Critical care explorations
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To develop and evaluate a novel strategy that automates the retrospective identification of sepsis using electronic health record data.
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To estimate the probability of a substitute decision maker choosing to withdraw life-sustaining therapy after hearing an affirmative patient response to the phrase "Do you want everything done?"
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We hypothesize that knowledge of a stable personalized baseline state and increased data sampling frequency would markedly improve the ability to detect progressive hypovolemia during hemorrhage earlier and with a lower false positive rate than when using less granular data.
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Acute Physiology and Chronic Health Evaluation is a well-validated method to risk-adjust ICU patient outcomes. However, predictions may be affected by inter-rater reliability for manually entered elements. We evaluated inter-rater reliability for Acute Physiology and Chronic Health Evaluation IV manually entered elements among clinician abstractors and assessed the impacts of disagreements on mortality predictions.
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Variability in hospital-level sepsis mortality rates may be due to differences in case mix, quality of care, or diagnosis and coding practices. Centers for Disease Control and Prevention's Adult Sepsis Event definition could facilitate objective comparisons of sepsis mortality rates between hospitals but requires rigorous risk-adjustment tools. We developed risk-adjustment models for Adult Sepsis Events using administrative and electronic health record data.