Cited by 161Antibiotics Pharmacokinetics and EfficacySepsis Diagnosis and TreatmentAntibiotic Use and Resistance

Timing of antibiotic therapy in the ICU

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Kollef, Marin H., Shorr, Andrew F., Bassetti, Matteo, Timsit, Jean-Francois, Micek, Scott T., Michelson, Andrew P., Garnacho-Montero, Jose·2021·Critical Care

Abstract

Severe or life threatening infections are common among patients in the intensive care unit (ICU). Most infections in the ICU are bacterial or fungal in origin and require antimicrobial therapy for clinical resolution. Antibiotics are the cornerstone of therapy for infected critically ill patients. However, antibiotics are often not optimally administered resulting in less favorable patient outcomes including greater mortality. The timing of antibiotics in patients with life threatening infections including sepsis and septic shock is now recognized as one of the most important determinants of survival for this population. Individuals who have a delay in the administration of antibiotic therapy for serious infections can have a doubling or more in their mortality. Additionally, the timing of an appropriate antibiotic regimen, one that is active against the offending pathogens based on in vitro susceptibility, also influences survival. Thus not only is early empiric antibiotic administration important but the selection of those agents is crucial as well. The duration of antibiotic infusions, especially for β-lactams, can also influence antibiotic efficacy by increasing antimicrobial drug exposure for the offending pathogen. However, due to mounting antibiotic resistance, aggressive antimicrobial de-escalation based on microbiology results is necessary to counterbalance the pressures of early broad-spectrum antibiotic therapy. In this review, we examine time related variables impacting antibiotic optimization as it relates to the treatment of life threatening infections in the ICU. In addition to highlighting the importance of antibiotic timing in the ICU we hope to provide an approach to antimicrobials that also minimizes the unnecessary use of these agents. Such approaches will increasingly be linked to advances in molecular microbiology testing and artificial intelligence/machine learning. Such advances should help identify patients needing empiric antibiotic therapy at an earlier time point as well as the specific antibiotics required in order to avoid unnecessary administration of broad-spectrum antibiotics.

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  • Approximately 47.3% of ICU patients with BSIs receive inadequate therapy within the first 24 h of symptom onset.

    “However, in some BSIs patients with less typical presentations delayed treatment is common: approximately 47.3% of ICU patients with BSIs receive inadequate therapy within the first 24 h of symptom onset [5–7].”

    From Early prediction of bloodstream infections in ICU patients using machine learning methods based on routine laboratory parameters (2025) · cites this paper as [5]

    The Conclusions (no page given) discuss delayed appropriate therapy in bloodstream infections, and the Abstract says ICU antibiotics are often not optimally administered. Neither passage reports that 47.3% of ICU patients with BSIs receive inadequate therapy within 24 hours of symptom onset, or links delays to less typical presentations. (LLM, conf=0.83)

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  1. [1]Vincent JL Sakr Y Singer M Martin-Loeches I Machado FR Marshall JC Prevalence and outcomes of infection among patients in intensive care units in 2017 JAMA 2020 323 1478 1487 10.1001/jama.2020.2717 32207816 PMC7093816 · DOI · Paper page
  2. [3]Wellcome Trust, United Kingdom Department of Health. 2014. Review on Antimicrobial Resistance. https://amr-review.org/. Accessed 26 Sep 2021.
  3. [5]Kollef MH Sherman G Ward S Fraser VJ Inadequate antimicrobial treatment of infections: a risk factor for hospital mortality among critically ill patients Chest 1999 115 462 474 10.1378/chest.115.2.462 10027448 · DOI · Paper page
  4. [6]Lodise TP Jr Patel N Kwa A Graves J Furuno JP Graffunder E Predictors of 30-day mortality among patients with Pseudomonas aeruginosa bloodstream infections: impact of delayed appropriate antibiotic selection Antimicrob Agents Chemother 2007 51 3510 3515 10.1128/AAC.00338-07 17646415 PMC2043259 · DOI
  5. [7]Kollef M Micek S Hampton N Doherty JA Kumar A Septic shock attributed to Candida infection: importance of empiric therapy and source control Clin Infect Dis 2012 54 1739 1746 10.1093/cid/cis305 22423135 · DOI · Paper page
  6. [8]Zasowski EJ Claeys KC Lagnf AM Davis SL Rybak MJ Time is of the essence: the impact of delayed antibiotic therapy on patient outcomes in hospital-onset enterococcal bloodstream infections Clin Infect Dis 2016 62 1242 1250 10.1093/cid/ciw110 26945013 PMC4845789 · DOI
  7. [9]Harris PNA Tambyah PA Lye DC Mo Y Lee TH Yilmaz M Effect of piperacillin-tazobactam vs meropenem on 30-day mortality for patients with E coli or Klebsiella pneumoniae bloodstream infection and ceftriaxone resistance: a randomized clinical trial JAMA 2018 320 984 994 10.1001/jama.2018.12163 30208454 PMC6143100 · DOI
  8. [12]Zilberberg MD Shorr AF Micek ST Vazquez-Guillamet MC Kollef MH Multi- drug resistance, inappropriate initial antibiotic therapy and mortality in Gram- negative severe sepsis and septic shock: a retrospective cohort study Crit Care 2014 18 596 10.1186/s13054-014-0596-8 25412897 PMC4264255 · DOI
  9. [13]Vazquez-Guillamet MC Scolari M Zilberberg MD Shorr AF Micek ST Kollef MH Using the number needed to treat to assess appropriate antimicrobial therapy as a determinant of outcome in severe sepsis and septic shock Crit Care Med 2014 42 2342 2349 10.1097/CCM.0000000000000516 25072764 · DOI
  10. [14]Bassetti M Rello J Blasi F Goossens H Sotjiu G Tavoschi L Systematic review of the impact of appropriate versus inappropriate initial antibiotic therapy on outcomes of patients with severe bacterial infections Int J Antimicrob Agents 2020 56 106184 10.1016/j.ijantimicag.2020.106184 33045353 · DOI
  11. [15]Klein Klouwenberg PM Cremer OL van Vught LA Ong DS Frencken JF Schultz MJ Likelihood of infection in patients with presumed sepsis at the time of intensive care unit admission: a cohort study Crit Care 2015 19 319 10.1186/s13054-015-1035-1 26346055 PMC4562354 · DOI · Paper page
  12. [16]Webb BJ Sorensen J Jephson A Mecham I Dean NC Broad-spectrum antibiotic use and poor outcomes in community-onset pneumonia: a cohort study Eur Respir J 2019 54 1900057 10.1183/13993003.00057-2019 31023851 · DOI · Paper page
  13. [17]Rhee C Kadri SS Dekker JP Danner RL Chen HC Fram D Prevalence of antibiotic-resistant pathogens in culture-proven sepsis and outcomes associated with inadequate and broad-spectrum empiric antibiotic use JAMA Netw Open 2020 3 e202899 10.1001/jamanetworkopen.2020.2899 32297949 PMC7163409 · DOI · Paper page
  14. [18]Timsit JF Bassetti M Cremer O Daikos G de Waele J Kallil A Rationalizing antimicrobial therapy in the ICU: a narrative review Intensive Care Med 2019 45 172 189 10.1007/s00134-019-05520-5 30659311 · DOI
  15. [19]van Vught LA Klein Klouwenberg PM Spitoni C Scicluna BP Wiewel MA Horn J Incidence, risk factors, and attributable mortality of secondary infections in the intensive care unit after admission for sepsis JAMA 2016 315 1469 1479 10.1001/jama.2016.2691 26975785 · DOI
  16. [20]Barbier F Pommier C Essaied W Garrouste-Orgeas M Schwebel C Ruckly S Colonization and infection with extended-spectrum beta-lactamase-producing Enterobacteriaceae in ICU patients: what impact on outcomes and carbapenem exposure? J Antimicrob Chemother 2016 71 1088 1097 10.1093/jac/dkv423 26755492 · DOI
  17. [21]Barbier F Bailly S Schwebel C Papazian L Azoulay E Kallel H Infection-related ventilator-associated complications in ICU patients colonised with extended-spectrum beta-lactamase-producing Enterobacteriaceae Intensive Care Med 2018 44 616 626 10.1007/s00134-018-5154-4 29663045 · DOI
  18. [22]Andremont O Armand-Lefevre L Dupuis C de Montmollin E Ruckly S Lucet JC Semi-quantitative cultures of throat and rectal swabs are efficient tests to predict ESBL-Enterobacterales ventilator-associated pneumonia in mechanically ventilated ESBL carriers Intensive Care Med 2020 46 1232 1242 10.1007/s00134-020-06029-y 32313993 PMC7222166 · DOI
  19. [23]Garrouste-Orgeas M Timsit JF Kallel H Ben Ali A Dumay MF Paoli B Colonization with methicillin-resistant Staphylococcus aureus in ICU patients: morbidity, mortality, and glycopeptide use Infect Control Hosp Epidemiol 2001 22 687 692 10.1017/S0195941700072659 11842988 · DOI
  20. [24]Kerneis S Visseaux B Armand-Lefevre L Timsit JF Molecular diagnostic methods for pneumonia: how can they be applied in practice? Curr Opin Infect Dis 2021 34 118 125 10.1097/QCO.0000000000000713 33395094 · DOI
  21. [25]Peri AM Stewart A Hume A Irwin A Harris PNA New microbiological techniques for the diagnosis of bacterial infections and sepsis in ICU including point of care Curr Infect Dis Rep 2021 23 12 10.1007/s11908-021-00755-0 34149321 PMC8207499 · DOI
  22. [26]Ripa M Rodriguez-Nunez O Cardozo C Naharro-Abellan A Almela M Marco F Influence of empirical double-active combination antimicrobial therapy compared with active monotherapy on mortality in patients with septic shock: a propensity score-adjusted and matched analysis J Antimicrob Chemother 2017 72 3443 3452 10.1093/jac/dkx315 28961801 · DOI
  23. [27]Gutierrez-Gutierrez B Salamanca E de Cueto M Hsueh PR Viale P Pano-Pardo JR Effect of appropriate combination therapy on mortality of patients with bloodstream infections due to carbapenemase-producing Enterobacteriaceae (INCREMENT): a retrospective cohort study Lancet Infect Dis 2017 17 726 734 10.1016/S1473-3099(17)30228-1 28442293 · DOI
  24. [28]Andes DR Safdar N Baddley JW Playford G Reboli AC Rex JH Impact of treatment strategy on outcomes in patients with candidemia and other forms of invasive candidiasis: a patient-level quantitative review of randomized trials Clin Infect Dis 2012 5 1110 1122 10.1093/cid/cis021 22412055 · DOI · Paper page
  25. [30]Garey KW Rege M Pai MP Mingo DE Suda KJ Turpin RS Time to initiation of fluconazole therapy impacts mortality in patients with candidemia: a multi-institutional study Clin Infect Dis 2006 43 25 31 10.1086/504810 16758414 · DOI · Paper page
  26. [31]Bassetti M Vena A Russo A Management of patients with septic shock due to Candida infection Hosp Pract 2018 46 258 265 10.1080/21548331.2018.1518104 30170000 · DOI
  27. [32]Bassetti M Righi E Ansaldi F Merelli M Scarparo C Antonelli M A multicenter multinational study of abdominal candidiasis: epidemiology, outcomes and predictors of mortality Intensive Care Med 2015 41 1601 1610 10.1007/s00134-015-3866-2 26077063 · DOI
  28. [33]Bassetti M Righi E Ansaldi F Merelli M Trucchi C De Pascale G A multicenter study of septic shock due to candidemia: outcomes and predictors of mortality Intensive Care Med 2014 40 839 845 10.1007/s00134-014-3310-z 24807083 · DOI · Paper page
  29. [34]Grim SA Berger K Teng C Gupta S Layden JE Janda WM Timing of susceptibility-based antifungal drug administration in patients with Candida bloodstream infection: correlation with outcomes J Antimicrob Chemother 2012 67 707 714 10.1093/jac/dkr511 22184469 · DOI
  30. [35]Pappas PG Kauffman CA Andes DR Clancy CJ Marr KA Ostrosky-Zeichner L Executive summary: clinical practice guideline for the management of Candidiasis: 2016 update by the infectious diseases Society of America Clin Infect Dis 2016 62 409 417 10.1093/cid/civ1194 26810419 · DOI · Paper page
  31. [37]Martin-Loeches I Antonelli M Cuenca-Estrella M Dimopoulos G Einav S De Waele J ESICM/ESCMID task force on practical management of invasive candidiasis in critically ill patients Intensive Care Med 2019 45 789 805 10.1007/s00134-019-05599-w 30911804 · DOI
  32. [38]Martinez-Jimenez MC Munoz P Valerio M Vena A Guinea J Bouza E Combination of Candida biomarkers in patients receiving empirical antifungal therapy in a Spanish tertiary hospital: a potential role in reducing the duration of treatment J Antimicrob Chemother 2015 70 3107 3115 10.1093/jac/dkv241 26311840 · DOI
  33. [39]Munoz P Vena A Machado M Gioia F Martinez-Jimenez MC Gomez E T2Candida MR as a predictor of outcome in patients with suspected invasive candidiasis starting empirical antifungal treatment: a prospective pilot study J Antimicrob Chemother 2018 73 6 12 10.1093/jac/dky047 29608751 · DOI
  34. [40]Bassetti M Peghin M Vena A Challenges and solution of invasive aspergillosis in non-neutropenic patients: a review Infect Dis Ther 2018 7 17 27 10.1007/s40121-017-0183-9 29273978 PMC5840102 · DOI
  35. [41]Teshome BF Vouri SM Hampton N Kollef MH Micek ST Duration of exposure to antipseudomonal β-lactam antibiotics in the critically ill and development of new resistance Pharmacotherapy 2019 39 261 270 10.1002/phar.2201 30506852 PMC6507412 · DOI · Paper page
  36. [42]Tabah A Bassetti M Kollef MH Zahar J-R Paiva J-A Timsit J-F Antimicrobial de-escalation in critically ill patients: a position statement from a task force of the European Society of Intensive Care Medicine (ESICM) and European Society of Clinical Microbiology and Infectious Diseases (ESCMID) Critically Ill Patients Study Group (ESGCIP) Intensive Care Med 2020 46 245 265 10.1007/s00134-019-05866-w 31781835 · DOI · Paper page
  37. [43]Tabah A Cotta MO Garnacho-Montero J Schouten J Roberts JA Lipman J A systematic review of the definitions, determinants, and clinical outcomes of antimicrobial de-escalation in the intensive care unit Clin Infect Dis 2016 62 1009 1017 10.1093/cid/civ1199 26703860 · DOI · Paper page
  38. [45]Garnacho-Montero J Díaz-Martín A Cantón-Bulnes L Ramírez P Sierra R Arias-Verdú D Initial antifungal strategy reduces mortality in critically ill patients with candidemia: a propensity score-adjusted analysis of a multicenter study Crit Care Med 2018 46 384 393 10.1097/CCM.0000000000002867 29189345 · DOI
  39. [46]De Bus L Depuydt P Steen J Dhaese S De Smet K Tabah A Antimicrobial de-escalation in the critically ill patient and assessment of clinical cure: the DIANA study Intensive Care Med 2020 46 1404 1417 10.1007/s00134-020-06111-5 32519003 PMC7334278 · DOI · Paper page
  40. [47]Heenen S Jacobs F Vincent J-L Antibiotic strategies in severe nosocomial sepsis: why do we not de-escalate more often? Crit Care Med 2012 40 1404 1409 10.1097/CCM.0b013e3182416ecf 22430235 · DOI
  41. [48]Garnacho-Montero J Gutiérrez-Pizarraya A Escoresca-Ortega A Corcia-Palomo Y Fernández-Delgado E Herrera-Melero I De-escalation of empirical therapy is associated with lower mortality in patients with severe sepsis and septic shock Intensive Care Med 2014 40 32 40 10.1007/s00134-013-3077-7 24026297 · DOI
  42. [49]Lew KY Ng TM Tan M Tan SH Lew EL Ling LM Safety and clinical outcomes of carbapenem de-escalation as part of an antimicrobial stewardship programme in an ESBL-endemic setting J Antimicrob Chemother 2015 70 1219 1225 25473028
  43. [50]Gonzalez L Cravoisy A Barraud D Conrad M Nace L Lemarié J Factors influencing the implementation of antibiotic de-escalation and impact of this strategy in critically ill patients Crit Care 2013 17 R140 10.1186/cc12819 23849321 PMC4055984 · DOI
  44. [51]Montravers P Augustin P Grall N Desmard M Allou N Marmuse J-P Characteristics and outcomes of anti-infective de-escalation during health care-associated intra-abdominal infections Crit Care 2016 20 83 10.1186/s13054-016-1267-8 27052675 PMC4823898 · DOI
  45. [52]De Bus L Denys W Catteeuw J Gadeyne B Vermeulen K Boelens J Impact of de-escalation of beta-lactam antibiotics on the emergence of antibiotic resistance in ICU patients: a retrospective observational study Intensive Care Med 2016 42 1029 1039 10.1007/s00134-016-4301-z 27025939 · DOI
  46. [53]Roberts JA Paul SK Akova M Bassetti M De Waele JJ Dimopoulos G DALI: defining antibiotic levels in intensive care unit patients: are current β-lactam antibiotic doses sufficient for critically ill patients? Clin Infect Dis 2014 58 1072 1083 10.1093/cid/ciu027 24429437 · DOI · Paper page
  47. [55]Cook AM Hatton-Kolpek J Augmented renal clearance Pharmacotherapy 2019 39 346 354 10.1002/phar.2231 30723936 · DOI
  48. [56]Carlier M Carrette S Roberts JA Stove V Verstraete A Hoste E Meropenem and piperacillin/tazobactam prescribing in critically ill patients: does augmented renal clearance affect pharmacokinetic/pharmacodynamic target attainment when extended infusions are used? Crit Care 2013 17 R84 10.1186/cc12705 23642005 PMC4056350 · DOI
  49. [57]Baptista JP Sousa E Martins PJ Augmented renal clearance in septic patients and implications for vancomycin optimisation Int J Antimicrob Agents 2012 39 420 423 10.1016/j.ijantimicag.2011.12.011 22386742 · DOI
  50. [58]Claus BO Hoste EA Colpaert K Robays H Decruyenaere J De Waele JJ Augmented renal clearance is a common finding with worse clinical outcome in critically ill patients receiving antimicrobial therapy J Crit Care 2013 28 695 700 10.1016/j.jcrc.2013.03.003 23683557 · DOI
  51. [59]Udy AA Dulhunty JM Roberts JA Davis JS Webb SAR Bellomo R Association between augmented renal clearance and clinical outcomes in patients receiving beta-lactam antibiotic therapy by continuous or intermittent infusion: a nested cohort study of the BLING-II randomised, placebo-controlled trial Int J Antimicrob Agents 2017 49 624 630 10.1016/j.ijantimicag.2016.12.022 28286115 · DOI
  52. [60]Burnham JP Micek ST Kollef MH Augmented renal clearance is not a risk factor for mortality in Enterobacteriaceae bloodstream infection treated with appropriate empiric antimicrobials PLoS ONE 2017 12 e0180247 10.1371/journal.pone.0180247 28678812 PMC5497982 · DOI
  53. [61]Kollef MH Chastre J Clavel M Restrepo MI Michiels B Kaniga K A randomized trial of 7-day doripenem versus 10-day imipenem-cilastatin for ventilator-associated pneumonia Crit Care 2012 16 R218 10.1186/cc11862 23148736 PMC3672596 · DOI
  54. [62]Roberts JA Webb S Paterson D Ho KM Lipman J A systematic review on clinical benefits of continuous administration of beta-lactam antibiotics Crit Care Med 2009 37 2071 2078 10.1097/CCM.0b013e3181a0054d 19384201 · DOI
  55. [63]Kasiakou SK Sermaides GJ Michalopoulos A Soteriades ES Falagas ME Continuous versus intermittent intravenous administration of antibiotics: a meta-analysis of randomised controlled trials Lancet Infect Dis 2005 5 581 589 10.1016/S1473-3099(05)70218-8 16122681 · DOI
  56. [64]Chant C Leung A Friedrich JO Optimal dosing of antibiotics in critically ill patients by using continuous/extended infusions: a systematic review and meta-analysis Crit Care 2013 17 R279 10.1186/cc13134 24289230 PMC4056781 · DOI
  57. [65]Falagas ME Tansarli GS Ikawa K Vardakas KZ Clinical outcomes with extended or continuous versus short-term intravenous infusion of carbapenems and piperacillin/tazobactam: a systematic review and meta-analysis Clin Infect Dis 2013 56 272 282 10.1093/cid/cis857 23074314 · DOI
  58. [67]Dulhunty JM Roberts JA Davis JS A multicenter randomized trial of continuous versus intermittent β-lactam infusion in severe sepsis Am J Respir Crit Care Med 2015 192 1298 1305 10.1164/rccm.201505-0857OC 26200166 · DOI
  59. [68]Sawyer AM Deal EN Labelle AJ Witt C Thiel SW Heard H Implementation of a real-time computerized sepsis alert in nonintensive care unit patients Crit Care Med 2011 39 469 473 10.1097/CCM.0b013e318205df85 21169824 · DOI · Paper page
  60. [69]Henry KE Hager DN Pronovost PJ Saria S A targeted real-time early warning score (TREWScore) for septic shock [Internet] Sci Transl Med. 2015 7 299ra122 10.1126/scitranslmed.aab3719 26246167 · DOI · Paper page
  61. [71]Reyna MA Josef CS Jeter R Shashikumar SP Westover MB Nemati S Early prediction of sepsis from clinical data: the PhysioNet/computing in cardiology challenge 2019 Crit Care Med 2020 48 210 217 10.1097/CCM.0000000000004145 31939789 PMC6964870 · DOI · Paper page
  62. [73]Mao Q Jay M Hoffman JL Calvert J Barton C Simabukuro D Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU BMJ Open 2018 8 e017833 10.1136/bmjopen-2017-017833 29374661 PMC5829820 · DOI · Paper page
  63. [74]Shivakumar N, Betthauser K, Gupta A, Lai A, Kollef MH, Payne P, et al. Comparison of Early warning scores for sepsis early identification and prediction in the general ward setting. JAMIA. 2021 (Epub ahead of print). PMC8607822 34820600
  64. [75]Shimabukuro DW Barton CW Feldman MD Mataraso SJ Das R Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial BMJ Open Respir Res 2017 4 e000234 10.1136/bmjresp-2017-000234 29435343 PMC5687546 · DOI · Paper page
  65. [76]Giannini HM Ginestra JC Chivers C Draugelis M Hanish A Schweickert WD A machine learning algorithm to predict severe sepsis and septic shock: development, implementation, and impact on clinical practice Crit Care Med 2019 47 1485 1492 10.1097/CCM.0000000000003891 31389839 PMC8635476 · DOI · Paper page
  66. [77]McGuire RJ Yu SC Payne PRO Lai AM Vazquez-Guillamet MC A pragmatic machine learning model to predict carbapenem resistance Antimicrob Agents Ch 2021 65 e00063 e121 10.1128/AAC.00063-21 PMC8218615 33972243 · DOI
  67. [78]Komorowski M Celi LA Badawi O Gordon AC Faisal AA The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care Nat Med 2018 24 1716 1720 10.1038/s41591-018-0213-5 30349085 · DOI · Paper page
  68. [79]Yu SC Betthauser KD Gupta A Lyons PG Lai AM Kollef MH Comparison of sepsis definitions as automated criteria Crit Care Med 2021 49 e433 e443 10.1097/CCM.0000000000004875 33591014 · DOI · Paper page
  69. [80]Petersen C Smith J Freimuth RR Goodman KW Jackson GP Kannry J Recommendations for the safe, effective use of adaptive CDS in the US healthcare system: an AMIA position paper J Am Med Inform Assn 2021 28 677 684 10.1093/jamia/ocaa319 PMC7973467 33447854 · DOI
  70. [81]Saria S Henry KE Too many definitions of sepsis Crit Care Med 2020 48 137 141 10.1097/CCM.0000000000004144 31939780 · DOI
  71. [82]Teshome BF Vouri SM Hampton NB Kollef MH Micek ST Evaluation of a ceiling effect on the association of new resistance development to antipseudomonal beta-lactam exposure in the critically ill Infect Control Hosp Epidemiol 2020 41 484 485 10.1017/ice.2019.381 31983358 · DOI

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Authors
Kollef, M. H., Shorr, A. F., Bassetti, M., Timsit, J.-F., Micek, S. T., Michelson, A. P., & Garnacho-Montero, J.
Affiliations
  • Washington University in St. Louis (US)
  • MedStar Washington Hospital Center (US)
  • Ospedale Policlinico San Martino (IT)
  • University of Health Sciences and Pharmacy (US)
Corresponding author
Kollef, Marin H.
Venue
Critical Care
Published
2021-10-15
Volume
25
Issue
1
Pages
360-360
ISSN
1364-8535 (ISSN-L)
OpenAlex
W3207639082
Cited by
161
References
9
Citations by year
2026: 30 · 2025: 42 · 2024: 40 · 2023: 33 · 2022: 15 · 2018: 1
FWCI
2.82
Type
journal article
Language
en
Open access
Locations
  • Critical Care (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Digital Commons@Becker (Washington University School of Medicine) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Pharmacology › Antibiotics Pharmacokinetics and Efficacy
  • Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
  • Life Sciences › Immunology and Microbiology › Applied Microbiology and Biotechnology › Antibiotic Use and Resistance
Keywords
timing of antibiotic administration, intensive care unit infections, sepsis, septic shock, empirical antibiotic therapy, antibiotic de-escalation, antibiotic resistance, antimicrobial exposure, critically ill patients, mortality
MeSH terms
Anti-Bacterial Agents, Humans, Intensive Care Units, Time Factors

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