title: Using electronic data to predict the probability of true bacteremia from positive blood cultures.
abstract: As part of a project to help physicians make more appropriate treatment decisions, we implemented a clinical prediction rule that computes the probability of true bacteremia for positive blood cultures and displays this information when culture results are viewed online. Prior to implementing the rule, we performed a revalidation study to verify the accuracy of the previously published logistic regression model. We randomly selected 114 cases of positive blood cultures from a recent one-year period and performed a paper chart review with the help of infectious disease experts to determine whether the cultures were true positives or contaminants. Based on the results of this revalidation study, we updated the probabilities reported by the model and made additional enhancements to improve the accuracy of the rule. Next, we implemented the rule into our hospital's laboratory computer system so that the probability information was displayed with all positive blood culture results. We displayed the prediction rule information on approximately half of the 2184 positive blood cultures at our hospital that were randomly selected during a 6-month period. During the study, we surveyed 54 housestaff to obtain their opinions about the usefulness of this intervention. Fifty percent (27/54) indicated that the information had influenced their belief of the probability of bacteremia in their patients, and in 28% (15/54) of cases it changed their treatment decision. Almost all (98% (53/54)) indicated that they wanted to continue receiving this information. We conclude that the probability information provided by this clinical prediction rule is considered useful to physicians when making treatment decisions.
authors.affiliation: Partners HealthCare System, Boston, MA, USA.
authors.forename: S J
authors.initials: SJ
authors.lastname: Wang
authors.forename: G J
authors.initials: GJ
authors.lastname: Kuperman
authors.forename: L
authors.initials: L
authors.lastname: Ohno-Machado
authors.forename: A
authors.initials: A
authors.lastname: Onderdonk
authors.forename: H
authors.initials: H
authors.lastname: Sandige
authors.forename: D W
authors.initials: DW
authors.lastname: Bates
mesh_terms: D000465:Algorithms; D001291:Attitude of Health Personnel; D016470:Bacteremia / Q000175:diagnosis*; D001769:Blood / Q000382:microbiology*; D003625:Data Collection; D005189:False Positive Reactions; D006751:Hospital Information Systems; D006801:Humans; D007396:Internship and Residency; D016015:Logistic Models; D011237:Predictive Value of Tests; D011336:Probability; D011897:Random Allocation; D015203:Reproducibility of Results