Perceptions of Quality in Interventional Oncology.

PURPOSE: To inductively characterize perceptions of quality in interventional oncology (IO) based on values and experiences of patients and referring providers. MATERIALS AND METHODS: Brief ethnographic interviews were completed with referring providers and patients before and after a variety of liver-directed procedures about their experiences, concerns, and perceptions of IO services at a single institution. Constructivist grounded theory was used to systematically analyze interview transcripts for themes until thematic saturation was achieved. All transcripts were analyzed by a reviewer with 3-years of experience performing such analyses, and 50% were randomly selected to be coded by 2 additional blinded reviewers. Interreviewer agreement was assessed via Cohen κ. RESULTS: Interviews with 22 patients (mean age, 65 y ± 13; 9 women) and 12 providers (mean age, 54 y ± 9; 6 women) were required to reach and confirm thematic saturation. Interreviewer agreement for interview themes was excellent (κ = 0.78; P < .001). Perceptions of high-quality IO care relied on interventional radiologists being responsive, friendly, and open; engaging in multidisciplinary collaboration; having thoughtful, dedicated support staff; and facilitating well-coordinated care after procedures and follow-up more than technical expertise and periprocedural comfort. Patient and provider perceptions of quality differed, but disjointed care after procedures was the most common critique among both groups. CONCLUSIONS: An inductive qualitative approach effectively characterized specific aspects of perceptions of high-quality IO care among patients and referring providers.

Brief Review and Primer of Key Terminology for Artificial Intelligence and Machine Learning in Hypertension.

Recent breakthroughs in artificial intelligence (AI) have caught the attention of many fields, including health care. The vision for AI is that a computer model can process information and provide output that is indistinguishable from that of a human and, in specific repetitive tasks, outperform a human's capability. The 2 critical underlying technologies in AI are used for supervised and unsupervised machine learning. Machine learning uses neural networks and deep learning modeled after the human brain from structured or unstructured data sets to learn, make decisions, and continuously improve the model. Natural language processing, used for supervised learning, is understanding, interpreting, and generating information using human language in chatbots and generative and conversational AI. These breakthroughs result from increased computing power and access to large data sets, setting the stage for releasing large language models, such as ChatGPT and others, and new imaging models using computer vision. Hypertension management involves using blood pressure and other biometric data from connected devices and generative AI to communicate with patients and health care professionals. AI can potentially improve hypertension diagnosis and treatment through remote patient monitoring and digital therapeutics.

Perceptions of Life Support and Advance Care Planning During the COVID-19 Pandemic: A Global Study of Twitter Users.

BACKGROUND: The COVID-19 pandemic has presented new challenges surrounding end-of-life planning and has been associated with increased online discussion about life support. RESEARCH QUESTION: How has online communication about advance care planning (ACP) and specific life-sustaining interventions (LSIs) changed during the pandemic? STUDY DESIGN AND METHODS: Conversations on Twitter containing references to LSIs (eg, "ECMO") or ACP (eg, "DNR/DNI") were collected between January 2019 and May 2021. User account metadata were used to predict user demographic information and to classify users as organizations, individuals, clinicians, or influencers. The number of impressions was compared across these user categories and the content of tweets analyzed by using natural language processing models to identify topics of discussion and associated emotional sentiment. RESULTS: There were 202,585 unique tweets about LSIs and 67,162 unique tweets about ACP. Users who were younger, male, or influencers were more likely to discuss LSIs online. Tweets about LSIs were associated with more positive emotional sentiment scores than tweets about ACP (LSIs, 0.3; ACP, -0.2; P < .001). Among tweets about ACP, most contained personal experiences related to the death of loved ones (27%) or discussed discrimination through do-not-resuscitate orders directed at the elderly and disabled (19%). Personal experiences had the greatest retweet-to-tweet-ratio (4.7), indicating high levels of user engagement. Tweets about discrimination contained the most negative net sentiment score (-0.5). INTERPRETATION: The observed increase in tweets regarding LSIs and ACP suggests that Twitter was consistently used to discuss treatment modalities and preferences related to intensive care during the pandemic. Future interventions to increase online engagement with ACP may consider leveraging influencers and personal stories. Finally, we identified do-not-resuscitate-related discrimination as a commonly held public fear, which should be further explored as a barrier to ACP completion and can be proactively addressed by clinicians during bedside goals-of-care discussions.

Snail cooperates with Kras G12D in vivo to increase stem cell factor and enhance mast cell infiltration.

UNLABELLED: Pancreatic ductal adenocarcinoma (PDAC) is associated with a pronounced fibro-inflammatory stromal reaction that contributes to tumor progression. A critical step in invasion and metastasis is the epithelial-to-mesenchymal transition (EMT), which can be regulated by the Snail family of transcription factors. Overexpression of Snail (Snai1) and mutant Kras(G12D) in the pancreas of transgenic mice, using an elastase (EL) promoter, resulted in fibrosis. To identify how Snail modulates inflammation in the pancreas, we examined the effect of expressing Snail in EL-Kras(G12D) mice (Kras(G12D)/Snail) on mast cell infiltration, which has been linked to PDAC progression. Using this animal model system, it was demonstrated that there are increased numbers of mast cells in the pancreas of Kras(G12D)/Snail mice compared with control Kras(G12D) mice. In addition, it was revealed that human primary PDAC tumors with increased Snail expression are associated with increased mast cell infiltration, and that Snail expression in these clinical specimens positively correlated with the expression of stem cell factor (SCF/KITLG), a cytokine known to regulate mast cell migration. Concomitantly, SCF levels are increased in the Kras(G12D)/Snail mice than in control mice. Moreover, overexpression of Snail in PDAC cells increased SCF levels, and the media conditioned by Snail-expressing PDAC cells promoted mast cell migration. Finally, inhibition of SCF using a neutralizing antibody significantly attenuated Snail-induced migration of mast cells. IMPLICATIONS: Together, these results elucidate how the EMT regulator Snail contributes to inflammation associated with PDAC tumors.