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1030. Risk Predictive Model for Candida Endocarditis in Patients with Candidemia: A 12-year Experience in a Single Tertiary Care Hospital

BACKGROUND: Candida endocarditis (CE) is a rare but an invasive infection associated with a high mortality rate. The current understanding of this infection is poorly defined from case reports, case series and small cohorts. This study aimed to assess the risk factors for CE in patients with candida...

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Autores principales: Foong, Kap Sum, Kronen, Ryan, Salazar, Ana S, Hsueh, Kevin, Lin, Charlotte, Powderly, William, Spec, Andrej
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6253236/
http://dx.doi.org/10.1093/ofid/ofy210.867
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author Foong, Kap Sum
Kronen, Ryan
Salazar, Ana S
Hsueh, Kevin
Lin, Charlotte
Powderly, William
Spec, Andrej
author_facet Foong, Kap Sum
Kronen, Ryan
Salazar, Ana S
Hsueh, Kevin
Lin, Charlotte
Powderly, William
Spec, Andrej
author_sort Foong, Kap Sum
collection PubMed
description BACKGROUND: Candida endocarditis (CE) is a rare but an invasive infection associated with a high mortality rate. The current understanding of this infection is poorly defined from case reports, case series and small cohorts. This study aimed to assess the risk factors for CE in patients with candida bloodstream infections (CBSI). METHODS: We conducted a retrospective analysis of all hospitalized patients diagnosed with CBSI at a large tertiary care hospital between 2002 and 2015. Data included demographics, comorbidities, laboratory parameters, and outcomes. Univariate and multivariable logistic regression analyses were used to build the predictive model. RESULTS: Of 1,873 cases of CBSI, 47 patients were identified to have CE. The most commonly isolated species were C. albicans (59.6%) followed by C. parapsilosis (16.2%). On univariate analysis, preexisting valvular disease (7.95, 95% CI [3.16, 20.02]) was associated with a higher risk of CE (P < 0.05). Factors such as isolation of C. glabrata (0.17, 95% CI [0.04, 0.68]), hematologic malignancy (0.09, 95% CI [0.01, 0.68]), and total parenteral nutrition (TPN) (0.40, 95% CI [0.17, 0.95]) were all associated with a lower risk of CE. In multivariable modeling, the factors of valvular disease (5.05, 95% CI [1.77, 14.43]), isolation of C. glabrata (0.19, 95% CI [0.05, 0.80]), hematologic malignancy (0.09, 95% CI [0.01, 0.66]), and total parenteral feeding (0.43, 95% CI [0.17, 1.09]) remained significant. The final model had a C-statistic of 0.82. The crude 90-day mortality for CE was 48.9%, similar to the overall CBSI mortality of 42.1%. CONCLUSION: In a population of patients with CBSI, previous valvular disease was the only factor associated with a greater risk of development of CE. Use of TPN, hematologic malignancy, and isolation of C. glabrata were protective factors. A predictive model may reduce the need for expensive and sometimes invasive diagnostic imaging such as trans-esophageal echocardiography, as a subset of patients may be at low enough risk for CE not to warrant them. DISCLOSURES: All authors: No reported disclosures.
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spelling pubmed-62532362018-11-28 1030. Risk Predictive Model for Candida Endocarditis in Patients with Candidemia: A 12-year Experience in a Single Tertiary Care Hospital Foong, Kap Sum Kronen, Ryan Salazar, Ana S Hsueh, Kevin Lin, Charlotte Powderly, William Spec, Andrej Open Forum Infect Dis Abstracts BACKGROUND: Candida endocarditis (CE) is a rare but an invasive infection associated with a high mortality rate. The current understanding of this infection is poorly defined from case reports, case series and small cohorts. This study aimed to assess the risk factors for CE in patients with candida bloodstream infections (CBSI). METHODS: We conducted a retrospective analysis of all hospitalized patients diagnosed with CBSI at a large tertiary care hospital between 2002 and 2015. Data included demographics, comorbidities, laboratory parameters, and outcomes. Univariate and multivariable logistic regression analyses were used to build the predictive model. RESULTS: Of 1,873 cases of CBSI, 47 patients were identified to have CE. The most commonly isolated species were C. albicans (59.6%) followed by C. parapsilosis (16.2%). On univariate analysis, preexisting valvular disease (7.95, 95% CI [3.16, 20.02]) was associated with a higher risk of CE (P < 0.05). Factors such as isolation of C. glabrata (0.17, 95% CI [0.04, 0.68]), hematologic malignancy (0.09, 95% CI [0.01, 0.68]), and total parenteral nutrition (TPN) (0.40, 95% CI [0.17, 0.95]) were all associated with a lower risk of CE. In multivariable modeling, the factors of valvular disease (5.05, 95% CI [1.77, 14.43]), isolation of C. glabrata (0.19, 95% CI [0.05, 0.80]), hematologic malignancy (0.09, 95% CI [0.01, 0.66]), and total parenteral feeding (0.43, 95% CI [0.17, 1.09]) remained significant. The final model had a C-statistic of 0.82. The crude 90-day mortality for CE was 48.9%, similar to the overall CBSI mortality of 42.1%. CONCLUSION: In a population of patients with CBSI, previous valvular disease was the only factor associated with a greater risk of development of CE. Use of TPN, hematologic malignancy, and isolation of C. glabrata were protective factors. A predictive model may reduce the need for expensive and sometimes invasive diagnostic imaging such as trans-esophageal echocardiography, as a subset of patients may be at low enough risk for CE not to warrant them. DISCLOSURES: All authors: No reported disclosures. Oxford University Press 2018-11-26 /pmc/articles/PMC6253236/ http://dx.doi.org/10.1093/ofid/ofy210.867 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Abstracts
Foong, Kap Sum
Kronen, Ryan
Salazar, Ana S
Hsueh, Kevin
Lin, Charlotte
Powderly, William
Spec, Andrej
1030. Risk Predictive Model for Candida Endocarditis in Patients with Candidemia: A 12-year Experience in a Single Tertiary Care Hospital
title 1030. Risk Predictive Model for Candida Endocarditis in Patients with Candidemia: A 12-year Experience in a Single Tertiary Care Hospital
title_full 1030. Risk Predictive Model for Candida Endocarditis in Patients with Candidemia: A 12-year Experience in a Single Tertiary Care Hospital
title_fullStr 1030. Risk Predictive Model for Candida Endocarditis in Patients with Candidemia: A 12-year Experience in a Single Tertiary Care Hospital
title_full_unstemmed 1030. Risk Predictive Model for Candida Endocarditis in Patients with Candidemia: A 12-year Experience in a Single Tertiary Care Hospital
title_short 1030. Risk Predictive Model for Candida Endocarditis in Patients with Candidemia: A 12-year Experience in a Single Tertiary Care Hospital
title_sort 1030. risk predictive model for candida endocarditis in patients with candidemia: a 12-year experience in a single tertiary care hospital
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6253236/
http://dx.doi.org/10.1093/ofid/ofy210.867
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