Objectives To describe the pathogen predominance and to evaluate the probability of covering the most common Gram-negative pathogens collectively in both empirical and early adjustment prescribing scenarios in ICU patients with respiratory infections. Methods Data were collected from an international cohort of hospitals as part of the SMART Surveillance Program (2018). Susceptibility testing (mg/L) was performed by broth microdilution methods. Results 7171 Gram-negative respiratory isolates from adult ICU patients across 209 hospitals from 56 different countries were studied. Overall, the most common ICU respiratory pathogens isolated were Pseudomonas aeruginosa (25%), Klebsiella pneumoniae (18%), Acinetobacter baumannii (14%), and Escherichia coli (11%), with inter-regional differences among these pathogens. Among Enterobacterales, 36% were ESBL positive. When the collective susceptibility profile of this set of pathogens (P. aeruginosa plus Enterobacterales; comprising 78% of all organisms isolated) was performed, ceftolozane/tazobactam (84%), followed by meropenem (81%), provided the most reliable in vitro activity in the empirical prescribing scenario compared with other β-lactam antibiotics. P. aeruginosa co-resistance was common among first-line β-lactam antibiotics. If P. aeruginosa was non-susceptible to piperacillin/tazobactam, less than one-third were susceptible to meropenem or ceftazidime. In contrast, ceftolozane/tazobactam offered in vitro coverage in over two-thirds of these resistant pathogens. Conclusions Ceftolozane/tazobactam demonstrated high cumulative susceptibility levels and in vitro activity in both empirical and adjustment antibiotic prescribing scenarios. High frequency of co-resistance undermines reliable coverage for Gram-negative pathogens already resistant to first-line agents. Ceftolozane/tazobactam would offer additional coverage in this setting.

Background Recent data have shown high rates of resistance and co-resistance of P. aeruginosa (PsA) to traditional first-line β-lactam antibiotics (piperacillin/tazobactam, ceftazidime, cefepime, and meropenem), with < 45% susceptibility to the others when resistance to one agent is present, driving a large medical need for newer agents. We compared the in vitro activity of newer Gram-negative antibiotics ceftolozane/tazobactam (CT), ceftazidime/avibactam (CA), and meropenem/vaborbactam (MV) against a global collection of PsA isolates. Methods Data were collected from multiple US hospitals as part of the SMART Surveillance Program (2019). Susceptibility testing (MIC, mg/L) was performed by broth microdilution, with susceptibility determined by CLSI breakpoints except for MV where EUCAST breakpoints were applied due to CLSI offering no susceptibility breakpoint criteria. Results 865 clinical P. aeruginosa isolates (one unique initial isolate per patient) were submitted from 21 US medical centers in 2019. 32% were from ICU patients; 71% were from lower respiratory tract infections. The phenotypic β-lactam susceptibility profile in this population was piperacillin/tazobactam (79%), ceftazidime (82%), cefepime (83%), and meropenem (78%). The table provides the comparative susceptibility rates. Co-resistance between commonly prescribed first line β-lactam antibiotics was common. CT, CA and MV were more active than traditional β-lactams, with CT having higher in vitro activity regardless of phenotype, followed by CA and then MV. Table. Probability of Coverage for P. aeruginosa when Non-Susceptibility or Resistance to a Given First Line β-lactam Antibiotic Conclusion To our knowledge, this is the largest multicenter head to head comparison of the activities of ceftolozane/tazobactam, ceftazidime/avibactam and meropenem/vaborbactam among P. aeruginosa with varying resistant phenotypes. Among the newer agents, ceftolozane/tazobactam demonstrated the most reliable in vitro activity against P. aeruginosa with resistance to traditional first-line β-lactams. Further studies are needed to translate the potential clinical relevance of these findings in different practice settings with varying rates of antimicrobial resistance among P. aeruginosa. Disclosures Pamela Moise, PharmD, Merck & Co., Inc. (Employee, Shareholder) C. Andrew DeRyke, PharmD, Merck & Co., Inc. (Employee, Shareholder) Marcela Gonzalez, MD, MSD (Employee, Shareholder) Irina Alekseeva, MD, PhD, MSD (Employee, Shareholder) Diego Lopez, MD, MSD (Employee, Shareholder) Brune Akrich, MD, MSD (Employee, Shareholder) Daniel F. Sahm, PhD, IHMA (Employee)Pfizer, Inc. (Consultant)Shionogi & Co., Ltd. (Independent Contractor) Katherine Young, MS, Merck & Co., Inc. (Employee, Shareholder)Merck & Co., Inc. (Employee, Shareholder) Mary Motyl, PhD, Merck & Co, Inc (Employee, Shareholder)

Swedish National Diabetes Register (NDR) for 35,238 persons with type 2 diabetes aged 30-74 years at diagnosis from January 1, 2004 to December 31, 2008 were analyzed using the conditional non-frailty Weibull model. To not underestimate the effect of BMI, two specifications of the model were estimated. Age at diagnosis, sex, hypoglycaemic treatment, diabetes duration, microalbuminuria and smoking were common covariates in both models. RESULTS: A total of 1409 patients had one MI event and 200 experienced two events. The results showed that the risk of a second MI differ from the risk of having a first MI. In addition, the effects of covariates were not constant between multiple events. Women had a lower risk for developing a first event compared to men, but a higher risk for a second event conditional on the first MI. Preliminary results indicate four times higher hazard of developing a MI conditional on a first MI during the follow up. CONCLUSIONS: The findings show the need for an update of simulation models including health-economic models and risk engines to include separate transition probabilities for first and subsequent events for correct predictions of costs and quality of life gains. Using recurrent event risk equations may reduce the bias from the previous assumption of constant transition probabilities for consecutive events in health economic models.