Zaira Raquel Palacios-Baena1, Belén Gutiérrez-Gutiérrez1, Marina De Cueto1,2, Pierluigi Viale3, Mario Venditti4, Alicia Hernández-Torres5, Antonio Oliver6, Luis Martínez-Martínez7, Esther Calbo8, Vicente Pintado9, Oriol Gasch10, Benito Almirante11, José Antonio Lepe1, Johann Pitout12, Murat Akova13, Carmen Peña-Miralles14, Mitchell J Schwaber15, Mario Tumbarello16, Evelina Tacconelli17, Julia Origüen18, Nuria Prim19, German Bou20, Helen Giamarellou21, Joaquín Bermejo22, Axel Hamprecht23, Federico Pérez24, Manuel Almela25, Warren Lowman26, Po-Ren Hsueh27, Carolina Navarro-San Francisco28, Julián Torre-Cisneros29, Yehuda Carmeli15, Robert A Bonomo30, David L Paterson31, Álvaro Pascual1,2, Jesús Rodríguez-Baño1,32. 1. Unidad Clínica de Enfermedades Infecciosas, Microbiología y Medicina Preventiva, Instituto de Biomedicina de Sevilla-IBiS, Hospitales Universitarios Virgen Macarena y Virgen del Rocío, Seville, Spain. 2. Departamento de Microbiología, Universidad de Sevilla, Seville, Spain. 3. Teaching Hospital Policlinico S. Orsola-Malpighi, Bologna, Italy. 4. Policlinico Umberto I, Rome, Italy. 5. Hospital Universitario Virgen de la Arrixaca, Murcia, Spain. 6. Hospital Universitario Son Espases, Palma de Mallorca, Spain. 7. Hospital Universitario Marqués de Valdecilla-IDIVAL, Departamento de Biología Molecular, Universidad de Cantabria, Santander, Spain. 8. Hospital Universitari Mútua de Terrassa, Barcelona, Spain. 9. Servicio de Enfermedades Infecciosas, Hospital Ramón y Cajal, Madrid, Spain. 10. Hospital Parc Taulí, Barcelona, Spain. 11. Servicio de Enfermedades Infecciosas, Hospital Vall d'Hebrón, Barcelona, Spain. 12. University of Calgary, Calgary, Canada. 13. Hacettepe University School of Medicine, Ankara, Turkey. 14. Hospital Universitari Bellvitge, Barcelona, Spain. 15. Tel Aviv Sourasky Medical Center, National Center for Infection Control, Israel Ministry of Health, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. 16. Catholic University of the Sacred Heart, Rome, Italy. 17. Tübingen University Hospital and Center for Infection Research (DZIF), Tübingen, Germany. 18. Unidad de Enfermedades Infecciosas, Hospital 12 de Octubre, Madrid, Spain. 19. Hospital de la Santa Creu i Sant Pau, Barcelona, Spain. 20. Complejo Hospitalario Universitario A Coruña, A Coruña, Spain. 21. Hygeia General Hospital, Athens, Greece. 22. Hospital Español, Rosario, Argentina. 23. Institut für Mikrobiologie, Immunologie und Hygiene, Universitätsklinikum Köln, Cologne, Germany. 24. Louis Stokes Cleveland Department of Veteran Affairs Medical Center, Case Western Reserve University, Cleveland, OH, USA. 25. Hospital Clinic, Barcelona, Spain. 26. Wits Donald Gordon Medical Centre, South Africa. 27. National Taiwan University Hospital, Taiwan. 28. Hospital La Paz, Madrid, Spain. 29. Maimonides Biomedical Research Institute of Córdoba (IMIBIC), Unidades de Gestión Clínica de Enfermedades Infecciosas y Microbiología, Reina Sofia University Hospital and University of Córdoba, Córdoba, Spain. 30. Research Service, Louis Stokes Cleveland Department of Veterans Affairs Medical Center and Departments of Medicine, Pharmacology, Biochemistry, Molecular Biology and Microbiology, Case Western Reserve University School of Medicine, Cleveland, OH, USA. 31. University of Queensland Centre for Clinical Research, Herston, Brisbane, Australia. 32. Departamento de Medicina, Universidad de Sevilla, Seville, Spain.
Abstract
Background: Bloodstream infections (BSIs) due to ESBL-producing Enterobacteriaceae (ESBL-E) are frequent yet outcome prediction rules for clinical use have not been developed. The objective was to define and validate a predictive risk score for 30 day mortality. Methods: A multinational retrospective cohort study including consecutive episodes of BSI due to ESBL-E was performed; cases were randomly assigned to a derivation cohort (DC) or a validation cohort (VC). The main outcome variable was all-cause 30 day mortality. A predictive score was developed using logistic regression coefficients for the DC, then tested in the VC. Results: The DC and VC included 622 and 328 episodes, respectively. The final multivariate logistic regression model for mortality in the DC included age >50 years (OR = 2.63; 95% CI: 1.18-5.85; 3 points), infection due to Klebsiella spp. (OR = 2.08; 95% CI: 1.21-3.58; 2 points), source other than urinary tract (OR = 3.6; 95% CI: 2.02-6.44; 3 points), fatal underlying disease (OR = 3.91; 95% CI: 2.24-6.80; 4 points), Pitt score >3 (OR = 3.04; 95 CI: 1.69-5.47; 3 points), severe sepsis or septic shock at presentation (OR = 4.8; 95% CI: 2.72-8.46; 4 points) and inappropriate early targeted therapy (OR = 2.47; 95% CI: 1.58-4.63; 2 points). The score showed an area under the receiver operating curve (AUROC) of 0.85 in the DC and 0.82 in the VC. Mortality rates for patients with scores of < 11 and ≥11 were 5.6% and 45.9%, respectively, in the DC, and 5.4% and 34.8% in the VC. Conclusions: We developed and validated an easy-to-collect predictive scoring model for all-cause 30 day mortality useful for identifying patients at high and low risk of mortality.
Background: Bloodstream infections (BSIs) due to ESBL-producing Enterobacteriaceae (ESBL-E) are frequent yet outcome prediction rules for clinical use have not been developed. The objective was to define and validate a predictive risk score for 30 day mortality. Methods: A multinational retrospective cohort study including consecutive episodes of BSI due to ESBL-E was performed; cases were randomly assigned to a derivation cohort (DC) or a validation cohort (VC). The main outcome variable was all-cause 30 day mortality. A predictive score was developed using logistic regression coefficients for the DC, then tested in the VC. Results: The DC and VC included 622 and 328 episodes, respectively. The final multivariate logistic regression model for mortality in the DC included age >50 years (OR = 2.63; 95% CI: 1.18-5.85; 3 points), infection due to Klebsiella spp. (OR = 2.08; 95% CI: 1.21-3.58; 2 points), source other than urinary tract (OR = 3.6; 95% CI: 2.02-6.44; 3 points), fatal underlying disease (OR = 3.91; 95% CI: 2.24-6.80; 4 points), Pitt score >3 (OR = 3.04; 95 CI: 1.69-5.47; 3 points), severe sepsis or septic shock at presentation (OR = 4.8; 95% CI: 2.72-8.46; 4 points) and inappropriate early targeted therapy (OR = 2.47; 95% CI: 1.58-4.63; 2 points). The score showed an area under the receiver operating curve (AUROC) of 0.85 in the DC and 0.82 in the VC. Mortality rates for patients with scores of < 11 and ≥11 were 5.6% and 45.9%, respectively, in the DC, and 5.4% and 34.8% in the VC. Conclusions: We developed and validated an easy-to-collect predictive scoring model for all-cause 30 day mortality useful for identifying patients at high and low risk of mortality.
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