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For 50% of these patients predictor data were abstracted from the EMR within 12 h of onset of acute pulmonary edema (93% within 48 h).
The risk based on the standard logistic regression model was: P Y i = 1 | X i = 1 1 + exp − α ^ standard + ∑ m ∈ 1, …, 6 x im ⋅ β ^ standard, m where P Yi = 1) is the predicted risk that a patient i will get PONV, given patients' predictor values X.
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In such patients, predictors of mortality are the severity of the pelvic fracture, the presence of coma, shock, and head and chest injuries.
For eligible patients, predictors were abstracted from medical records within 48 hours of the alert.
11 These factors affect treatment decisions, as they are, as for the youngest patients, predictors of the risk of relapse.
Furthermore, the association between patient predictor variables and treatment failure has not been described in our setting.
Odds ratios [OR] and 95% confidence intervals [95% CI] were used to determine which of the 15 potential informal caregiver and seven patient predictor variables were associated with dying at home.
For each treatment, clinic and patient predictor of discontinuation reported in the systematic review, we noted how many studies investigated it and, from these, how many found a statistically significant positive and/or negative association with discontinuation from treatment.
However, asthma outcomes research has almost invariably concentrated on identifying and addressing patient predictors.
Consequently, identifying patient predictors of health status is essential.
Data on treatment, clinic and patient predictors of discontinuation are mainly inconclusive.
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