Background Pressure on the US health care system has been increasing due to a combination of aging populations, rising health care expenditures, and most recently, the COVID-19 pandemic. Responses to this pressure are hindered in part by reliance on a limited supply of highly trained health care professionals, creating a need for scalable technological solutions. Digital symptom checkers are artificial intelligence–supported software tools that use a conversational “chatbot” format to support rapid diagnosis and consistent triage. The COVID-19 pandemic has brought new attention to these tools due to the need to avoid face-to-face contact and preserve urgent care capacity. However, evidence-based deployment of these chatbots requires an understanding of user demographics and associated triage recommendations generated by a large general population. Objective In this study, we evaluate the user demographics and levels of triage acuity provided by a symptom checker chatbot deployed in partnership with a large integrated health system in the United States. Methods This population-based descriptive study included all web-based symptom assessments completed on the website and patient portal of the Sutter Health system (24 hospitals in Northern California) from April 24, 2019, to February 1, 2020. User demographics were compared to relevant US Census population data. Results A total of 26,646 symptom assessments were completed during the study period. Most assessments (17,816/26,646, 66.9%) were completed by female users. The mean user age was 34.3 years (SD 14.4 years), compared to a median age of 37.3 years of the general population. The most common initial symptom was abdominal pain (2060/26,646, 7.7%). A substantial number of assessments (12,357/26,646, 46.4%) were completed outside of typical physician office hours. Most users were advised to seek medical care on the same day (7299/26,646, 27.4%) or within 2-3 days (6301/26,646, 23.6%). Over a quarter of the assessments indicated a high degree of urgency (7723/26,646, 29.0%). Conclusions Users of the symptom checker chatbot were broadly representative of our patient population, although they skewed toward younger and female users. The triage recommendations were comparable to those of nurse-staffed telephone triage lines. Although the emergence of COVID-19 has increased the interest in remote medical assessment tools, it is important to take an evidence-based approach to their deployment.

PURPOSE Acute care use (ACU) is a major driver of oncologic costs and is penalized by a Centers for Medicare & Medicaid Services quality measure, OP-35. Targeted interventions reduce preventable ACU; however, identifying which patients might benefit remains challenging. Prior predictive models have made use of a limited subset of the data in the electronic health record (EHR). We aimed to predict risk of preventable ACU after starting chemotherapy using machine learning (ML) algorithms trained on comprehensive EHR data. METHODS Chemotherapy patients treated at an academic institution and affiliated community care sites between January 2013 and July 2019 who met inclusion criteria for OP-35 were identified. Preventable ACU was defined using OP-35 criteria. Structured EHR data generated before chemotherapy treatment were obtained. ML models were trained to predict risk for ACU after starting chemotherapy using 80% of the cohort. The remaining 20% were used to test model performance by the area under the receiver operator curve. RESULTS Eight thousand four hundred thirty-nine patients were included, of whom 35% had preventable ACU within 180 days of starting chemotherapy. Our primary model classified patients at risk for preventable ACU with an area under the receiver operator curve of 0.783 (95% CI, 0.761 to 0.806). Performance was better for identifying admissions than emergency department visits. Key variables included prior hospitalizations, cancer stage, race, laboratory values, and a diagnosis of depression. Analyses showed limited benefit from including patient-reported outcome data and indicated inequities in outcomes and risk modeling for Black and Medicaid patients. CONCLUSION Dense EHR data can identify patients at risk for ACU using ML with promising accuracy. These models have potential to improve cancer care outcomes, patient experience, and costs by allowing for targeted, preventative interventions.