Literature DB >> 8263576

Inconsistency of a model aimed at predicting bacteremia in hospitalized patients.

B Mozes1, D Milatiner, C Block, Z Blumstein, H Halkin.   

Abstract

Clinical prediction rules can help physicians determine the necessity for blood cultures in specific patients and/or in whom empiric antibiotic treatment should be administered. Before adopting a prediction rule its validity must be evaluated in different settings. We revealed independent predictors of true bacteremia and developed a risk score based on them in one group of adult hospitalized patients (n = 474; derivation set). An attempt was made to validate this risk score in a second group of in-patients at the same hospital (n = 438; validation set). The derivation set included 540 blood culture episodes and the validation set 516. A blood culture episode was defined as one or more of all blood specimens withdrawn for culture from one patient over one 24 hour period. Independent multivariate predictors of true bacteremia were: temperature of 39 degrees C or higher, current immunosuppressive therapy, serum alkaline phosphatase > 100 IU and hospitalization in an intensive care unit. In the low risk group, defined by the absence of the said predictors, the rates of true bacteremia were 5.1 and 4.6% for the derivation and validation sets, respectively. As raised temperature is the main clinical feature guiding physicians to suspect bacteremia, we examined the probability of true bacteremia in patients with a temperature of less than 38 degrees C and found it to be 5.6% in the two sets. The model identified high risk subset patient groups demonstrating true bacteremia in 38% of all episodes in the derivation set and the comparatively low rate of 12.1% (p < 0.01) for the validation set.(ABSTRACT TRUNCATED AT 250 WORDS)

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Year:  1993        PMID: 8263576     DOI: 10.1016/0895-4356(93)90171-v

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  7 in total

Review 1.  Updated review of blood culture contamination.

Authors:  Keri K Hall; Jason A Lyman
Journal:  Clin Microbiol Rev       Date:  2006-10       Impact factor: 26.132

2.  Why models predicting bacteremia in general medical patients do not work.

Authors:  J J Allison; R M Centor
Journal:  J Gen Intern Med       Date:  1996-02       Impact factor: 5.128

3.  Factors associated with positive blood cultures in outpatients with suspected bacteremia.

Authors:  K Wildi; S Tschudin-Sutter; S Dell-Kuster; R Frei; H C Bucher; R Nüesch
Journal:  Eur J Clin Microbiol Infect Dis       Date:  2011-04-20       Impact factor: 3.267

4.  Continuous quality improvement for introduction of automated blood culture instrument.

Authors:  M Alfa; S Sanche; S Roman; Y Fiola; P Lenton; G Harding
Journal:  J Clin Microbiol       Date:  1995-05       Impact factor: 5.948

5.  Machine learning for fast identification of bacteraemia in SIRS patients treated on standard care wards: a cohort study.

Authors:  Franz Ratzinger; Helmuth Haslacher; Thomas Perkmann; Matilde Pinzan; Philip Anner; Athanasios Makristathis; Heinz Burgmann; Georg Heinze; Georg Dorffner
Journal:  Sci Rep       Date:  2018-08-15       Impact factor: 4.379

6.  Early Detection of Bacteraemia Using Ten Clinical Variables with an Artificial Neural Network Approach.

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

7.  Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time.

Authors:  Kyoung Hwa Lee; Jae June Dong; Subin Kim; Dayeong Kim; Jong Hoon Hyun; Myeong-Hun Chae; Byeong Soo Lee; Young Goo Song
Journal:  Diagnostics (Basel)       Date:  2022-01-03
  7 in total

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