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Solid Organ Transplantation (SOT) and Data Mining: Bloodstream Infections (BSI) Have a Significant Impact on One-Year Survival, and qSOFA ≥ 2 Predicts 30-Day Mortality

BACKGROUND: We created a retrospective and prospective database of SOT recipients using innovative data mining tools. This study describing the epidemiology of BSI in SOT serves as a proof of concept of such techniques in clinical research. METHODS: The design of the study was a retrospective, singl...

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Autores principales: Liu, Terrence, Xie, Donglu, Adams-Huet, Beverley, Le, Jade, Yek, Christina, Ranganathan, Dipti, Haley, Robert W, Greenberg, David, Hoz, Ricardo La
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5632017/
http://dx.doi.org/10.1093/ofid/ofx162.025
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author Liu, Terrence
Xie, Donglu
Adams-Huet, Beverley
Le, Jade
Yek, Christina
Ranganathan, Dipti
Haley, Robert W
Greenberg, David
Hoz, Ricardo La
author_facet Liu, Terrence
Xie, Donglu
Adams-Huet, Beverley
Le, Jade
Yek, Christina
Ranganathan, Dipti
Haley, Robert W
Greenberg, David
Hoz, Ricardo La
author_sort Liu, Terrence
collection PubMed
description BACKGROUND: We created a retrospective and prospective database of SOT recipients using innovative data mining tools. This study describing the epidemiology of BSI in SOT serves as a proof of concept of such techniques in clinical research. METHODS: The design of the study was a retrospective, single-center, cohort study. Data mining tools were used to extract information from the electronic medical record and merged it with data from the SRTR (Figure 1). First SOT from January 1, 2010 to December 31, 2015 were included. Charts of subjects with positive blood cultures were manually reviewed and adjudicated using CDC/NHSN and SCCM/ESICM criteria. The 1-year cumulative incidence was calculated using the Kaplan–Meier method. Cox proportional hazards models were used to identify risk factors for BSI and 1-year mortality. BSI was analyzed as a time-dependent covariate in the mortality model. Fisher’s exact test and chi-square were used to identify risk factors for 30-day mortality and MDRO. RESULTS: A total of 917 SOT recipients met inclusion criteria. Seventy-five patients experienced at least one BSI. The cumulative incidence was 8.4% (95% CI 6.8–10.4) (Figure 2). The onset of the first BSI episode was: 30 episodes (40%) <1 month, 33 (44%) 1–6 months, and 12 (16%) >6 months. The most common pathogens were Klebsiella sp. (16%), Vancomycin-resistant E. faecium (12%), E. coli (12%), CoNS (12%), and Candida sp. (9.3%). Nineteen isolates (25%) were identified as MDRO; the risk of MDRO was highest <1 month compared with 1–6 and >6 months (44.8 vs. 12.1 vs. 16.7; P = 0.01). The most common source of BSI was CLABSI (29%) (Figure 3). In multivariable analysis, the risk of BSI was associated with organ type (HR [95% CI] = Multiorgan 3.5 [1.1–11.6], liver 2.5 [1.1–5.4], heart 2.4 [1.1–5.1]) and acquisition of a BSI was associated with a higher 1-year mortality (HR = 8.7 [5.1–14.7]). In univariable analysis, a polymicrobial BSI (14.7 vs. 57.1%; P = 0.02), qSOFA ≥ 2 (0.0 vs. 25.5%; P = 0.02) and septic shock (3.9 vs. 52.2%; P < 0.001) were associated with an increased risk of death at 30 days. CONCLUSION: A BSI significantly affects the 1-year survival of SOT recipients. A qSOFA ≥ 2 can be used to identify patients at risk for death. Additionally, this study illustrates the potential of data mining tools to study infectious complications. DISCLOSURES: All authors: No reported disclosures.
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spelling pubmed-56320172017-11-07 Solid Organ Transplantation (SOT) and Data Mining: Bloodstream Infections (BSI) Have a Significant Impact on One-Year Survival, and qSOFA ≥ 2 Predicts 30-Day Mortality Liu, Terrence Xie, Donglu Adams-Huet, Beverley Le, Jade Yek, Christina Ranganathan, Dipti Haley, Robert W Greenberg, David Hoz, Ricardo La Open Forum Infect Dis Abstracts BACKGROUND: We created a retrospective and prospective database of SOT recipients using innovative data mining tools. This study describing the epidemiology of BSI in SOT serves as a proof of concept of such techniques in clinical research. METHODS: The design of the study was a retrospective, single-center, cohort study. Data mining tools were used to extract information from the electronic medical record and merged it with data from the SRTR (Figure 1). First SOT from January 1, 2010 to December 31, 2015 were included. Charts of subjects with positive blood cultures were manually reviewed and adjudicated using CDC/NHSN and SCCM/ESICM criteria. The 1-year cumulative incidence was calculated using the Kaplan–Meier method. Cox proportional hazards models were used to identify risk factors for BSI and 1-year mortality. BSI was analyzed as a time-dependent covariate in the mortality model. Fisher’s exact test and chi-square were used to identify risk factors for 30-day mortality and MDRO. RESULTS: A total of 917 SOT recipients met inclusion criteria. Seventy-five patients experienced at least one BSI. The cumulative incidence was 8.4% (95% CI 6.8–10.4) (Figure 2). The onset of the first BSI episode was: 30 episodes (40%) <1 month, 33 (44%) 1–6 months, and 12 (16%) >6 months. The most common pathogens were Klebsiella sp. (16%), Vancomycin-resistant E. faecium (12%), E. coli (12%), CoNS (12%), and Candida sp. (9.3%). Nineteen isolates (25%) were identified as MDRO; the risk of MDRO was highest <1 month compared with 1–6 and >6 months (44.8 vs. 12.1 vs. 16.7; P = 0.01). The most common source of BSI was CLABSI (29%) (Figure 3). In multivariable analysis, the risk of BSI was associated with organ type (HR [95% CI] = Multiorgan 3.5 [1.1–11.6], liver 2.5 [1.1–5.4], heart 2.4 [1.1–5.1]) and acquisition of a BSI was associated with a higher 1-year mortality (HR = 8.7 [5.1–14.7]). In univariable analysis, a polymicrobial BSI (14.7 vs. 57.1%; P = 0.02), qSOFA ≥ 2 (0.0 vs. 25.5%; P = 0.02) and septic shock (3.9 vs. 52.2%; P < 0.001) were associated with an increased risk of death at 30 days. CONCLUSION: A BSI significantly affects the 1-year survival of SOT recipients. A qSOFA ≥ 2 can be used to identify patients at risk for death. Additionally, this study illustrates the potential of data mining tools to study infectious complications. DISCLOSURES: All authors: No reported disclosures. Oxford University Press 2017-10-04 /pmc/articles/PMC5632017/ http://dx.doi.org/10.1093/ofid/ofx162.025 Text en © The Author 2017. 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
Liu, Terrence
Xie, Donglu
Adams-Huet, Beverley
Le, Jade
Yek, Christina
Ranganathan, Dipti
Haley, Robert W
Greenberg, David
Hoz, Ricardo La
Solid Organ Transplantation (SOT) and Data Mining: Bloodstream Infections (BSI) Have a Significant Impact on One-Year Survival, and qSOFA ≥ 2 Predicts 30-Day Mortality
title Solid Organ Transplantation (SOT) and Data Mining: Bloodstream Infections (BSI) Have a Significant Impact on One-Year Survival, and qSOFA ≥ 2 Predicts 30-Day Mortality
title_full Solid Organ Transplantation (SOT) and Data Mining: Bloodstream Infections (BSI) Have a Significant Impact on One-Year Survival, and qSOFA ≥ 2 Predicts 30-Day Mortality
title_fullStr Solid Organ Transplantation (SOT) and Data Mining: Bloodstream Infections (BSI) Have a Significant Impact on One-Year Survival, and qSOFA ≥ 2 Predicts 30-Day Mortality
title_full_unstemmed Solid Organ Transplantation (SOT) and Data Mining: Bloodstream Infections (BSI) Have a Significant Impact on One-Year Survival, and qSOFA ≥ 2 Predicts 30-Day Mortality
title_short Solid Organ Transplantation (SOT) and Data Mining: Bloodstream Infections (BSI) Have a Significant Impact on One-Year Survival, and qSOFA ≥ 2 Predicts 30-Day Mortality
title_sort solid organ transplantation (sot) and data mining: bloodstream infections (bsi) have a significant impact on one-year survival, and qsofa ≥ 2 predicts 30-day mortality
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5632017/
http://dx.doi.org/10.1093/ofid/ofx162.025
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