Cargando…
Early prediction of blood stream infection in a prospectively collected cohort
BACKGROUND: Blood stream infection (BSI) and sepsis are serious clinical conditions and identification of the disease-causing pathogen is important for patient management. The RISE (Rapid Identification of SEpsis) study was carried out to collect a cohort allowing high-quality studies on different a...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8017733/ https://www.ncbi.nlm.nih.gov/pubmed/33810788 http://dx.doi.org/10.1186/s12879-021-05990-3 |
_version_ | 1783674104766791680 |
---|---|
author | Nestor, David Andersson, Hanna Kihlberg, Pernilla Olson, Sara Ziegler, Ingrid Rasmussen, Gunlög Källman, Jan Cajander, Sara Mölling, Paula Sundqvist, Martin |
author_facet | Nestor, David Andersson, Hanna Kihlberg, Pernilla Olson, Sara Ziegler, Ingrid Rasmussen, Gunlög Källman, Jan Cajander, Sara Mölling, Paula Sundqvist, Martin |
author_sort | Nestor, David |
collection | PubMed |
description | BACKGROUND: Blood stream infection (BSI) and sepsis are serious clinical conditions and identification of the disease-causing pathogen is important for patient management. The RISE (Rapid Identification of SEpsis) study was carried out to collect a cohort allowing high-quality studies on different aspects of BSI and sepsis. The aim of this study was to identify patients at high risk for BSI who might benefit most from new, faster, etiological testing using neutrophil to lymphocyte count ratio (NLCR) and Shapiro score. METHODS: Adult patients (≥ 18 years) presenting at the emergency department (ED) with suspected BSI were prospectively included between 2014 and 2016 at Örebro University Hospital. Besides extra blood sampling, all study patients were treated according to ED routines. Electronic patient charts were retrospectively reviewed. A modified Shapiro score (MSS) and NLCR were extracted and compiled. Continuous score variables were analysed with area under receiver operator characteristics curves (AUC) to evaluate the ability of BSI prediction. RESULTS: The final cohort consisted of 484 patients where 84 (17%) had positive blood culture judged clinically significant. At optimal cut-offs, MSS (≥3 points) and NLCR (> 12) showed equal ability to predict BSI in the whole cohort (AUC 0.71/0.74; sensitivity 69%/67%; specificity 64%/68% respectively) and in a subgroup of 155 patients fulfilling Sepsis-3 criteria (AUC 0.71/0.66; sensitivity 81%/65%; specificity 46%/57% respectively). In BSI cases only predicted by NLCR> 12 the abundance of Gram-negative to Gram-positive pathogens (n = 13 to n = 4) differed significantly from those only predicted by MSS ≥3 p (n = 7 to n = 12 respectively) (p < 0.05). CONCLUSIONS: MSS and NLCR predicted BSI in the RISE cohort with similar cut-offs as shown in previous studies. Combining the MSS and NLCR did not increase the predictive performance. Differences in BSI prediction between MSS and NLCR regarding etiology need further evaluation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-021-05990-3. |
format | Online Article Text |
id | pubmed-8017733 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-80177332021-04-02 Early prediction of blood stream infection in a prospectively collected cohort Nestor, David Andersson, Hanna Kihlberg, Pernilla Olson, Sara Ziegler, Ingrid Rasmussen, Gunlög Källman, Jan Cajander, Sara Mölling, Paula Sundqvist, Martin BMC Infect Dis Research Article BACKGROUND: Blood stream infection (BSI) and sepsis are serious clinical conditions and identification of the disease-causing pathogen is important for patient management. The RISE (Rapid Identification of SEpsis) study was carried out to collect a cohort allowing high-quality studies on different aspects of BSI and sepsis. The aim of this study was to identify patients at high risk for BSI who might benefit most from new, faster, etiological testing using neutrophil to lymphocyte count ratio (NLCR) and Shapiro score. METHODS: Adult patients (≥ 18 years) presenting at the emergency department (ED) with suspected BSI were prospectively included between 2014 and 2016 at Örebro University Hospital. Besides extra blood sampling, all study patients were treated according to ED routines. Electronic patient charts were retrospectively reviewed. A modified Shapiro score (MSS) and NLCR were extracted and compiled. Continuous score variables were analysed with area under receiver operator characteristics curves (AUC) to evaluate the ability of BSI prediction. RESULTS: The final cohort consisted of 484 patients where 84 (17%) had positive blood culture judged clinically significant. At optimal cut-offs, MSS (≥3 points) and NLCR (> 12) showed equal ability to predict BSI in the whole cohort (AUC 0.71/0.74; sensitivity 69%/67%; specificity 64%/68% respectively) and in a subgroup of 155 patients fulfilling Sepsis-3 criteria (AUC 0.71/0.66; sensitivity 81%/65%; specificity 46%/57% respectively). In BSI cases only predicted by NLCR> 12 the abundance of Gram-negative to Gram-positive pathogens (n = 13 to n = 4) differed significantly from those only predicted by MSS ≥3 p (n = 7 to n = 12 respectively) (p < 0.05). CONCLUSIONS: MSS and NLCR predicted BSI in the RISE cohort with similar cut-offs as shown in previous studies. Combining the MSS and NLCR did not increase the predictive performance. Differences in BSI prediction between MSS and NLCR regarding etiology need further evaluation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-021-05990-3. BioMed Central 2021-04-02 /pmc/articles/PMC8017733/ /pubmed/33810788 http://dx.doi.org/10.1186/s12879-021-05990-3 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Nestor, David Andersson, Hanna Kihlberg, Pernilla Olson, Sara Ziegler, Ingrid Rasmussen, Gunlög Källman, Jan Cajander, Sara Mölling, Paula Sundqvist, Martin Early prediction of blood stream infection in a prospectively collected cohort |
title | Early prediction of blood stream infection in a prospectively collected cohort |
title_full | Early prediction of blood stream infection in a prospectively collected cohort |
title_fullStr | Early prediction of blood stream infection in a prospectively collected cohort |
title_full_unstemmed | Early prediction of blood stream infection in a prospectively collected cohort |
title_short | Early prediction of blood stream infection in a prospectively collected cohort |
title_sort | early prediction of blood stream infection in a prospectively collected cohort |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8017733/ https://www.ncbi.nlm.nih.gov/pubmed/33810788 http://dx.doi.org/10.1186/s12879-021-05990-3 |
work_keys_str_mv | AT nestordavid earlypredictionofbloodstreaminfectioninaprospectivelycollectedcohort AT anderssonhanna earlypredictionofbloodstreaminfectioninaprospectivelycollectedcohort AT kihlbergpernilla earlypredictionofbloodstreaminfectioninaprospectivelycollectedcohort AT olsonsara earlypredictionofbloodstreaminfectioninaprospectivelycollectedcohort AT ziegleringrid earlypredictionofbloodstreaminfectioninaprospectivelycollectedcohort AT rasmussengunlog earlypredictionofbloodstreaminfectioninaprospectivelycollectedcohort AT kallmanjan earlypredictionofbloodstreaminfectioninaprospectivelycollectedcohort AT cajandersara earlypredictionofbloodstreaminfectioninaprospectivelycollectedcohort AT mollingpaula earlypredictionofbloodstreaminfectioninaprospectivelycollectedcohort AT sundqvistmartin earlypredictionofbloodstreaminfectioninaprospectivelycollectedcohort |