Anesthesiology
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Risk stratification helps guide appropriate clinical care. Our goal was to develop and validate a broad suite of predictive tools based on International Classification of Diseases, Tenth Revision, diagnostic and procedural codes for predicting adverse events and care utilization outcomes for hospitalized patients. ⋯ Predictive analytical modeling based on administrative claims history can provide individualized risk profiles at hospital admission that may help guide patient management. Similar results from six different modeling approaches suggest that we have identified both the value and ceiling for predictive information derived from medical claims history.
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Improper endotracheal tube (ETT) positioning is frequently observed and potentially hazardous in the intensive care unit. The authors developed a deep learning-based automatic detection algorithm detecting the ETT tip and carina on portable supine chest radiographs to measure the ETT-carina distance. This study investigated the hypothesis that the algorithm might be more accurate than frontline critical care clinicians in ETT tip detection, carina detection, and ETT-carina distance measurement. ⋯ A deep learning-based algorithm can match or even outperform frontline critical care clinicians in ETT tip detection, carina detection, and ETT-carina distance measurement.