BACKGROUND: Previous attempts to predict bacteremia have focused on selecting significant variables. However, these approaches have had limitations such as poor reproducibility in prediction accuracy and inconsistency in predictor selection. Here we propose a Bayesian approach to predict bacteremia based on the statistical distributions of clinical variables of previous patients, which has recently become possible through the adoption of electronic medical records. METHODS: In a derivation cohort, Bayesian prediction models were derived and their discriminative performance was compared with previous models under varying combinations of predictors. Then the Bayesian models were prospectively tested in a validation cohort. According to Bayesian probabilities of bacteremia, patients in both cohorts were grouped into bacteremia risk groups. RESULTS: Using the same prediction variables, the Bayesian predictions were more accurate than conventional rule-based predictions. Moreover, their better discriminative performance remained consistent despite variations in clinical variables. The receiver operating characteristic (ROC) area of the Bayesian model with 20 predictors was 0.70 ± 0.007 in the derivation cohort and 0.70 ± 0.018 in the validation cohort. The prevalence of bacteremia in groups I, II, and VI (grouped according to probability ratio) were 1.9%, 3.4%, and 20.0% in the derivation cohort, and 0.4%, 3.2%, and 18.4% in the validation cohort, respectively. The overall prevalence of bacteremia was 6.9% in both cohorts. CONCLUSIONS: In the present study, the Bayesian prediction model showed stable performance in predicting bacteremia and identifying risk groups, as the previous models did. The clinical significance of the Bayesian approach is expected to be demonstrated through a multicenter trial.
BACKGROUND: Previous attempts to predict bacteremia have focused on selecting significant variables. However, these approaches have had limitations such as poor reproducibility in prediction accuracy and inconsistency in predictor selection. Here we propose a Bayesian approach to predict bacteremia based on the statistical distributions of clinical variables of previous patients, which has recently become possible through the adoption of electronic medical records. METHODS: In a derivation cohort, Bayesian prediction models were derived and their discriminative performance was compared with previous models under varying combinations of predictors. Then the Bayesian models were prospectively tested in a validation cohort. According to Bayesian probabilities of bacteremia, patients in both cohorts were grouped into bacteremia risk groups. RESULTS: Using the same prediction variables, the Bayesian predictions were more accurate than conventional rule-based predictions. Moreover, their better discriminative performance remained consistent despite variations in clinical variables. The receiver operating characteristic (ROC) area of the Bayesian model with 20 predictors was 0.70 ± 0.007 in the derivation cohort and 0.70 ± 0.018 in the validation cohort. The prevalence of bacteremia in groups I, II, and VI (grouped according to probability ratio) were 1.9%, 3.4%, and 20.0% in the derivation cohort, and 0.4%, 3.2%, and 18.4% in the validation cohort, respectively. The overall prevalence of bacteremia was 6.9% in both cohorts. CONCLUSIONS: In the present study, the Bayesian prediction model showed stable performance in predicting bacteremia and identifying risk groups, as the previous models did. The clinical significance of the Bayesian approach is expected to be demonstrated through a multicenter trial.
Authors: Benjamin A Goldstein; Ann Marie Navar; Michael J Pencina; John P A Ioannidis Journal: J Am Med Inform Assoc Date: 2016-05-17 Impact factor: 4.497
Authors: Franz Ratzinger; Michel Dedeyan; Matthias Rammerstorfer; Thomas Perkmann; Heinz Burgmann; Athanasios Makristathis; Georg Dorffner; Felix Lötsch; Alexander Blacky; Michael Ramharter Journal: PLoS One Date: 2014-09-03 Impact factor: 3.240
Authors: Kyoung Hwa Lee; Jae June Dong; Su Jin Jeong; Myeong-Hun Chae; Byeong Soo Lee; Hong Jae Kim; Sung Hun Ko; Young Goo Song Journal: J Clin Med Date: 2019-10-02 Impact factor: 4.241