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Background. Increasing antimicrobial resistance among pathogens causing complicated intra-abdominal infections (cIAIs) supports the development of new antimicrobials. Ceftolozane/tazobactam, a novel antimicrobial therapy, is active against multidrug-resistant Pseudomonas aeruginosa and most extended-spectrum β-lactamase (ESBL)–producing Enterobacteriaceae. Methods. ASPECT-cIAI (Assessment of the Safety Profile and Efficacy of Ceftolozane/Tazobactam in Complicated Intra-abdominal Infections) was a prospective, randomized, double-blind trial. Hospitalized patients with cIAI received either ceftolozane/tazobactam (1.5 g) plus metronidazole (500 mg) every 8 hours or meropenem (1 g) every 8 hours intravenously for 4–14 days. The prospectively defined objectives were to demonstrate statistical noninferiority in clinical cure rates at the test-of-cure visit (24–32 days from start of therapy) in the microbiological intent-to-treat (primary) and microbiologically evaluable (secondary) populations using a noninferiority margin of 10%. Microbiological outcomes and safety were also evaluated. Results. Ceftolozane/tazobactam plus metronidazole was noninferior to meropenem in the primary (83.0% [323/389] vs 87.3% [364/417]; weighted difference, −4.2%; 95% confidence interval [CI], −8.91 to .54) and secondary (94.2% [259/275] vs 94.7% [304/321]; weighted difference, −1.0%; 95% CI, −4.52 to 2.59) endpoints, meeting the prespecified noninferiority margin. In patients with ESBL-producing Enterobacteriaceae, clinical cure rates were 95.8% (23/24) and 88.5% (23/26) in the ceftolozane/tazobactam plus metronidazole and meropenem groups, respectively, and 100% (13/13) and 72.7% (8/11) in patients with CTX-M-14/15 ESBLs. The frequency of adverse events (AEs) was similar in both treatment groups (44.0% vs 42.7%); the most common AEs in either group were nausea and diarrhea. Conclusions. Treatment with ceftolozane/tazobactam plus metronidazole was noninferior to meropenem in adult patients with cIAI, including infections caused by multidrug-resistant pathogens.

Background. The incidence of Clostridium difficile infection (CDI) has risen dramatically during the last decade. Although patients respond well to medical therapy such as vancomycin, 20%–30% of patients treated suffer a recurrence of CDI. Methods. We developed a simple/practical scoring rule (logistic regression model) for recurrent CDI using data from 2 large phase 3 clinical trials. Seventy-seven baseline CDI factors were classified: demographics, comorbidity, medications, vital signs, laboratory tests, severity, and symptoms. Predictors with the highest discrimination in each class (using receiver operating characteristics curve) were selected. For the final model, stepwise selection was performed. Discrimination, calibration, and internal validation were used to assess the model. Results. The final model with a simple scoring rule was developed. It includes 4 independent risk factors that are readily available when the patient makes initial contact: age (<75 vs ≥75 years), number of unformed bowel movements during previous 24 hours (<10 vs ≥10), serum creatinine leves (<1.2 mg/dL vs ≥1.2 mg/dL) and prior episode of CDI (yes vs no). In addition, the model includes choice of treatment (vancomycin or fidaxomicin). Conclusions. The prediction model for recurrence may be useful for treatment decision. Clinical Trials Registration. NCT00314951 and NCT00468728.

Despite best efforts, some crucial information used to design a confirmatory trial may not be available, or may be available but with a high degree of uncertainty, at the design stage. When this happens, it may be prudent to check the validity of those assumptions using interim data from the study and make midcourse adjustment if necessary. One such adjustment is to modify the sample size. In this article, we focus on sample size reestimation (SSR) for phase III and IV studies. The discussion is relevant to both continuous and binary endpoints even though the basis for SSR might differ for those two cases. We review commonly used approaches to adjust sample size and provide recommendations on how SSR should be implemented to achieve the objectives and maintain the integrity of the trial. The recommendations cover scientific, procedural, and logistic considerations.