Point of Care Ultrasound Identification and Aspiration of a Neck Lymph Node.

The tissue diagnosis and staging of all types of lung cancer is foundational for prognosis and establishing the optimal treatment plan. In order to appropriately stage lung cancer, the highest stage should be established using the 8th edition TNM criteria, where tumor size (T), nodal involvement (N), and metastasis (M) are all taken into account. Establishing a tissue diagnosis may involve the use of CT guided biopsy, navigational bronchoscopy, endobronchial biopsy, EBUS, percutaneous lymph node biopsy and/or excisional biopsy of supraclavicular nodes. It is recommended to proceed with the method that is considered least invasive and provides the highest staging. We present a case of recurrent lung adenocarcinoma diagnosed with real time ultrasound-guided fine needle aspiration of a neck lymph node.

In-Hospital Cardiac Arrest Outcomes During the Early COVID-19 Pandemic in RI: A Qualitative Analysis.

Throughout the COVID-19 pandemic, there has been growing but limited data describing the poor mortality outcomes in COVID-19 patients who experienced In-Hospital Cardiac Arrest (IHCA). This study evaluated the baseline characteristics and outcomes of COVID-19 patients who underwent cardiopulmonary resuscitation (CPR) during hospitalization in the early phases of the pandemic and compared them to that of several national and international centers. A list of all the IHCA events in the Lifespan hospital network from March 2020 to April 2021 was generated, and data, including de-identified patient characteristics, comorbidities, and details of the IHCA event, were examined. The primary outcome of all-cause mortality was then calculated. Forty-three patients with COVID-19 who experienced an IHCA event and underwent CPR were identified. Return of spontaneous circulation (ROSC) was achieved in 23 (53%) patients, and all-cause in-hospital mortality was 97.67%, with only one patient surviving until discharge. During the early pandemic, experiencing an IHCA event while admitted with COVID-19 carried an extremely poor prognosis, even if ROSC was achieved. This outcome likely reflects the lack of clear management guidelines or established therapeutic agents and the prevalence of the Delta strain during this time period.

From Personal Observations to Recommendation of Tailored Interventions based on Causal Reasoning: a case study of Falls Prevention in Elderly Patients.

OBJECTIVE: While the challenge of estimating the efficacy of therapies using observational data has received a lot of attention, little work has been done on estimating the treatment effect from interventions. In this paper, we tackle this problem by proposing an early guidance system based on a causal Bayesian network (CBN) for recommending personalized interventions. We are interested in the elderly fall prevention context. The objective is to develop a practical tool to help doctors estimate the effects of each intervention (or compound interventions) on a given patient and then choose the one that best fits each patient's health situation to reduce the risk of falling. METHODS: On a real-world elderly information base, we undertake an empirical investigation for the proposed approach, which is based on a 44-node CBN. Then, we describe what is possible to achieve using state-of-the-art machine learning methods, namely Support Virtual Machine (SVM), Decision Tree (DT), and Bayesian Network (BN), and how well these methods can be used in recommending personalized interventions compared to the proposed approach. RESULTS: 1174 elderly patients from Lille University Hospital, between January 2005 and December 2018 are included. The results reveal that none of the classifiers is significantly superior to the others, even if BN delivers somewhat better results (41.6%) and DT most often slightly lower performance (31.2%). Results also show that none of these three classifiers performs comparable to the proposed system (89.7%). The interventions recommended by the system are in agreement with the expert's judgment to a satisfactory level. The reaction of the physicians to the proposed system in its first trial version was very favorable. CONCLUSION: The study showed the efficacy and utility of the causality-based strategy in recommending tailored interventions to prevent elderly falls compared to automated learning methods that had failed to infer a solid interventional paradigm for precision medicine.