Chest
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Patients with non-small cell lung cancer (NSCLC) and preexisting interstitial lung disease (ILD) are often excluded from clinical trials of immune checkpoint inhibitors (ICIs), leaving a gap in knowledge. ⋯ Programmed cell death protein 1/programmed cell death ligand 1 inhibitors had favorable efficacy in NSCLC with preexisting ILD. CIP is frequent in patients with preexisting ILD who receive ICI therapy but is often mild and easily manageable. Clinicians should be cautious when using ICIs in patients with preexisting ILD.
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A cardiopulmonary exercise test (CPET) is ideally suited to quantify exercise tolerance and evaluate the pathophysiological mechanism(s) of dyspnea and exercise limitation in people with chronic respiratory disease. Although there are several statements on CPET and many outstanding resources detailing the cardiorespiratory and perceptual responses to exercise, limited information is available to support the health care provider in conducting a practical CPET evaluation. ⋯ Information on CPET protocol, as well as how to evaluate maximal patient effort, peak rate of oxygen consumption, ventilatory demand, pulmonary gas exchange, ventilatory reserve, operating lung volumes, and exertional dyspnea, is presented. Two case examples are also described to highlight how these parameters are evaluated to provide a clinical interpretation of CPET data.
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Although maintaining some amount of positive end-expiratory pressure (PEEP) seems essential, selecting and titrating a specific level for patients with ARDS remains challenging despite extensive research on the subject. Although an "open lung" approach to ventilation is popular and has some degree of biological plausibility, it is not without risk. ⋯ Here we present a pragmatic approach based on simple measurements available on all ventilators, focused on achieving balance between the potential risks and benefits of PEEP. Acknowledging "best PEEP" as an impossible goal, we aim for a straightforward method to achieve "better PEEP."
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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.