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Presymptomatic diagnosis of postoperative infection and sepsis using gene expression signatures
PURPOSE: Early accurate diagnosis of infection ± organ dysfunction (sepsis) remains a major challenge in clinical practice. Utilizing effective biomarkers to identify infection and impending organ dysfunction before the onset of clinical signs and symptoms would enable earlier investigation and inte...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9281215/ https://www.ncbi.nlm.nih.gov/pubmed/35831640 http://dx.doi.org/10.1007/s00134-022-06769-z |
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author | Lukaszewski, Roman A. Jones, Helen E. Gersuk, Vivian H. Russell, Paul Simpson, Andrew Brealey, David Walker, Jonathan Thomas, Matt Whitehouse, Tony Ostermann, Marlies Koch, Alexander Zacharowski, Kai Kruhoffer, Mogens Chaussabel, Damien Singer, Mervyn |
author_facet | Lukaszewski, Roman A. Jones, Helen E. Gersuk, Vivian H. Russell, Paul Simpson, Andrew Brealey, David Walker, Jonathan Thomas, Matt Whitehouse, Tony Ostermann, Marlies Koch, Alexander Zacharowski, Kai Kruhoffer, Mogens Chaussabel, Damien Singer, Mervyn |
author_sort | Lukaszewski, Roman A. |
collection | PubMed |
description | PURPOSE: Early accurate diagnosis of infection ± organ dysfunction (sepsis) remains a major challenge in clinical practice. Utilizing effective biomarkers to identify infection and impending organ dysfunction before the onset of clinical signs and symptoms would enable earlier investigation and intervention. To our knowledge, no prior study has specifically examined the possibility of pre-symptomatic detection of sepsis. METHODS: Blood samples and clinical/laboratory data were collected daily from 4385 patients undergoing elective surgery. An adjudication panel identified 154 patients with definite postoperative infection, of whom 98 developed sepsis. Transcriptomic profiling and subsequent RT-qPCR were undertaken on sequential blood samples taken postoperatively from these patients in the three days prior to the onset of symptoms. Comparison was made against postoperative day-, age-, sex- and procedure- matched patients who had an uncomplicated recovery (n =151) or postoperative inflammation without infection (n =148). RESULTS: Specific gene signatures optimized to predict infection or sepsis in the three days prior to clinical presentation were identified in initial discovery cohorts. Subsequent classification using machine learning with cross-validation with separate patient cohorts and their matched controls gave high Area Under the Receiver Operator Curve (AUC) values. These allowed discrimination of infection from uncomplicated recovery (AUC 0.871), infectious from non-infectious systemic inflammation (0.897), sepsis from other postoperative presentations (0.843), and sepsis from uncomplicated infection (0.703). CONCLUSION: Host biomarker signatures may be able to identify postoperative infection or sepsis up to three days in advance of clinical recognition. If validated in future studies, these signatures offer potential diagnostic utility for postoperative management of deteriorating or high-risk surgical patients and, potentially, other patient populations. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00134-022-06769-z. |
format | Online Article Text |
id | pubmed-9281215 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-92812152022-07-14 Presymptomatic diagnosis of postoperative infection and sepsis using gene expression signatures Lukaszewski, Roman A. Jones, Helen E. Gersuk, Vivian H. Russell, Paul Simpson, Andrew Brealey, David Walker, Jonathan Thomas, Matt Whitehouse, Tony Ostermann, Marlies Koch, Alexander Zacharowski, Kai Kruhoffer, Mogens Chaussabel, Damien Singer, Mervyn Intensive Care Med Original PURPOSE: Early accurate diagnosis of infection ± organ dysfunction (sepsis) remains a major challenge in clinical practice. Utilizing effective biomarkers to identify infection and impending organ dysfunction before the onset of clinical signs and symptoms would enable earlier investigation and intervention. To our knowledge, no prior study has specifically examined the possibility of pre-symptomatic detection of sepsis. METHODS: Blood samples and clinical/laboratory data were collected daily from 4385 patients undergoing elective surgery. An adjudication panel identified 154 patients with definite postoperative infection, of whom 98 developed sepsis. Transcriptomic profiling and subsequent RT-qPCR were undertaken on sequential blood samples taken postoperatively from these patients in the three days prior to the onset of symptoms. Comparison was made against postoperative day-, age-, sex- and procedure- matched patients who had an uncomplicated recovery (n =151) or postoperative inflammation without infection (n =148). RESULTS: Specific gene signatures optimized to predict infection or sepsis in the three days prior to clinical presentation were identified in initial discovery cohorts. Subsequent classification using machine learning with cross-validation with separate patient cohorts and their matched controls gave high Area Under the Receiver Operator Curve (AUC) values. These allowed discrimination of infection from uncomplicated recovery (AUC 0.871), infectious from non-infectious systemic inflammation (0.897), sepsis from other postoperative presentations (0.843), and sepsis from uncomplicated infection (0.703). CONCLUSION: Host biomarker signatures may be able to identify postoperative infection or sepsis up to three days in advance of clinical recognition. If validated in future studies, these signatures offer potential diagnostic utility for postoperative management of deteriorating or high-risk surgical patients and, potentially, other patient populations. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00134-022-06769-z. Springer Berlin Heidelberg 2022-07-13 2022 /pmc/articles/PMC9281215/ /pubmed/35831640 http://dx.doi.org/10.1007/s00134-022-06769-z Text en © Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Lukaszewski, Roman A. Jones, Helen E. Gersuk, Vivian H. Russell, Paul Simpson, Andrew Brealey, David Walker, Jonathan Thomas, Matt Whitehouse, Tony Ostermann, Marlies Koch, Alexander Zacharowski, Kai Kruhoffer, Mogens Chaussabel, Damien Singer, Mervyn Presymptomatic diagnosis of postoperative infection and sepsis using gene expression signatures |
title | Presymptomatic diagnosis of postoperative infection and sepsis using gene expression signatures |
title_full | Presymptomatic diagnosis of postoperative infection and sepsis using gene expression signatures |
title_fullStr | Presymptomatic diagnosis of postoperative infection and sepsis using gene expression signatures |
title_full_unstemmed | Presymptomatic diagnosis of postoperative infection and sepsis using gene expression signatures |
title_short | Presymptomatic diagnosis of postoperative infection and sepsis using gene expression signatures |
title_sort | presymptomatic diagnosis of postoperative infection and sepsis using gene expression signatures |
topic | Original |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9281215/ https://www.ncbi.nlm.nih.gov/pubmed/35831640 http://dx.doi.org/10.1007/s00134-022-06769-z |
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