Emerging Waves of Carbapenem Resistance among Gram-negative Pathogens at a Tertiary Center Presenter: Julie Paronish Faculty Advisor: Nancy W. Glynn, PhD Internship Preceptor: Ryan K. Shields, PharmD, MS

Background Antibiotic resistance has emerged as a healthcare crisis around the world In the United States alone, more than $30 billion dollars a year are spent on treating drug-resistant infections Given the limited therapeutic options for treatment of antibiotic-resistant pathogens, patient morbidity and mortality are disproportionately high Carbapenems are considered one of the last lines of defense against drug-resistant bacteria; however, rates of carbapenem-resistant organisms (CROs) have increased

Background Despite recent global trends, the epidemiology of CROs at individual centers remains poorly defined So too are patient or hospital factors associated with the emergence of CROs At UPMC Presbyterian hospital, carbapenem resistance is now common across many Gram-negative pathogens Overall burden of CROs, and the pathogen-specific impact on patient outcomes are unknown

Objectives & Hypothesis Define the epidemiology of CROs at UPMC Identify associations between trends in antibiotic use and CROs Describe characteristics and outcomes of patients with CROs Hypotheses Carbapenem resistance has emerged over time across many pathogens Increased carbapenem usage is associated with increasing rates of CROs at UPMC Patient outcomes vary by pathogen, severity of illness, and underlying diseases

Methods Microbiology data extracted from 2000 – 2015 Data sources: MISYS and Sunquest Inpatient locations only – UPMC Presbyterian Carbapenem resistance was defined by the 2016 CLSI interpretive criteria, and applied retrospectively throughout the study Unique patients were identified by pathogen for epidemiology analysis (e.g. patients may be re-included for each pathogen) For outcome analysis, unique patients were identified by the first CRO isolated (e.g. patients were not re-included) Social Security Death Index to determine time to death Inpatient was defined by institution antibiogram methodology.

Statistical Analysis Epidemiological trends in CROs and antibiotic daily defined doses were determined by linear regression Time series, cross-correlation regression analysis was used to compare trends in CROs as a function of antibiotic consumption Kaplan-meier graphs were used to plot survival over time, and compared by the log-rank test In univariate analysis to identify predictors of death, continuous and categorical variables were compared by student’s t-test and chi-square, respectively In multivariate analysis, a logistic regression model was built with stepwise backward selection procedures using variables with a P-value <0.10 Using coefficients from the logistic regression model, a prediction equation was derived by fitting the data to an inverse probability model

Gram-negative pathogens at UPMC 97,747 isolates from 42,070 patients were identified Organism n % CRO baumannii 2,954 36.46% C. freundii 2,209 2.72% E. coli 28,676 1.14% E. aerogenes 3,230 5.91% E. cloacae 6,487 6.81% K. oxytoca 3,370 2.05% K. pneumoniae 15,864 9.30% M. morganii 1,199 4.25% P. mirabilis 6,372 2.57% P. aeruginosa 22,851 22.58% S. marcescens 4,535 4.26% Organism n % CRO baumannii 2,954 36.46% C. freundii 2,209 2.72% E. coli 28,676 1.14% E. aerogenes 3,230 5.91% E. cloacae 6,487 6.81% K. oxytoca 3,370 2.05% K. pneumoniae 15,864 9.30% M. morganii 1,199 4.25% P. mirabilis 6,372 2.57% P. aeruginosa 22,851 22.58% S. marcescens 4,535 4.26%

Targeted CROs (2000 – 2015) 84,597 isolates from 37,823 patients 8,864 isolates from 4,994 patients were defined as CRO In our tertiary center, Pseudomonas has consistently been the cause of the largest quantity of unique patients over the pas 15 years. KLPN and ACAT both showed increases beginning in 2006, while an increase of ENT in 2009 is noted. *Did any testing methods occur during these years? What else could account for drastic increases.

Rates of Carbapenem Resistance by Pathogen Time series linear regression showed that Carbapenem resistance has increased over time for all organisms (P<.05). The most substantial increase is seen in ACAT beginning in 2006.

Incidence of Carbapenem Resistance Pathogen n Rate per 1,000 patient days 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 A. baumannii 547 0.04 0.05 0.02 0.07 0.19 0.32 0.36 0.39 0.34 0.2 0.18 E. coli 372 0.03 0.08 0.1 0.14 0.11 0.12 Enterobacter 536 0.01 0.16 0.3 0.38 0.48 K. pneumoniae 650 0.06 0.24 0.26 0.43 0.52 0.33 P. aeruginosa 2267 0.74 0.7 0.64 0.53 0.61 0.68 0.81 0.82 0.88 0.83 0.96 0.95 1.03 1.11 1.09 S. marcescens 127

Recurrence rates by pathogen Note that recurrence for PSAR same pathogen is significantly higher than recurrence of other organisms for PSAR.

Antibiotic daily defined doses (DDDs) P-VALUE R-SQUARED 0.0000 0.9242 0.0001 0.7005 0.0641 0.2240 Carbapenem DDD’s/1000 patient days increased over the study period (P<.001) ; cephalosporin, piperacillin/tazobactam, and aminoglycoside DDDs were unchanged, while fluoroquinolone DDDs were decreased (P<0.001).

Association between CROs and DDDs Cross-correlation regression analysis showed that increased carbapenem use and the emergence of CROs occurred simultaneously All CROs By pathogen LAG R-squared ACAT -3 0.6106 ECOL 0.8868 ENT 0.8534 KLPN 0.8790 PSAR 0.8616 SERM 0.6258 TOTAL 0.9070 By cross-correlation regression analysis, carbapenem DDDs correlated with the emergence of CROs at a zero-lag time interval (R2=0.90, P<0.001), indicating that increased consumption and emergence of CROs occurred simultaneously.

Patient characteristics  Factor All Patients (n=4,994) Median Age, years (std dev) 57.8(± 15.9) Male, no. (%) 2,811(56) ICU at time of culture, no. (%) 2,064(41) Solid Organ Transplant Recipient, no. (%) 1,297(26) CROs were identified from 4,994 unique patients; mean age was 57.8 years (± 15.9), 56% were men, and 41% resided in the intensive care unit (ICU) at the time of isolation. 26% of patients were solid-organ transplant recipients. 30- and 90-day mortality rates were 19% and 32%, respectively. 30d mortality rates were higher for Acat (28%, P<0.001), and lower for Psar (17%, P<0.001), compared to other pathogens.

Kaplan-meier survival estimates By log rank test: ACAT was associated with a higher rate of 30- and 90- mortality compared to all other pathogens PSAR was associated with a lower rate of 30- and 90- mortality compared to ACAT and KLPN

Mortality Rate by Culture Source   Respiratory Urine D. Wound Blood S. Wound Other ACAT (589) 182 8 30 19 4 ECOL (232) 17 12 14 ENT (487) 71 39 15 2 KLPN (750) 103 33 54 3 PSAR (2794) 600 53 114 SERM (142) 24 7 TOTAL 999 171 235 150 26

Logistic Regression Male 2,237 (55.6%) 574 (59.1%) 0.0900 Age > 65 Factor Alive (n=4,022) Dead (n=972) P-value Male 2,237 (55.6%) 574 (59.1%) 0.0900 Age > 65 226 (5.6%) 406 (41.8%) <0.0001 Residence in ICU 1,410 (35.1%) 654 (67.3%) SOT 1,104 (27.4%) 193 (19.9%) 0.0020 Organism Baumannii 422 (10.5%) 167 (28.4%) E. coli 195 (4.8%) 37 (15.9%) 0.1936  Enterobacter 396 (9.8%) 91 (18.7%)  0.6921 K. Pneumoniae 588 (14.6%) 162 (21.6%)  0.1204 Pseudomonas 2,311 (57.5%) 483 (17.3%)  <0.0001 S. marcescens 110 (2.7%) 32 (22.5%)  0.4063 Culture Respiratory 2,233 (55.5%) 614 (63.2%) Urine 661 (16.4%) 89 (9.2%) D. Wound 72 (18.1%) 143 (14.7%) Blood 288 (7.2%) 105 (10.8%)  0.0002 S. Wound 91 (2.3%) 19 (2.0%)  0.6419

Estimating mortality using prediction model Rationale Can use coefficients from logistic regression model to make predictions for individual patients Equation p= 𝑒 𝑎+𝐵1𝑋1+𝐵2𝑋2+..𝐵𝑝𝑋𝑝 1+ 𝑒 𝑎+𝐵1𝑋1+𝐵2𝑋2+..𝐵𝑝𝑋𝑝 Variable β-coefficient Intercept -4.03225 Age 0.020666 Male 0.113142 ICU 1.290581 SOT -0.30932 Organism ACAT 0.273369 ECOL -0.28304 ENT -0.23665 KLPN 0.073806 PSAR -0.19427 Culture RESP 0.862593 URINE 0.514527 DWOUN 0.822352 BLOOD 1.20741 SWOUN 1.002972

Estimating mortality using prediction model Example 1 65 year old, non-transplant man in the ICU with A. baumanii in blood cultures Predicted mortality = 54.87% Example 2 45 year old, liver transplant female on a medical ward with K. pneumoniae in urine cultures Predicted mortality = 5.61% Example 3 79 year old, non-transplant man in the ICU with P. aeruginosa in respiratory cultures Predicted mortality = 41.88%

Conclusions CROs have emerged in waves over time at UPMC Antibiotic consumption does not appear to be associated with the epidemiology of CROs CROs are associated with high rates of mortality Identification of A. baumannii, residence in the ICU, and older age are important predictors of death Solid-organ transplant recipients, on the other hand, have lower rates of death following isolation of CROs Active surveillance and infection control strategies will be useful to identify and control the emergence of other CROs, respectively