Anesthesiology
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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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Randomized Controlled Trial Comparative Study
Respiratory effects of biased-ligand oliceridine in older volunteers: a pharmacokinetic-pharmacodynamic comparison with morphine.
Oliceridine is a G protein-biased µ-opioid, a drug class that is associated with less respiratory depression than nonbiased opioids, such as morphine. The authors quantified the respiratory effects of oliceridine and morphine in elderly volunteers. The authors hypothesized that these opioids differ in their pharmacodynamic behavior, measured as effect on ventilation at an extrapolated end-tidal Pco2 at 55 mmHg, V̇E55. ⋯ Oliceridine and morphine differ in their respiratory pharmacodynamics with a more rapid onset and offset of respiratory depression for oliceridine and a smaller magnitude of respiratory depression over time.
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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.