A prediction rule to identify low-risk patients with heart failure
Auble TE, Hsieh M, Gardner W, Cooper GF, Stone RA, McCausland JB, Yealy DM. A prediction rule to identify low-risk patients with heart failure. Academic Emergency Medicine 12 (2005) 514-521. PMID: 15930402
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. Key words: heart failure; decision support techniques; emergency service; hospital.