Chest
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Basic critical care echocardiography emphasizes two-dimensional (2D) findings, such as ventricular function, inferior vena cava size, and pericardial assessment, while generally excluding quantitative findings and Doppler-based techniques. Although this approach offers advantages, including efficiency and expedited training, it complicates attempts to understand the hemodynamic importance of any 2D abnormalities detected. ⋯ An estimate of SV allows 2D findings to be placed into better context in terms of both hemodynamic significance and acuity. This article describes the technique of SV determination, reviews common confounding factors and pitfalls, and suggests a systematic approach for using SV measurements to help integrate important 2D findings into the clinical context.
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Predictive analytic models leveraging machine learning methods increasingly have become vital to health care organizations hoping to improve clinical outcomes and the efficiency of care delivery for all patients. Unfortunately, predictive models could harm populations that have experienced interpersonal, institutional, and structural biases. Models learn from historically collected data that could be biased. ⋯ This strategy follows the lifecycle of machine learning models in health care, namely, identifying the clinical problem, model design, data collection, model training, model validation, model deployment, and monitoring after deployment. To illustrate this approach, we use a hypothetical case of a health system developing and deploying a machine learning model to predict the risk of mortality in 6 months for patients admitted to the hospital to target a hospital's delivery of palliative care services to those with the highest mortality risk. The core ethical concepts of equity and transparency guide our proposed framework to help ensure the safe and effective use of predictive algorithms in health care to help everyone achieve their best possible health.
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Smoking at the time of surgical treatment for lung cancer increases the risk for perioperative morbidity and mortality. The prevalence of persistent smoking in the postoperative period and its association with long-term oncologic outcomes are poorly described. ⋯ Persistent smoking after surgery for stage I NSCLC is common and is associated with inferior overall survival. Providers should continue to assess smoking habits in the postoperative period given its disproportionate impact on long-term outcomes after potentially curative treatment for early-stage lung cancer.