Risk Factors for Pseudomonas aeruginosa to Guide Empiric
Therapy for Gram-Negative Infections
Contact Information:
[email protected]
Krutika N. Mediwala, PharmD1; W. Cliff Rutter, PharmD, MS1,2; Donna R. Burgess, RPh1,2; Craig A. Martin, PharmD, MBA1,2;
Katie Wallace, PharmD, BCPS1,2, David S. Burgess, PharmD, FCCP2
1. University of Kentucky HealthCare Department of Pharmacy Services, Lexington, KY; 2. University of Kentucky College of Pharmacy, Lexington, KY
Abstract (Revised)
Results
Table 1. Baseline Characteristics
Variable
Age, Median (IQR)
Male Gender, n (%)
Comorbidities, n (%)
Diabetes Mellitus
Coronary Artery Disease
Chronic Kidney Disease
End Stage Renal Disease
Chronic Obstructive Pulmonary Disease
Liver dysfunction
Solid tumor
Liquid tumor
Charlson Comorbidity Index, median (IQR)
Immunosuppression composite, n (%)
Immunosuppressive agent exposure
Immunosuppression
Parenteral nutrition, n (%)
Neurological disease, n (%)
Prosthetic device, n (%)
Fungal colonization/infection, n (%)
Elderly
Severity of illness – increasing APACHE II score, ICU admission
Immunosuppressed – dialysis, cirrhosis, malignancy
Previous multi-drug resistant infection
117 (32)
108 (29)
100 (27)
32 (9)
45 (12)
14 (4)
58 (16)
31 (8)
5 (3-8)
53 (14)
51 (14)
2 (0.5)
1 (0.3)
0 (0)
13 (4)
10 (3)
0.93
0.89
0.0003
0.001
0.015
0.007
0.57
0.002
0.0002
<0.0001
<0.0001
0.78
1
0.96
0.55
0.09
E. aerogenes
K. oxytoca
P. mirabilis
80
70
53.9
50
ENT
PSA
37.9
40
p-value
0.0005
0.0005
0.0002
2145 (89)
341 (14)
170 (7)
180 (8)
371 (15)
372 (16)
48 (2)
338 (14)
262 (11)
73 (3)
487 (20)
311 (84)
64 (17)
20 (5)
45 (12)
81 (22)
82 (22)
13 (4)
79 (21)
30 (8)
26 (7)
137 (37)
0.002
0.14
0.27
0.003
0.002
0.002
0.09
0.0004
0.12
0.0002
<0.0001
Male gender
Receipt of antibiotics prior to the 48 hours preceding culture
Charlson Comorbidity Index ≥5
Hematological malignancy
Liver dysfunction
Hospital-acquired infection
PSA positive culture within 30 days
Pulmonary source
Immunosuppression and corticosteroid use
30
Figure 4. Risk Score distribution in
patients
20
10
C. freundii
PSA
266 (67)
15 (8-32)
6 (2-13)
Variable
90
60
ENT
1027 (58)
13 (6-26)
4 (1-9)
Table 3. Pseudomonas aeruginosa Risk Score
100
S. marcescens
Points
2
3
3.5
2.5
-3.5
-2
10
3
2.5
Max = 26.5
Min = -5.5
Figure 5. Receiver Operating Characteristic
(ROC) curve
0
0
 To identify risk factors for PSA infections in order to guide empiric therapy
 To create a risk-factor score based on comparison between patients with PSA vs
Enterobacteriaceae (ENT) infections
Variable
Hospital-acquired, n (%)
Length of stay in days, Median (IQR)
Time to culture collection in days, Median
(IQR)
Survival at discharge, n (%)
Invasive device, n (%)
Urinary indwelling catheter, n (%)
Central Venous Catheter, n (%)
Mechanical ventilation, n (%)
Circulatory shock composite, n (%)
Circulatory shock
Vasopressor exposure
Invasive procedure, n (%)
Bronchiectasis, n (%)
Corticosteroid exposure, n (%)
Figure 2. Previous Antibiotic Exposure*
E. cloacae
Objectives
Yes
20
40
60
80
100
Percentage of ENT cultures
*p < 0.0001
Exposure
Figure 3. Sources of Culture Collection
100
80
Percentage
 This was an IRB-approved single-center, case-control study
 Patients admitted from January 1st, 2010 - December 31st, 2014 at UK HealthCare were screened
 Inclusion Criteria:
•Case: Patients ≥ 18 years who had a POSITIVE culture for PSA
•Control: Patients ≥ 18 years who had a NEGATIVE culture for PSA, but were CULTURE-POSITIVE for
ENT
 Exclusion Criteria: Cystic Fibrosis patients and polymicrobial infections
 Data Collection: Center for Clinical and Translational Science (CCTS) Enterprise Data Trust
•Patient demographics, comorbidities, severity of illness, vital signs, medication administration data,
laboratory data, microbiological data, previous and current visit details
 Statistical Analysis: Descriptive statistics were calculated using the student’s t test and the Wilcoxon
rank-sum test. Chi-squared test was utilized for categorical data. Variables were identified using a
multivariate regression model. A risk-score was constructed using the corresponding model betacoefficients. Various point cutoffs were assessed with a receiver operator characteristic (ROC) curve,
with the point closest to maximum sensitivity and specificity being selected. All tests were two-tailed
with a predefined alpha of 0.05.
766 (32)
687 (29)
451 (19)
109 (5)
196 (8)
188 (8)
407 (17)
106 (4)
4 (2-8)
165 (7)
146 (6)
20 (0.8)
10 (0.4)
4 (0.2)
104 (4)
33 (1)
K. pneumoniae
 Recent susceptibilities for common empiric agents at the University of Kentucky HealthCare range
from 69-72% for PSA. Thus, it is important to differentiate between patients at risk of developing
these infections.
Methods
p-value
0.995
<0.0001
E. coli
Microorganism
 Current literature highlights several risk factors for Pseudomonas aeruginosa (PSA) infections:1,2,3,4
PSA (n=371)
59 (49-68)
224 (60)
Figure 1. Microbiological breakdown for ENT
Background
Table 2. Inpatient Visit Details
ENT (n=2399)
59 (46-70)
1125 (47)
Percentage of patients
Background: Pseudomonas aeruginosa (PSA) is associated with high morbidity and mortality, and there is
no PSA-specific risk factor score to guide empiric therapy. The objective of this study was to identify risk
factors and develop a PSA-risk score.
Methods: This was a retrospective, case-control study where clinical data from 1/1/2010 through 12/31/14
were obtained from the University of Kentucky Center for Clinical and Translational Science Enterprise Data
Trust. Cases were defined as adults with PSA-positive cultures, while controls had Enterobacteriaceae
(ENT)-positive cultures. Exclusion criteria included cystic fibrosis and polymicrobial infections. Basic
descriptive statistics were performed and multivariable logistic regression was utilized to identify risk
factors for PSA infections.
Results: 2770 patients were evaluated (2399 ENT vs. 371 PSA). Male gender (60% vs. 40%, p<0.0001) and
comorbidities including CKD, ESRD, COPD, immunosuppression and hematologic malignancies were
significantly more prevalent in the PSA group. Patients with prior history of PSA cultures (p<0.001) and
antibiotic exposure were more likely to have PSA infections (54% vs. 38%, p<0.0001). They were also more
likely to have central-lines, mechanical ventilation, and circulatory shock (12% vs 7%, p=0.003; 22% vs. 15%,
p=0.002; 22% vs. 16%, p=0.002 respectively). Charlson Comorbidity Index (CCI) score and frequency of
hospital-acquired infections were higher in PSA group [median(IQR) 4(2-8) vs. 5(3-8) p=0.002 and 67% vs.
58%, p=0.0005, respectively]. Prior antibiotic exposure or PSA culture, hematological malignancy,
immunosuppression, CCI ≥ 5, and pulmonary source were independent predictors for PSA in our
population. A risk-factor score cutoff of 5.5 had the most optimal combination of sensitivity (64.2%),
specificity (76%), accuracy (74.4%), negative predictive value (93.2%), and positive predictive value (29.2%).
Conclusions: A score cutoff of 5.5 was optimal in our population. Score distributions for ENT and PSA
patients were remarkably similar, making it difficult to distinguish between them in order to direct
antibiotic therapy.
Previous antibiotic exposure
Prior hospitalization
Invasive device – catheter, tracheostomy
Respiratory source of bacteremia
Contact Information
Email: [email protected]
Phone: (210) 269-7885
ENT
60
39.1
40
20
46.0
PSA
29.4
11.8 11.3
18.0
10.6
7.0
13.6 13.2
0
Blood
Pulmonary
Urine
Wound
Other
Pathogen site
Conclusions
 At our institution, characteristics of patients with PSA infections included male gender, renal dysfunction, respiratory
disease such as COPD, hematological malignancy, greater risk of mortality, higher rate of mechanical ventilation, presence
of central venous catheters and vasopressor and corticosteroid use.
 A score cutoff of 5.5 was optimal in our population. This provided 64% sensitivity, 76% specificity and 74.4% accuracy.
 Score distributions for ENT and PSA patients were remarkably similar, making it difficult to distinguish between them in
order to direct antibiotic therapy.
 With current susceptibilities in the lower-70 percentile, this may warrant thorough evaluation of empiric regimens and
possible double-coverage with aminoglycosides.
References
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2.
3.
4.
Al-Hassan et al. Clin Microbiol Infect. 2013; 19:948-954
Cheong et al. Am J Med 2008; 121:709-714
Osih et al. Antimicrob Agents Chemother. 2007; 51(3):839-844
Schechner et al. Clin Infect Dis. 2009; 48:580–586
The project described was supported by the National Center for Advancing Translational Sciences,
National Institutes of Health, through grant number UL1TR000117. The content is solely the
responsibility of the authors and does not necessarily represent the official views of the NIH.
IDWeek 2016, New Orleans, LA. October 26-30, 2016