Tree-structured survival analysis of patients with Pseudomonas aeruginosa bacteremia: A multicenter observational cohort study

    Tree-structured survival analysis of patients with Pseudomonas aeruginosa bacteremia: A multicenter observational cohort study Young Kyung Yoon, Hyun Ah Kim, Seong Yeol Ryu, Eun Jung Lee, Mi Suk Lee, Jieun Kim, Seong Yeon Park, Kyung Sook Yang, Shin Woo Kim PII: DOI: Reference:

S0732-8893(16)30332-7 doi: 10.1016/j.diagmicrobio.2016.10.008 DMB 14215

To appear in:

Diagnostic Microbiology and Infectious Disease

Received date: Revised date: Accepted date:

3 June 2016 13 September 2016 3 October 2016

Please cite this article as: Yoon Young Kyung, Kim Hyun Ah, Ryu Seong Yeol, Lee Eun Jung, Lee Mi Suk, Kim Jieun, Park Seong Yeon, Yang Kyung Sook, Kim Shin Woo, Tree-structured survival analysis of patients with Pseudomonas aeruginosa bacteremia: A multicenter observational cohort study, Diagnostic Microbiology and Infectious Disease (2016), doi: 10.1016/j.diagmicrobio.2016.10.008

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ACCEPTED MANUSCRIPT Tree-structured survival analysis of patients with Pseudomonas aeruginosa bacteremia: A

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multicenter observational cohort study

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Running Title: Pseudomonas aeruginosa bacteremia

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Young Kyung Yoona, Hyun Ah Kimb, Seong Yeol Ryub, Eun Jung Leec, Mi Suk Leed, Jieun Kime,

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Seong Yeon Parkf, Kyung Sook Yangg, Shin Woo Kimh: Antibiotic Stewardship Study Group

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Division of Infectious Diseases, Department of Internal Medicine, aKorea University Anam Hospital, bKeimyung University Dongsan Hospital, cSoonchunhyang University Seoul Hospital, Kyung Hee University Medical Center, eHanyang University Kuri Hospital, fDongguk

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d

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University Ilsan Hospital, gKyungpook National University Hospital, hDepartment of

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Biostatistics, Korea University College of Medicine, Republic of Korea

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Corresponding author: Shin Woo Kim, MD, PhD Division of Infectious Diseases, Department of Internal Medicine, Kyungpook National University Hospital, 130 Dongdoek-ro Jung-gu Daegu 700-721, Republic of Korea Tel: +82-53-200-6525; Fax: +82-53-426-2046; E-mail: [email protected]

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ACCEPTED MANUSCRIPT Abstract (142/150 words)

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This study aimed to construct a prediction algorithm, which is readily applicable in the clinical

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setting, to determine the mortality rate for patients with P. aeruginosa bacteremia. A multicenter observational cohort study was performed retrospectively in seven university-affiliated hospitals

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in Korea from March 2012 to February 2015. In total, 264 adult patients with monomicrobial P.

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aeruginosa bacteremia were included in the analyses. Among the predictors independently associated with 30-day mortality in the Cox regression model, Pitt bacteremia score >2 and high-

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risk source of bacteremia were identified as critical nodes in the tree-structured survival analysis. Particularly, the empirical combination therapy was not associated with any survival benefit in

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the Cox regression model compared to the empirical monotherapy. This study suggests that

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determining the infection source and evaluating the clinical severity are critical to predict the

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clinical outcome in patients with P. aeruginosa bacteremia.

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Keywords: Pseudomonas aeruginosa, bacteremia, mortality

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ACCEPTED MANUSCRIPT 1. Introduction

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Pseudomonas aeruginosa, first isolated by Gessard in 1882, causes many human infections.

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Particularly, P. aeruginosa is the third most common gram-negative pathogen causing bacteremia. In 1997, a cross-sectional study performed in North and Latin America showed that

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it is associated with a high mortality rate ranging from 26% to 39% (Al-Hasan et al., 2008;

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Diekema et al., 1997; Parkins et al., 2010; Wisplinghoff et al., 2004). The poor outcomes associated with P. aeruginosa bacteremia may be attributed to both host and microbial factors.

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Therefore, it is imperative to consider several factors that can influence patient outcomes, such as optimal antibiotic treatment, widespread antibiotic resistance and host immunity (Gellatly et al.,

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2013; Lodise et al., 2007; Sadikot et al., 2005).

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In order to improve the clinical outcome and to accurately predict the prognosis, it is essential to identify the predictors associated with mortality. In previous studies, the risk factors

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associated with adverse outcomes in patients with P. aeruginosa bacteremia included severe

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underlying diseases, neutropenia, pneumonia, severe sepsis, septic shock, an increasing Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) score, prolonged length of hospital stay prior to the blood culture, and inadequacy of the initial empirical antimicrobial therapy (Bisbe et al., 1988; Hilf et al., 1989; Ibrahim et al. 2000; Kang et al., 2003; Kim et al., 2014; Kuikka et al., 1998; Sadikot et al., 2005). Antibiotic combination therapy is a common therapeutic approach implemented for decades against infections with P. aeruginosa (Bodey et al., 1985; Mutlu et al., 2006). Combination therapy for P. aeruginosa bacteremia provides an increased possibility of adequate empirical coverage, prevention of the emergence of bacterial resistance during antibiotic therapy, and in 3

ACCEPTED MANUSCRIPT vitro antibiotic synergy (Micek et al., 2005; van Delden C et al., 2007). However, despite the anticipatable advantages of combination antibiotic therapy, it has not been clearly established

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whether empirical combination antibiotic therapy essentially improves survival in patients with P.

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aeruginosa bacteremia (Bowers et al., 2013; Hu et al., 2013; Park et al., 2012). The most widely used models for identification of risk factors of mortality have been the

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Cox proportional hazard or multivariate logistic regression analyses. However, tree-structured

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survival analysis is considered an alternative to these traditional models. Its final output is expressed by a tree-structured diagram, which is understood and explained easily as an approach

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for segmentation, classification, and prediction by applying a series of simple rules. However, data using the tree-structured survival analysis of patients with P. aeruginosa bacteremia is

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lacking. (Kang et al., 2003; Lodise et al., 2007; Micek et al., 2005).

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The purpose of this multicenter cohort study was to construct a prediction algorithm, in order to be readily applicable in the clinical setting, to determine the mortality rate for patients with P.

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aeruginosa bacteremia. Furthermore, we aimed to evaluate the impact of combination antibiotic

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therapy as empirical or definitive antibiotic therapy compared to monotherapy on mortality.

2. Materials and methods

2.1.Study design and patients We conducted a retrospective observational cohort study in seven university-affiliated hospitals in the Republic of Korea from March 2012 to February 2015. Adult patients (≥18 years) with positive blood culture results for P. aeruginosa were identified from the microbiologic 4

ACCEPTED MANUSCRIPT laboratory database. All hospitalized adult patients with P. aeruginosa bacteremia were eligible for the study. Patients with polymicrobial bacteremia were excluded from the study. Only the

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first episodes of bacteremia from each patient were included in the analysis. The decision to

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perform blood cultures and the choice of therapeutic approach were at the discretion of the attending physicians.

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In all the analyses that were performed, 30-day mortality was considered the main outcome.

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The primary endpoint was to identify the predictors of 30-day mortality in patients with monomicrobial bacteremia involving P. aeruginosa. The secondary endpoint was to evaluate the

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impact of empirical combination therapy on 30-day all-cause mortality from the date of index culture among them.

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The study protocol was approved by the institutional review board of Korea University

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Anam Hospital (No. AN15073-001). The need for informed patient consent was waived because

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2.2. Definitions

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of the retrospective and observational nature of this study.

P. aeruginosa bacteremia was defined as the observation of at least one positive blood culture result. If microorganisms other than P. aeruginosa were detected from the blood culture within a 24-hour period, the bacteremia was defined as polymicrobial. Bacteremia was considered nosocomially acquired if it occurred at least 48 hours after hospital admission and if there was no evidence of infection before then. Healthcare-associated bacteremia was defined according to the criteria described in a previous study (Friedman et al., 2002). The sources of bacteremia were determined according to the medical records, imaging studies, surgical findings, and microbiological evidence and were categorized into high-risk and low-risk sources. High-risk 5

ACCEPTED MANUSCRIPT sources originated from the lower respiratory tract, abdomen, soft tissue, and sources of unknown origin. Low-risk sources were from the pancreaticobiliary tract, intravenous catheters,

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and the urinary tract (Peña et al. 2013). Severe sepsis and septic shock were classified based on

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the criteria previously published by the American College of Chest Physicians/Society of Critical Care Medicine (ACCP/SCCM) (Bone et al., 1992). Neutropenia and thrombocytopenia were

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defined by an absolute neutrophil count of <500 cells/mm3 and a platelet count of <1.0 × 105

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cells/mm3, respectively.

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2.3. Antimicrobial therapy

Empirical antibiotic therapy was defined when antibiotics were administered within 24 hours of

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extraction of the index blood culture test and before antibiotic susceptibility test results were

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available. Empirical antibiotic therapy was considered adequate if at least one of the antimicrobial drugs administered was later proven active against P. aeruginosa isolates in vitro,

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that is, if it had been administered at the recommended dose and the pattern of administration

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within 48 hours after the index blood culture was drawn. Antibiotic treatment that was continued or initiated on the day that antibiotic susceptibility was reported was considered definitive therapy. Taking into account the controversy in the literature regarding whether aminoglycoside monotherapy is associated with poor clinical outcomes in patients with P. aeruginosa bacteremia, aminoglycoside monotherapy was categorized into inadequate therapy (Kuikka et al., 1998; Osih et al., 2007).

2.4.Variables

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ACCEPTED MANUSCRIPT Pertinent data electronically collected for this analysis included demographic characteristics, comorbidities, Charlson comorbidity index (Charlson et al., 1994), severity of illness including

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severe sepsis, septic shock, and Pitt bacteremia score within 24 h of index culture (Chow et al.,

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1991), source of bacteremia, microbiological data, laboratory data, including platelet count and serum albumin, antimicrobial therapy, length of hospital stay, and 30-day mortality. Predisposing

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factors within 30 days before the first identification of bacteremia were included in this analysis.

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Data for clinical variables related to the surgical intervention therapy and retention of foreign

2.5. Microbiologic evaluation

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bodies were also collected for analysis.

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Blood cultures were processed in the clinical microbiology laboratory using the automated blood

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culture system (BACTEC 9240 system; Becton Dickinson Diagnostic Instrument Systems, Sparks, MD, USA). Bacterial identification and antimicrobial susceptibility tests were performed

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at each study site using a VITEK II (bioMérieux, Hazelwood, MO) or MicroScan Pos Combo

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panel type 6 system (Baxter Diagnostics, West Sacramento, CA). The Clinical and Laboratory Standards Institute (CLSI) criteria were used to determine the susceptibility threshold breakpoints of antibiotics tested against P. aeruginosa (Clinical and Laboratory Standards Institute, 2012). Isolates with intermediate susceptibility according to these criteria were considered resistant.

2.6. Statistical analysis Categorical variables were indicated as count variables (proportion). Groups of categorical variables were compared using the Pearson's chi-square test or Fisher’s exact test. Continuous 7

ACCEPTED MANUSCRIPT variables were expressed as the mean ± standard deviation or median (inter-quartile range, IQR), as appropriate. Groups of normally and non-normally distributed continuous variables were

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compared using the two-sample Student's t-test and the Mann–Whitney U test, respectively.

threshold breakpoint using conditional inference trees.

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Continuous variables were transformed into categorical variables at the most significant

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To identify predictors associated with 30-day mortality from the data of index culture in patients

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with P. aeruginosa bacteremia, multivariate logistic regression analysis using the backward stepwise variable selection based on the Wald statistic was used. The models were evaluated

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using the Hosmer–Lemeshow goodness-of-fit tests. Furthermore, the Cox proportional hazard model was used for adjustment of confounding factors. Tree-structured survival analysis of the

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relevant variables, using a conditional inference tree, was conducted to extract useful variables

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that are routinely monitored in clinical settings and the information was arranged as treestructured diagrams that can be easily interpreted (Shin et al., 2016).

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Kaplan–Meier failure methods were plotted and were compared using the log-rank test among

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different groups for the node terminals. Variables were included in the multivariate analysis if they had at least 10% significance as predictors of 30-day mortality in univariate analysis. Results of the multivariate analyses are reported as odds ratio (OR) or hazard ratio (HR) with 95% confidence intervals (CI). All tests of significance were 2-tailed, and P < 0.05 was considered to indicate statistical significance. IBM SPSS Statistics version 20.0 (IBM Corporation, Armonk, NY, USA), R 3.2.3 (The R Foundation for Statistical Computing, Vienna, Austria), and SAS 9.4 (SAS Institute Inc., Cary, NC, USA) were used for all statistical analyses.

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ACCEPTED MANUSCRIPT 3. Results

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3.1.Patient and clinical characteristics

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During the study period, 318 patients were initially identified. Among them, 54 patients had polymicrobial bacteremia. Finally, 264 patients were included in the analyses. Demographic and

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baseline characteristics of these patients are summarized in Table 1. Among the patients included

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in the analyses, 168 patients (63.6%) were men. The median age was 57 years (IQR, 68–75 years), and the median Charlson comorbidity index was 3 (IQR, 1–6). Old age (≥70 years),

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neutropenia, and high-risk sources of bacteremia were more frequent in the nonsurvivors than in the survivors (Table 1). Compared with the survivors, nonsurvivors showed more severe clinical

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signs including septic shock, higher Pitt bacteremia scores, thrombocytopenia, and

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hypoalbuminemia (Table 1). For the P. aeruginosa blood isolates, the susceptibility rates to ceftazidime, gentamicin, ciprofloxacin, piperacillin/tazobactam, and imipenem were 81.1%,

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89.8%, 79.9%, 76.9%, and 83.7%, respectively. The 30-day mortality rate was 31.8% (84

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patients). Surprisingly, 40.5% (34 patients) of all the mortality cases occurred in the first 2 days after index blood cultures.

3.2.Antimicrobial treatment Among the 264 patients, 155 (58.7%) received adequate empirical antibiotic therapy (Table 1). Antibiotic agents used for adequate empirical and definitive therapy are shown in the supplementary table. Adequate empirical antibiotic therapy was significantly more common in patients receiving the empirical combination therapy than in those receiving empirical monotherapy (60/85 [70.6%] versus 95/179 [53.1%], P = 0.007, respectively). Adequate 9

ACCEPTED MANUSCRIPT definitive antibiotic therapy was significantly more common in patients infected with imipenemsusceptible P. aeruginosa than in those infected with imipenem-resistant P. aeruginosa (214/221

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[96.8%] versus 38/43 [88.4%], P=0.015, respectively). However, no significant difference was

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observed in the frequency of adequate empirical antibiotic therapy among patients infected with imipenem-susceptible and -resistant P. aeruginosa (134/221 [60.6%] versus 21/43 [48.8%],

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P=0.151, respectively).

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Of the 264 patients, 12 received inadequate definitive antibiotic therapy. Of these 12 patients, five had carbapenem-resistant P. aeruginosa bacteremia, and seven including three who did not

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receive antibiotics died within 72 hours after admission.

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3.3.Predictors associated with 30-day mortality

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In the multiple logistic regression model, the predictors significantly associated with 30-day

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mortality included age ≥70 years (OR 2.44, 95% CI 1.20–4.97), neutropenia (OR 2.87, 95% CI 1.36–6.05), Pitt bacteremia score > 2 (OR 10.68, 95% CI 5.27–21.64), high-risk source (OR 3.72,

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95% CI 1.74–7.97), thrombocytopenia (OR 3.04, 95% CI 1.49–6.24), and inadequate empirical antimicrobial therapy (OR 2.20, 95% CI 1.11–4.37) (Table 2). The P-value for the Hosmer– Lemeshow goodness-of-fit test was 0.241, which was higher than the 0.05 significance threshold; therefore, no significant evidence for lack of fit was observed in any of the final models. Similarly, the Cox regression model to control the variables associated with mortality also revealed the predictors associated with 30-day mortality among them. Adjusted 30-day mortality did not differ between patients receiving empirical combination antimicrobial therapy and empirical single-drug antimicrobial therapy (Table 3).

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ACCEPTED MANUSCRIPT Using the Kaplan-Meier estimator, unadjusted probabilities of survival until day 30 were plotted by the appropriateness of empirical antibiotic therapy for patients with P. aeruginosa bacteremia.

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Cumulative probability of survival significantly differed between the two groups (P = 0.001, log-

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rank test) (Fig. 1A). However, there was no significant difference between empirical single-drug therapy and empirical combination therapy, regardless of their appropriateness (P = 0.270, log-

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rank test) (Fig. 1B).

3.4. Tree-structured survival analysis on 30-day mortality

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Tree-structured survival analysis was performed using the nine clinical variables: age ≥ 70 years, high risk source of bacteremia, Pitt bacteremia score > 2, neutropenia, platelets <100,000/mm3,

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septic shock, serum albumin < 3 g/dL, appropriate empirical antibiotic therapy, and pneumonia.

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With a minimum criterion of 0.85 in conditional inference trees analysis, the tree had an initial split on Pitt bacteremia score >2, and four terminal subgroups were formed. The variables

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determining the structure of the tree included Pitt bacteremia score >2, thrombocytopenia and

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initial appropriate and inappropriate empirical antibiotic therapy. The structure of the survival tree and corresponding survival curves from the four groups are demonstrated in Fig 2. The longest surviving subgroup included 157 patients with a Pitt bacteremia score ≤2. The shortest surviving subgroup included 72 patients with a Pitt bacteremia score >2, thrombocytopenia and inadequate empirical antibiotic therapy.

4. Discussion

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ACCEPTED MANUSCRIPT This multicenter study sought to investigate risk factors for mortality in patients with P. aeruginosa bacteremia and evaluate the effects of empirical combination antibiotic therapy on

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clinical outcome. The results of our study suggest that combination therapy is not associated with

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survival benefit in patients with P. aeruginosa bacteremia, even after adjusting for confounding variables. However, a delay in the administration of adequate empirical antibiotic therapy was

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significantly associated with higher mortality.

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Further, 40.5% of all the mortality cases were observed within the first 48 hours of diagnosis due to P. aeruginosa bacteremia; hence, the rapid progression of the infection imposes a heavy

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burden on the clinicians during the initial treatment. Despite the development of various antipseudomonal antibiotics, poor outcomes have been observed; therefore, clinicians prescribe

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combination antibiotic therapy for the clinical benefit of in vitro synergy during the treatment of

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Pseudomonas bacteremia. Nevertheless, there is no consensus regarding the necessity of combination antibiotic therapy as opposed to single-drug antibiotic therapy. A prospective

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multicenter study suggested a significant mortality benefit for patients who received combination

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therapy (47% versus 23%, P=0.023) (Hilf et al., 1989). However, this is currently controversial since subsequent studies that amended the study limitations have shown contradicting results (Bowers et al., 2013; Peña et al., 2013). Particularly, a meta-analysis demonstrated that combination therapy did not show a significant benefit on mortality rates in patients with P. aeruginosa bacteremia (Hu et al., 2013). Our results are in accordance with the findings of the meta-analysis regarding empirical antimicrobial treatments. As high mortality rates are particularly observed in the first 3-5 days following bacteremia involving P. aeruginosa, an initial decision for prompt and adequate antibiotic therapy appears to be essential. In agreement with our results, several studies have shown that inadequate empirical 12

ACCEPTED MANUSCRIPT antibiotic therapy is associated with an increased mortality rate (Cheong et al., 2008; Lodise et al., 2007; Micek et al., 2005). However, severe illness at the onset of bacteremia can result in

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early mortality that is independent of the adequacy of antibiotic therapy. In other words, this

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could dilute the importance of adequate definitive antimicrobial therapy and could lead to an underestimation of the effect of inadequate therapy on the outcome.

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Even though combination therapy does not seem to be associated with improved outcome once

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susceptibilities are known, it is still questionable whether the number of empirical antibiotic therapies affects patient outcomes. The most promising benefit of combination therapy is the

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increased likelihood of adequate empirical therapy rather than in vitro synergy or prevention of emergence of antibiotic resistance (Traugott et al., 2011). In our study, an adequate empirical

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antibiotic therapy was significantly more common in the empirical combination therapy group

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than in the empirical monotherapy group (60/85 [70.6%] versus 95/179 [53.1%], P = 0.007). However, of the 85 patients who received combination therapy, 51 (60.0%) were treated with

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combination therapy in which only one antibiotic was active against Pseudomonas. This was

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most likely due to poor decision making, using a drug that was not antipseudomonal. Finally, our findings, after adjusting for confounding factors, did not reveal any benefit of combination therapy on mortality when investigating only adequate therapy. Therefore, to strengthen the advantage of empirical combination therapy, a wide variation in the distribution and susceptibility of clinical pathogens should be reflected to achieve adequate empirical antibiotic therapy for better outcomes. Additionally, negative effects such as drug toxicity, increased possibility of superinfection occurrence, or increased costs were not evaluated in our study. As expected, the independent predictors of mortality in our study included age ≥70 years, neutropenia, Pitt bacteremia score >2, high-risk bacteremia source, thrombocytopenia and 13

ACCEPTED MANUSCRIPT inadequate empirical antimicrobial therapy, which is remarkably consistent with previous findings (Bisbe et al., 1998; Hilf et al., 1989; Ibrahim et al., 2000; Kuikka et al., 1998; Vidal et

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al., 1996). While the mechanism of thrombocytopenia in sepsis was not well understood, several

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studies have shown that the mortality rates in critically ill patients were significantly higher for those with thrombocytopenia than in those without the disease (Hilf et al., 1989; Ibrahim et al.,

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2000). The primary site of infection has been demonstrated as a strong prognostic factor for

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mortality in patients with bacteremia or in patients with neutropenia and malignancies (Kang et al., 2005; Nørgaard et al., 2016). In addition, inadequate empirical antibiotic therapy may

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adversely affect the mortality associated with P. aeruginosa bacteremia (Joo et al., 2011; Kim et al., 2014). Particularly, old age and severity of illness are well-known critical factors for

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mortality in patients with P. aeruginosa bacteremia (Aliaga et al., 2002; Hirsch et al., 2012;

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Kang et al., 2003; Lodise et al., 2007; Micek et al., 2005; Osih et al., 2007; Tam et al., 2010). Tree-structured analysis of survival data identified the important prognostic factors for

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mortality in patients with P. aeruginosa bacteremia and alleviated the problems associated with

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the bedside utility of the Cox multivariate analyses. This enables clinicians to estimate survival for an individual patient and integrate the available prognostic information into patient management. Moreover, the analyses conducted in this study included variables that were considered important based on multivariate models and were applied to the conditional inference tree, and the clinical variables to stratify patient survival were validated. Our study had several limitations. Firstly, we conducted a retrospective observational cohort study and were unable to control the clinical practice of providing antibiotic or non-antibiotic therapy. Antibiotics were prescribed by physicians, which could introduce bias. Secondly, our definition of adequate therapy included any agent with in vitro susceptibility to the isolate. 14

ACCEPTED MANUSCRIPT Therefore, we could not evaluate the synergy between an active and inactive agent as well as a combination of two inactive agents. Finally, 27 patients died before the results on antibiotic

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susceptibility were reported. To evaluate the effect of empirical antibiotic therapy on clinical

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outcomes, we included them in our analysis.

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5. Conclusions

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In conclusion, this study suggests that the use of adequate empirical antibiotic therapy and evaluation of the clinical severity are important for predicting the clinical outcomes in patients

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with P. aeruginosa bacteremia. Interestingly, we did not observe any difference in mortality outcomes associated with the number of adequate agents administered during empirical therapy

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for P. aeruginosa bacteremia, that is, when at least one agent was active. Furthermore, education

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regarding the prognostic classification index using tree-based models may help clinicians

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intuitively assess the clinical outcomes of patients with P. aeruginosa bacteremia.

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ACCEPTED MANUSCRIPT Acknowledgments

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None.

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Role of the funding source

This study was supported by the Korean Society for Chemotherapy. The funder did not

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participate in study design, data collection, data analysis, data interpretation, or writing the report.

Declaration of interest

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The authors declare that they have no conflicts of interest.

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Osih RB, McGregor JC, Rich SE, Moore AC, Furuno JP, Perencevich EN, et al. Impact of empiric antibiotic therapy on outcomes in patients with Pseudomonas aeruginosa bacteremia.

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Antimicrob Agents Chemother 2007;51(3):839–44.

Park SY, Park HJ, Moon SM, Park KH, Chong YP, Kim MN, et al. Impact of adequate empirical

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combination therapy on mortality from bacteremic Pseudomonas aeruginosa pneumonia. BMC

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Infect Dis 2012;12:308.

Parkins MD, Gregson DB, Pitout JD, Ross T, Laupland KB. Population-based study of the

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2010;38(1):25–32.

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epidemiology and the risk factors for Pseudomonas aeruginosa bloodstream infection. Infection

Peña C, Suarez C, Ocampo-Sosa A, Murillas J, Almirante B, Pomar V, et al. Effect of adequate single-drug vs combination antimicrobial therapy on mortality in Pseudomonas aeruginosa bloodstream infections: A post Hoc analysis of a prospective cohort. Clin Infect Dis 2013;57(2):208–16. Sadikot RT, Blackwell TS, Christman JW, Prince AS. Pathogen-host interactions in Pseudomonas aeruginosa pneumonia. Am J Respir Crit Care Med 2005;171(11):1209–23. Shin H, Lee Y, Lee E. Comparison of various classification tree methods with clinical data. J Health Info Stat 2016;41(1):135–46. 20

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2010;54(9):3717–22.

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Traugott KA, Echevarria K, Maxwell P, Green K, Lewis JS 2nd. Monotherapy or combination therapy? The Pseudomonas aeruginosa conundrum. Pharmacotherapy 2011;31(6):598–608.

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van Delden C. Pseudomonas aeruginosa bloodstream infections: how should we treat them? Int J

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Antimicrob Agents 2007;30 Suppl 1:S71-5.

Vidal F, Mensa J, Almela M, Martínez JA, Marco F, Casals C, et al. Epidemiology and outcome

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of Pseudomonas aeruginosa bacteremia, with special emphasis on the influence of antibiotic treatment. Analysis of 189 episodes. Arch Intern Med 1996;156(18):2121–6.

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Wisplinghoff H, Bischoff T, Tallent SM, Seifert H, Wenzel RP, Edmond MB. Nosocomial

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bloodstream infections in US hospitals: Analysis of 24,179 cases from a prospective nationwide

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surveillance study. Clin Infect Dis 2004;39(3):309–17.

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Table 1. Demographic and clinical characteristics of 264 patients with bacteremia caused by Pseudomonas aeruginosa according to the

Non-survivors

(n = 264)

(n = 84, 31.8%)

Male sex, n (%)

168 (63.6)

54 (64.3)

Age ≥70 years, n (%)

120 (45.5)

46 (54.8)

2 (0–19)

1 (0–17)

114 (63.3)

0.881

74 (41.1)

0.038

0.390

D TE

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(IQR) Category of infection, n (%)

value*

3 (0–20)

22 (26.2)

48 (26.7)

20 (23.8)

34 (18.9)

42 (50.0)

98 (54.4)

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Admission to diagnosis of Bacteremia (days), median

P-

(n = 180, 68.2%)

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Variables

Survivors

NU S

All

CR I

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treatment outcomes on admission day 30.

Community-acquired

70 (26.5)

Healthcare-associated†

54 (20.5)

Nosocomial

140 (53.0)

0.639

Comorbidity, n (%) 22

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118 (44.7)

36 (42.9)

82 (45.6)

0.681

Central nervous system

39 (14.8)

14 (16.7)

25 (13.9)

0.554

Malignancy

111 (42.0)

34 (40.5)

77 (42.8)

0.724

Renal

42 (15.9)

12 (14.3)

30 (16.7)

0.622

Hepatic

31 (11.7)

8 (9.5)

Respiratory

16 (6.1)

4 (4.8)

Metabolic

55 (20.8)

16 (19.0)

3 (1–6)

3 (2–6)

23 (12.8)

0.444

12 (6.7)

0.546

39 (21.7)

0.626

3 (1–6)

0.482

D

MA

NU S

CR I

PT

Cardiovascular

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median (IQR)

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Charlson comorbidity index‡,

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Predisposing factors, n (%) Prior admission

155 (58.7)

50 (59.5)

105 (58.3)

0.855

Prior surgical operation

42 (15.9)

15 (17.9)

27 (15.0)

0.554

Neutropenia

161 (61.0)

61 (72.6)

100 (55.6)

0.008

Prior antibiotic use

87 (33.0)

27 (32.1)

60 (33.3)

0.848

Primary focus of bacteremia, n (%) 23

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66 (78.6)

92 (51.1)

<0.001

CR-BSI

22 (8.3)

5 (8.0)

17 (9.4)

0.339

Urinary tract infection

51 (19.3)

13 (15.5)

38 (21.1)

0.280

Biliary tract infection

33 (12.5)

0

33 (18.3)

<0.001

106 (40.2)

18 (21.4)

68 (25.8)

35 (41.7)

Surgical wound infection

9 (3.4)

3 (3.6)

Skin and soft tissue infection

7 (2.7)

3 (3.6)

Bone and joint infection

1 (0.4)

Cardiovascular infection

CR I

NU S

<0.001

33 (18.3)

<0.001

6 (3.3)

1.000

4 (2.2)

0.683

0

1 (0.6)

1.000

4 (1.5)

0

4 (2.2)

0.310

Intra-abdominal infection

14 (5.3)

13 (15.5)

1 (0.6)

<0.001

Central nervous system infection

1 (0.4)

0

1 (0.6)

1.000

77 (29.2)

23 (27.4)

54 (30.0)

0.663

23 (8.7)

8 (9.5)

15 (8.3)

0.749

Unknown

TE

CE P

Pneumonia

MA

88 (48.9)

AC

High risk

PT

158 (59.8)

D

Low risk

Clinical severity, n (%) Development of severe

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79 (29.9)

51 (60.7)

28 (15.6)

Pitt bacteremia score§ >2, n (%)

107 (40.5)

64 (76.2)

43 (23.9)

Platelets ≤100,000 cells/μL

88 (33.3)

42 (50.0)

Albumin < 3.0 g/dL

120 (45.6)

52 (62.7)

Foreign body retention

27 (10.2)

Surgical intervention therapy

41 (15.5)

CR I

<0.001

68 (37.8)

<0.001

21 (11.7)

0.259

6 (7.1)

35 (19.4)

0.010

1 (1.2)

10 (5.6)

MA

<0.001

D CE P

6 (7.1)

AC

Empirical antimicrobial therapy

<0.001

46 (25.6)

Complicated case, n (%)

Antibiotic therapy, n (%)

NU S

Laboratory findings, n (%)

PT

Development of septic shock

TE

sepsis

Adequate empirical combination 11 (4.2) therapy 0.005 Adequate empirical single therapy

144 (54.5)

37 (44.0)

107 (59.4)

Inadequate empirical therapy

109 (41.3)

46 (54.8)

63 (35.0) 25

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Adequate empirical antibiotic therapy

155 (58.7)

38 (45.2)

117 (65.0)

85 (32.2)

30 (35.7)

55 (30.6)

16 (6.1)

7 (8.3)

236 (89.4)

69 (82.1)

12 (4.5)

8 (9.5)

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Combination empirical antibiotic

Adequate definitive combination

252 (95.5)

Combination definitive antibiotic

Antibiotic susceptibility, n (%)

D

76 (90.5)

9 (5.0) 0.014

167 (92.8) 4 (2.2) 176 (97.8)

0.021

8 (9.5)

20 (11.1)

0.696

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28 (10.6) therapy

TE

Adequate definitive antibiotic therapy

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Inadequate definitive therapy

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therapy

NU S

Definitive antimicrobial therapy

Adequate definitive single therapy

0.403

CR I

therapy

0.002

Gentamicin

237 (89.8)

80 (95.2)

157 (87.2)

0.045

Ceftazidime

214 (81.1)

70 (83.3)

144 (80.0)

0.520

Cefepime

214 (81.1)

72 (85.7)

142 (78.9)

0.187

26

211 (79.7)

71 (84.5)

140 (77.8)

0.202

Piperacillin-tazobactam

203 (76.9)

66 (78.6)

137 (76.1)

0.659

Imipenem

221 (83.7)

71 (84.5)

150 (83.3)

0.807

13 (4-26)

4 (1-12)

21 (8-45)

11 (2-27)

40 (15.2)

CR I

PT

Ciprofloxacin

TE

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Hospital stay after bacteremia

MA

(days), median (IQR)

(days), median (IQR)

8 (3.0)

34 (40.5)

CE P

Relapse within 1-month

17 (8-35)

<0.001

27 (11-56)

<0.001

6 (3.3)

<0.001

8 (4.4)

0.058

D

Overall length of hospital stay

P. aeruginosa-related mortality

NU S

Outcomes, n (%)

0

*

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CR-BSI, catheter-related bloodstream infection; CRP, C-reactive protein; IQR, interquartile range; P-values were obtained using Student’s t-test, Mann–Whitney U test, Wilcoxon’s signed-rank test, Fisher’s exact test, chi-squared test, or

McNemar’s test, as appropriate. †Bloodstream

infection diagnosed within 48 hours of hospital admission was considered healthcare-associated infection if the patient

presented with healthcare-associated factors in the preceding 3 months. 27

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bacteremia score within 24 h of index culture was calculated based on temperature (35.1–36 °C or 39.0–39.9 °C: 1 point, ≤ 35 or ≥

CR I

§Pitt

comorbidity index was calculated within 24 h of index culture.

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‡Charlson

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40°C: 2 points), blood pressure (hypotension requiring intravenous vasopressor agents or systolic blood pressure < 90 mm Hg or acute hypotensive event with drop in systolic blood pressure >30mm Hg and diastolic blood pressure >20 mm Hg: 2 points), mental status

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(disorientation: 1 point, stupor: 2 points, coma: 4 points), respiratory status (mechanical ventilation: 2 points) and cardiac status (cardiac arrest:

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4 points).

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Table 2. Multiple logistic regression analysis of risk factors associated with 30-day mortality in 264 patients with bacteremia caused by

CR I

PT

Pseudomonas aeruginosa

Independent variable

OR (95% CI for OR)

NU S

Univariate logistic regression analysis

P-value

1.73 (1.03–2.92) 3.18 (1.79–5.66)

<0.001

High risk source of bacteremia

3.51 (1.93–6.38)

(Yes)

3.72 (1.74–7.97)

0.001

<0.001

10.68 (5.27–21.64)

<0.001

8.39 (4.63–15.21)

<0.001

MA

<0.001

10.19 (5.55–18.72)

0.039

0.009

2.87 (1.36–6.05)

0.006

2.91 (1.69–5.02)

<0.001

3.04 (1.49–6.24)

0.002

Serum albumin < 3.0 g/dL

2.76 (1.62–4.73)

<0.001

1.80 (0.91-3.53)

0.090

2.25 (1.33–3.81)

0.003

2.20 (1.11-4.37)

0.025

4.63 (1.35–15.85)

0.015

3

Inadequate empirical antimicrobial therapy Inadequate definite antimicrobial therapy Empirical combination antibiotic therapy

AC

2.12 (1.21–3.72)

Platelets <100,000/mm

CE P

Neutropenia

0.014

TE

Pitt bacteremia score > 2 Septic shock (Yes)

2.44 (1.20–4.97)

D

Age ≥ 70 years Pneumonia (Yes)

Multivariate logistic regression analysis with backward variable selection based on LR OR (95% CI for OR) P-value

0.57 (0.15–2.10) 95% CI, 95% confidence interval; LR, likelihood ratio; OR, odds ratio

0.397

29

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T

with bacteremia caused by Pseudomonas aeruginosa

RI P

95% confidence interval Hazard ratio

P-value

Upper

1.08

2.68

0.023

4.83

0.002

1.44

6.26

0.003

1.11

4.00

0.022

1.70

High risk source of bactere mia

2.64

Pitt bacteremia score >2

3.01

Septic shock

2.11

Neutropenia

1.79

1.06

3.03

0.029

Platelets <100,000 cells/mm3

1.67

1.06

2.64

0.026

Inadequate empirical antimi crobial therapy

1.74

1.07

2.85

0.026

0.85

0.51

1.41

0.519

Serum albumin <3.0 g/dL

1.18

0.74

1.88

0.489

Inadequate definitive antimi crobial therapy

2.21

0.91

5.37

0.080

Empirical combination anti biotic therapy

0.97

0.30

3.20

0.967

AC

CE

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1.44

MA

PT

Pneumonia

SC

Age ≥70 years

ED

Lower

30

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Fig. 1. Cumulative risk of survival for patients with P. aeruginosa bacteremia. A. Comparison

RI P

between adequate empirical antibiotic therapy and inadequate empirical antibiotic therapy. B. Comparison between empirical combination antibiotic therapy and empirical single-drug

NU

SC

antibiotic therapy.

AC

CE

PT

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caused by Pseudomonas aeruginosa.

MA

Fig. 2. Survival tree analysis for prediction of 30-day mortality in patients with bacteremia

31

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SC

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Fig. 1

32

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Fig. 2

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PT

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SC

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Highlights Predictors for mortality were evaluated for patients with P. aeruginosa bacteremia. Combination therapy does not render survival benefit in these patients. Survival tree analysis is useful for evaluating clinical outcomes in patients.

34