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Objectives: To derive a prediction rule using data available in the emergency department (ED) to identify a group of patients hospitalized for the treatment of heart failure who are at low risk of death and serious complications. Methods: The authors analyzed data for all 33,533 patients with a primary hospital discharge diagnosis of heart failure in 1999 who were admitted from EDs in Pennsylvania. Candidate predictors were demographic and medical history variables and the most abnormal examination or diagnostic test values measured in the ED (vital signs only) or on the first day of hospitalization. The authors constructed classification trees to identify a subgroup of patients with an observed rate of death or serious medical complications before discharge ,2%; the tree that identified the subgroup with the lowest rate of this outcome and an inpatient mortality rate ,1% was chosen. Results: Within the entire cohort, 4.5% of patients died and 6.8% survived to hospital discharge after experiencing a serious medical complication. The prediction rule used 21 prognostic factors to classify 17.2% of patients as low risk; 19 (0.3%) died and 59 (1.0%) survived to hospital discharge after experiencing a serious medical complication. Conclusions: This clinical prediction rule identified a group of patients hospitalized from the ED for the treatment of heart failure who were at low risk of adverse inpatient outcomes. Model performance needs to be examined in a cohort of patients with an ED diagnosis of heart failure and treated as outpatients or hospitalized.

This clinical prediction rule identified a group of patients hospitalized from the ED for the treatment of heart failure who were at low risk of adverse inpatient outcomes. Model performance needs to be examined in a cohort of patients with an ED diagnosis of heart failure and treated as outpatients or hospitalized.

We introduce an algorithm for learning patient-specific models from clinical data to predict outcomes. Patient-specific models are influenced by the particular history, symptoms, laboratory results, and other features of the patient case at hand, in contrast to the commonly used population-wide models that are constructed to perform well on average on all future cases. The patient-specific algorithm uses Markov blanket (MB) models, carries out Bayesian model averaging over a set of models to predict the outcome for the patient case at hand, and employs a patient-specific heuristic to locate a set of suitable models to average over. We evaluate the utility of using a local structure representation for the conditional probability distributions in the MB models that captures additional independence relations among the variables compared to the typically used representation that captures only the global structure among the variables. In addition, we compare the performance of Bayesian model averaging to that of model selection. The patient-specific algorithm and its variants were evaluated on two clinical datasets for two outcomes. Our results provide support that the performance of an algorithm for learning patient-specific models can be improved by using a local structure representation for MB models and by performing Bayesian model averaging.

This study found a 5.6% incidence of hyponatremia in marathon runners requiring medical treatment.

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