Clinical prediction rules for bacteremia and in-hospital death based on clinical data at the time of blood withdrawal for culture: An evaluation of their development and use
- PMID: 17100868
- DOI: 10.1111/j.1365-2753.2006.00637.x
Clinical prediction rules for bacteremia and in-hospital death based on clinical data at the time of blood withdrawal for culture: An evaluation of their development and use
Abstract
Rationale, aims and objectives: To develop clinical prediction rules for true bacteremia, blood culture positive for gram-negative rods, and in-hospital death using the data at the time of blood withdrawal for culture.
Methods: Data on all hospitalized adults who underwent blood cultures at a tertiary care hospital in Japan were collected from an integrated medical computing system. Logistic regression was used for developing prediction rules followed by the jackknife cross validation.
Results: Among 739 patients, 144 (19.5%) developed true bacteremia, 66 (8.9) were positive for gram-negative rods, and 203 (27.5%) died during hospitalization. Prediction rule based on the data at the time of blood withdrawal for culture stratified them into five groups with probabilities of true bacteremia 6.5, 9.6, 21.9, 30.1, and 59.6%. For blood culture positive for gram-negative rods, the probabilities were 0.6, 4.7, 8.6, and 31.7%, and for in-hospital death, those were 6.7, 15.5, 26.0, 35.5, and 56.1%. The area of receiver operating characteristic for true bacteremia, blood culture positive for gram-negative rods, and in-hospital death were 0.73, 0.64, and 0.64, respectively, in original cohort and 0.72, 0.64, and 0.64 in validation respectively.
Conclusions: The clinical prediction rules are helpful for improved clinical decision making for bacteremia patients.
Similar articles
-
Predictive model of antimicrobial-resistant gram-negative bacteremia at the ED.Am J Emerg Med. 2007 Jul;25(6):597-607. doi: 10.1016/j.ajem.2006.11.024. Am J Emerg Med. 2007. PMID: 17606081
-
How long does it take to "rule out" bacteremia in children with central venous catheters?Pediatrics. 2008 Jan;121(1):135-41. doi: 10.1542/peds.2007-1387. Pediatrics. 2008. PMID: 18166567
-
Projected impact of monoclonal anti-endotoxin antibody therapy.Arch Intern Med. 1994 Jun 13;154(11):1241-9. Arch Intern Med. 1994. PMID: 8203991
-
[Nosocomial bacteremia].Przegl Epidemiol. 2006;60(1):35-41. Przegl Epidemiol. 2006. PMID: 16758736 Review. Polish.
-
Blood cultures for febrile patients in the acute care setting: too quick on the draw?J Am Acad Nurse Pract. 2008 Nov;20(11):539-46. doi: 10.1111/j.1745-7599.2008.00356.x. J Am Acad Nurse Pract. 2008. PMID: 19128337 Review.
Cited by
-
A risk prediction model for screening bacteremic patients: a cross sectional study.PLoS One. 2014 Sep 3;9(9):e106765. doi: 10.1371/journal.pone.0106765. eCollection 2014. PLoS One. 2014. PMID: 25184209 Free PMC article. Clinical Trial.
-
Identifying Patients with Bacteremia in Community-Hospital Emergency Rooms: A Retrospective Cohort Study.PLoS One. 2016 Mar 29;11(3):e0148078. doi: 10.1371/journal.pone.0148078. eCollection 2016. PLoS One. 2016. PMID: 27023336 Free PMC article.
-
Isopropyl alcohol compared with isopropyl alcohol plus povidone-iodine as skin preparation for prevention of blood culture contamination.J Clin Microbiol. 2009 Jan;47(1):54-8. doi: 10.1128/JCM.01425-08. Epub 2008 Oct 29. J Clin Microbiol. 2009. PMID: 18971366 Free PMC article.
-
Evaluation of an intervention to improve blood culture practices: a cluster randomised trial.Eur J Clin Microbiol Infect Dis. 2014 Dec;33(12):2207-13. doi: 10.1007/s10096-014-2154-3. Epub 2014 Jul 2. Eur J Clin Microbiol Infect Dis. 2014. PMID: 24981390 Clinical Trial.
-
Predicting Bacteremia among Septic Patients Based on ED Information by Machine Learning Methods: A Comparative Study.Diagnostics (Basel). 2022 Oct 15;12(10):2498. doi: 10.3390/diagnostics12102498. Diagnostics (Basel). 2022. PMID: 36292187 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Medical