Anesthesiology
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Under the hypothesis that mechanical power ratio could identify the spontaneously breathing patients with a higher risk of respiratory failure, this study assessed lung mechanics in nonintubated patients with COVID-19 pneumonia, aiming to (1) describe their characteristics; (2) compare lung mechanics between patients who received respiratory treatment escalation and those who did not; and (3) identify variables associated with the need for respiratory treatment escalation. ⋯ In this COVID-19 cohort, tidal volume was similar in patients undergoing treatment escalation and in patients who did not; mechanical power, its ratio, and pressure-rate index were the variables presenting the highest association with the clinical outcome.
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Electromyography has advantages over mechanomyography and acceleromyography. Previously, agreement of the train-of-four counts between acceleromyography and electromyography was found to be fair. The objective of this study was to assess the agreement of posttetanic count including agreement of neuromuscular blockade status (intense block, posttetanic count equal to 0; or deep block, posttetanic count 1 or greater and train-of-four count equal to 0) between acceleromyography and electromyography. ⋯ Acceleromyography frequently counted more twitches than electromyography in posttetanic count monitoring. Acceleromyography- and electromyography-posttetanic counts cannot be used interchangeably to assess the degree of neuromuscular blockade.
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Monitoring and controlling lung stress and diaphragm effort has been hypothesized to limit lung injury and diaphragm injury. The occluded inspiratory airway pressure (Pocc) and the airway occlusion pressure at 100 ms (P0.1) have been used as noninvasive methods to assess lung stress and respiratory muscle effort, but comparative performance of these measures and their correlation to diaphragm effort is unknown. The authors hypothesized that Pocc and P0.1 correlate with diaphragm effort and lung stress and would have strong discriminative performance in identifying extremes of lung stress and diaphragm effort. ⋯ Pocc and P0.1 correlate with lung stress and diaphragm effort in the preceding hour. Diagnostic performance of Pocc and P0.1 to detect extremes in these parameters is reasonable to excellent. Pocc is more accurate in detecting high diaphragm effort.
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Multicenter Study
Predicting Intensive Care Delirium with Machine Learning: Model Development and External Validation.
Delirium poses significant risks to patients, but countermeasures can be taken to mitigate negative outcomes. Accurately forecasting delirium in intensive care unit (ICU) patients could guide proactive intervention. Our primary objective was to predict ICU delirium by applying machine learning to clinical and physiologic data routinely collected in electronic health records. ⋯ Machine learning models trained with routinely collected electronic health record data accurately predict ICU delirium, supporting dynamic time-sensitive forecasting.
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The authors previously reported a broad suite of individualized Risk Stratification Index 3.0 (Health Data Analytics Institute, Inc., USA) models for various meaningful outcomes in patients admitted to a hospital for medical or surgical reasons. The models used International Classification of Diseases, Tenth Revision, trajectories and were restricted to information available at hospital admission, including coding history in the previous year. The models were developed and validated in Medicare patients, mostly age 65 yr or older. The authors sought to determine how well their models predict utilization outcomes and adverse events in younger and healthier populations. ⋯ Predictive analytical modeling based on administrative claims history provides individualized risk profiles at hospital admission that may help guide patient management. Similar predictive performance in Medicare and in younger and healthier populations indicates that Risk Stratification Index 3.0 models are valid across a broad range of adult hospital admissions.