Racial Disparities and Other Socioeconomic Predictors of Mortality in Acute Pulmonary Embolism Treatment from the National Inpatient Sample.

PURPOSE: To explore the significance of socioeconomic factors such as race and ethnicity as predictors of mortality in sub-massive and massive acute pulmonary embolism (PE). MATERIALS AND METHODS: Hospitalizations aged > 18 years with acute, non-septic PE from 2016 to 2019 were identified in the National Inpatient Sample and divided into IR (CDT and thrombectomy) and non-IR (tPA) treatments. Statistical analyses calculated significant odds ratios via 95% confidence intervals. The primary outcome of interest was mortality rate. Comorbidities affecting mortality were examined secondarily. RESULTS: Non-Hispanic (NH) Black, Hispanic, and Asian/Pacific Islander patients were significantly less likely to undergo an IR procedure for acute, non-septic PE compared to White patients (NH Black 0.83 [0.76 - 0.90], p<0.05; Hispanic 0.78 [0.68 - 0.89], p=0.06; Asian/Pacific Islander 0.71 [0.51 - 0.98], p=0.72; OR [95% CI]); however, these differences were eliminated when propensity score matching for age, biological sex, and primary insurance-type or primary insurance-type alone. NH Black patients were significantly more likely than White patients to die regardless of undergoing non-IR or an IR treatment. Overall risk of death was 41% higher for NH Black patients compared to White patients (RR [95% CI] 1.41 [1.24 - 1.60], p<0.001). CONCLUSION: NH Black patients have a higher risk of mortality from acute, non-septic PE than White patients. Independent of race, undergoing IR management for acute, non-septic pulmonary embolisms was associated with a lower mortality rate. Matching for primary insurance-type eliminates difference in mortality between races suggest socioeconomic status (SES) may determine outcomes in acute PE.

Novel combination approaches to locoregional and systemic therapy in the management of primary and metastatic liver tumors.

Several pathways and mutations must develop or be in place for the onset of cancer. Therefore, therapies should ideally target as many of these pathways as possible to improve outcomes. Combining several agents has proven to be more effective than the use of monotherapy in the treatment of renal cell carcinoma, hepatocellular carcinoma, and other cancers. Combination therapy can also include locoregional therapies such as ablation and embolization with systemic agents for synergistic effects. This review article discusses the current literature and clinical trials covering these multifactorial combination therapies in primary and metastatic liver tumors.

ChatGPT Interactive Medical Simulations for Early Clinical Education: Case Study.

BACKGROUND: The transition to clinical clerkships can be difficult for medical students, as it requires the synthesis and application of preclinical information into diagnostic and therapeutic decisions. ChatGPT-a generative language model with many medical applications due to its creativity, memory, and accuracy-can help students in this transition. OBJECTIVE: This paper models ChatGPT 3.5's ability to perform interactive clinical simulations and shows this tool's benefit to medical education. METHODS: Simulation starting prompts were refined using ChatGPT 3.5 in Google Chrome. Starting prompts were selected based on assessment format, stepwise progression of simulation events and questions, free-response question type, responsiveness to user inputs, postscenario feedback, and medical accuracy of the feedback. The chosen scenarios were advanced cardiac life support and medical intensive care (for sepsis and pneumonia). RESULTS: Two starting prompts were chosen. Prompt 1 was developed through 3 test simulations and used successfully in 2 simulations. Prompt 2 was developed through 10 additional test simulations and used successfully in 1 simulation. CONCLUSIONS: ChatGPT is capable of creating simulations for early clinical education. These simulations let students practice novel parts of the clinical curriculum, such as forming independent diagnostic and therapeutic impressions over an entire patient encounter. Furthermore, the simulations can adapt to user inputs in a way that replicates real life more accurately than premade question bank clinical vignettes. Finally, ChatGPT can create potentially unlimited free simulations with specific feedback, which increases access for medical students with lower socioeconomic status and underresourced medical schools. However, no tool is perfect, and ChatGPT is no exception; there are concerns about simulation accuracy and replicability that need to be addressed to further optimize ChatGPT's performance as an educational resource.