Skip to main content

Evaluation of the INCREMENT-CPE, Pitt Bacteremia and qPitt Scores in Patients with Carbapenem-Resistant Enterobacteriaceae Infections Treated with Ceftazidime–Avibactam

Abstract

Background

The aim of this study was to evaluate the predictive performance of the INCREMENT-CPE (ICS), Pitt bacteremia score (PBS) and qPitt for mortality among patients treated with ceftazidime–avibactam for carbapenem-resistant Enterobacteriaceae (CRE) infections.

Methods

Retrospective, multicenter, cohort study of patients with CRE infections treated with ceftazidime–avibactam between 2015 and 2019. The primary outcome was 30-day all-cause mortality. Predictive performance was determined by assessing discrimination, calibration and precision.

Results

In total, 109 patients were included. Thirty-day mortality occurred in 18 (16.5%) patients. There were no significant differences in discrimination of the three scores [area under the curve (AUC) ICS 0.7039, 95% CI 0.5848–0.8230, PBS 0.6893, 95% CI 0.5709–0.8076, and qPitt 0.6847, 95% CI 0.5671–0.8023; P > 0.05 all pairwise comparisons]. All scores showed adequate calibration and precision. When dichotomized at the optimal cut-points of 11, 3, and 2 for the ICS, PBS, and qPitt, respectively, all scores had NPV > 90% at the expense of low PPV. Patients in the high-risk groups had a relative risk for mortality of 3.184 (95% CI 1.35–8.930), 3.068 (95% CI 1.094–8.606), and 2.850 (95% CI 1.016–7.994) for the dichotomized ICS, PBS, and qPitt, scores respectively. Treatment-related variables (early active antibiotic therapy, combination antibiotics and renal ceftazidime–avibactam dose adjustment) were not associated with mortality after controlling for the risk scores.

Conclusions

In patients treated with ceftazidime–avibactam for CRE infections, mortality risk scores demonstrated variable performance. Modifications to scoring systems to more accurately predict outcomes in the era of novel antibiotics are warranted.

FormalPara Key Summary Points
The INCREMENT CPE, Pitt Bacteremia and qPitt scores have recently been validated in patients with bacteremic and non-bacteremic carbapenem-resistant Enterobacteriaceae (CRE) infections.
However, these studies included no or few patients treated with newer anti-CRE antibiotics.
In patients treated with ceftazidime–avibactam for CRE infections, the mortality risk scores demonstrated variable performance.
Modifications to scoring systems to more accurately predict outcomes in the era of novel antibiotics is warranted.

Introduction

Enterobacteriaceae are among the most frequent cause of bacterial infections in patients of all ages in both community and inpatient settings [1]. The remarkable ability of these organisms to acquire a growing array of mechanisms to evade the activity of broad-spectrum antibiotics therefore presents a growing challenge. In particular, the emergence and spread of carbapenem-hydrolyzing beta-lactamases (carbapenemases) limits our ability to treat many life-threatening infections [2]. Due to the well-rehearsed challenges of conducting randomized controlled trials (RCTs) in patients with carbapenem-resistant Enterobacteriaceae (CRE) infections, current management strategies are largely informed by observational data and expert opinion [3]. Observational studies can provide valuable insight into the real-world effectiveness of treatment alternatives in circumstances where execution of RCTs is not feasible. However, because treatment is not randomly assigned, confounding by indication may lead to a biased estimate of the treatment effect. Covariate adjustment is especially important in CRE studies due to the wide spectrum of disease severity and underlying health status of the patients who acquire these infections, which clearly influences both the management approach and outcomes. Risk scores are useful for covariate adjustment in these circumstances, and they can also facilitate comparisons of populations across different studies. Furthermore, some risk scores have been used clinically to define high-risk subgroups for whom more intensive management strategies may be targeted [4, 5]. A number of risk scores have recently been developed and/or validated specifically in patients with CRE infections [4, 6, 7]. The INCREMENT-CPE score (ICS) was developed to predict 14-day mortality in patients with carbapenemase-producing Enterobacteriaceae (CPE) bacteremia [4]. It was subsequently modified and validated for 30-day mortality in patients with bacteremic and non-bacteremic CPE infections [6]. The Pitt bacteremia score (PBS) and an abbreviated version of the PBS, the qPitt, were also recently validated in a large cohort of patients with bacteremic and non-bacteremic CRE infections [7]. Notably, these analyses included no [4, 6] or few [7] patients treated with newer antibiotics with activity against CRE. Their performance in the era of more effective and safer antibiotics [8, 9] is unclear. Therefore, the objective of this study was to evaluate the predictive performance of the ICS, PBS, and qPitt for mortality among patients with CRE infections treated with ceftazidime–avibactam.

Methods

Study Design and Population

This was a secondary analysis of a multicenter, retrospective, observational, cohort study conducted at six geographically diverse academic and community medical centers in the U.S. between May 2015 and February 2019 [10]. Approval was obtained from each participating center’s Institutional Review Board (IRB) with a waiver for informed consent (Supplementary Appendix 1). Wayne State University served as the master IRB. Pharmacy records were screened for all patients who received ceftazidime–avibactam between January 2015 and February 2019. Inclusion criteria were: (1) age ≥ 18 years, (2) receipt of ceftazidime–avibactam for ≥ 72 h, and (3) CRE infection [10].

Data Collection and Study Definitions

Relevant demographic, clinical, microbiological, and treatment data were extracted from the electronic medical record (EMR) by study investigators at each center and entered into a secure online data collection form [11]. Bacterial identification and antibiotic susceptibilities were performed by local laboratories according to standard procedures. CRE was defined by current US Centers for Disease Control and Prevention criteria [7]. Ceftazidime–avibactam susceptibility was determined using disk diffusion or gradient strips, where available. Hospital-acquired CRE infection was defined as the first CRE-positive culture collected ≥ 48 h after admission. Sources of infection were based on physician notes and available clinical, microbiological, and diagnostic data. Where available, resistance markers were identified by VERIGENE BC-GN (Luminex, Austin, TX, USA). Infection onset was defined as the day the index CRE culture was collected. Early active therapy was receipt of at least one in vitro active antibiotic within 48 h of infection onset. Early ceftazidime–avibactam therapy was defined as ceftazidime–avibactam initiated within 48 h of infection onset. Ceftazidime–avibactam combination therapy was the receipt of a concomitant antibiotic with Gram-negative activity for ≥ 48 h.

The primary outcome was 30-day all-cause mortality, measured from infection onset. Data were collected for up to 30 days after discharge (i.e., from health system outpatient clinics, rehabilitation centers, emergency departments, and hospital re-admissions, where available), and patients discharged before day 30 were assumed to have survived if death was not documented during this follow-up. The ICS, PBS, and qPitt were calculated for each patient using the worst physiological values recorded within 24 h of infection onset. The variables included in the ICS were severe sepsis/septic shock [12], PBS ≥ 6, Charlson comorbidity score ≥ 2, and non-biliary or non-urinary tract source of infection [6]. Scores range from 0 to 15 and patients with an ICS < 8 and ≥ 8 have been considered to be at low and high risk for mortality, respectively [4, 6]. The PBS is based on five variables: temperature, blood pressure, mechanical ventilation, cardiac arrest, and mental status [13]. The maximum PBS score is 14, with scores ≥ 4 generally considered to indicate increased risk of death [7, 13]. qPitt includes the same five variables as the PBS, but temperature and mental status are dichotomized rather than graded [14]. The qPitt ranges from 0 to 5, with scores ≥ 2 indicating increased mortality risk in previous studies [7, 14]. No patient in our cohort experienced cardiac arrest around infection onset, and therefore this variable was not included in the scores.

Statistical Analyses

Descriptive statistics were used to characterize the cohort. Continuous variables were reported as medians (interquartile ranges, IQR) whereas categorical variables were expressed as counts and percentages. Unadjusted comparisons were performed using Chi squared, Fisher’s exact or Mann–Whitney U tests, as appropriate.

Scoring system performance was assessed by determining discrimination, calibration and precision. Discrimination in this setting refers to the ability to correctly classify those who died and those who survived. It was evaluated by calculating the area under to receiver operating characteristic curve (AUC). An AUC of 1.0 indicates perfect discrimination while a value 0.5 indicates no better than chance [15]. Although there are no universally agreed thresholds, values ≥ 0.90, ≥ 0.80 and ≥ 0.70 are generally considered to be excellent, good, and satisfactory, respectively [16, 17]. The non-parametric Delong–Delong test was used for pairwise AUC comparisons [18].

Calibration refers to agreement between observed and predicted mortality across deciles of risk, and was assessed using the Hosmer–Lemeshow goodness-of-fit test [19]. To account for the smaller sample size, and therefore reduced power to detect a lack of fit, a conservative P value < 0.10 was considered to indicate lack of fit [20].

Precision was measured by calculating the Brier score (mean squared difference between observed and predicted mortality). Brier scores can range from 0 for a perfect model to 0.25 for a non-informative model with an outcome incidence of 50% [16, 21].

The performance characteristics of each score as a binary classification tool were examined by calculating the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) at selected cut-points. The Youden Index J (J = sensitivity + specificity − 1 maximized) was chosen as the most appropriate summary measure. Modified Poisson regression analysis, using a robust error variance, was performed to estimate the relative risk (RR) and 95% confidence intervals for mortality at select cut-points.

The impact of early active antibiotic therapy, early ceftazidime–avibactam and combination therapy was evaluated by Poisson regression after adjustment for each score individually modeled as a continuous variable and as a categorical variable dichotomized at the score corresponding to the maximum J.

Analyses were performed using SAS 9.4 Statistical Software (SAS Institute, Cary, NC, USA) and SPSS, v.24 (IBM, Armonk, NY, USA). Unless otherwise stated, a two-tailed p < 0.05 was considered statistically significant.

Results

A total of 109 patients were included. The median age was 63 (53–74) years and 34.9% were admitted from a skilled nursing facility or long-term acute care hospital (Table 1). Over half (54.1%) of patients were in the intensive care unit (ICU) at infection onset and 16.5% were admitted during the remainder of their admission. The most common infection sources were respiratory (34.9%), intra-abdominal (21.1%) and urinary (20.2%). Nine (8.3%) patients had a positive CRE blood culture. A total of 113 CRE strains were isolated. Klebsiella pneumoniae was the most commonly identified pathogen, isolated in 71 (65.1%) patients followed by Escherichia coli in 16 (14.7%) and Enterobacter spp. in 12 (11.0%). Regarding antimicrobial susceptibility, among K. pneumoniae isolates tested for ceftazidime-avibactam susceptibility (n = 48), two were resistant, including one that carried New Delhi metallo-beta-lactamase and OXA enzymes.

Table 1 Baseline patient and infection characteristics stratified by 30-day mortality

Overall, 30-day mortality was 16.5%, and was highest in patients with primary bacteremia (42.9%) or pneumonia (23.7%) and lowest in patients with urinary tract infection (4.5%). Chronic pulmonary disease was significantly more prevalent among patients who died compared to those who survived (p = 0.004). In addition, patients who died were significantly more likely to reside in the ICU, require mechanical ventilation, or have severe sepsis/septic shock at infection onset (Table 1). There were no associations between 30-day mortality and early active antibiotic therapy, early ceftazidime–avibactam, nor the use of combination therapy in univariate analyses. Patients who died were significantly more likely to have their ceftazidime–avibactam dose adjusted for decreased renal function (72.2% vs. 42.9% in non-survivors vs. survivors, respectively). The median ICS, PBS, and qPitt were significantly higher in patients who died compared to those who survived (Table 1).

Discrimination for 30-day mortality for the ICS, PBS, and qPitt was 0.704, 0.689, and 0.685, respectively (Fig. 1; Table 2). No significant differences in discrimination were found for all pairwise comparisons (Supplementary Appendix 2). All models showed adequate calibration for 30-day mortality according to the Hosmer–Lemeshow goodness-of-fit test (Table 2). Precision, as measured by the Brier score, ranged from 0.128 for the ICS score to 0.132 for the qPitt score for 30-day mortality.

Fig. 1
figure1

Area under the curve (AUC) for 30-day mortality prediction

Table 2 Risk score discrimination, calibration, and precision for 30-day mortality

The performance characteristics of the ICS, PBS, and qPitt as binary classification tools for 30-day mortality at selected cut-points are summarized in Table 3. Using an ICS cutoff ≥ 11 to indicate high mortality risk provided the best performance with a sensitivity, specificity, PPV, NPV, PLR, and NLR of 72.2%, 60.4%, 26.5%, 91.7%, 1.82, and 0.46, respectively. ICS ≥ 11 was significantly associated with 30-day mortality (RR 3.184, 95% CI 1.35–8.930). The post-test probability of mortality increases from 16.5% to 26.5% and decreases from 16.5% to 8.2% among patients with ICS ≥ 11 and < 11, respectively. The optimal cutoff scores to indicate high 30-day mortality risk for the PBS and qPitt were ≥ 3 and ≥ 2, respectively. The RR of mortality was 3.068 (95% CI 1.094–8.606) and 2.850 (95% CI 1.016–7.994) for patients with PBS ≥ 3 and qPitt ≥ 2, respectively. RRs and 95% CIs for the individual components of each score are shown in Supplementary Appendix 3. Although the confidence intervals were wide, all components of the ICS had RRs > 2. With regard to the components of the PBS and qPitt, altered mental status and respiratory failure/mechanical ventilation consistently had RRs > 2, while the association between temperature and mortality was variable. Survival curves for the dichotomized ICS, PBS, and qPitt are shown in Fig. 2a–c. Early and sustained separation between curves was seen for all scores, and the log rank tests were significant for ICS ≥ 11 and PBS ≥ 3 (p = 0.0315 and p = 0.0242, respectively) but not qPitt ≥ 2 (p = 0.0521).

Table 3 Risk score performance characteristics for 30-day mortality at selected cut-points
Fig. 2
figure2

Survival curves for a INCREMENT-CPE ≥ 11 vs. < 11, b Pitt Bacteremia score ≥ 3 vs < 3 and c qPitt ≥ 2 vs. < 2. ICS log-rank p = 0.0315. PBS log rank p = 0.0242. qPitt log rank p = 0.0521. ICS INCREMENT-CPE score, PBS: Pitt Bacteremia score

No significant associations between 30-day mortality and early active, early ceftazidime–avibactam, combination ceftazidime–avibactam therapy nor ceftazidime–avibactam renal dose adjustment were found after controlling for each risk score (data not shown).

Discussion

We conducted a retrospective, multicenter study evaluating the predictive performance of the ICS, PBS, and qPitt for mortality in patients with CRE infections treated with ceftazidime–avibactam. Overall, the scoring systems demonstrated adequate calibration and precision in our cohort. According to conventional thresholds to categorize score discrimination [16, 17], the ICS demonstrated satisfactory discrimination while the PBS and qPitt had poor discrimination. However, differences between scores were very small and not statistically significant.

RCTs to help guide treatment selection for patients with CRE infections are scarce. Most available clinical studies are observational and retrospective with important limitations, including selection bias and inadequate control for confounding. Risk scores are useful for confounding adjustment when analyzing observational data. However, they may perform less well in external cohorts, and, although a model may be successful at one point in time, antimicrobial resistance patterns, pathogen virulence, and standards of care change with time and therefore models must be updated. Our findings do not fully align with those of Henderson et al., who evaluated the performance of the PBS and qPitt for 14-day mortality in 475 patients with non-bacteremic and bacteremic CRE infections from the CRACKLE-1 database [7]. In their evaluation, the discriminatory ability of the PBS and qPitt was considerably higher than in our cohort (0.853 and 0.851, respectively). With regard to the ICS, which was recently validated in 42 patients with non-bacteremic and bacteremic K. pneumoniae carbapenemase-producing K. pneumoniae infections, 30-day mortality was substantially higher than in our cohort (57.1% vs. 16.5%), but discrimination was similar (AUC 0.78 vs. 0.70). The optimal cut-point to identify patients at high risk for 30-day mortality was 11 in our cohort versus 8 in the validation study by Cano et al. [6].

Several factors may account for these discrepancies. First, there are important differences between the cohorts with regard to infection source (less bacteremia and more respiratory in our cohort) and pathogen species (more diverse in this study vs. primarily K. pneumoniae in the other studies). It is notable, however, that Henderson et al. found the performance of the PBS and qPitt to be similar when analyses were restricted to the subgroup of patients with non-bacteremic CRE infections [7]. Furthermore, an important property of the most useful scoring systems is that they perform similarly across different target populations. Second, patients who died within 72 h of infection onset were excluded from our study (patients had to receive ≥ 72 h of ceftazidime–avibactam), and variables such as severe sepsis/septic shock may best predict very early versus later deaths. However, observational studies evaluating antibiotic alternatives typically have inclusion criteria based on receipt of ≥ 48–72 h of the antibiotics of interest [9, 22, 23]. Prediction scores that discriminate for later deaths may be more suited for adjustment in these studies. The ICS was developed and validated specifically in patients with CPE infections. We did not confirm carbapenemase production, and a proportion of patients were likely infected with non-carbapenemase-producing CRE which have been shown to confer a lower risk of poor outcomes compared to CPE [24]. However, as was the case in our study, these data are not always available for observational analyses.

As noted previously, the validation studies for the ICS, PBS, and qPitt were conducted largely in the era before the introduction of newer antibiotics with activity against CRE [6, 7]. Two recent observational studies found improved survival in patients with CRE infection treated with ceftazidime–avibactam compared to historical controls treated with colistin-, aminoglycoside-, and carbapenem-based regimens [8, 9]. It is plausible that the use of ceftazidime–avibactam in all patients in our cohort may have partly contributed to the observed differences in score performance. However, it is important to remember that other changes have occurred in recent years that may have influenced the relationship between baseline variables and outcomes. Rapid genomic and phenotypic methods are now available to accelerate the identification of CRE [25]. A great deal of progress has also been made with regard to our understanding of key aspects of the complex pharmacokinetics of polymyxins enabling the design of dosing regimens more likely to achieve therapeutic concentrations [26]. Ten percent of patients in this study received a polymyxin with ceftazidime–avibactam. Furthermore, at most institutions, the use of new antibiotics must be approved by the antimicrobial stewardship team or infectious diseases consult service. All patients in our study were managed by the infectious diseases consult service. The value of specialist involvement in the management of multidrug-resistant infections is well established [27,28,29,30,31].

The PBS and qPitt scores are based on the important severity of illness-related variables, while the ICS also adds comorbidities and site of infection. None of the scores incorporate key treatment-related factors that could influence outcomes. We observed that mortality was significantly higher in patients who had their ceftazidime–avibactam dose renally adjusted. The association was no longer significant after adjustment based on the scores, raising the possibility that need for dose adjustment may be a surrogate for other prognostic variables. Renal impairment and the need for renal replacement therapy have been identified by other investigators as risk factors for poor outcomes in patients treated with ceftazidime–avibactam [32,33,34] Antibiotic blood and tissue concentrations as well as minimum inhibitory concentrations may impact patient response, and inclusion in prognostic modeling could improve predictive performance at the expense of model simplicity. Conversely, it may be worthwhile to revisit and revise protocols pertaining to ceftazidime–avibactam renal dose adjustment criteria [35].

Our study has several important limitations. First, the study is subject to inherent bias with its retrospective, observational design. However, in-depth EMR reviews allowed us to obtain detailed patient level data that may not be available in validation studies that obtain data from large healthcare administrative databases. Second, although our data spanned 4 years and we enrolled patients from six medical centers, the number of patients included was relatively small owing to the infrequency of CRE infections and slow incorporation of new antibiotics into clinical practice. Our event rate was also low, which may partly explain the variable performance of the models. We calculated the scores using the worst physiological measurements within 24 h of culture collection as a proxy for infection onset. However, for patients with community-onset infections as well as those with chronic relapsing infections, the time-frame used may not have reflected the true infection onset. We included only patients treated with ceftazidime–avibactam. Evaluations of scoring system performance in patients treated with other novel agents would be valuable.

Conclusion

There is an ongoing trend in infectious diseases toward algorithmic approaches for risk stratification and treatment. With respect to clinical use, the ICS, PBS, and qPitt all have the advantages of being based on readily available variables and being simple to calculate compared to the more time-intensive APACHE II or SOFA scores. However, none performed well enough in our cohort to be used for clinical decision-making in individual patients. Updating the scores to more accurately predict outcomes in the era of novel antibiotics, rapid diagnostics, and infectious diseases specialist involvement would be worthwhile, not only to improve their utility as tools in observational research but also so that clinicians can use them as supplementary information when making appropriate decisions about the management of patients with CRE infections.

References

  1. 1.

    Grundmann H, Livermore DM, Giske CG, et al. Carbapenem-non-susceptible Enterobacteriaceae in Europe: conclusions from a meeting of national experts. Euro Surveill. 2010. https://doi.org/10.2807/ese.15.46.19711-en.

    Article  PubMed  Google Scholar 

  2. 2.

    Trecarichi EM, Tumbarello M. Therapeutic options for carbapenem-resistant Enterobacteriaceae infections. Virulence. 2017;4:470–84.

    Article  Google Scholar 

  3. 3.

    Doi Y, Bonomo RA, Hooper DC, et al. Gram-negative bacterial infections: research priorities, accomplishments, and future directions of the antibacterial resistance leadership group. Clin Infect Dis. 2017;1:S30–5.

    Article  Google Scholar 

  4. 4.

    Gutierrez-Gutierrez B, Salamanca E, de Cueto M, et al. A predictive model of mortality in patients with bloodstream infections due to carbapenemase-producing Enterobacteriaceae. Mayo Clin Proc. 2016;10:1362–71.

    Article  Google Scholar 

  5. 5.

    Justo JA, Bookstaver PB, Kohn J, Albrecht H, Al-Hasan MN. Combination therapy vs. monotherapy for gram-negative bloodstream infection: matching by predicted prognosis. Int J Antimicrob Agents. 2018;3:488–92.

    Article  Google Scholar 

  6. 6.

    Cano A, Gutierrez-Gutierrez B, Machuca I, et al. Risks of infection and mortality among patients colonized with Klebsiella pneumoniae carbapenemase-producing K. pneumoniae: validation of scores and proposal for management. Clin Infect Dis. 2018;8:1204–10.

    Article  Google Scholar 

  7. 7.

    Henderson H, Luterbach CL, Cober E, et al. The Pitt bacteremia score predicts mortality in non-bacteremic infections. Clin Infect Dis. 2019. https://doi.org/10.1093/cid/ciz528.

    Article  PubMed  Google Scholar 

  8. 8.

    van Duin D, Lok JJ, Earley M, et al. Colistin versus ceftazidime–avibactam in the treatment of infections due to carbapenem-resistant Enterobacteriaceae. Clin Infect Dis. 2018;2:163–71.

    Article  Google Scholar 

  9. 9.

    Shields RK, Nguyen MH, Chen L, et al. Ceftazidime–avibactam is superior to other treatment regimens against carbapenem-resistant Klebsiella pneumoniae bacteremia. Antimicrob Agents Chemother. 2017. https://doi.org/10.1128/AAC.00883-17.

    Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Jorgensen SCJ, Trinh TD, Zasowski EJ, et al. Real-world experience with ceftazidime–avibactam for multidrug-resistant gram-negative bacterial infections. Open Forum Infect Dis. 2019;12:ofz522.

    Article  Google Scholar 

  11. 11.

    Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;2:377–81.

    Article  Google Scholar 

  12. 12.

    Singer M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA. 2016;8:801–10.

    Article  Google Scholar 

  13. 13.

    Chow JW, Yu VL. Combination antibiotic therapy versus monotherapy for gram-negative bacteraemia: a commentary. Int J Antimicrob Agents. 1999;1:7–12.

    Article  Google Scholar 

  14. 14.

    Battle SE, Augustine MR, Watson CM, et al. Derivation of a quick Pitt bacteremia score to predict mortality in patients with gram-negative bloodstream infection. Infection. 2019;47(4):571–8.

    Article  Google Scholar 

  15. 15.

    Hosmer DW Jr, Lemeshow S. Sturdivant RX (2013) Applied logistic regression. 3rd ed. New York: Wiley; 2013.

    Book  Google Scholar 

  16. 16.

    Raj R, Skrifvars M, Bendel S, et al. Predicting six-month mortality of patients with traumatic brain injury: usefulness of common intensive care severity scores. Crit Care. 2014;2:R60.

    Article  Google Scholar 

  17. 17.

    Sakoulas G, Rose W, Rybak MJ, et al. Evaluation of endocarditis caused by methicillin-susceptible Staphylococcus aureus developing nonsusceptibility to daptomycin. J Clin Microbiol. 2008;1:220–4.

    Article  Google Scholar 

  18. 18.

    DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;3:837–45.

    Article  Google Scholar 

  19. 19.

    Lemeshow S, Hosmer DW Jr. A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol. 1982;1:92–106.

    Article  Google Scholar 

  20. 20.

    Stevens V, Lodise TP, Tsuji B, et al. The utility of acute physiology and chronic health evaluation II scores for prediction of mortality among intensive care unit (ICU) and non-ICU patients with methicillin-resistant Staphylococcus aureus bacteremia. Infect Control Hosp Epidemiol. 2012;6:558–64.

    Article  Google Scholar 

  21. 21.

    Steyerberg EW, Harrell FE Jr, Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001;8:774–81.

    Article  Google Scholar 

  22. 22.

    Wang R, Cosgrove SE, Tschudin-Sutter S, et al. Cefepime therapy for cefepime-susceptible extended-spectrum beta-lactamase-producing Enterobacteriaceae bacteremia. Open Forum Infect Dis. 2016;3:ofw132.

    Article  Google Scholar 

  23. 23.

    Gutierrez-Gutierrez B, Perez-Galera S, Salamanca E, et al. A multinational, preregistered cohort study of beta-lactam/beta-lactamase inhibitor combinations for treatment of bloodstream infections due to extended-spectrum-beta-lactamase-producing Enterobacteriaceae. Antimicrob Agents Chemother. 2016;7:4159–69.

    Article  Google Scholar 

  24. 24.

    Tamma PD, Goodman KE, Harris AD, et al. Comparing the outcomes of patients with carbapenemase-producing and non-carbapenemase-producing carbapenem-resistant Enterobacteriaceae bacteremia. Clin Infect Dis. 2017;3:257–64.

    Article  Google Scholar 

  25. 25.

    Banerjee R, Humphries R. Clinical and laboratory considerations for the rapid detection of carbapenem-resistant Enterobacteriaceae. Virulence. 2017;4:427–39.

    Article  Google Scholar 

  26. 26.

    Tsuji BT, Pogue JM, Zavascki AP, et al. International Consensus Guidelines for the optimal use of the polymyxins: endorsed by the American College of Clinical Pharmacy (ACCP), European Society of Clinical Microbiology and Infectious Diseases (ESCMID), Infectious Diseases Society of America (IDSA), International Society for Anti-infective Pharmacology (ISAP), Society of Critical Care Medicine (SCCM), and Society of Infectious Diseases Pharmacists (SIDP). Pharmacotherapy. 2019;1:10–39.

    Article  Google Scholar 

  27. 27.

    Hamandi B, Husain S, Humar A, Papadimitropoulos EA. Impact of infectious disease consultation on the clinical and economic outcomes of solid organ transplant recipients admitted for infectious complications. Clin Infect Dis. 2014;8:1074–82.

    Article  Google Scholar 

  28. 28.

    Bai AD, Showler A, Burry L, et al. Impact of infectious disease consultation on quality of care, mortality, and length of stay in Staphylococcus aureus bacteremia: results from a large multicenter cohort study. Clin Infect Dis. 2015;10:1451–61.

    Article  Google Scholar 

  29. 29.

    Lee RA, Zurko JC, Camins BC, et al. Impact of infectious disease consultation on clinical management and mortality in patients with candidemia. Clin Infect Dis. 2019;9:1585–7.

    Article  Google Scholar 

  30. 30.

    Kim SH, Huh K, Cho SY, Kang CI, Chung DR, Peck KR. Factors associated with the recurrence of acute pyelonephritis caused by extended-spectrum beta-lactamase-producing Escherichia coli: the importance of infectious disease consultation. Diagn Microbiol Infect Dis. 2019;1:55–9.

    Article  Google Scholar 

  31. 31.

    Burnham JP, Olsen MA, Stwalley D, Kwon JH, Babcock HM, Kollef MH. Infectious diseases consultation reduces 30-day and 1-year all-cause mortality for multidrug-resistant organism infections. Open Forum Infect Dis. 2018;3:ofy026.

    Google Scholar 

  32. 32.

    Shields RK, Nguyen MH, Chen L, Press EG, Kreiswirth BN, Clancy CJ. Pneumonia and renal replacement therapy are risk factors for ceftazidime–avibactam treatment failures and resistance among patients with carbapenem-resistant Enterobacteriaceae infections. Antimicrob Agents Chemother. 2018;5.

  33. 33.

    Mazuski JE, Gasink LB, Armstrong J, et al. Efficacy and safety of ceftazidime–avibactam plus metronidazole versus meropenem in the treatment of complicated intra-abdominal infection: results from a randomized, controlled, double-blind, phase 3 program. Clin Infect Dis. 2016;11:1380–9.

    Article  Google Scholar 

  34. 34.

    Jorgensen SCJ, McDonald P, Mynatt RP, et al. Averting the post-antibiotic era: successful use of meropenem/vaborbactam for carbapenem-resistant Serratia marcescens and Enterobacter aerogenes bacteraemia in a haemodialysis patient. J Antimicrob Chemother. 2018;12:3529–31.

    Google Scholar 

  35. 35.

    Bidell MR, Lodise TP. Suboptimal clinical response rates with newer antibiotics among patients with moderate renal impairment: review of the literature and potential pharmacokinetic and pharmacodynamic considerations for observed findings. Pharmacotherapy. 2018;12:1205–15.

    Article  Google Scholar 

Download references

Acknowledgements

Funding

No funding or sponsorship was received for this study or the publication of this article.

Authorship

All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.

Prior Presentation

This work was presented, in part, at ASM Microbe June 20–25 2019, San Francisco, CA, USA, poster CIV–142.

Disclosures

Sarah C. J. Jorgensen: Current affiliation: Department of Pharmacy, Mount Sinai Hospital, Toronto, ON, Canada. Michael J. Rybak: Research support, consultant or speaker for Allergan, Melinta, Merck, Motif, Nabriva, Paratek, Tetraphase and Shionogi. Michael J. Rybak is is the Editor-in-Chief of this journal. Susan L. Davis: Consultant for Spero and Tetraphase. Sandra J. Estrada: Employee of T2 Biosystems. Joshua R. Rosenberg: Consulting agreements or is on the speakers bureau with Allergan, Merck, Shinogi, Tetraphase, Melinta, Paratek.

Trang D. Trinh, Evan J. Zasowski, Abdalhamid M. Lagnf, Sahil Bhatia, Sarah M. Melvin, Samuel P. Simon, Molly E. Steed and Taylor Morrisette have nothing to disclose.

Compliance with Ethical Guidelines

Approval was obtained from each participating center’s Institutional Review Board (IRB) with a waiver for informed consent (Appendix 1). Wayne State University served as the master IRB.

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Michael J. Rybak.

Additional information

Enhanced Digital Features

To view enhanced digital features for this article go to https://doi.org/10.6084/m9.figshare.11800800.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 18 kb)

Rights and permissions

This article is published under an open access license. Please check the 'Copyright Information' section either on this page or in the PDF for details of this license and what re-use is permitted. If your intended use exceeds what is permitted by the license or if you are unable to locate the licence and re-use information, please contact the Rights and Permissions team.

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jorgensen, S.C.J., Trinh, T.D., Zasowski, E.J. et al. Evaluation of the INCREMENT-CPE, Pitt Bacteremia and qPitt Scores in Patients with Carbapenem-Resistant Enterobacteriaceae Infections Treated with Ceftazidime–Avibactam. Infect Dis Ther 9, 291–304 (2020). https://doi.org/10.1007/s40121-020-00288-4

Download citation

Keywords

  • Carbapenem-resistant Enterobacteriaceae
  • Ceftazidime–avibactam
  • INCREMENT-CPE
  • Pitt bacteremia