Skip to main content

Advertisement

SpringerLink
Log in

Derivation of a quick Pitt bacteremia score to predict mortality in patients with Gram-negative bloodstream infection

  • Original Paper
  • Published:
Infection Aims and scope Submit manuscript

Abstract

Purpose

This retrospective cohort study derived a “quick” version of the Pitt bacteremia score (qPitt) using binary variables in patients with Gram-negative bloodstream infections (BSI). The qPitt discrimination was then compared to quick sepsis-related organ failure assessment (qSOFA) and systemic inflammatory response syndrome (SIRS).

Methods

Hospitalized adults with Gram-negative BSI at Palmetto Health hospitals in Columbia, SC, USA from 2010 to 2013 were identified. Multivariate Cox proportional hazards regression was used to determine variables associated with 14-day mortality.

Results

Among 832 patients with Gram-negative BSI, median age was 65 years and 449 (54%) were women. After adjustments for age and Charleston comorbidity score, all five components of qPitt were independently associated with mortality: temperature < 36 °C [hazard ratio (HR) 3.02, 95% confidence interval (CI) 1.95–4.62], systolic blood pressure < 90 mmHg or vasopressor use (HR 2.40, 95% CI 1.37–4.13), respiratory rate ≥ 25/min or mechanical ventilation (HR 3.01, 95% CI 1.81–5.14), cardiac arrest (HR 5.35, 95% CI 2.81–9.43), and altered mental status (HR 3.99, 95% CI 2.44–6.80). The qPitt had higher discrimination to predict mortality [area under receiver operating characteristic curve (AUROC) 0.85] than both qSOFA (AUROC 0.77, p < 0.001) and SIRS (AUROC 0.63, p < 0.001). There was a significant difference in mortality between appropriate and inappropriate empirical antimicrobial therapy in patients with qPitt ≥ 2 (24% vs. 49%, p < 0.001), but not in those with qPitt < 2 (3% vs. 5%, p = 0.36).

Conclusions

The qPitt had good discrimination in predicting mortality following Gram-negative BSI and identifying opportunities for improved survival with appropriate empirical antimicrobial therapy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Goto M, Al-Hasan MN. Overall burden of bloodstream infection and nosocomial bloodstream infection in North America and Europe. Clin Microbiol Infect. 2013;19:501–9.

    Article  CAS  PubMed  Google Scholar 

  2. Uslan DZ, Crane SJ, Steckelberg JM, et al. Age-and sex-associated trends in bloodstream infection. Arch Intern Med. 2007;167:834–9.

    Article  PubMed  Google Scholar 

  3. McGregor JC, Rich SE, Harris AD, et al. A systematic review of the methods used to assess the association between appropriate antibiotic therapy and mortality in bacteremic patients. Clin Infect Dis. 2007;45:329–37.

    Article  PubMed  Google Scholar 

  4. Al-Hasan MN, Lahr BD, Eckel-Passow JE, Baddour LM. Predictive scoring model of mortality in gram-negative bloodstream infection. Clin Microbiol Infect. 2013;19:948–54.

    Article  CAS  PubMed  Google Scholar 

  5. Al-Hasan MN, Juhn YJ, Bang DW, et al. External validation of Gram-negative bloodstream infection mortality risk score in a population-based cohort. Clin Microbiol Infect. 2014;20:886–91.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  6. Retamar P, Portillo MM, López-Prieto MD, et al. Impact of inadequate empirical therapy on the mortality of patients with bloodstream infections: a propensity score-based analysis. Antimicrob Agents Chemother. 2012;56:472–8.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  7. Cain SE, Kohn J, Bookstaver PB, et al. Stratification of the impact of inappropriate empirical antimicrobial therapy for gram-negative bloodstream infections by predicted prognosis. Antimicrob Agents Chemother. 2015;59:245–50.

    Article  CAS  PubMed  Google Scholar 

  8. Korvick JA, Bryan CS, Farber B, et al. Prospective observational study of Klebsiella bacteremia in 230 patients: outcome for antibiotic combinations versus monotherapy. Antimicrob Agents Chemother. 1992;36:2639–44.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  9. Paterson DL, Ko WC, Von Gottberg A, et al. International prospective study of Klebsiella pneumoniae bacteremia: implications of extended-spectrum beta-lactamase production in nosocomial Infections. Ann Intern Med. 2004;140:26–32.

    Article  PubMed  Google Scholar 

  10. Rhee JY, Kwon KT, Ki HK, et al. Scoring systems for prediction of mortality in patients with intensive care unit-acquired sepsis: a comparison of the Pitt bacteremia score and the Acute Physiology and Chronic Health Evaluation II scoring systems. Shock. 2009;31:146–50.

    Article  PubMed  Google Scholar 

  11. Seymour CW, Liu VX, Iwashyna TJ, et al. Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA. 2016;315:762–74.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Singer M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA. 2016;315:801–10.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. Sundén-Cullberg J, Rylance R, Svefors J, Norrby-Teglund A, Björk J, Inghammar M. Fever in the emergency department predicts survival of patients with severe sepsis and septic shock admitted to the ICU. Crit Care Med. 2017;45:591–9.

    Article  PubMed  Google Scholar 

  14. Laupland KB, Church DL. Population-based epidemiology and microbiology of community-onset bloodstream infections. Clin Microbiol Rev. 2014;27:647–64.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  15. Horan TC, Andrus M, Dudeck MA. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control. 2008;36:309–32.

    Article  PubMed  Google Scholar 

  16. Friedman ND, Kaye KS, Stout JE, et al. Health care-associated bloodstream infections in adults: a reason to change the accepted definition of community-acquired infections. Ann Intern Med. 2002;137:791–7.

    Article  PubMed  Google Scholar 

  17. Bone RC, Balk RA, Cerra FB, et al. American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference: definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Crit Care Med 1992;20:864–874.

    Article  Google Scholar 

  18. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36.

    Article  CAS  Google Scholar 

  19. Raith EP, Udy AA, Bailey M, et al. Prognostic accuracy of the SOFA score, SIRS criteria, and qSOFA score for in-hospital mortality among adults with suspected infection admitted to the intensive care unit. JAMA. 2017;317:290–300.

    Article  PubMed  Google Scholar 

  20. Freund Y, Lemachatti N, Krastinova E, et al. Prognostic accuracy of sepsis-3 criteria for in-hospital mortality among patients with suspected infection presenting to the emergency department. JAMA. 2017;317:301–8.

    Article  PubMed  Google Scholar 

  21. Moskowitz A, Patel PV, Grossestreuer AV, et al. Quick sequential organ failure assessment and systemic inflammatory response syndrome criteria as predictors of critical care intervention among patients with suspected infection. Crit Care Med. 2017;45:1813–9.

    Article  PubMed Central  PubMed  Google Scholar 

  22. Finkelsztein EJ, Jones DS, Ma KC, et al. Comparison of qSOFA and SIRS for predicting adverse outcomes of patients with suspicion of sepsis outside the intensive care unit. Crit Care. 2017;21:73.

    Article  PubMed Central  PubMed  Google Scholar 

  23. Fernando SM, Tran A, Taljaard M, et al. Prognostic accuracy of the quick sequential organ failure assessment for mortality in patients with suspected infection: a systematic review and meta-analysis. Ann Intern Med. 2018;168:266–75.

    Article  PubMed  Google Scholar 

  24. Maitra S, Som A, Bhattacharjee S. Accuracy of quick sequential organ failure assessment (qSOFA) score and systemic inflammatory response syndrome (SIRS) criteria for predicting mortality in hospitalized patients with suspected infection: a meta-analysis of observational studies. Clin Microbiol Infect. 2018;24:1123–9.

    Article  CAS  PubMed  Google Scholar 

  25. Gransden WR, Leibovici L, Eykyn SJ, et al. Risk factors and a clinical index for diagnosis of Pseudomonas aeruginosa bacteremia. Clin Microbiol Infect. 1995;1:119–23.

    Article  PubMed  Google Scholar 

  26. Hammer KL, Justo JA, Bookstaver PB, Kohn J, Albrecht H, Al-Hasan MN. Differential effect of prior β-lactams and fluoroquinolones on risk of bloodstream infections secondary to Pseudomonas aeruginosa. Diagn Microbiol Infect Dis. 2017;87:87–91.

    Article  CAS  PubMed  Google Scholar 

  27. Augustine MR, Testerman TL, Justo JA, et al. Clinical risk score for prediction of extended-spectrum beta-lactamase producing Enterobacteriaceae in bloodstream isolates. Infect Control Hosp Epidemiol. 2017;38:266–72.

    Article  PubMed  Google Scholar 

  28. Battle SE, Kohn J, Bookstaver B, Albrecht H, Al-Hasan MN. Association between inappropriate empirical antimicrobial therapy and hospital length of stay in gram-negative bloodstream infections: stratification by prognosis. J Antimicrob Chemother. 2017;72:299–304.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank Prisma Health Antimicrobial Stewardship and Support Team in South Carolina, USA for their help in facilitating the conduct of this study. SEB, MRA and MNA have full access to all of the data in the study and take responsibility for the integrity of the data and accuracy of the analysis. The preliminary results of this study were presented in part at IDWeek annual meeting on October 7, 2017 in San Diego, CA, USA.

Funding

The study received internal funding from the Grant-in-Aid, Palmetto Health Richland Research and Education Foundation, Columbia, SC, USA. There were no other sources of funding for this study.

Author information

Authors and Affiliations

  1. Department of Medicine, Palmetto Health University of South Carolina Medical Group, Columbia, SC, USA

    Sarah E. Battle, William B. Owens & Majdi N. Al-Hasan

  2. University of South Carolina School of Medicine, 2 Medical Park, Suite 502, Columbia, SC, 29203, USA

    Matthew R. Augustine, William B. Owens & Majdi N. Al-Hasan

  3. Department of Acute Care Surgery, Prisma Health Richland Hospital, Columbia, SC, USA

    Christopher M. Watson

  4. Department of Clinical Pharmacy and Outcomes Sciences, University of South Carolina College of Pharmacy, Columbia, SC, USA

    P. Brandon Bookstaver

  5. Department of Pharmacy, Prisma Health Richland Hospital, Columbia, SC, USA

    P. Brandon Bookstaver & Joseph Kohn

  6. Department of Medicine, Division of Infectious Diseases, Mayo Clinic, Rochester, MN, USA

    Larry M. Baddour

Authors
  1. Sarah E. Battle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthew R. Augustine
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christopher M. Watson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. P. Brandon Bookstaver
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Joseph Kohn
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. William B. Owens
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Larry M. Baddour
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Majdi N. Al-Hasan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Majdi N. Al-Hasan.

Ethics declarations

Conflict of interest

MNA: Continuing medical education steering committee, Rockpointe Corporation. PBB: Advisory board member, CutisPharma; Speaker’s Bureau, Melinta Therapeutics; speaker and continuing medical education steering committee, Rockpointe Corporation. SEB, MRA, CMW, WO, JK, LMB: no conflicts.

Electronic supplementary material

Below is the link to the electronic supplementary material.

15010_2019_1277_MOESM1_ESM.tif

Supplementary material 1 Supplemental Figure 1: Calibration plot of qPitt model. The observed frequency of 14-day mortality plotted by deciles of predicted probability from the qPitt model (black dots). Perfect calibration is represented by the grey Y=X line (TIF 37 KB)

15010_2019_1277_MOESM2_ESM.tif

Supplementary material 2 Supplemental Figure 2: Receiver operating characteristic plot of qPitt model. Black line indicates receiver operating characteristic curve. Light color tangent line highlights the point in the curve that represents the best performance of the model. Area under receiver operating characteristic curve= 0.85 (TIF 48 KB)

15010_2019_1277_MOESM3_ESM.tif

Supplementary material 3 Supplemental Figure 3: Kaplan–Meier survival curves of patients with bloodstream infection and qPitt < 2 by appropriateness of empirical antimicrobial therapy (TIF 44 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Battle, S.E., Augustine, M.R., Watson, C.M. et al. Derivation of a quick Pitt bacteremia score to predict mortality in patients with Gram-negative bloodstream infection. Infection 47, 571–578 (2019). https://doi.org/10.1007/s15010-019-01277-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s15010-019-01277-7

Keywords

Search

Navigation