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Title: Development and Validation of a Clinical Prediction Rule for Bacteremia among Maintenance Hemodialysis Patients in Outpatient Settings.
Authors: Sasaki, Sho
Hasegawa, Takeshi
Kawarazaki, Hiroo
Nomura, Atsushi
Uchida, Daisuke
Imaizumi, Takahiro
Furusho, Masahide
Nishiwaki, Hiroki
Fukuma, Shingo  kyouindb  KAKEN_id
Shibagaki, Yugo
Fukuhara, Shunichi  kyouindb  KAKEN_id
Author's alias: 福間, 真悟
Issue Date: 12-Jan-2017
Publisher: Public Library of Science
Journal title: PLOS ONE
Volume: 12
Issue: 1
Thesis number: e0169975
Abstract: Background: To our knowledge, no reliable clinical prediction rule (CPR) for identifying bacteremia in hemodialysis (HD) patients has been established. The aim of this study was to develop a CPR for bacteremia in maintenance HD patients visiting the outpatient department. Methods: This multicenter cohort study involved consecutive maintenance HD patients who visited the outpatient clinic or emergency room of seven Japanese institutions between August 2011 and July 2013. The outcome measure was bacteremia diagnosed based on the results of blood cultures. The candidate predictors for bacteremia were extracted through a literature review. A CPR for bacteremia was developed using a coefficient-based multivariable logistic regression scoring method, and calibration was performed. The test performance was then assessed for the CPR. Results: Of 507 patients eligible for the study, we analyzed the 293 with a complete dataset for candidate predictors. Of these 293 patients, 48 (16.4%) were diagnosed with bacteremia. At the conclusion of the deviation process, body temperature ≥ 38.3°C, heart rate ≥ 125 /min, C-reactive protein ≥ 10 mg/dL, alkaline phosphatase >360 IU/L, and no prior antibiotics use within the past week were retained and scored. The CPR had a good fit for the model on calibration. The AUC of the CPR was 0.76, and for score CPR ≥ 2, the sensitivity and specificity were 89.6% and 51.4%, respectively. Conclusions: We established a simple CPR for bacteremia in maintenance HD patients using routinely obtained clinical information in an outpatient setting. This model may facilitate more appropriate clinical decision making.
Rights: © 2017 Sasaki et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
URI: http://hdl.handle.net/2433/218029
DOI(Published Version): 10.1371/journal.pone.0169975
PubMed ID: 28081211
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