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
Creators: name: Kordo Saeed
name: Wilson, Darius
name: Bloos, Frank
name: Schuetz, Philipp
name: Does, Yuri
name: Melander, Olle
name: Hausfater, Pierre
name: Legramante, Jacopo
name: Yann-Erick Claessens
name: Deveendra Amin
name: Rosenqvist, Mari
name: White, Graham
name: Mueller, Beat
name: Limper, Maarten
name: Callejo, Carlota
name: Brandi, Antonella
name: Marc-Alexis Macchi
name: Cortes, Nicholas
name: Kutz, Alexander
name: Patka, Peter
name: MarĂ­A YaĂąez
name: Bernardini, Sergio
name: Beau, Nathalie
name: Dryden, Matthew
name: Gorp, Eric
name: Minieri, Marilena
name: Chan, Louisa
name: Pleunie Rood
name: Castillo, Juan
Publisher: name: figshare
Year: 2019
Subjects: subject: Space Science
subject: Medicine
subject: Biotechnology
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: FOS: Chemical sciences
subject: Sociology
subject: FOS: Sociology
subject: FOS: Sociology
subject: 69999 Biological Sciences not elsewhere classified
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Cancer
subject: Hematology
subject: Plant Biology
subject: 60506 Virology

description: 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)