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1. The early identification of disease progression in patients with suspected infection presenting to the emergency department: a multi-centre derivation and validation study

Author

Kordo Saeed, Darius Cameron Wilson, Frank Bloos, Philipp Schuetz, Yuri van der Does, Olle Melander, Pierre Hausfater, Jacopo M. Legramante, Yann-Erick Claessens, Deveendra Amin, Mari Rosenqvist, Graham White, Beat Mueller, Maarten Limper, Carlota Clemente Callejo, Antonella Brandi, Marc-Alexis Macchi, Nicholas Cortes, Alexander Kutz, Peter Patka, María Cecilia Yañez, Sergio Bernardini, Nathalie Beau, Matthew Dryden, Eric C. M. van Gorp, Marilena Minieri, Louisa Chan, Pleunie P. M. Rood, and Juan Gonzalez del Castillo

Subjects
MR-proADM, Sepsis, SOFA, qSOFA, Disease progression, Emergency department, Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

Abstract Background There is a lack of validated tools to assess potential disease progression and hospitalisation decisions in patients presenting to the emergency department (ED) with a suspected infection. This study aimed to identify suitable blood biomarkers (MR-proADM, PCT, lactate and CRP) or clinical scores (SIRS, SOFA, qSOFA, NEWS and CRB-65) to fulfil this unmet clinical need. Methods An observational derivation patient cohort validated by an independent secondary analysis across nine EDs. Logistic and Cox regression, area under the receiver operating characteristic (AUROC) and Kaplan-Meier curves were used to assess performance. Disease progression was identified using a composite endpoint of 28-day mortality, ICU admission and hospitalisation > 10 days. Results One thousand one hundred seventy-five derivation and 896 validation patients were analysed with respective 28-day mortality rates of 7.1% and 5.0%, and hospitalisation rates of 77.9% and 76.2%. MR-proADM showed greatest accuracy in predicting 28-day mortality and hospitalisation requirement across both cohorts. Patient subgroups with high MR-proADM concentrations (≥ 1.54 nmol/L) and low biomarker (PCT

Published
2019
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2. Correction to: The early identification of disease progression in patients with suspected infection presenting to the emergency department: a multi-centre derivation and validation study

Author

Kordo Saeed, Darius Cameron Wilson, Frank Bloos, Philipp Schuetz, Yuri van der Does, Olle Melander, Pierre Hausfater, Jacopo M. Legramante, Yann-Erick Claessens, Deveendra Amin, Mari Rosenqvist, Graham White, Beat Mueller, Maarten Limper, Carlota Clemente Callejo, Antonella Brandi, Marc-Alexis Macchi, Nicholas Cortes, Alexander Kutz, Peter Patka, María Cecilia Yañez, Sergio Bernardini, Nathalie Beau, Matthew Dryden, Eric C. M. van Gorp, Marilena Minieri, Louisa Chan, Pleunie P. M. Rood, and Juan Gonzalez del Castillo

Subjects
Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

In the publication of this article [1], there are two errors in contributing author affiliations. This has now been included in this correction article.

Published
2019
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3. The early identification of disease progression in patients with suspected infection presenting to the emergency department: a multi-centre derivation and validation study

Author

Louisa Chan, Darius Cameron Wilson, Peter Patka, Jacopo M. Legramante, Pleunie P.M. Rood, Marilena Minieri, Beat Mueller, Kordo Saeed, Alexander Kutz, Matthew Dryden, Mari Rosenqvist, Pierre Hausfater, Marc Alexis MacChi, Maarten Limper, Yuri van der Does, Sergio Bernardini, Frank Bloos, María Cecilia Yañez, Nicholas Cortes, Yann Erick Claessens, Antonella Brandi, Juan González del Castillo, Eric C. M. van Gorp, Graham White, Nathalie Beau, Deveendra Amin, Olle Melander, Carlota Clemente Callejo, Philipp Schuetz, Gestionnaire, Hal Sorbonne Université, University of Southampton, Jena University Hospital [Jena], University of Basel (Unibas), Erasmus University Medical Center [Rotterdam] (Erasmus MC), Skane University Hospital [Malmo], Lund University [Lund], Service d'Accueil des Urgences [CHU Pitié-Salpêtrière] (SAU), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Sorbonne Université (SU), Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Research Unit on Cardiovascular and Metabolic Diseases (ICAN), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut de Cardiométabolisme et Nutrition = Institute of Cardiometabolism and Nutrition [CHU Pitié Salpêtrière] (IHU ICAN), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Università degli Studi di Roma Tor Vergata [Roma], Hôpital Princesse Grace [Monaco], University Medical Center [Utrecht], Public Health England [London], Service des Urgences [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Institute of cardiometabolism and nutrition (ICAN), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Emergency Medicine, Internal Medicine, and Virology

Subjects
Validation study, Settore MED/09 - Medicina Interna, MR-proADM, Critical Care and Intensive Care Medicine, Adrenomedullin, 03 medical and health sciences, 0302 clinical medicine, qSOFA, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Sepsis, Humans, Medicine, SOFA, In patient, Derivation, Protein Precursors, Multi centre, Disease progression, business.industry, Emergency department, Research, Settore BIO/12, lcsh:Medical emergencies. Critical care. Intensive care. First aid, 030208 emergency & critical care medicine, lcsh:RC86-88.9, medicine.disease, 3. Good health, Hospitalization, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Identification (biology), [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Medical emergency, Emergency Service, Hospital, business
Abstract

Background There is a lack of validated tools to assess potential disease progression and hospitalisation decisions in patients presenting to the emergency department (ED) with a suspected infection. This study aimed to identify suitable blood biomarkers (MR-proADM, PCT, lactate and CRP) or clinical scores (SIRS, SOFA, qSOFA, NEWS and CRB-65) to fulfil this unmet clinical need. Methods An observational derivation patient cohort validated by an independent secondary analysis across nine EDs. Logistic and Cox regression, area under the receiver operating characteristic (AUROC) and Kaplan-Meier curves were used to assess performance. Disease progression was identified using a composite endpoint of 28-day mortality, ICU admission and hospitalisation > 10 days. Results One thousand one hundred seventy-five derivation and 896 validation patients were analysed with respective 28-day mortality rates of 7.1% and 5.0%, and hospitalisation rates of 77.9% and 76.2%. MR-proADM showed greatest accuracy in predicting 28-day mortality and hospitalisation requirement across both cohorts. Patient subgroups with high MR-proADM concentrations (≥ 1.54 nmol/L) and low biomarker (PCT

Published
2019
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4. Additional file 1: of The early identification of disease progression in patients with suspected infection presenting to the emergency department: a multi-centre derivation and validation study

Author

Kordo Saeed, Wilson, Darius, Bloos, Frank, Schuetz, Philipp, Does, Yuri, Melander, Olle, Hausfater, Pierre, Legramante, Jacopo, Yann-Erick Claessens, Deveendra Amin, Rosenqvist, Mari, White, Graham, Mueller, Beat, Limper, Maarten, Callejo, Carlota, Brandi, Antonella, Marc-Alexis Macchi, Cortes, Nicholas, Kutz, Alexander, Patka, Peter, MarĂ­A YaĂąez, Bernardini, Sergio, Beau, Nathalie, Dryden, Matthew, Gorp, Eric, Minieri, Marilena, Chan, Louisa, Pleunie Rood, and Castillo, Juan

Abstract

Table S1. Initial clinical diagnoses and infectious source (derivation cohort). Table S2. 28-day mortality validation cohort characteristics. Table S3. Sepsis-2 and sepsis-3 classification (derivation cohort). Table S4. Application of the 28-day mortality derivation MR-proADM cut-off (validation cohort). Table S5. Pooled derivation and validation cohorts for 28-day mortality prediction. Table S6. Cox regression between high and low severity populations (derivation and validation cohorts). Table S7. Uncomplicated infection and disease progression events in the low 28-day mortality biomarker/score value subgroups. Table S8. Subgroups with low MR-proADM and low biomarker/score values showing enrichment for uncomplicated infections. Table S9. Subgroups with high MR-proADM and low biomarker/score values showing enrichment for disease progression events. Table S10. Significance between uncomplicated infection or disease progression subgroups for hospitalisation duration, 28-day mortality and ICU admission rates. Table S11. Univariate logistic regression for hospitalisation decisions. Table S12. Multivariate logistic regression for hospitalisation decisions. Table S13. Application of the optimised derivation MR-proADM cut-off for hospitalisation (derivation and validation cohort). Table S14. Derivation and validation logistic regression for hospitalisation using optimised derivation cut-offs. Table S15. Combined derivation and validation cohort meta-analysis for hospitalisation decisions. Figure S1. Meta-analysis assessing derivation and validation heterogeneity for 28-day mortality. Figure S2. Fagan nomogram calculating post-test probabilities for 28-day mortality. Figure S3. Kaplan-Meier using the optimised derivation lactate cut-off. Figure S4. Kaplan-Meier using the optimised derivation PCT cut-off. Figure S5. Kaplan-Meier using the optimised derivation qSOFA cut-off. Figure S6. Kaplan-Meier using the optimised derivation SOFA cut-off. Figure S7. Kaplan-Meier using the optimised derivation SIRS cut-off. Figure S8. Kaplan-Meier using the optimised derivation NEWS cut-off. Figure S9. Meta-analysis assessing derivation and validation heterogeneity for hospitalisation decisions. Figure S10. Fagan nomogram calculating post-test probabilities for hospitalisation decisions. Figure S11. Conventional and virtual MR-proADM guided-triage (derivation cohort). Figure S12. Conventional and virtual MR-proADM guided-triage (validation cohort). (DOCX 1318 kb)

Published
2019
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