Tag: prediction

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Resuscitated cardiac arrest is associated with high mortality; however, the ability to estimate risk of adverse outcomes using existing illness severity scores is limited. Using in-hospital data available within the first 24 hours of admission, we aimed to develop more accurate models of risk prediction using both logistic regression (LR) and machine learning (ML) techniques, with a combination of demographic, physiologic, and biochemical information. ML... Read More | Comment
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Researchers from MIT and Massachusetts General Hospital (MGH) have developed a predictive model that could guide clinicians in deciding when to give potentially life-saving drugs to patients being treated for sepsis in the emergency room. Early prediction could, among other things, prevent an unnecessary ICU stay for a patient that doesn’t need vasopressors, or start early preparation for the ICU for a patient that does.... Read More | Comment
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MIT researchers have developed a machine-learning model that groups patients into subpopulations by health status to better predict a patient’s risk of dying during their stay in the ICU. This technique outperforms “global” mortality-prediction models and reveals performance disparities of those models across specific patient subpopulations. By training on patients grouped by health status, neural network can better estimate if patients will die in the... Read More | Comment
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While both ICU delirium prediction models have moderate-to-good performance, the PRE-DELIRIC model predicts delirium better. However, ICU physicians rated the user convenience of E-PRE-DELIRIC superior to PRE-DELIRIC. In low-risk patients the delirium prediction further improves after an update with the PRE-DELIRIC model after 24 h. In total 2178 patients were included. The area under the receiver operating characteristic curve was significantly greater for PRE-DELIRIC (0.74... Read More | Comment
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No models completely satisfy our requirements for planning, identifying unexpectedly long ICU length of stay, or for benchmarking purposes. Physicians using these models to predict ICU length of stay should interpret them with reservation. The number of admissions ranged from 253 to 178,503. Median ICU length of stay was between 2 and 6.9 days. Read More | Comment
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We would like to add to the recent editorial by McMahon on biomarkers of acute kidney injury (AKI), with a specific focus on biomarkers in the clinical setting of cardiac surgery-associated AKI (CSA-AKI). We agree with McMahon that biomarkers may aid in the early diagnosis of AKI and that they represent an excellent tool for predicting treatment response. Their role may be pivotal in CSA-AKI,... Read More | Comment