Henriksen DP, Pottegård A, Laursen CB, Jensen TG, Hallas J, Pedersen C, et al. Risk factors for hospitalization due to community-acquired sepsis—a population-based case-control study. PLoS ONE. 2015. https://doi.org/10.1371/journal.pone.0124838.
Article
PubMed
PubMed Central
Google Scholar
Timbrook TT, Spivak ES, Hanson KE. Current and future opportunities for rapid diagnostics in antimicrobial stewardship. Med Clin N Am. 2018;102:899–911. https://doi.org/10.1016/j.mcna.2018.05.004.
Article
PubMed
Google Scholar
Florio W, Morici P, Ghelardi E, Barnini S, Lupetti A. Recent advances in the microbiological diagnosis of bloodstream infections. Crit Rev Microbiol. 2018;44:351–70. https://doi.org/10.1080/1040841X.2017.1407745.
Article
PubMed
Google Scholar
Peker N, Couto N, Sinha B, Rossen JW. Diagnosis of bloodstream infections from positive blood cultures and directly from blood samples: recent developments in molecular approaches. Clin Microbiol Infect. 2018;24:944–55. https://doi.org/10.1016/j.cmi.2018.05.007.
Article
CAS
PubMed
Google Scholar
Mangioni D, Viaggi B, Giani T, Arena F, D’Arienzo S, Forni S, et al. Diagnostic stewardship for sepsis: the need for risk stratification to triage patients for fast microbiology workflows. Future Microbiol. 2019;14:169–74. https://doi.org/10.2217/fmb-2018-0329.
Article
CAS
PubMed
Google Scholar
Bates DW. Contaminant blood cultures and resource utilization. JAMA. 1991;265:365. https://doi.org/10.1001/jama.1991.03460030071031.
Article
CAS
PubMed
Google Scholar
Coburn B, Morris AM, Tomlinson G, Detsky AS. Does this adult patient with suspected bacteremia require blood cultures? JAMA. 2012;308:502. https://doi.org/10.1001/jama.2012.8262.
Article
CAS
PubMed
Google Scholar
Eliakim-Raz N, Bates DW, Leibovici L. Predicting bacteraemia in validated models—a systematic review. Clin Microbiol Infect. 2015;21:295–301. https://doi.org/10.1016/j.cmi.2015.01.023.
Article
CAS
PubMed
Google Scholar
Ward L, Andreassen S, Astrup JJ, Rahmani Z, Fantini M, Sambri V. Clinical- vs. model-based selection of patients suspected of sepsis for direct-from-blood rapid diagnostics in the emergency department: a retrospective study. Eur J Clin Microbiol Infect Dis. 2019. https://doi.org/10.1007/s10096-019-03581-4.
Article
PubMed
Google Scholar
Royal College of Physicians. National Early Warning Score (NEWS) 2: standardising the assessment of acute-illness severity in the NHS. London: Royal College of Physicians; 2017.
Google Scholar
Shapiro NI, Wolfe RE, Moore RB, Smith E, Burdick E, Bates DW. Mortality in Emergency Department Sepsis (MEDS) score: a prospectively derived and validated clinical prediction rule. Crit Care Med. 2003;31:670–5. https://doi.org/10.1097/01.CCM.0000054867.01688.D1.
Article
PubMed
Google Scholar
Bone RC, Balk RA, Cerra FB, Dellinger RP, Fein AM, Knaus WA, et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. CHEST J. 1992;101:1644–55.
Article
CAS
Google Scholar
Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315:801. https://doi.org/10.1001/jama.2016.0287.
Article
CAS
PubMed
PubMed Central
Google Scholar
Shapiro NI, Wolfe RE, Wright SB, Moore R, Bates DW. Who needs a blood culture? A prospectively derived and validated prediction rule. J Emerg Med. 2008;35:255–64. https://doi.org/10.1016/j.jemermed.2008.04.001.
Article
PubMed
Google Scholar
Ward L, Møller JK, Eliakim-Raz N, Andreassen S. Prediction of bacteraemia and of 30-day mortality among patients with suspected infection using a CPN model of systemic inflammation. IFAC-PapersOnLine. 2018;51:116–21. https://doi.org/10.1016/j.ifacol.2018.11.657.
Article
Google Scholar
Poses RM, Anthony M. Availability, wishful thinking, and physicians’ diagnostic judgments for patients with suspected bacteremia. Med Decis Mak. 1991;11:159–68. https://doi.org/10.1177/0272989X9101100303.
Article
CAS
Google Scholar
Pawlowicz A, Holland C, Zou B, Payton T, Tyndall JA, Allen B. Implementation of an evidence-based algorithm reduces blood culture overuse in an adult emergency department. Gen Intern Med Clin Innov. 2016;1:26–9. https://doi.org/10.15761/gimci.1000108.
Article
Google Scholar
Jessen MK, Mackenhauer J, Hvass AMSW, Ellermann-Eriksen S, Skibsted S, Kirkegaard H, et al. Prediction of bacteremia in the emergency department. Eur J Emerg Med. 2016;23:44–9. https://doi.org/10.1097/MEJ.0000000000000203.
Article
PubMed
Google Scholar
Arboe B, Laub RR, Kronborg G, Knudsen JD. Evaluation of the decision support system for antimicrobial treatment, TREAT, in an acute medical ward of a university hospital. Int J Infect Dis. 2014;29:156–61. https://doi.org/10.1016/j.ijid.2014.08.019.
Article
PubMed
Google Scholar
Ward LM, Møller J, Østergaard C, Mogensen M, Paul M, Leibovici L, et al. Prediction of bacteraemia in a low-bacteraemia-prevalence cohort using the Treat decision support system. In: Conference of The International Society for Medical Innovation and Technology, iSMIT. Baden-Baden. 2013.
Paul M, Andreassen S, Tacconelli E, Nielsen AD, Almanasreh N, Frank U, et al. Improving empirical antibiotic treatment using TREAT, a computerized decision support system: cluster randomized trial. J Antimicrob Chemother. 2006;58:1238–45. https://doi.org/10.1093/jac/dkl372.
Article
CAS
PubMed
Google Scholar
Ward L, Paul M, Andreassen S. Automatic learning of mortality in a CPN model of the systemic inflammatory response syndrome. Math Biosci. 2017;284:12–20. https://doi.org/10.1016/j.mbs.2016.11.004.
Article
PubMed
Google Scholar
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837. https://doi.org/10.2307/2531595.
Article
CAS
PubMed
Google Scholar
Hunter JD. Matplotlib: a 2D graphics environment. Comput Sci Eng. 2007;9:99–104. https://doi.org/10.1109/MCSE.2007.55.
Article
Google Scholar
Perl B, Gottehrer NP, Raveh D, Schlesinger Y, Rudensky B, Yinnon AM. Cost-effectiveness of blood cultures for adult patients with cellulitis. Clin Infect Dis. 1999;29:1483–8. https://doi.org/10.1086/313525.
Article
CAS
PubMed
Google Scholar
National Institute for Health and Care Excellence. Tests for rapidly identifying bloodstream bacteria and fungi (LightCycler SeptiFast Test MGRADE, SepsiTest and IRIDICA BAC BSI assay). 2016. https://www.nice.org.uk/guidance/dg20/chapter/4-Outcomes. Accessed 28 2017.
Seymour CW, Liu VX, Iwashyna TJ, Brunkhorst FM, Rea TD, Scherag A, et al. Assessment of clinical criteria for sepsis. JAMA. 2016;315:762. https://doi.org/10.1001/jama.2016.0288.
Article
CAS
PubMed
PubMed Central
Google Scholar
Zhao J, He Y, Xu P, Liu J, Ye S, Cao Y. Serum ammonia levels on admission for predicting sepsis patient mortality at D28 in the emergency department. Medicine (Baltimore). 2020;99:e19477. https://doi.org/10.1097/MD.0000000000019477.
Article
CAS
Google Scholar
Xia Y, Zou L, Li D, Qin Q, Hu H, Zhou Y, et al. The ability of an improved qSOFA score to predict acute sepsis severity and prognosis among adult patients. Medicine (Baltimore). 2020;99:e18942. https://doi.org/10.1097/MD.0000000000018942.
Article
CAS
Google Scholar
Gonzalez Del Castillo J, Wilson DC, Clemente-Callejo C, Román F, Bardés-Robles I, Jiménez I, et al. Biomarkers and clinical scores to identify patient populations at risk of delayed antibiotic administration or intensive care admission. Crit Care. 2019. https://doi.org/10.1186/s13054-019-2613-4.
Article
PubMed
PubMed Central
Google Scholar
Kim H, Hur M, Struck J, Bergmann A, Di Somma S. Circulating biologically active adrenomedullin predicts organ failure and mortality in sepsis. Ann Lab Med. 2019;39:454–63. https://doi.org/10.3343/alm.2019.39.5.454.
Article
CAS
PubMed
PubMed Central
Google Scholar
Saeed K, Wilson DC, Bloos F, Schuetz P, van der Does Y, Melander O, et al. The early identification of disease progression in patients with suspected infection presenting to the emergency department: a multi-centre derivation and validation study. Crit Care. 2019;23:40. https://doi.org/10.1186/s13054-019-2329-5.
Article
PubMed
PubMed Central
Google Scholar
Abdullah SMOB, Sørensen RH, Dessau RBC, Sattar SMRU, Wiese L, Nielsen FE. Prognostic accuracy of qSOFA in predicting 28-day mortality among infected patients in an emergency department: a prospective validation study. Emerg Med J. 2019;36:emermed-2019-208456. https://doi.org/10.1136/emermed-2019-208456.
Article
Google Scholar
Yu H, Nie L, Liu A, Wu K, Hsein YC, Yen DW, et al. Combining procalcitonin with the qSOFA and sepsis mortality prediction. Medicine (United States). 2019;98:e15981. https://doi.org/10.1097/MD.0000000000015981.
Article
CAS
Google Scholar
Prabhakar SM, Tagami T, Liu N, Samsudin MI, Ng JCJ, Koh ZX, et al. Combining quick sequential organ failure assessment score with heart rate variability may improve predictive ability for mortality in septic patients at the emergency department. PLoS ONE. 2019;14:e0213445. https://doi.org/10.1371/journal.pone.0213445.
Article
CAS
PubMed
PubMed Central
Google Scholar
García-Lamberechts EJ, Martín-Sánchez FJ, Julián-Jiménez A, Llopis F, Martínez-Ortizde Zarate M, Arranz-Nieto MJ, et al. Infection and systemic inflammatory response syndrome in older patients in the emergency department: a 30-day risk model. Emergencias Rev la Soc Esp Med Emergencias. 2018;30:241–6.
Google Scholar
Li D, Zhou Y, Yu J, Yu H, Xia Y, Zhang L, et al. Evaluation of a novel prognostic score based on thrombosis and inflammation in patients with sepsis: a retrospective cohort study. Clin Chem Lab Med. 2018;56:1182–92. https://doi.org/10.1515/cclm-2017-0863.
Article
CAS
PubMed
Google Scholar
Zhao Y, Jia Y, Li C, Fang Y, Shao R. The risk stratification and prognostic evaluation of soluble programmed death-1 on patients with sepsis in emergency department. Am J Emerg Med. 2018;36:43–8. https://doi.org/10.1016/j.ajem.2017.07.002.
Article
PubMed
Google Scholar
Innocenti F, Tozzi C, Donnini C, De Villa E, Conti A, Zanobetti M, et al. SOFA score in septic patients: incremental prognostic value over age, comorbidities, and parameters of sepsis severity. Intern Emerg Med. 2017;13:405–12. https://doi.org/10.1007/s11739-017-1629-5.
Article
PubMed
Google Scholar
Wang J-Y, Chen Y-X, Guo S-B, Mei X, Yang P. Predictive performance of quick Sepsis-related Organ Failure Assessment for mortality and ICU admission in patients with infection at the ED. Am J Emerg Med. 2016;34:1788–93. https://doi.org/10.1016/j.ajem.2016.06.015.
Article
PubMed
Google Scholar
Mirijello A, Tosoni A, Zaccone V, Impagnatiello M, Passaro G, Vallone CV, et al. MEDS score and Vitamin D status are independent predictors of mortality in a cohort of Internal Medicine patients with microbiological identified Sepsis. Eur Rev Med Pharmacol Sci. 2019;23:4033–43. https://doi.org/10.26355/eurrev_201905_17834.
Article
CAS
PubMed
Google Scholar
Brink A, Alsma J, Verdonschot RJCG, Rood PPM, Zietse R, Lingsma HF, et al. Predicting mortality in patients with suspected sepsis at the Emergency Department; A retrospective cohort study comparing qSOFA, SIRS and National Early Warning Score. PLoS ONE. 2019;14:e0211133. https://doi.org/10.1371/journal.pone.0211133.
Article
CAS
PubMed
PubMed Central
Google Scholar
Ramos JGR, da Hora Passos R, Teixeira MB, Gobatto ALN, Coutinho RVdS, Caldas JR, et al. Prognostic ability of quick-SOFA across different age groups of patients with suspected infection outside the intensive care unit: a cohort study. J Crit Care. 2018;47:178–84. https://doi.org/10.1016/j.jcrc.2018.07.008.
Article
PubMed
Google Scholar